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A finite element model was developed for a range of human head-sUAS impacts to provide multiple case scenarios of impact severity at two response regions of interest: global and local. The hypothesis was that for certain impact scenarios, local response injuries of the brain (frontal, parietal, occipital, temporal lobes, and cerebellum) have a higher severity level compared to global response injury, the response at the Center of Gravity (CG) of the head. This study is the first one to predict and quantify the influence of impact parameters such as impact velocity, location, offset, and angle of impact to severity of injury. The findings show that an sUAS has the potential of causing minimal harm under certain impact scenarios, while other scenarios cause fatal injuries. Additionally, results indicate that the human head’s global response as a less viable response region of interest when measuring injury severity for clinical diagnosis. It is hoped that the results from this research can be useful to assist decision making for treatments and may offer different perspectives in sUAS designs or operation environments.

Thesis (M. Agricultural Management (Plant Production)) -- University of Limpopo, 2020 / Tomato is a major vegetable crop produced in Limpopo province by subsistence and commercial farmers. Biotic and abiotic factors such as diseases and drought affect its productivity and yield negatively. The most economically important tomato disease in Limpopo is early blight, caused by Alternaria solani. Early blight affects tomato growing under stressful conditions such as drought. The main aim of this study was to evaluate water management in tomato production and its effect on tomato yield, early blight occurrence and severity under smallholder farming systems in Giyani. A survey study and field experiments on early blight occurrence and severity were conducted in the Greater Giyani municipality in Limpopo province. Firstly, a survey of early blight was conducted in tomato fields selected from three service centres namely Guwela, Hlaneki and Mhlava Willem. Twenty five potential tomato grower’s farms were randomly selected for survey based on the location and production management strategies. A second study was carried out by establishing field experiments at two farms in the same area, namely: A hi tirheni Mqekwa and Duvadzi farms. The experiments were laid out in a randomized complete block design (RCBD) in a split plot arrangement, with irrigation as the main plot treatment and tomato variety as the subplot treatment. Each treatment was replicated four times. The irrigation treatments consisted of full irrigation up to field capacity and deficit irrigation. The tomato varieties planted were HTX14, Rodade, STAR9006 and Commander, and were irrigated with a drip irrigation system. The overall survey results showed that Guwela s/c had high disease incidence and the lowest disease severity as compared to the other two service centers. The lowest disease incidence was observed at Hlaneki s/c whilst Mhlava willem s/c had the highest disease severity. The two water treatments (full and deficit irrigation) in both farms have shown no significant pairwise differences among xx the volumetric water content means. There was no significant difference (P≥0.05) in early blight disease severity in both farms. Results shows that, all tomato varieties did not differ significantly (P≥0.05) in yield observed between treatments at Duvadzi and A hi tirheni Mqekwa Farm. Deficit irrigation can be used effectively to produce high yields in tomato production provided the cropping season window does not coincide with the hottest time of the year. Full and deficit irrigation did not have influence on the early blight disease incidence on tomato plants. Key words: Alternaria solani; disease incidence; disease severity and drip irrigation

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Adverse Childhood Experiences (ACEs) including experiences of neglect and abuse have been shown to negatively impact the victims’ health outcomes. While the ACE score methodology has been widely accepted as the method for measuring childhood trauma, the severity of the abuse has not been accounted for through research. The aim of this research was to assess the duration and frequency of childhood emotional and physical abuse in addition to prevalence among college aged adults, to bridge this gap in the literature. A modified ACE and health behavior questionnaire was administered online at one university beginning July through December of 2014. A sample of 965 participants aged ≥ 18 were included in this study. Two ACE questions, “did a parent or other adult in the household often or very often swear at you, insult you, put you down, or humiliate you? Or, act in any way that made you afraid that you might be physically hurt?” and “did a parent or other adult in the household often or very often push, grab, slap, or throw something at you? Or ever hit you so hard that you had marks or were injured?” were asked to measure the prevalence of emotional and physical abuse, respectively. Descriptive statistics were completed in SAS for age, race, gender, emotional abuse and physical abuse. Frequencies, proportions and corresponding p-values were reported. The sample distribution included: female (69%), age average 20 years (M = 20.21, SD = 4.07), and white (84.9%). In total 29.01% of the sample reported exposure to childhood emotional or physical abuse and 37.5% reported both experiences. Females reported a higher proportion of emotional abuse and less proportion of physical abuse (27.59% and 12.29%) compared to males (24.50% and 15.44%). However, gender differences were not significant, X2 = 1.01, p>0.05 and X2 = 1.77, p>0.05. Of those reporting abuse, about two thirds of emotional abuse and one third of physical abuse occurred frequently defined as occurring either ‘daily’, ‘at least once a week’ or ‘at least once a month’. Modal frequency was ‘at least once a week’ for both female (30.74%) and male (20.31%). More than half of the abusive experiences (60.3% emotional and 53.9% physical) happened for more than two years. Experiences of childhood emotional and physical abuse are common among this sample of college age adults and the occurrence of one type of abuse is most often accompanied by the occurrence of another. This result is consistent with national research that has been done on adult populations. In cases where abuse occurred for this sample, the experiences were frequent and for an extended period of time. An understanding of the prevalence of childhood physical and emotional abuse among college aged adults and the discussion of its implications should be included in college health and counseling programs. Knowledge of the frequency and duration of abuse is critical in identifying the high risk population and developing personalized and targeted programs addressing their specific needs.

Human activities and rapid global climate change are altering fire regimes with potential threat to the stability of aspen ecosystems in North America. Aspen is an early successional species that plays an important role in post-fire forest reestablishment, but chronic browsing on juvenile aspen by large ungulate herbivores after fire can be detrimental and lead to regeneration failure. Although larger and more severe fires are expected to become more prominent, whether and how this may influence aspen and ungulate communities remains unclear. The objective of this research was to examine how the relationship between aspen and ungulate communities might be influenced by variation in fire severity and size. In 2012, we examined browse patterns, growth responses and defense chemistry (phenolic glycoside and condensed tannins) concentrations of regenerating aspen that experienced variable burn severity in the 2010 Twitchell Canyon Fire, Utah, USA. We found that greater light availability in higher severity burn environments enhanced aspen tolerance and resistance against herbivory by increasing growth potential and defense chemistry concentrations of aspen. These results suggest that burn severity influences plant-herbivore interactions through bottom-up and top-down forces, and that higher fire severity increases post-disturbance vegetation recruitment potential by increasing resilience to herbivory. In 2013, we characterized aspen and ungulate patterns of 25 fires that spread across five National Forests (Uinta-Wasatch-Cache NF, Ashley NF, Fishlake NF, Dixie NF, and Manti-La Sal NF) in the state of Utah. We identified interaction effects between fire size and severity that strongly influenced aspen and ungulate densities. Fire size and severity are important ecological filters that can interact to affect forest reestablishment and community response. This information is useful in developing decision-making tools for wildfire and ungulate management that can more effectively increase the long-term resilience of forests systems.

Introduction: The notion that depression is a disorder that moves along a continuum is well-established. Similarly, the belief in the continuity of mental illness is considered an important element in the stigma process. Against this background, it is the aim of this study to examine whether public continuum beliefs vary with the severity of depressive symptoms. Methods: Analyses were based on computer-assisted telephone interviews (CATIs) conducted in winter 2019/2020 in Germany (N = 1,009, response rate 46.8%). Using three vignettes representing mild, moderate, and severe depressive symptoms, beliefs regarding the continuity of symptoms, specifically a fundamental difference, were assessed with seven items. Sociodemographic characteristics and own experiences with depression (affliction and contact) were introduced as covariates. Results: Significant differences between the three groups of severity were found for the majority of the items measuring continuum beliefs or perceived fundamental difference. However, only few items showed a linear trend indicating a parallel between symptom severity and beliefs. Multivariate regression models showed that a moderate degree of depression was positively associated with stronger continuum beliefs but also with greater perceived difference compared to the mild degree, while no significant associations emerged for the severe vignette. Limitations: Although a comparison of our sample with official statistics supports the external validity, we cannot rule out a selection bias. It is arguable in how far short case vignettes convey a holistic picture of a person affected by depressive symptoms. Conclusion: Our results do not indicate a parallel between symptom severity and public continuum beliefs.

Severe liver and kidney dysfunction are prevalent in 10%-30% of the Southeast Asian population. Dengue virus infection has been reported as a modifiable risk factor for liver and kidney dysfunction, especially among children in Southeast Asia. Epidemiologic studies assessing this relationship are sparse, often failed to include children, and did not adjust for important covariates. Therefore, we evaluated the relationship between dengue virus infection and liver and kidney dysfunction among hospitalized children in Cambodia (n=551). Participants with a serologically confirmed dengue virus infection were categorized according to increasing severity of infection (i.e. dengue fever, dengue hemorrhagic fever, or dengue shock syndrome) using clinical assessment. Laboratory assays were used to assess liver (i.e. albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total protein) and kidney protein levels (i.e. creatinine and urea). Descriptive statistics were used to assess the impact of severity of dengue virus infection on kidney dysfunction. Additionally, descriptive statistics and linear mixed modeling were used to assess the impact of severity of dengue virus infection on liver dysfunction while adjusting for important risk factors. Approximately 75% of all participants had abnormal liver or kidney protein level(s) over the first four days of follow-up. Overall, a negative association was observed between increasing dengue disease severity and albumin (ßadj = -0.08, 95% CI = -0.45 to 0.29), ALT (ßadj = -18.02, 95% CI = -51.59 to 15.55), and AST (ßadj = -7.18, 95% CI = -49.57 to 35.21) protein levels. A positive association was observed between increasing dengue disease severity and total protein levels (ßadj = 7.14, 95% CI = 1.15 to 13.13). While abnormal liver function (increases in ALT and/or AST levels and decreases in albumin and/or total protein levels) is a common clinical finding in dengue infections, we did not find evidence for a significant association with more severe forms of dengue virus infection (i.e. dengue hemorrhagic fever or dengue shock syndrome) and greater liver dysfunction as compared to patients with dengue fever in pediatric populations in Cambodia.

Statistics show that signalized intersections are among the most dangerous locations of a roadway network. Different approaches including crash frequency and severity models have been used to establish the relationship between crash occurrence and intersection characteristics. In order to model crash occurrence at signalized intersections more efficiently and eventually to better identify the significant factors contributing to crashes, this dissertation investigated the temporal, spatial, and site correlations for total, rear-end, right-angle and left-turn crashes. Using the basic regression model for correlated crash data leads to invalid statistical inference, due to incorrect test statistics and standard errors based on the misspecified variance. In this dissertation, the Generalized Estimating Equations (GEEs) were applied, which provide an extension of generalized linear models to the analysis of longitudinal or clustered data. A series of frequency models are presented by using the GEE with a Negative Binomial as the link function. The GEE models for the crash frequency per year (using four correlation structures) were fitted for longitudinal data; the GEE models for the crash frequency per intersection (using three correlation structures) were fitted for the signalized intersections along corridors; the GEE models were applied for the rear-end crash data with temporal or spatial correlation separately. For right-angle crash frequency, models at intersection, roadway, and approach levels were fitted and the roadway and approach level models were estimated by using the GEE to account for the "site correlation"; and for left-turn crashes, the approach level crash frequencies were modeled by using the GEE with a Negative Binomial link function for most patterns and using a binomial logit link function for the pattern having a higher proportion of zeros and ones in crash frequencies. All intersection geometry design features, traffic control and operational features, traffic flows, and crashes were obtained for selected intersections. Massive data collection work has been done. The autoregression structure is found to be the most appropriate correlation structure for both intersection temporal and spatial analyses, which indicates that the correlation between the multiple observations for a certain intersection will decrease as the time-gap increase and for spatially correlated signalized intersections along corridors the correlation between intersections decreases as spacing increases. The unstructured correlation structure was applied for roadway and approach level right-angle crashes and also for different patterns of left-turn crashes at the approach level. Usually two approaches at the same roadway have a higher correlation. At signalized intersections, differences exist in traffic volumes, site geometry, and signal operations, as well as safety performance on various approaches of intersections. Therefore, modeling the total number of left-turn crashes at intersections may obscure the real relationship between the crash causes and their effects. The dissertation modeled crashes at different levels. Particularly, intersection, roadway, and approach level models were compared for right-angle crashes, and different crash assignment criteria of "at-fault driver" or "near-side" were applied for disaggregated models. It shows that for the roadway and approach level models, the "near-side" models outperformed the "at-fault driver" models. Variables in traffic characteristics, geometric design features, traffic control and operational features, corridor level factor, and location type have been identified to be significant in crash occurrence. In specific, the safety relationship between crash occurrence and traffic volume has been investigated extensively at different studies. It has been found that the logarithm of traffic volumes per lane for the entire intersection is the best functional form for the total crashes in both the temporal and spatial analyses. The studies of right-angle and left-turn crashes confirm the assumption that the frequency of collisions is related to the traffic flows to which the colliding vehicles belong and not to the sum of the entering flows; the logarithm of the product of conflicting flows is usually the most significant functional form in the model. This study found that the left-turn protection on the minor roadway will increase rear-end crash occurrence, while the left-turn protection on the major roadway will reduce rear-end crashes. In addition, left-turn protection reduces Pattern 5 left-turn crashes (left-turning traffic collides with on-coming through traffic) specifically, but it increases Pattern 8 left-turn crashes (left-turning traffic collides with near-side crossing through traffic), and it has no significant effect on other patterns of left-turn crashes. This dissertation also investigated some other factors which have not been considered before. The safety effectiveness of many variables identified in this dissertation is consistent with previous studies. Some variables have unexpected signs and a justification is provided. Injury severity also has been studied for Patterns 5 left-turn crashes. Crashes were located to the approach with left-turning vehicles. The "site correlation" among the crashes occurred at the same approach was considered since these crashes may have similar propensity in crash severity. Many methodologies and applications have been attempted in this dissertation. Therefore, the study has both theoretical and implementational contribution in safety analysis at signalized intersections.

Arterial roads constitute the majority of the centerline miles of the Florida State Highway System. Severe injury involvements on these roads account for a quarter of the total severe injuries reported statewide. This research focuses on driver injury severity analysis of statewide multilane high speed arterials using crash data for the years 2002 to 2004. The first goal is to test different ways of analyzing crash data (by road entity and crash types) and find the best method of driver injury severity analysis. A second goal is to find driver, vehicle, road and environment related factors that contribute to severe involvements on multilane arterials. Exploratory analysis using one year of crash data (2004) using binary logit regression was used to measure the risk of driver severe injury given that a crash occurs. A preliminary list of significant factors was obtained. A massive data preparation effort was undertaken and a random sample of multivehicle crashes was selected for final analysis. The final injury severity analysis consisted of six road entity models and twenty crash type models. The data preparation and sampling was successful in allowing a robust dataset. The overall model was a good candidate for the analysis of driver injury severity on multilane high speed roads. Driver injury severity resulting from angle and left turn crashes were best modeled by separate non-signalized intersection crash analysis. Injury severity from rear end and fixed object crashes was best modeled by combined analysis of pure segment and non-signalized intersection crashes. The most important contributing factors found in the overall analysis included driver related variables such as age, gender, seat belt use, at-fault driver, physical defects and speeding. Crash and vehicle related contributing factors included driver ejection, collision type (harmful event), contributing cause, type of vehicle and off roadway crash. Multivehicle crashes and interactions with intersection and off road crashes were also significant. The most significant roadway related variables included speed limit, ADT per lane, access class, lane width, roadway curve, sidewalk width, non-high mast lighting density, type of friction course and skid resistance. The overall model had a very good fit but some misspecification symptoms appeared due to major differences in road entities and crash types by land use. Two additional models of crashes for urban and rural areas were successfully developed. The land use models' goodness of fit was substantially better than any other combination by road entity or the overall model. Their coefficients were substantially robust and their values agreed with scientific or empirical principles. Additional research is needed to prove these results for crash type models found most reliable by this investigation. A framework for injury severity analysis and safety improvement guidelines based on the results is presented. Additional integration of road characteristics (especially intersection) data is recommended for future research. Also, the use of statistical methods that account for correlation among crashes and locations are suggested for use in future research.