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Addressing the Impact of Media Coverage on Public Perception of Active Shooter Incidents
No full textThe study examines the predictors of a high rate of media coverage for active shooter incidents in the United States using a sample of 100 incidents drawn from the ALERRT active shooter database spanning 2000 to 2023. The research evaluates the independent variables, including incident severity measured by casualties, location type, the most powerful weapon used, resolution of incident, and regional zones. The news reports were retrieved from LexisNexis, examining the initial wave of news coverage within a year of the date of the incident. Results of the multiple linear regression indicate that the total number of individuals killed is the most significant predictor of high levels of media coverage. Additionally, incidents in which offenders fled garnered more media reports when compared to active shooter incidents that resolved in the suicide of the offender, which could be linked to law enforcement using the media as a tool to assist apprehension. In conclusion, the findings contribute to the overall understanding of the characteristics of active shooter events that shape media framing and public perception of the prevalence of active shooter incidents
RiWiX: Towards Automatic River Water Width Extraction From High Resolution Satellite Images Using Swin Transformer
No full textThe increasing availability of high-resolution remote sensing data has enabled new opportunities for large-scale river studies, reducing dependence on manual field-based width measurements. While existing automated methods, ranging from thresholding to deep learning approaches such as CNNs, DeepLabV3+, and U-Net, have shown reasonable performance for wide rivers (Width\u3e30m), they remain less effective for narrow rivers due to limited semantic representation and high computational demands. Recently, vision transformers have emerged as powerful models, offering improved semantic understanding by capturing long-range dependencies through self-attention. However, their adoption in remote sensing remains limited due to the high processing cost. In this thesis, I introduce the ‘River Water Width Extraction’ (RiWiX), a lightweight, encoder-only river water surface segmentation model integrated with a novel width extraction module. The model employs a hierarchical encoder that captures multi-scale contextual features through patch-wise self-attention. At the same time, a shallow up-sampling module efficiently restores spatial resolution without relying on a heavy transformer decoder. The accompanying width extraction algorithm further constructs a graph-based river centerline. It computes perpendicular distances along the flow direction, enabling precise, continuous width estimation even in complex river geometries. Experimental evaluation shows that RiWiX surpasses the baseline transformers both in accuracy and efficiency, achieving a dice score of 0.60 with the lowest computational cost of 2830.85 GFlops. Validation on georeferenced data further confirms its consistent performance across varying river scales, yielding a mean absolute error of 0.02% for wide rivers (width\u3e500m), 0.06% for moderate rivers (5
LC-MS Discriminates Between Effects of Sulfur Nanoparticles vs Molybdenum Disulfide Nanoparticles on Lettuce (Lactuca Sativa)
No full textNanotechnology is gaining attention in agriculture, majorly due to their potential benefits in farming practices. Sulfur and molybdenum disulfide nanoparticles are reported to enhance plant growth, protection, and nutrient delivery. However, there is a need to understand how nano-fertilizers affect the metabolites and nutritional benefits of plants. Here, lettuce was cultivated under greenhouse in one of three treatments: control, 50 ppm S NPs, and 20 ppm MoS2 NPs. A method was developed on Liquid Chromatography – Mass Spectrometry (LC-MS) instrument to quantify the concentration of amino acids and antioxidants in the harvested lettuce plants. Inductively Coupled Plasma – Mass Spectrometry (ICP-MS) was used to analyze elemental compositions of macro and micronutrients of the plants. Other phenotypic parameters such as biomass, chlorophyll content, and oxidative stress were also measured to determine the overall effect of the nanoparticles on plant health. Interestingly, no significant difference was seen in the biomass, chlorophyll content, and lipid peroxidation between treatments. The elemental concentrations were mostly not affected except for a decrease in S content and an increase in Mo content of plants treated with MoS2 NPs. Significant changes of up to 164% were detected in the levels of six antioxidants in the nanoparticle-treated plants including glutathione, tocopherol, ferulic acid, vanillic acid, p-coumaric acid, and trifluoromethyl cinnamic acid. Although most of the detected amino acids show no significant difference across treatments, the levels of leucine, isoleucine, serine, and asparagine were up-regulated by up to 50%, and glutamine level was down-regulated by 58%, in plants exposed to MoS2 NPs compared to control and S NPs. The findings revealed that S NPs and MoS2 NPs caused dysregulation in antioxidants and amino acids but did not affect growth and oxidative stress. Furthermore, an LC-MS method was developed that effectively differentiated between metabolite dysregulation in lettuce exposed to S NPs and MoS2 NPs
Interaction of Tau With Pegylated Gold Nanoparticles
No full textTau protein aggregation is a hallmark of Alzheimer’s disease and related tauopathies, where the formation of toxic oligomeric intermediates leads to neuronal dysfunction and cell death. In this study, the interaction between Tau protein and PEGylated gold nanoparticles (AuNPs-PEG) was investigated to explore their potential role in modulating the fibrillation. The gold nanoparticles were made via a two-step process incorporating thiol-terminated polyethylene glycol (PEG-SH) and characterized using dynamic light scattering (DLS) and localized surface plasmon resonance (LSPR). The effect of AuNP-PEG on the kinetics of Tau aggregation was examined using thioflavin T(ThT), Congo red, and Nile red assays, which investigate β-sheet formation, amyloid binding, and hydrophobicity measurement, respectively. The outcome of the above assays clearly demonstrated that the AuNP-PEG inhibited Tau fibril formation, being able to increase the lag-time period length and decreasing the change into mature fibrils, which inhibited the generation of the more toxic oligomeric intermediate stage. LC-MS analysis confirms that PEGylation alters the proteomic landscape, influencing protein abundance, binding behavior and the biological response. Overall, these findings demonstrate that AuNPs-PEG can act as effective inhibitors of Tau aggregation through modulation of early nucleation events. This study provides mechanistic insight into the design of PEG-based nanoparticle systems as potential nanotherapeutic platforms for mitigating Tau-mediated neurodegeneration in Alzheimer’s disease
Predicting Real Estate Prices Using Deep Learning Regression Models on Socio Spatial Data
No full textABSTRACT
Cities keep their own kind of ledger. Every block, bus stop, corner store, and year that slips by leaves a small entry about what homes are worth. That ledger is what we call socio-spatial data: simple facts about what a home is (its age), where it sits (latitude/longitude), how easy it is to get around (distance to the nearest MRT station), what’s nearby (number of convenience stores), and when it sold (transaction date). This thesis asks a practical question in that everyday language: given these common clues, can we predict home prices more accurately and explain why? Using 414 recorded transactions, we compared three approaches that rise in complexity: Ordinary Least Squares (OLS), Ridge regression, and a compact Multi-Layer Perceptron (MLP). We trained with a 70–15–15 split and early stopping, then judged performance out of sample using MAE, RMSE, and R², with visual checks (actual-vs-predicted, residual distributions), and a partial dependence view of how price changes with MRT distance. The results are consistent and practical. The MLP reduced typical error and cut large mistakes relative to linear baselines: MAE 5.01 vs. 5.49 (OLS) and 5.41 (Ridge); RMSE 6.59 vs. 7.20 and 7.14; R² 0.740 vs. 0.689 and 0.694 meaning tighter price bands where it matters. The distance story is clear and human: prices fall steeply as you move from the station out to roughly 600–700 m, then flatten to a gentle decline beyond ~1.5 km, a familiar pattern of convenience paying a premium. Two lessons follow. First, much of housing value really is written in the map: accessibility and neighborhood context carry weight even in small feature sets. Second, a modest neural model, used responsibly and explained with simple graphics, can turn those everyday clues into more reliable, fewer-surprise predictions. These balance strong linear baselines for transparency, a compact MLP for accuracy, and clear diagnostics offers a practical blueprint for valuation teams, planners, and lenders who work with the city’s ledger every day
The Long-Term Psychological Effects of Violent Crime on Victims: A Review of Evidence and Supportive Interventions
No full textThis study utilizes anonymous survey data collected from 26 mental health professionals in various regions of Canada to understand which psychological disturbances are the most prevalent among victims of violent crime as well as which psychological interventions are most beneficial for treating violent crime victims. The data revealed that Post-Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), Generalized Anxiety Disorder (GAD), anger, guilt, self-blame, and shame are each highly prevalent. Findings also revealed that certain therapeutic methods including Eye Movement Desensitization Reprocessing (EMDR), Supportive Counselling (SC), Progressive Relaxation (PR), Cognitive Reprocessing Therapy (CPT) and Cognitive Restructuring (CR) are associated with advantageous outcomes for victims. Additionally, the findings also establish the importance of several components in promoting therapeutic success, including the integration of multiple therapies, an individualized, trauma-informed approach, victim readiness and willingness to engage in treatment, strong family and social support, clinician cultural competency, and continuous follow-up care. The final chapter of this thesis summarizes the core findings of the study and provides a discussion of the limitations of this study as well as suggestions for further research
The Effect of Hypochlorous Acid on Cultured Osteoblast and Escherichia Coli
No full textHypochlorous acid (HOCl), a naturally occurring compound in neutrophils, has been used as an antimicrobial in wound treatment. Osteoblasts, bone-forming cells, within the bone tissue play a large role in bone fracture healing. Orthopedic infections are challenging to treat and highly morbid, including surgical site infection of arthroplasty procedures or infection in the setting of compound fractures. HOCl is being considered as a surgical antiseptic to decrease infections in orthopedic surgical procedures. This study therefore seeks to determine the effects of HOCl on osteoblasts and E. coli. Cultured osteoblasts were incubated with varying concentrations of HOCl to examine osteoblast survivability. Upon examining osteoblasts in the presence of varying concentrations of HOCl, it was determined that survivability is not affected at 10 ppm and was 51% at 50 ppm HOCl. The most common pathogens associated with osteomyelitis (bone infection) following compound fracture are Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. To examine antimicrobial effects of HOCl, this work looked at the ability of 5 and 50 ppm HOCl to inhibit growth of E. coli. Bacterial cultures were exposed to HOCl for 5 or 15 minutes, plated on nutrient agar (NA) or Eosin-Methylene Blue (EMB) agar, incubated 24-48 hours, and then colony forming units were used to determine percent kill. E. coli was killed to over 99% with 50 ppm HOCl, while ddH2O showed a better ability to kill E. coli than HEPES solution in the 5 ppm HOCl concentration. A growing body of research shows that HOCl is a safe and effective antiseptic in various medical settings. This study suggests HOCl may have a role in orthopedic surgical procedures
A Retrieval Augmented Approach to Improving Accuracy of Biomedical Term Normalization by Large Language Models
No full textOntology normalization is crucial in biomedical text processing, as it enables the mapping of medical expressions to standardized ontology terms and their corresponding identifiers. This thesis explores the feasibility of using large language models (LLMs) for ontology normalization, with a specific focus on the Human Phenotype Ontology and Gene Ontology. Prior research studies indicated that LLMs employing zero-shot learning tend to exhibit low accuracy and are prone to frequent hallucinations. We propose a retrieval augmented generation (RAG) approach to address these limitations and enhance normalization accuracy. We generated synthetic test sets of ontology-derived synonyms to evaluate normalization performance and developed a validation and classification method based on BioBERT embeddings and cosine similarity. The normalization task aimed to match each generated synonym back to its original seed term. We utilized three large language models (GPT-4o, Llama 3.3 70B, and Phi-4) in our experiments to ensure robustness and generalization of results. The results provide insights into the strengths and limitations of each model, emphasizing the importance of candidate list retrieval quality, embedding strategies, and the inherent capabilities of the language models in influencing normalization performance. Across all test cases, RAG consistently outperformed zero-shot learning and maintained stable performance regardless of term frequency in both ontologies. The findings of this thesis also highlight the effectiveness of LLM-based ontology normalization and underscore the potential of RAG to enhance accuracy and robustness in biomedical text processing. These insights contribute to advancing automated biomedical knowledge integration and retrieval, thereby improving interoperability and facilitating data-driven decision-making in healthcare and the life sciences
The Effect of Polyethylene Glycol Surface Density of Dense Brush Gold Nanoparticles on Insulin Fibrillation
No full textThe development of various neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease has been linked to the fibrillation of various proteins within the brain. Parkinson’s disease is specifically linked to the fibrillation of α-Synuclein. During the fibrillation process, the proteins take the form of oligomers, and exposure to these oligomers has been linked to various cytotoxic and neurotoxic effects. Once the fibrillation process is complete, the fibrils made have been shown to be much less toxic than the oligomer stage. This fibrillation process is thermodynamically favorable and is largely irreversible, so one course of action for treatment of these disease is to limit exposure to protein oligomers that have been linked to various diseases. This can be done by either preventing the fibrillation process or by accelerating it to limit exposure to the oligomer stage of the protein. Gold nanoparticles show promise for affecting this fibrillation process. This investigation looks into the effect of surface density of polyethylene glycol coated gold nanoparticles on the fibrillation of insulin as a model protein for α-Synuclein, in hopes of establishing a link between acceleratory and inhibitory fibrillation patterns and different physical properties of the nanoparticles
Nexus of Fear, Deterrence, and Prestige: The Role of Russia\u27s Exotic Nuclear Toolkit
No full textThis thesis examines Russia’s development of “exotic” nuclear weapon systems, specifically the Poseidon nuclear-powered torpedo, the Avangard hypersonic glide vehicle, the Kinzhal hypersonic air-launched ballistic missile, and the Burevestnik nuclear-powered cruise missile, to determine how and why Russia diverged from traditional nuclear platforms. Drawing on open-source data and comparative analysis against the standard nuclear triad, it investigates the doctrinal, technical, and political motivations driving Moscow’s nuclear modernization, including paranoia over U.S. missile defense, aspirations for great-power status, and domestic economic influences. Scenario-based assessments illustrate the potential destabilizing effects of these systems, which compress decision-making time and raise risks of inadvertent escalation. Despite formidable engineering and financial obstacles, Russia’s pursuit of these weapons signals a commitment to ensuring the credibility of its deterrent by circumventing existing defense architectures. By highlighting the gaps in arms control frameworks and underscoring the broader implications for the international security environment, this research offers insights into how evolving nuclear technologies may alter future crisis management, deterrence theory, and international security policy