The spread of new technologies takes place at different speeds across different places and between different individuals in a society. This essay focuses on examining and analyzing how the distribution pattern and pace of electric and hybrid cars, commonly called electric vehicles, has taken place in Sweden since the beginning of the 2000s and the following two decades. This trend analysis has been done at national, regional, and local level. Furthermore, a binary logistical regression analysis has been conducted at the individual level for the corresponding geography, based on the goal of finding the personal conditions that affect the acquisition of an electric vehicle.  The study shows that Sweden is at a turning point regarding the diffusion of innovation where the next phase that follows is the “early acceptance”- face in a society. Regional primary locations lead the development and the individuals who most likely own electric vehicles are generally married, highly educated men who own their own housing, have a yearly income above the national median income level and are between 40-80 years old.

The science of making a computer understand text and process it, natural language processing, is a topic of great interest among researchers. This study aims to further that research by comparing the BERT algorithm and classic logistic regression when identifying names of public organizations. The results show that BERT outperforms its competitor in the task from the data which consisted of public state inquiries and reports. Furthermore a literature study was conducted as a way of exploring how a system for NER can be implemented into the management of an organization. The study found that there are many ways of doing such an implementation but mainly suggested three main areas that should be focused to ensure success - recognising the right entities, trusting the system and presentation of data. / Vetenskapen kring hur datorer ska förstå och arbeta med fria texter, språkteknologi, är ett område som blivit populärt bland forskare. Den här uppsatsen vill utvidga det området genom att jämföra BERT med logistisk regression för att undersöka nämnandet av svenska myndigheter genom NER. BERT visar bättre resultat i att identifiera namnen på myndigheter från texter i statliga utredningar och rapporter än modellen med logistisk regression. Det genomfördes även en litteraturstudie för att undersöka hur ett system för NER kan implementeras i en organisation. Studien visade att det finns flera sätt att genomföra detta men föreslår framförallt tre områden som bör fokuseras på för en lyckad implementation - användande av rätt entiteter, trovärdighet i system och presentation av data.

In the United States, intersections are among the most frequent locations for crashes. One of the major problems at signalized intersection is the dilemma zone, which is caused by false driver behavior during the yellow interval. This research evaluated driver behavior during the yellow interval at signalized intersections and compared different dilemma zone countermeasures. The study was conducted through four stages. First, the driver behavior during the yellow interval were collected and analyzed. Eight variables, which are related to risky situations, are considered. The impact factors of drivers' stop/go decisions and the presence of the red-light running (RLR) violations were also analyzed. Second, based on the field data, a logistic model, which is a function of speed, distance to the stop line and the lead/follow position of the vehicle, was developed to predict drivers' stop/go decisions. Meanwhile, Cellular Automata (CA) models for the movement at the signalized intersection were developed. In this study, four different simulation scenarios were established, including the typical intersection signal, signal with flashing green phases, the intersection with pavement marking upstream of the approach, and the intersection with a new countermeasure: adding an auxiliary flashing indication next to the pavement marking. When vehicles are approaching the intersection with a speed lower than the speed limit of the intersection approach, the auxiliary flashing yellow indication will begin flashing before the yellow phase. If the vehicle that has not passed the pavement marking before the onset of the auxiliary flashing yellow indication and can see the flashing indication, the driver should choose to stop during the yellow interval. Otherwise, the driver should choose to go at the yellow duration. The CA model was employed to simulate the traffic flow, and the logistic model was applied as the stop/go decision rule. Dilemma situations that lead to rear-end crash risks and potential RLR risks were used to evaluate the different scenarios. According to the simulation results, the mean and standard deviation of the speed of the traffic flow play a significant role in rear-end crash risk situations, where a lower speed and standard deviation could lead to less rear-end risk situations at the same intersection. High difference in speed are more prone to cause rear-end crashes. With Respect to the RLR violations, the RLR risk analysis showed that the mean speed of the leading vehicle has important influence on the RLR risk in the typical intersection simulation scenarios as well as intersections with the flashing green phases' simulation scenario. Moreover, the findings indicated that the flashing green could not effectively reduce the risk probabilities. The pavement marking countermeasure had positive effects on reducing the risk probabilities if a platoon's mean speed was not under the speed used for designing the pavement marking. Otherwise, the risk probabilities for the intersection would not be reduced because of the increase in the RLR rate. The simulation results showed that the scenario with the pavement marking and an auxiliary indication countermeasure, which adds a flashing indication next to the pavement marking, had less risky situations than the other scenarios with the same speed distribution. These findings suggested the effectiveness of the pavement marking and an auxiliary indication countermeasure to reduce both rear-end collisions and RLR violations than other countermeasures.

The objective of this research study is to explore trajectories of health-related quality of life (HRQoL) in older breast cancer survivors, along with their predictors. HRQoL is important because patients who show severe symptoms may wish to consider therapies or treatment plans that lead to better HRQoL. Older people are more vulnerable to low HRQoL scores since old age is associated with deteriorating health, multiple comorbidities, and low-socioeconomic status. To examine the HRQoL trajectory among older women with breast cancer, we used the data queried from the Surveillance, Epidemiology and End Results Medicare Health Outcomes Survey database. A total of 1,089 older (≥ 65 years) women who were diagnosed with breast cancer in 1998-2012 and participated in the survey before and after the cancer diagnosis were identified. HRQoL was measured using SF-36/VR-12 questionnaire and summarized as Physical Component Summary (PCS) Score and Mental Component Summary (MCS) Score. Latent Class Growth Mixture Modeling was conducted to identify distinct groups of women with a similar trajectory of HRQoL. The results showed that there were three latent classes of HRQoL trajectories for PCS: the high-declining (46.5% of the sample), mid-declining (36.0%), and the low-improving (17.5%). Two latent classes of HRQoL trajectories were identified for MCS: high-stable (76.5%) and low-declining (23.5%). The results showed that age at diagnosis, BMI, level of education, geographic region, tumor grade, tumor size, and number of comorbidities were some of the major predictors of health-related quality of life. These predictors were further explored using multinomial logistic regression analysis which identified number of comorbidities as the most significant predictor for HRQoL-PCS scores and level of education as the most significant predictor for HRQoL-MCS scores. This suggests that future research needs to be conducted, identifying the most common comorbidities in older breast cancer survivors to develop interventions that better the physical HRQoL in patients, in addition to the development of mental HRQoL interventions for patients that are less educated.

Can public organization managers respond to rapidly changing environmental conditions given that stability is a primary societal function of bureaucracies? If so, how might they successfully bring about change? Bennis (1966) states that examinations of organizational change fall into two categories: 1) those attempting to explain why organizations change and 2) those attempting to explain how organizations change. Those exploring why organizations change question the degree to which managers can serve as successful stimuli for transformation. This is true across public and private realms, but is particularly germane to public organizations, where managerial agency is restricted by accountability, civil service employee protections, and democratic norms. A Rational Adaptive philosophy of change suggests that managers can and do change organizations; however, with the exception of transformation in the face of budgetary and managerial crises, little evidence exists for manager-initiated, public organization change. Furthermore, prescriptions for how to bring about change are largely anecdotal and private-sector oriented. While Contingency Theory posits that no one-sizeits-all formula will suffice across all organization types and contexts, public administration scholars have proposed testable propositions regarding drivers of bureaucratic transformation. Using descriptive statistics, qualitative analysis, and logistic regression, this study examined one U.S. national agency’s attempt at transformational change to determine 1) the degree to which managers achieved success and 2) under what circumstances success was achieved. A theoretical typology for U.S. hierarchical, national agencies attempting internal policy change was proposed using the testable propositions, and a derivative model of change was tested to determine the types of manager-initiated efforts that yielded organizational change versus those that did not. Results suggest that, while transformational organizational change is challenging, managers of public organizations can create changes in employee attitudes and behavior in the absence of immediate crisis by communicating the vision for the change effectively and incorporating change-related routines in employees’ workplaces. Managerial support for the change may influence employee attitudes regarding the change and encourage support, while change-related incentives may promote behavior changes and adoption. Furthermore, adequate provision of change-related resources may be an important component in ensuring employees who desire to change can do so.

Bottomland hardwood forests and pine forests in the southern United States provide valuable ecosystem services such as timber, recreation, wildlife habitat, carbon sequestration, floodwater storage, and sediment and nutrient retention. However, these forest ecosystems are threatened because of intensive forest management, forest land conversion, and urbanization. As private landownership dominates in this region, landowner participation is crucial for success of conservation programs facilitating ecosystem services. This research focused on three different aspects of private land conservation programs. First, it estimated the impact of environmental contextual factors, private land attributes and sociodemographic characteristics on landowner concern about environmental issues. Second, it determined the influence of private land attributes, environmental concerns, frequency of contacts with federal agencies and socioeconomic characteristics on landowner satisfaction with available conservation programs. Third, it estimated the monetary compensation required by landowners to implement conservation practices focused on increasing provision of ecosystem services. Data were collected using a mail survey and from online sources. Data were analyzed using seemingly unrelated regression and logistic regression models. Results indicated that private land attributes, particularly size of agricultural land owned, and landownership goals such as providing ecosystem services and profitability, had a greater magnitude of positive association with landowner concerns about environmental issues than other factors. Similarly, size of agricultural land owned, landownership goals such as profitability and personal recreation, concerns about wildlife habitat losses and frequent contacts with federal agencies were positively related to landowner satisfaction with conservation programs. Landowner willingness to participate in a conservation program was positively related to payment amount, concerns about wildlife habitat losses, frequency of contact with the Natural Resources Conservation Service (NRCS), and education level. Similarly, provision of clean water as landownership objective, concerns about hurricanes and tornadoes, and landowner age were negatively associated with landowner interests to participate in conservation programs. This research also quantified landowner median willingness to accept (WTA) compensation which was $229.98 ha-1 yr-1 for participation in a conservation program related to bottomland hardwood or pine forests. The findings help identify likely participants or landowners for conservation programs facilitating ecosystem services and determine actual conservation costs at a regional level.

Developing high quality software is the goal of every software development organization. Software quality models are commonly used to assess and improve the software quality. These models, based on the past releases of the system, can be used to identify the fault-prone modules for the next release. This information is useful to the open-source software community, including both developers and users. Developers can use this information to clean or rebuild the faulty modules thus enhancing the system. The users of the software system can make informed decisions about the quality of the product. This thesis builds quality models using logistic regression, neural networks, decision trees, and genetic algorithms and compares their performance. Our results show that an overall accuracy of 65 ? 85% is achieved with a type II misclassification rate of approximately 20 ? 35%. Performance of each of the methods is comparable to the others with minor variations.

Retention and matriculation are topics of heavy debate and inquiry in higher education as rising tuition costs, coupled with declining state support, have fueled the need for increased accountability. In Mississippi, few studies have been conducted that are unique to the public universities in the state in order to analyze success factors in higher education. The purpose of this study was to analyze these success factors at Mississippi’s public universities as measured by successful degree completion within a six-year time period. This study analyzed High School GPA, ACT Scores, Parental Income Levels, Parental Education Levels, Ethnicity, and Gender. Academic, demographic and socioeconomic data were gathered on two cohorts of resident first-time, full-time students attending a Mississippi Institution of Higher Learning. A total of 5,603 students were included in the study from the fall 2001 and 2002 semesters. Transfer students were not included in the study. A successful completer was defined as completing a bachelor’s degree within six-years of enrollment. Students still enrolled in the seventh year were not included. In addition, students seeking an Associate’s Degree were also not included. Descriptive statistics revealed that graduation rates fluctuated depending on high school GPA, ACT scores, income and parental education levels. The most significant differences in graduation rates occurred when analyzing high school GPA and income statistics. These findings were supported when logistic regression analysis was employed. Logistic regression analysis was used to analyze these factors compared to graduation rates for the state, and by type of institution. In Mississippi, there are four regional universities and four research universities. High school GPA and parental income were significant predictors in all three models, while ACT was significant when analyzing data for the system. For research universities, the education level of the father was significant. For regional universities, ethnicity was a significant predictor. In all, universities should develop a deeper understanding of the socioeconomic background of students in order to ensure that proper scaffolding is in place to ensure successful matriculation.

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