Exploring Risk Factors Influencing Motorists’ Crash Injury Severities in District Peshawar, Pakistan

Traveling in a motor car is a popular mode of conveyance across Pakistan. Similarly, motorists constitute about 66% of total registered cars in Peshawar District, which observed a 200% increase from 2006 to 2016. The current research estimates a Random Parameter Logit (RPL) Model by heterogeneousness means & variations in order to identify various parameters that contribute to the motorists\u27 severity of injury. The effects of motorist traits, temporal characteristics, motor vehicle features, roadway attributes, weather characteristics, and the effects of speed limits were predominantly considered for this analysis. Generally, typical approximation results show that the chance and severity of injuries increase for accidents involving young drivers, winter indicators, and crashes that occurred between 10 AM-02 PM. Likewise, minor wound smashes are more likely to involve senior drivers, occurring during sunny weather, in the autumn season, and in the month of August. Safety measures are suggested based on the findings of this research study to improve motorists\u27 safety, e.g., educating drivers about traffic rules and safety, zero tolerance for driving without a valid license, and enforcing speed limits on the roads. The results of this study will help in formulating strategies to improve motorists\u27 safety

Investigating HpaB as a Modulator of Gut-Brain Connectivity through Neurotransmitter Metabolism by Gut Microbiota

The gut-brain axis represents a bidirectional communication network linking the gastrointestinal tract and the central nervous system. A growing body of research implicates gut microbiota in modulating host neurochemistry, largely through microbial metabolism of neurotransmitters. This thesis investigates the flavin-dependent monooxygenase HpaB as a putative microbial enzyme involved in host dopamine metabolism, thereby influencing gut-brain signaling. Traditionally characterized for its role in the homoprotocatechuate (HPC) pathway, HpaB catalyzes the hydroxylation of 4-hydroxyphenylacetate (4-HPA) to 3,4-dihydroxyphenylacetate (3,4-DHPA)—a reaction structurally analogous to eukaryotic dopamine oxidation to DOPAC. This structural and chemical convergence suggests that HpaB may act on host-derived catecholamines. In vivo expression data, phylogenetic analysis, and gut colonization models for strains of human microbiota further the relevance of HpaB homologs in host-associated bacterial taxa. Notably, behavioral phenotypes in animal models resulting from host-associated strains overexpressing HpaB—such as impaired locomotion, altered sensory perception, and reproduction—mirror symptoms associated with dopaminergic imbalance. This work proposes a novel mechanism by which bacterial aromatic monooxygenases may deplete or modify host neurotransmitters in situ, with implications for host gut motility, immune function, and behavior. If validated through biochemical assays and animal models, HpaB could represent a novel enzyme-mediated mechanism at the interface of environmental metabolism and neurochemical regulation. The findings expand the conceptual framework of microbial endocrinology and open new avenues for targeting microbial enzymes to influence host neurological health

Using Human Interaction with Natural Language Processing Techniques to Reinforce Vocabulary Comprehension and Usage

This dissertation proposes SENCE: SENtence Curation and Evaluation - a Natural Language Processing (NLP) aid to be used in an educational setting for children. SENCE is designed as an AI-augmented tool for educators such as general and special education teachers and practicing school-based speech-language pathologists who work with children. While several commercially available products incorporate NLP techniques for teaching adults language skills, the field is still nascent for incorporating NLP into teaching aids for children with learning disorders. SENCE uses NLP techniques to reinforce vocabulary comprehension and usage in children. Additionally, it integrates human interaction with NLP techniques, allowing domain specialists to improve results before they are presented to students. SENCE uses off-the-shelf NLP libraries such as spaCy and Stanza in combination with NLP techniques such as lemmatization, part-of-speech tagging, and vocabulary similarity. These methods are integrated to identify key vocabulary words and sentences using those keywords. An evaluation is created based on these keywords and sentences. SENCE thereby creates an automated process to gauge students’ vocabulary comprehension over time. The evaluations can be shared between classes and instructors. Further, students can be quickly assessed for retention of words taught earlier in the school year. Through these methods, SENCE provides a novel and easy-to-use NLP-powered application for non-computer scientists to use NLP for everyday classroom tasks

Consumer Returns in Electronic Commerce

This dissertation has two empirical essays that explore consumer returns in electronic commerce. The goal of this work is to help online retailers to improve customer satisfaction, enhance customer loyalty, and eventually increase profitability by optimizing consumer returns management strategies. The first essay explores the effects of customer procrastination and reverse logistics time on customer loyalty from the customer co-production perspective. We construct econometric models for the entire online return process. Using a proprietary dataset from one reverse logistics management firm, we apply logistic regression and the Cox proportional hazards regression models to verify our hypotheses. We find that both customer procrastination and reverse logistics time negatively affect customer loyalty. Our results suggest that reducing customer procrastination or shortening reverse logistics time can decrease consumer return rates and enhance customer retention. Further analysis using consumer psychology theories indicates that customer procrastination is associated with demographic factors like gender and ethnicity. These results suggest that online retailers should reconsider consumer returns not merely as an unavoidable cost of doing business but as a strategic opportunity for fostering positive customer engagement. By leveraging returns as a touch-point for enhancing customer experience, retailers can drive future sales and achieve long-term profitability. Motivated by differences in customer procrastination across gender and ethnicity, the second essay examines how gender and ethnic diversities impact consumer returns in the luxury fashion industry. Although both gender and ethnicity are well studied in consumer behavior and retail operations, the academic literature lacks insights into how a consumer’s gender and ethnicity drive their return behaviors. We address this gap by analyzing 1.8 million online transactions across the US, using algorithms to predict customers\u27 gender and ethnicity from their names and neighborhood demographics. Through logistic regression and mediation analysis, we identify distinct return patterns across different demographic groups. These findings enable targeted operational strategies to reduce returns and increase retention, allowing firms to tailor marketing approaches to accommodate varying return tendencies among consumer groups, ultimately improving operational efficiency and supporting localized marketing strategies