This thesis investigates the relationship between share repurchase decisions and severalfinancial variables the year before. We link financial theories such as signaling,substitution, leverage, excess capital, corporate governance, employee stock option, andlegitimacy theory to this relationship and create a hypothesis to test in a quantitative study.This study uses publicly listed firms headquartered in Nordic countries and uses mainlyfinancial data collected between 2015-2019. The study intends to define what factors mayaffect a share repurchase decision and by which degree. This study is also among the firstto test Refinitiv ESG pillar scores toward this relationship. The purpose of this is tounderstand the process better and could potentially allow management to understand betterwhen a share repurchase program can be initiated. It can also better inform stakeholders infirms' decisions on the market in regards to share repurchases. This deductive andquantitative research is based on secondary data gathered from the Eikon financial databaseto create an observational study.We find that share repurchasing firms have more cash flows, lower leverage ratio, morestock options programs, more board members, and fewer independent board members. Wefind that firms with excess cash flows are more likely to undertake a share repurchase eventfrom the regression analysis consistent with the excess cash flow theory. We find arelationship between having a high number of board members, where few are independent,increases the likelihood of a share repurchase event in the following year, which wasagainst our initial corporate governance hypothesis. We found that a higher governancepillar score increases the likelihood of a share repurchases event that shows value tolegitimacy theory but cannot conclusively answer that firms use sustainability disclosure asa tool to legitimize themselves. This result more likely links to corporate governancetheory. We did not find a relation for the undervaluation, substitution, leverage, employeestock options theory. We conclude that firms with more cash flows, lower leverage ratio,more stock options programs, more board members, and fewer independent board membersuse share repurchases more than their counterparts. We note that ESG scores are relativelynew and have seen more widespread use in the latest years.We look forward to reading more research in this field as more data is collected.

A college degree provides benefits to individuals and society, but education is an expensive endeavor. College costs are high and they continue to rise while the median family income shows only modest increases. By lowering the cost of attendance, financial aid makes it possible for many students, especially those from low and middle-income families to attend college. FAFSA is the main instrument used in distributing financial assistance although completing the form is not an easy task. Each year, many students do not file the FAFSA or file it too late, missing valuable financial resources. The focus of this research was on students who file FAFSA late. The purpose of the study was two-fold: to explore the relationship between the timing of FASFA filing and the characteristics of financial aid applicants, and to assess the impact of late filing on student finances. Logistic regression analysis was used to examine how much of the variation in timing of FAFSA filing could be explained by students characteristics. The findings indicate late FAFSA filers tend to be in-state, male students, coming from single households, with weak high school academic performance. Focusing on low-income group, the study found the odds of filing late were nearly 2.8 times higher for in-state students than they were for out-of-state students. Being male increased the chances of late filing; the odds of filing late for low-income male students were 1.53 times higher than they were for low-income females. The impact of late FAFSA filing on student finances was assessed through linear regression analyses. The results show late filers received less grant aid but larger loan amounts. Compared to on time filers, late FAFSA filers received, on average, $2,815 less in grant aid and $662 more in loans. The current study shed light on several key factors that make students more likely to miss the FAFSA deadlines. In addition, it demonstrated that late filing has major financial consequences for students and their families. The findings can be used by high school guidance offices, college administrators, state and federal governments, and higher education leaders concerned with improving college affordability. / Ph. D. / Higher education provides benefits to individuals and society. Benefits aside, education is expensive, and most students need financial assistance to offset the college price. By lowering the cost of attendance, financial aid makes it possible for many students, especially those from low and middle income families to attend college. Financial assistance is key for a successful degree completion, while FAFSA remains the main instrument used to distribute the aid. Filing a FAFSA is a critical step in securing financial assistance, although completing the form is not an easy task. The combination of several barriers such as complexity of the form, confusing deadlines, low predictability, and lack of information about the student aid system make the FAFSA application process challenging. Because of that, many students fail to complete or file the FAFSA on time. However, due to limited resources, the timing of the FAFSA filing matters. The purpose of this study was to explore the relationship between the timing of FAFSA filing and characteristics of financial aid recipients. Logistic regression analysis was used to examine how much of the variation in timing of FAFSA filing could be explained by students demographic and socioeconomic characteristics. The findings indicate late FAFSA filers tend to be in-state, male students, coming from single households, with weak high school academic performance. Additionally, the current study assessed the impact of late FAFSA filing on the amount of grants and loans received by the applicants in their first year in college. The results of the impact assessment show late FAFSA filers received significantly more loans and less grant aid. The current study identified key factors that make students more likely to file a late FAFSA. It also demonstrated that late filing has major financial consequences for students and their families. The findings can be used by high school guidance offices, college administrators, state and federal governments, and higher education leaders concerned with improving college affordability.

With the proliferation of fairly powerful mobile devices and ubiquitous wireless technology, traditional mobile ad hoc networks (MANETs) now migrate into a new era of service-oriented MANETs wherein a node can provide and receive service from other nodes it encounters and interacts with. This dissertation research concerns trust management and its applications for service-oriented MANETs to answer the challenges of MANET environments, including no centralized authority, dynamically changing topology, limited bandwidth and battery power, limited observations, unreliable communication, and the presence of malicious nodes who act to break the system functionality as well as selfish nodes who act to maximize their own gain. We propose a context-aware trust management model called CATrust for service-oriented ad hoc networks. The novelty of our design lies in the use of logit regression to dynamically estimate trustworthiness of a service provider based on its service behavior patterns in a context environment, treating channel conditions, node status, service payoff, and social disposition as 'context' information. We develop a recommendation filtering mechanism to effectively screen out false recommendations even in extremely hostile environments in which the majority recommenders are malicious. We demonstrate desirable convergence, accuracy, and resiliency properties of CATrust. We also demonstrate that CATrust outperforms contemporary peer-to-peer and Internet of Things trust models in terms of service trust prediction accuracy against collusion recommendation attacks. We validate the design of trust-based service management based on CATrust with a node-to-service composition and binding MANET application and a node-to-task assignment MANET application with multi-objective optimization (MOO) requirements. For either application, we propose a trust-based algorithm to effectively filter out malicious nodes exhibiting various attack behaviors by penalizing them with trust loss, which ultimately leads to high user satisfaction. Our trust-based algorithm is efficient with polynomial runtime complexity while achieving a close-to-optimal solution. We demonstrate that our trust-based algorithm built on CATrust outperforms a non-trust-based counterpart using blacklisting techniques and trust-based counterparts built on contemporary peer-to-peer trust protocols. We also develop a dynamic table-lookup method to apply the best trust model parameter settings upon detection of rapid MANET environment changes to maximize MOO performance. / Ph. D.

Most estimates of wood supply do not reflect the true availability of wood resources. The availability of wood resources ultimately depends on collective wood harvesting decisions across the landscape. Both social and biophysical constraints impact harvesting decisions and thus the availability of wood resources. While most constraints do not completely inhibit harvesting, they may significantly reduce the probability of harvest. Realistic assessments of woody availability and distribution are needed for effective forest management and planning. This study focuses on predicting the probability of harvest at forested FIA plot locations in Virginia. Classification and regression trees, conditional inferences trees, random forest, balanced random forest, conditional random forest, and logistic regression models were built to predict harvest as a function of social and biophysical availability constraints. All of the models were evaluated and compared to identify important variables constraining harvest, predict future harvests, and estimate the available wood supply. Variables related to population and resource quality seem to be the best predictors of future harvest. The balanced random forest and logistic regressions models are recommended for predicting future harvests. The balanced random forest model is the best predictor, while the logistic regression model can be most easily shared and replicated. Both models were applied to predict harvest at recently measured FIA plots. Based on the probability of harvest, we estimate that between 2012 and 2017, 10 – 21 percent of total wood volume on timberland will be available for harvesting. / Master of Science

Motivated by real-time monitoring of dialysis, we aim at detecting difference between groups of Raman spectra generated from dialyzates at different time in one session. Baseline correction being a critical procedure in use of Raman Spectra, existing methods may not perform well on dialysis spectra due to nature of dialyzates, which contain numerous chemicals compounds. We first developed a new baseline correction method, Iterative Smoothing-spline with Root Error Adjustment (ISREA), which automatically adjusts intensities and employs smoothing-spline to produce a baseline in each iteration, providing better performance on dialysis spectra than a popular method Goldindec, and better accuracy regardless of types of samples. We proposed a two sample hypothesis testing on groups of baseline-corrected Raman spectra with ISREA. The uniqueness of the test lies in nature of the tested data. Instead of using Raman spectra as curves, we also consider a vector whose elements are peak intensities of biomarkers, meaning the data is regarded as mixed data and that a spectrum curve and a vector compose one observation. Our method tests on equality of the means of the two groups of mixed data. This method is based on asymptotic properties of the covariance of mixed data and FPCA. Simulation studies shows that our method is applicable to small sample size with proper power and size control. Meanwhile, to locate regions that contribute most to significant difference between two groups of univariate functional data, we developed a method to estimate the a sparse coefficient function by using a L1 norm penalty in functional logistic regression, and compared its performance with other methods. / Doctor of Philosophy / In U.S., there are more than 709,501 patients with End-Stage Renal Disease (ESRD). For those patients, dialysis is a standard treatment. While dialysis is time-consuming, expensive, and uncomfortable, it requires patients to take three sessions every week in facilities, and each session lasts for four hours regardless of patients' condition. An affordable, fast, and widely-applied technique called Raman spectroscopy draws attention. Spectral data from used dialysate samples collected at different time in one session can give information on the dialysis process and thus make real-time monitoring possible. With spectral data, we want to develop a statistical method that helps real-time monitoring on dialysis. This method can provide physicians with statistical evidence on dialysis process to improve their decision making, therefore increases efficiency of dialysis and better serve patients. On the other hand, Raman spectroscopy demands preprocessing called baseline correction on the raw spectra. A baseline is generated because of the nature of Raman technique and its instrumentation, which adds complexity to the spectra and interfere with analysis. Despite popularity of this technique and many existing baseline correction method, we found performance on dialysate spectra under expectation. Hence, we proposed a baseline correction method called Iterative Smoothing-spline with Root Error Adjustment (ISREA) and ISREA can provide better performance than existing methods. In addition, we come up with a method that is able to detect difference between the two groups of ISREA baseline-corrected spectra from dialysate collected at different time. Furthermore, we proposed and applied sparse functional logistic regression on two groups to locate regions where the significant difference comes from.

Fixed-object crashes have long time been considered as major roadway safety concerns. While previous relevant studies tended to address such crashes in the context of roadway departures, and heavily relied on police-reported accidents data, this study integrated the SHRP2 NDS and RID data for analyses, which fully depicted the prior to, during, and after crash scenarios. A total of 1,639 crash, near-crash events, and 1,050 baseline events were acquired. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. Logistic regression analyses identified 16 and 10 significant variables with significance levels of 0.1, relevant to driver, roadway, environment, etc. for two responses respectively. The logistic regression analyses led to a series of findings regarding the effects of explanatory variables on fixed-object event occurrence and associated severity level. SVM classifiers and ANN models were also constructed to predict these two responses. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods obtained satisfactory prediction performance, that was around 88% for fixed-object event occurrence and 75% for event severity level, which indicated the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses. / Master of Science / Fixed-object crashes happen when a single vehicle strikes a roadway feature such as a curb or a median, or runs off the road and hits a roadside feature such as a tree or utility pole. They have long time been considered as major highway safety concerns due to their high frequency, fatality rate, and associated property cost. Previous studies relevant to fixed-object crashes tended to address such crashes in the contexture of roadway departures, and heavily relied on police-reported accident data. However, many fixed-object crashes involved objects in roadway such as traffic control devices, roadway debris, etc. The police-reported accident data were found to be weak in depicting scenarios prior to, during crashes. Also, many minor crashes were often kept unreported. The Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) is the largest NDS project launched across the country till now, aimed to study driver behavior or, performance-related safety problems under real-world scenarios. The data acquisition systems (DASs) equipped on participated vehicles collect vehicle kinematics, roadway, traffic, environment, and driver behavior data continuously, which enable researchers to address such crash scenarios closely. This study integrated SHRP2 NDS and roadway information database (RID) data to conduct a comprehensive analysis of fixed-object crashes. A total of 1,639 crash, near-crash events relevant to fixed objects and animals, and 1,050 baseline events were used. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. The logistic regression analyses identified 16 and 10 variables with significance levels of 0.1 for fixed-object event occurrence and severity level models respectively. The influence of explanatory variables was discussed in detail. SVM classifiers and ANN models were also constructed to predict the fixed-object crash occurrence and severity level. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods achieved satisfactory prediction accuracies of around 88% for crash occurrence prediction and 75% for crash severity level prediction, which suggested the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses.

Improved definition of risk factors for equine colic is necessary to develop effective interventions to reduce colic incidence. A one-year prospective study was conducted to estimate colic incidence and to identify risk factors. Farms with greater than 20 horses were randomly selected from two adjacent counties of Virginia and Maryland. Management information was recorded by questionnaire for 31 farms with 1427 horses. Owners kept calendars to record occurrence of specified events. Colic was reported by the owner when a horse exhibited signs of abdominal pain. The incidence of colic was 10.6 colic cases per 100 horse-years, based on 104 cases per 983.5 horse-years. Twenty-five deaths occurred from all causes, the proportional mortality rate of colic was 7/25 (28%). Risk factors were analyzed by logistic regression at the farm-level and the horse-level with farm as a random effects variable. No farm-level variables were significant. Significant horse variables were: age 2-10 years, odds ratio (OR)=2.8 (95% confidence interval, 1.2-6.5); previous colic, OR=3.6(1.9-6.8); changes in concentrate feeding during the year, OR=3.6(1.6-5.4); more than one change in hay feeding during the year, OR=2.1(1.2-3.8); feeding high levels of concentrate (>2.5 kg/day dry matter, OR=4.8(1.4-16), >5 kg/day dry matter, OR=6.3(1.8-22)); and vaccination with monocytic ehrlichiosis vaccine during the study, OR=2.0(1.8-22). Feeding whole grain with or without other concentrates had less risk than diets without whole grain included. Variables related to concentrate feeding frequency or concentrate type could be substituted for the concentrate level variable. A nested analysis examined risk for the time period following an event. The odds ratio was determined for the proportion of cases with an event within 14 days prior to the colic-date, relative to the proportion of horses without colic with an event within 14 days of a date chosen at random from the observation time. Weather events were analyzed for the three days before the colic or assigned date. Foaling was analyzed for three time periods: before, 0-60 and 60-150 days post-foaling. Significant events were recent vaccination, OR=3.31(1.9-6.0); recent transport, OR=3.3(1.2-5.5); 60-150 days post-foaling, OR=5.9(1.8-13); and recent fever, OR=20(2.5-169). Snow on the day of the colic, OR=2.8(1.0-7) and humidity <50% the day before the colic OR=1.6(1.0-2.9) were marginally significant. / Ph. D.

Headwater streams change in wet length in response to storm events and seasonal moisture conditions. These low-order channels with temporary flow are pervasive across arid and humid environments yet receive little attention in comparison to perennial waterways. This dissertation examines headwater stream length dynamics at multiple spatial and temporal scales across the Appalachians. I mapped wet stream length in four Appalachian physiographic provinces--the Appalachian Plateau, Blue Ridge, New England, and Valley and Ridge--to characterize seasonal expansion and contraction of the wet network at a broad, regional scale. Conversely, most existing field studies of stream length in headwaters are limited to a single study area or geographic setting. Field mappings showed that wet stream length varies widely within the Appalachians; network dynamics correlated with regional geology as well as local site lithology, geologic structure, and the depth, size, and spatial distribution of surficial sediment deposits. I used the field data to create logistic regression models of the wet network in each physiographic province at high and low runoffs. Topographic metrics derived from elevation data were able to explain the discontinuous pattern of headwater streams at different flow conditions with high classification accuracy. Finally, I used flow intermittency sensors in a single Valley and Ridge catchment to record channel wetting and drying at a high temporal resolution. The sensors indicated stream length hysteresis during storms with low antecedent moisture, with a higher wet network proportion on the rising limb than on the falling limb of events. As a result, maximum network extension can precede peak runoff by minutes to hours. Accurate maps of headwater streams and an understanding of wet network dynamics through time are invaluable for applications surrounding watershed management and environmental policy. These findings will contribute to the burgeoning research on temporary streams and are additionally relevant for studies of runoff generation, biogeochemical cycling, and mass fluxes of material from headwaters. / Ph. D. / During a rain storm, we may think of streams increasing in depth, width, and velocity. However, we may not necessarily envision streams also getting longer. Headwaters, which form the upstream extremities of river systems, consist of many temporary streams that expand and contract in length due to storms and changes in seasonal moisture conditions. Headwaters are spatially expansive, comprising a majority of total river length, and serve as a primary control on downstream water quality. Therefore, understanding stream length dynamics can inform policy and land use decisions to effectively conserve and manage headwater regions and protect water sources for human use and consumption. This dissertation examines changes in stream length across four study areas of the Appalachian Mountains. I mapped the wet, or active, stream network multiple times at different flow conditions in each study area. Stream length dynamics varied considerably across the Appalachians and demonstrated the same range of network expansion and contraction as other studies observed in diverse settings around the world. Wet stream length greatly depended on regional and local geology. I then sought to predict the location of wet streams at high and low flows using metrics such as slope and drainage area that I calculated from digital elevation information. Comparisons with the field maps I made showed that simple terrain metrics explained the location, length, and disconnected nature of wet networks in each province with high accuracy. I also observed stream length dynamics during storm events in one watershed using sensors that recorded the presence or absence of water. These observations demonstrated that stream length was often higher for a given flow at the beginning of a storm on the rising limb than on the falling limb when flow was decreasing, particularly if conditions were dry before the storm. The findings of this dissertation contribute to existing knowledge of temporary streams and are relevant for future studies investigating the hydrology, biology, and ecology of headwaters.

This project deals with exploring 1) travel-related health risk perception, and 2) actions taken to mitigate that health risk. Ordered logistic regression models were used to identify factors associated with the perceived risk of contracting influenza at work, school, daycare, stores, restaurants, libraries, hospitals, doctor’s offices, public transportation, and family or friends’ homes. Based on the models, factors influencing risk perception of contracting influenza in public places for discretionary activities (stores, restaurants, and libraries) are consistent but differ from models of discretionary social visits to someone’s home. Mandatory activities (work, school, daycare) seem to have a few unique factors (e.g., age, gender, work exposure), as do different types of health-related visits (hospitals, doctors’ offices). Across all of the models, recent experience with the virus, of either an individual or a household member, was the most consistent set of factors increasing risk perception. Using such factors in examining transportation implications will require tracking virus outbreaks for use in conjunction with other factors. Subsequently, social-health risk mitigation strategies were studied with the objective of understanding how risk perception influences an individual’s protective behavior. For this objective, this study analyzes travel-actions associated with two scenarios during an outbreak of influenza: 1) A sick person avoiding spreading the disease and 2) A healthy person avoiding getting in contact with the disease. Ordered logistic regression models were used to identify factors associated with mitigation behavior in the first scenario: visiting a doctor’s office, avoiding public places, avoiding public transit, staying at home; and in the second scenario: avoiding public places, avoiding public transit, staying at home. Based on the models for Scenario 1, the factors affecting the decision of avoiding public places, avoiding public transit, and staying at home were fairly consistent but differ for visiting a doctor’s office. However, Scenario 2 models were consistent with their counterpart mitigation models in Scenario 1 except for two factors: gender and household characteristics. Across all the models from Scenario 1, gender was the most significant factor, and for Scenario 2, the most significant factor was the ratio of household income to the household size. / Master of Science / Transmission of a communicable disease depends on the social interactions of the members of society. Generally, individuals associate their health-protection behavior to the perception of health risk associated with that activity. Hence, individuals with high health-risk perception are likely to participate in a protective action to reduce the threat of getting infected with influenza. However, in some cases, even if a high health risk is perceived, an individual might have a decreased likelihood to take actions to mitigate that risk. This behavior could be associated with their inability to carry out recommendations, such as vaccination (due to the cost of vaccination) or adopting protective behaviors such as social isolation (switching from public transit to personal vehicle due to the associated cost). This behavior, of either adopting or rejecting protective action, can be explained by protection motivation theory. This theory explains the individual’s perception of the severity of an event (i.e., threat appraisal), and individual’s expectancy of carrying out recommendations (risk mitigation strategies) to reduce threat (i.e., coping appraisal). Both, health risk perception and risk-mitigation strategies are studied for changes in travel decisions.

This study extends the research of Rodgers (1999) on the relationship between parenting processes and adolescent sexual risk-taking. Parenting behaviors considering were parental monitoring, parent-adolescent communication, and parenting styles. Sexual risk-taking was determined by assessing number of lifetime sexual partners as well as use of condoms during last sexual intercourse. A sample (n=286) of 9th-12th grade males and females who reporting having had sexual intercourse were separated into two groups-those engaging in low sexual risk-taking or high sexual risk-taking. Logistic regression analysis revealed gender differences in the relationship between parents' behaviors and adolescent sexual risk-taking. For females, parenting monitoring of the adolescent's after-school whereabouts was related to a decrease in the odds that a daughter would take sexual risks. For males, parental monitoring of whom the adolescent male goes out with was related to a decrease in the odds of a son taking sexual risks. Several significant interaction effects were also found. / Master of Science