The importance of traffic safety has prompted considerable research on predicting driver risk and evaluating the impact of risk factors. Driver risk modeling is challenging due to the rarity of motor vehicle crashes and heterogeneity in individual driver risk. Statistical modeling and analysis of such driver data are often associated with Big Data, considerable noise, and lacking informative predictors. This dissertation aims to develop several systematic techniques for traffic safety modeling, including finite sample bias correction, decision-adjusted modeling, and effective risk factor construction. Poisson and negative binomial regression models are primary statistical analysis tools for traffic safety evaluation. The regression parameter estimation could suffer from the finite sample bias when the event frequency (e.g., the total number of crashes) is low, which is commonly observed in safety research. Through comprehensive simulation and two case studies, it is found that bias adjustment can provide more accurate estimation when evaluating the impacts of crash risk factors. I also propose a decision-adjusted approach to construct an optimal kinematic-based driver risk prediction model. Decision-adjusted modeling fills the gap between conventional modeling methods and the decision-making perspective, i.e., on how the estimated model will be used. The key of the proposed method is to enable a decision-oriented objective function to properly adjust model estimation by selecting the optimal threshold for kinematic signatures and other model parameters. The decision-adjusted driver-risk prediction framework can outperform a general model selection rule such as the area under the curve (AUC), especially when predicting a small percentage of high-risk drivers. For the third part, I develop a Multi-stratum Iterative Central Composite Design (miCCD) approach to effectively search for the optimal solution of any "black box" function in high dimensional space. Here the "black box" means that the specific formulation of the objective function is unknown or is complicated. The miCCD approach has two major parts: a multi-start scheme and local optimization. The multi-start scheme finds multiple adequate points to start with using space-filling designs (e.g. Latin hypercube sampling). For each adequate starting point, iterative CCD converges to the local optimum. The miCCD is able to determine the optimal threshold of the kinematic signature as a function of the driving speed. / Doctor of Philosophy / When riding in a vehicle, it is common to have personal judgement about whether the driver is safe or risky. The drivers’ behavior may affect your opinion, for example, you may think a driver who frequently hard brakes during one trip is a risky driver, or perhaps a driver who almost took a turn too tightly may be deemed unsafe, but you do not know how much riskier these drivers are compared to an experienced driver. The goal of this dissertation is to show that it is possible to quantify driver risk using data and statistical methods. Risk quantification is not an easy task as crashes are rare and random events. The wildest driver may have no crashes involved in his/her driving history. The rareness and randomness of crash occurrence pose great challenges for driver risk modeling. The second chapter of this dissertation deals with the rare-event issue and provides more accurate estimation. Hard braking, rapid starts, and sharp turns are signs of risky driving behavior. How often these signals occur in a driver’s day-to-day driving reflects their driving habits, which is helpful in modeling driver risk. What magnitude of deceleration would be counted as a hard brake? How hard of a corner would be useful in predicting high-risk drivers? The third and fourth chapter of this dissertation attempt to find the optimal threshold and quantify how much these signals contribute to the assessment of the driver risk. In Chapter 3, I propose to choose the threshold based on the specific application scenario. In Chapter 4, I consider the threshold under different speed limit conditions. The modeling and results of this dissertation will be beneficial for driver fleet safety management, insurance services, and driver education programs.

Do people become more optimistic about future adjustment to negative life events after rumination? Past research using a "top of the head" paradigm indicates that people estimate they would adjust more poorly for severe events and better for mild negative events than their peers. Selective focus (i.e., differential accessibility of information about assets and liabilities for coping) has been provided as an explanation for this effect, which is counter to research on "optimistic bias". Martin and Tesser's (1989) rumination model was applied to beliefs about one's comparative adjustment to negative life events. One hundred twenty undergraduate subjects were asked to imagine experiencing a Severe (HIV+) or Mild (Herpes) negative event at some future time, then to designate items on a reaction time task as either an Asset or Liability in coping with the event. The reaction time task and subsequent comparative adjustment ratings were made either immediately, after a delay that allowed for rumination, or after a delay without an opportunity for rumination. A thought-listing analysis of the audiotaped ruminations revealed that, as predicted, subjects became more optimistic over time. They initially discussed liabilities in coping with the Severe event but gradually considered assets. Comparative adjustment ratings for the Severe event were not significantly different than for the Mild event, even in the Rumination Absent condition. It was suggested that temporarily making assets for coping accessible through the reaction time task had the same effect on comparative adjustment ratings as did problem-solving through rumination. The reaction time data provided convergent evidence regarding selective focus and complimented a thought-listing paradigm used in previous studies. / Master of Science

The design and development of technology applications has to deal with many variables. Reference is obviously made to established hardware and software support, particularly with regard to the choice of appropriate operating systems, development model, environment and programming language. With the growth of networked and web-exposed systems, we are increasingly dealing with IoT (Internet-of-Things) systems: complex applications consisting of a network of often heterogeneous elements to be managed like an orchestra, using existing elements and creating new ones. Among the many fields affected by this phenomenon, two in particular are considered here: industry and medical, key sectors of modern society. Given the inherently parallel nature of such networks and the fact that it is commonly necessary to manage them via the Web, the most prevalent de facto model employs an architecture relying on a paradigm based on data flows, representing the entire system as a kind of assembly line in which each entity acquires input data and returns an output in a perfectly asynchronous manner. This thesis highlights some notable limitations of this approach and proposes an evolution that resolves some key issues. This has been done not only on a purely theoretical level, but with actual implementations currently operational and thus demonstrated in the field. Rather than proposing an abstract formalisation of a new solution, the basic principles of a whole new architecture are presented here instead, going into more detail on some key features and with experimental and practical feedback implemented as a full blown software platform. A first contribution is the definition of the principles of a new programming architecture, disseminated with some published articles and a speech in an international congress. A second contribution concerns a lightweight data synchronisation strategy, which is particularly useful for components that need to continue working during offline periods. A third contribution concerns a method of storing a symmetric encryption key combined with a peculiar retrieval and verification technique: this has resulted in an international patent, already registered. A fourth contribution concerns a new data classification model, which is particularly effective for processing information asynchronously. Issues related to possible integrations with artificial intelligence systems have also been addressed, for which a number of papers are being written, introduced by a presentation that has just been published.

This dissertation demonstrates how patients' mental illness treatment careers depend on the change and/or stability among differing levels of social structure. Theorists of the mental illness career tend to ignore the role that higher levels of social structural change have on individuals' mental illness career. Researchers using an organizational perspective tend to focus on the organizational environment but ignore the treatment process from the individual's point of view. Both perspectives neglect what the nation-state's broader socio-political and economic circumstances could imply for people seeking treatment for mental disorders. Organizational theory and theories of the mental illness career are independent theoretical streams that remain separate. This dissertation connects these independent theoretical streams by developing a unifying theoretical framework based on historical analysis. This historical analysis covers three phases of treatment beginning at the end of World War II to the present. This framework identifies mechanisms through which changes in larger levels of social structure can change the experience and career of mental patients. This new perspective challenges current conceptions of the mental illness career as static by accounting for the various levels of social structure that play a part in the mental illness treatment career. Taken together, the inclusion of differing levels of social structure and the subsequent reciprocal relationship between these levels of analysis produce a narrative that explains why and how stability and change within the mental health sector shape the mental illness treatment career. / Ph. D.

Corporate crisis events such as cyber attacks, executive scandals, facility accidents, fraud, and product recalls can damage customer trust and firm reputation severely, which may lead to tremendous loss in sales and firm equity value. My research aims to integrate information available on the market to assist firms in tackling crisis events, and to provide insight for better decision making. We first study the impact of crisis events on firm performance. We build a hybrid deep learning model that utilizes information from financial news, social media, and historical stock prices to predict firm stock performance during firm crisis events. We develop new methodologies that can extract, select, and represent useful features from textual data. Our hybrid deep learning model achieves 68.8% prediction accuracy for firm stock movements. Furthermore, we explore the underlying mechanisms behind how stakeholders adopt and propagate event information on social media, as well as how this would impact firm stock movements during such events. We adopt an extended epidemiology model, SEIZ, to simulate the information propagation on social media during a crisis. The SEIZ model classifies people into four states (susceptible, exposed, infected, and skeptical). By modeling the propagation of firm-initiated information and user-initiated information on Twitter, we simulate the dynamic process of Twitter stakeholders transforming from one state to another. Based on the modeling results, we quantitatively measure how stakeholders adopt firm crisis information on Twitter over time. We then empirically evaluate the impact of different information adoption processes on firm stock performance. We observe that investors often react very positively when a higher portion of stakeholders adopt the firm-initiated information on Twitter, and negatively when a higher portion of stakeholders adopt user-initiated information. Additionally, we try to identify features that can indicate the firm stock movement during corporate events. We adopt Layer-wised Relevance Propagation (LRP) to extract language features that can be the predictive variables for stock surge and stock plunge. Based on our trained hybrid deep learning model, we generate relevance scores for language features in news titles and tweets, which can indicate the amount of contributions these features made to the final predictions of stock surge and plunge. / Doctor of Philosophy / Corporate crisis events such as cyber attacks, executive scandals, facility accidents, fraud, and product recalls can damage customer trust and firm reputation severely, which may lead to tremendous loss in sales and firm equity value. My research aims to integrate information available on the market to assist firms in tackling crisis events and providing insight for better decision making. We first study the impact of crisis events on firm performance. We investigate five types of crisis events for SandP 500 companies, with 14,982 related news titles and 4.3 million relevant tweets. We build an event-driven hybrid deep learning model that utilizes information from financial news, social media, and historical stock prices to predict firm stock performance during firm crisis events. Furthermore, we explore how stakeholders adopt and propagate event information on social media, as well as how this would impact firm stock movements during the events. Social media has become an increasingly important channel for corporate crisis management. However, little is known on how crisis information propagates on social media. We observe that investors often react very positively when a higher portion of stakeholders adopt the firm-initiated information on Twitter, and negatively when a higher portion of stakeholders adopt user-initiated information. In addition, we find that the language used in the crisis news and social media discussions can have surprising predictive power on the firm stock. Thus, we develop a methodology to identify the importance of text features associated with firm performance during crisis events, such as predictive words or phrases.

The City of Stockholm has undergone a curious process of considering whether to launch a bid for the 2026 Winter Olympic Games. That Stockholm has contemplated launching a bid is not surprising from a regional perspective—the Olympic Games have not been held in a Scandinavian country since Lillehammer, Norway played host in 1994 and Sweden has never hosted the Winter Olympics. A potential bid from Stockholm would also be consistent with Sweden's self-identification and embracement of being a 'sportive nation'. Failed applications by the Swedish cities of Gothenburg, Falun, and Östersund to host the Winter Olympic Games confirm the long-standing interest of the Swedish Olympic Committee to secure the Games, although it should be noted that the Swedish Olympic Committee did not submit a bid for the 2006, 2010, 2014 or 2018 Winter Olympic Games competitions. Although recent reports indicate that Stockholm will not vie for the 2026 Winter Olympic Games, the notion that the city was even considering the option remains surprising. Stockholm had withdrawn its bid from the 2022 bidding competition citing a variety of concerns including a lack of government and public support, financial uncertainty, as well as the post-event viability of purpose-built infrastructure. Stockholm's withdrawal from the 2022 competition resonates with the growing apprehension by potential bid cities (especially those emerging from democratic countries) towards the Olympic Games. This thesis seeks to illustrate that Stockholm's Olympic hopes have book-ended a transformative period in the Olympic bidding process and to expose the struggle that bid cities have in adjusting to the demands of the IOC's bidding process. / Master of Science

Adults aged 50 years and older are a growing segment of the population and are more likely than their younger counterparts to experience significant stressors such as the death of a relative or friend, onset of chronic conditions, and increased health burden. The current studies use Pearlin's stress process model to evaluate the impact of these stressors on outcomes of depression. Study 1 used Wave 1 data from the ORANJ BOWL research panel of 5,688 New Jersey residents aged 50 and older to explore the relationship between relational life events, private religious practices, and depressed mood. Cross-sectional structural equation modeling was used to evaluate these relationships. Results showed that relational life events had a significant positive influence on depressive symptoms and this relationship was partially mediated by private religious practices. Findings indicate that non-personal life events are important sources of stress that may otherwise be overlooked when assessing risk factors among older adults. Study 2 used data from 640 men from the Research on Older Adults with HIV (ROAH) study to evaluate the impact of HIV-related health burden on depressed mood and to assess the mitigating effects of social support adequacy. Structural equation modeling showed that greater health burden was associated with more depressive symptoms; this relationship was significantly partially mediated by emotional support adequacy, which was a measure of unmet social need. Findings indicate that health burden has a cumulative impact on psychological health and programs and supports that target social wellness can improve this relationship. These studies point to the importance of understanding sources of risk and resilience among older people and in an attempt to improve overall health outcomes. / Ph. D.

The present study examined a social marketing approach to a health promotion program in stress management education that combined various aspects of large scale mass-market campaigns and individually tailored interventions. The study was conducted in two major phases using two groups from the main population of retired university faculty members. The intervention was a series of stress management seminars which was presented in each phase. Program evaluation took place at several intervals throughout the study. The first phase of the study served to assess the retirees' needs and to develop the program content and delivery style by using the target population's administrative committee. This committee became the focus group. The presentation of the stress management seminars to the focus group was specifically tailored to the group through frequent interactions and participation by the group members. On evaluation, the program was shown to be effective on a number of dimensions, but it was also labor intensive. A second phase was conducted on a larger sample from the target population of retirees. The sample was found to be equivalent to the focus group on demographic variables, stress levels, and stress management practices. This phase utilized the same program content that was developed in the first phase, but further examined program delivery. Two styles of program delivery were compared. The first was a didactic, lecture-style frequently used in large scale educational campaigns; the second was an interactive, discussion style, used more frequently in individual interventions. Overall, the program participants from both phases improved in their abilities to identify their stress symptoms, stress management strategies that they felt they would use, and increased their levels of perceived control over their stress. Factor analysis was one method used to evaluate program effectiveness and to replicate the factor structure of coping strategies from another study. The utility of factor analysis as an assessment procedure was developed and supported. No major significant differences between delivery styles were found. Thus, indirect tailoring of the program for the target population through the representative focus group was as effective as directly tailoring the program with the target population. Both the interactive and didactic approaches can be integrated into a single educational program to obtain an optimal combination of cost-effectiveness and informativeness. Once the program content was developed through the intensive process of tailoring in the first phase, the more efficient didactic delivery style could be used equally successfully with a matched population. Clinically, the study served as a cost-effective prototype of a stress-management education program for the mass-market. / Ph. D.

Nowcasting – short-term forecasting using current observations – is a key challenge that human activities have to face on a daily basis. We heavily rely on short-term meteorological predictions in domains such as aviation, agriculture, mobility, and energy production. One of the most important and challenging task for meteorology is the nowcasting of extreme events, whose anticipation is highly needed to mitigate risk in terms of social or economic costs and human safety. The goal of this thesis is to contribute with new machine learning methods to improve the spatio-temporal precision of nowcasting of extreme precipitation events. This work relies on recent advances in deep learning for nowcasting, adding methods targeted at improving nowcasting using ensembles and trained on novel original data resources. Indeed, the new curated multi-year radar scan dataset (TAASRAD19) is introduced that contains more than 350.000 labelled precipitation records over 10 years, to provide a baseline benchmark, and foster reproducibility of machine learning modeling. A TrajGRU model is applied to TAASRAD19, and implemented in an operational prototype. The thesis also introduces a novel method for fast analog search based on manifold learning: the tool leverages the entire dataset history in less than 5 seconds and demonstrates the feasibility of predictive ensembles. In the final part of the thesis, the new deep learning architecture ConvSG based on stacked generalization is presented, introducing novel concepts for deep learning in precipitation nowcasting: ConvSG is specifically designed to improve predictions of extreme precipitation regimes over published methods, and shows a 117% skill improvement on extreme rain regimes over a single member. Moreover, ConvSG shows superior or equal skills compared to Lagrangian Extrapolation models for all rain rates, achieving a 49% average improvement in predictive skill over extrapolation on the higher precipitation regimes.

yes / River morphological dynamics are complex phenomena in natural and environmental flows. In particular, the sediment transport around braid mid-channel bars has not gained enough understanding from previous research. The effect of submergence ratio on the turbulence behavior in the proximity of the bar has been investigated in this study. The spatial distribution of turbulent flow in the proximity of bar has been studied by plotting the depth-averaged two-dimensional contours of turbulent kinetic energy. The high value of TKE has been observed in regions just downstream from the bar. It is due to the vortex shedding occurring in that region. The interaction of sweep and ejection events have been analyzed using the parameter Dominance Function obtained from the ratio of occurrence probability of ejection events to the occurrence probability of sweep events. This outcome indicates that the depth averaged parameter Dominance Function has successfully predicted the high scouring region which makes it an ideal parameter for analyzing the scour phenomena in real-world water management projects. The high scouring zone lies in the close proximity of the bar. This shows that the scouring effect from the bar is limited to its close region. The magnitude of scouring occurring at the upstream region of the bar also increases with the increment of submergence ratio. The relationship of quadrant event inclination angles with the sediment transport occurring in the proximity of bar has been also studied, where an Angle Ratio parameter has been utilized for linking the bed elevation change with the inclination angle. The results indicate that the AR parameter has been successfully tested in this study to show its competence to represent the turbulent burst-induced bed sedimentation and scouring. / The author has confirmed that no changes were made to the content of this proof on publication, although the paper is watermarked uncorrected proof.