According to documented statistics, intersections are among the most hazardous locations on roadway systems. Many studies have extensively analyzed safety of signalized intersections, but did not put their major focus on the most frequent type of intersections, unsignalized intersections. Unsignalized intersections are those intersections with stop control, yield control and no traffic control. Unsignalized intersections can be differentiated from their signalized counterparts in that their operational functions take place without the presence of a traffic signal. In this dissertation, multiple approaches of analyzing safety at unsignalized intersections were conducted. This was investigated in this study by analyzing total crashes, the most frequent crash types at unsignalized intersections (rear-end as well as angle crashes) and crash injury severity. Additionally, an access management analysis was investigated with respect to the different median types identified in this study. Some of the developed methodological techniques in this study are considered recent, and have not been extensively applied. In this dissertation, the most extensive data collection effort for unsignalized intersections was conducted. There were 2500 unsignalized intersections collected from six counties in the state of Florida. These six counties were Orange, Seminole, Hillsborough, Brevard, Leon and Miami-Dade. These selected counties are major counties representing the central, western, eastern, northern and southern parts in Florida, respectively. Hence, a geographic representation of the state of Florida was achieved. Important intersections' geometric and roadway features, minor approach traffic control, major approach traffic flow and crashes were obtained. The traditional negative binomial (NB) regression model was used for modeling total crash frequency for two years at unsignalized intersections. This was considered since the NB technique is well accepted for modeling crash count data suffering from over-dispersion. The NB models showed several important variables affecting safety at unsignalized intersections. These include the traffic volume on the major road and the existence of stop signs, and among the geometric characteristics, the configuration of the intersection, number of right and/or left turn lanes, median type on the major road, and left and right shoulder widths. Afterwards, a new approach of applying the Bayesian updating concept for better crash prediction was introduced. Different non-informative and informative prior structures using the NB and log-gamma distributions were attempted. The log-gamma distribution showed the best prediction capability. Crash injury severity at unsignalized intersections was analyzed using the ordered probit, binary probit and nested logit frameworks. The binary probit method was considered the best approach based on its goodness-of-fit statistics. The common factors found in the fitted probit models were the logarithm of AADT on the major road, and the speed limit on the major road. It was found that higher severity (and fatality) probability is always associated with a reduction in AADT, as well as an increase in speed limit. A recently developed data mining technique, the multivariate adaptive regression splines (MARS) technique, which is capable of yielding high prediction accuracy, was used to analyze rear-end as well as angle crashes. MARS yielded the best prediction performance while dealing with continuous responses. Additionally, screening the covariates using random forest before fitting MARS model was very encouraging. Finally, an access management analysis was performed with respect to six main median types associated with unsignalized intersections/access points. These six median types were open, closed, directional (allowing access from both sides), two-way left turn lane, undivided and mixed medians (e.g., directional median, but allowing access from one side only). Also, crash conflict patterns at each of these six medians were identified and applied to a dataset including median-related crashes. In this case, separating median-related and intersection-related crashes was deemed significant in the analysis. From the preliminary analysis, open medians were considered the most hazardous median type, and closed and undivided medians were the safest. The binomial logit and bivariate probit models showed significant median-related variables affecting median-related crashes, such as median width, speed limit on the major road, logarithm of AADT, logarithm of the upstream and downstream distances to the nearest signalized intersection and crash pattern. The results from the different methodological approaches introduced in this study could be applicable to diagnose safety deficiencies identified. For example, to reduce crash severity, prohibiting left turn maneuvers from minor intersection approaches is recommended. To reduce right-angle crashes, avoiding installing two-way left turn lanes at 4-legged intersections is essential. To reduce conflict points, closing median openings across from intersections is recommended. Since left-turn and angle crash patterns were the most dominant at undivided medians, it is recommended to avoid left turn maneuvers at unsignalized intersections having undivided medians at their approach. This could be enforced by installing a left-turn prohibition sign on both major and minor approaches.

This dissertation traces debates about youth crime and justice in Canada. On a substantive level, I ask how the social problem of youth crime and justice is constructed, focusing specifically on debates over the culpability of young offenders. I also examine debates over the degree and severity of youth crime and connect the divergent positions on this question to how young offenders are conceptualized. Related to these debates, I examine the search for solutions to youth crime. I argue that positions regarding how to address youth crime are rendered ambiguous given the creation of a hybridized youth justice context which combines various competing goals. On a theoretical level, I explore the relationship between how formulations of "deviant identities" (in this case "young offenders") are related to other areas of advocacy over a social problem. I explore the dynamics of a social problem debate which persists without resolution over an extended period of time. I also address the ways in which social context impacts upon claims made over a social problem. / Thesis / Doctor of Philosophy (PhD)

Objectives: In this study, we examine the public’s knowledge about different levels of depression severity in Germany. Methods: Data stem from a national telephone survey in Germany. A total of 1,009 persons participated, response rate was 46.8%. A vignette was presented with signs of mild, moderate or severe depression. Participants were asked what they think the person has, which persons and services are helpful and how effective different treatment options are. Differences between the three vignettes were tested with 95% confidence intervals and χ 2 -tests. Results: 55.3% of the respondents identified depression as the health problem in question. Participants who heard the vignette with moderate symptomatology recognized depression more often. Across groups, a general practitioner was named most frequently concerning helpful persons/services. Effectiveness of psychotherapy received high levels of approval, online therapy and books were less often rated as effective. There were only few significant differences between the three vignettes. Conclusions: This is the first study examining public depression literacy for different severity levels. Small differences between severity levels indicate a lack of knowledge, which may have adverse consequences for adherence to treatment, especially for mild depression.

Budgets for transportation improvements are limited so it is important for governments to focus on improving locations most in need of safety funding. The objective of the Two-Output Model for Safety (TOMS) is to provide the Utah Department of Transportation (UDOT) a reliable method to prioritize safety improvements on state-owned roadways among the different regions. This research will improve the existing Crash Analysis Methodology for Segments (CAMS) and Intersection Safety Analysis Methodology (ISAM) being used to analyze crashes on Utah roadways. The scope of this project is improving on the existing CAMS and ISAM to work together within R, to incorporate segment and intersection severity in safety hot spot analysis, to develop overall severity distributions, and to develop limited recommendations and conclusions related to the research. TOMS uses UDOT data to create a statistical input. Each segment is homogenous with respect to five variables: average annual daily traffic, functional class, number of through lanes, speed limit, and urban code. Intersections are provided as a separate dataset. In the statistical analyses performed on the data, five years of crash data (2016-2020) are used to determine a weighted risk score for segments and intersections of similar characteristics. Those segments or intersections with excess weighted risk scores are designated as crash hot spots. Two-page technical reports with road characteristics and crash data are created for the top 10 hot spots for segments and intersections in Utah. The reports are sent to UDOT where region engineers may review and determine which locations might be addressed.

Human-caused forest fires have increased in magnitude and frequency, affecting global vegetation and requiring a re-evaluation of fire regimes. Changing fire regimes have led to reduced burned areas in fire- dependent ecosystems and increased areas in fire-independent ecosystems, resulting in changes in land cover and posing a threat to native plant communities. This study focuses on monitoring vegetation recovery after fires in California, USA, using the Enhanced Vegetation Index (EVI) from MODIS time series. The goal is to determine the full recovery time and half recovery time (HRT) after forest fires in year 2017 and analyze the influence of burn severity on three land cover classes in two different climate zones in California.Analyzes show that the "Closed Forest" land cover type exhibits the longest recovery period, followed by the "Open Forest" type and “Herbaceous/Shrub” type in both climate zones but no general connection between recovery time and climate zone was observed. It is found that burn severity degree affects HRT but not the full recovery time in both Mediterranean and Semi-arid climate zones. The study mainly highlights the variations in forest fire recovery patterns between land cover types, as well as differences observed between climate zones.

<p>The methodology leverages medical notes already annotated according to the General Assessment of Functioning (GAF) scale to develop a disease severity PDM for schizophrenia, bipolar type I or mixed bipolar and non-psychotic patients.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Psychiatric patients require continuous monitoring on par with their severity status. Unfortunately, current assessment instruments are often time-consuming. The present thesis introduces several passive digital markers (PDMs) that can help reduce this burden by automating the assessment using medical notes. The methodology leverages medical notes already annotated according to the General Assessment of Functioning (GAF) scale to develop a disease severity PDM for schizophrenia, bipolar type I or mixed bipolar and non-psychotic patients. Topic words that are representative of three disease severity levels (severe impairment, serious impairment, moderate to no impairment) are identified and the top 50 words from each severity level are used to summarize the raw text of the medical notes. The summary of the text is processed by a classifier that generates a disease severity level. Two classifiers are considered: BERT PDM and Clinical BERT PDM. The evaluation of these classifiers showed that the BERT PDM delivered the best performance. The PDMs developed using the BERT PDM can assign medical notes from each encounter to a severe impairment level with a positive predictive value higher than 0.84. These PDMs are generalizable and their development was facilitated by the availability of a substantial number of medical notes from multiple institutions that were annotated by several health care providers. The methodology introduced in the present thesis can support the automated monitoring of the progression of the disease severity for psychiatric patients by digitally processing the medical note produced at each encounter without additional burden on the health care system. Applying the same methodology to other diseases is possible subject to availability of the necessary data.

Recognizing the distinct non-motorist injury severity profiles by crash location (segment or intersection), we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The data for our analysis is drawn from the Central Florida region for the years of 2015 to 2021. The model system explicitly accounts for temporal heterogeneity across the two dimensions. A comprehensive set of independent variables including non-motorist user characteristics, driver and vehicle characteristics, roadway attributes, weather and environmental factors, temporal and sociodemographic factors are considered for the analysis. We also conducted an elasticity analysis to show the actual magnitude of the independent variables on non-motorist injury severity at the two locations. The results highlight the importance of examining the effect of various independent variables on non-motorist injury severity outcome by different crash locations.

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