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Advancing structural applications of nickel electrodeposition onto additively manufactured polymers
No full textMetal electrodeposition onto polymeric substrates yields a favorable method to produce high-quality components for injection molding and other manufacturing and engineering applications. This work examines the fundamental electrochemical mechanisms involved in the deposition of nickel from a Watts-type electrolyte. It outlines the complex interplay of electrolyte composition and process parameters to optimize deposition quality. Furthermore, this thesis contrasts traditional primary metallization methods with advanced techniques such as silver conductive paint revealing distinct advantages. Evaluations of additive-free and saccharin-enhanced Watts electrolytes demonstrate that saccharin significantly improves surface smoothness, reduces porosity, enhances hardness to over 550 HV, and increases tensile strength up to 800 MPa, although with increased brittleness address by post processing heat treatment. This work also explores novel methods of electrodeposition of nitinol binary alloy with promising composition results revealing near equal deposition by weight
Advancements in Gunshot Detection: AI, IoT, and Deep Learning for Real-Time Response
Get PDFGun violence is a growing public safety concern, highlighting the need for fast and accurate gunshot detection systems (GDS) to support real-time law enforcement response. This review explores the integration of deep learning (DL), artificial intelligence (AI), and the Internet of Things (IoT) in enhancing GDS performance. It summarizes recent advances ranging from physics-based acoustic modeling to data-driven methods like CNNs, RNNs, and hybrid neural networks, along with edge-based IoT frameworks for low-latency alerts. The review also examines challenges such as differentiating gunshots from similar sounds, ensuring robustness across environments, and addressing surveillance-related privacy concerns. Case studies from urban, indoor, and wildlife deployments illustrate real-world applications. By synthesizing current methods and deployment strategies, this work identifies emerging research opportunities to guide the development of scalable, ethical, and real-time safety solutions.
YouYube Video Link: https://youtu.be/NNXk5iZ4id
Study of the relationship between interoceptive sensibility and distress tolerance in community and college samples
Get PDFInteroceptive sensibility involves the detection, integration, and interpretation of internal bodily signals, primarily from cardiovascular, pulmonary, or gastrointestinal systems. Dysfunction in interoceptive processing has been increasingly recognized as a clinically relevant factor in numerous neurological, psychiatric, and behavioral disorders. This study aimed to elucidate the relationship between interoceptive sensibility and distress tolerance, hypothesizing that individuals with heightened interoceptive sensibility may report poorer distress tolerance. This relationship could help identify patterns between interoceptive sensibility and tendencies toward maladaptive coping strategies or emotional dysregulation. To explore these relationships, two studies were conducted utilizing self-report measures of interoceptive sensibility and distress tolerance in community and college samples. In a sample of college students, a significant positive correlation between interoceptive sensibility and poorer distress tolerance was found. In a sample of participants from a southeastern United States metropolitan area, a non-significant positive relationship between interoceptive sensibility and poorer distress tolerance was observed. Implications, limitations, and future directions are discussed
Enhancing Bystander Intervention in Sexual Harassment Through Brief Empathy Training
No full textSexual harassment (SH) persists as a significant barrier to healthy, productive work environments, particularly in male-dominated fields like academia and industry. Traditional compliance-based training often yields limited behavioral change, especially among men, highlighting the need for innovative approaches that foster empathy and proactive intervention. This presentation reports on two studies testing a brief, scalable empathy training module designed to enhance bystander intentions by promoting perspective-taking and emotional connection to SH victims. Grounded in empathy theory (Batson et al., 1997; Decety & Jackson, 2004), the module uses narrative reflection on real SH incidents to cultivate empathic concern and moral motivation, aligning with the conference theme by emphasizing human-centric strategies to optimize performance in digitally monitored workplaces—where AI tools may detect misconduct, but human empathy drives effective, supportive responses. In both studies, U.S. adults were recruited via CloudResearch Connect and randomized to conditions. The SH empathy training prompted participants to recall a real SH incident, retell it in first-person perspective, identify short- and long-term consequences, and reflect on evoked emotions. Study 1 (N=257) compared this to burglary empathy training (non-SH empathy control) and time management training (non-empathy control). Measures included empathic concern/distress (Batson et al., 1997), sense of oneness (Aron et al., 1992), and bystander intentions (Liang & Park, 2022). Study 2 (N=251) refined comparisons to standard SH compliance training (covering definitions, boundaries, and power dynamics) and a waitlist control, adding state empathy (Powell & Roberts, 2017) and perspective-taking for SH (Ventura et al., 2021) scales, plus social desirability checks. Study 1 found no significant condition differences but positive correlations between empathy and bystander intentions, with women reporting higher empathy and intentions. Study 2 revealed that SH empathy training significantly increased state empathy and perspective-taking over standard training and control, mediating higher bystander intentions (explaining 24-32% variance). Gender differences persisted in baseline empathy, but training effects were equivalent across genders. These results underscore empathy training\u27s potential to supplement compliance programs, fostering proactive cultures that enhance employee engagement and performance amid AI-driven monitoring. Limitations include self-report reliance and short-term assessment; future work should test behavioral outcomes and longevity. This 60-minute interactive session, including Q&A, will discuss practical implications for I-O professionals, offering evidence-based tools to build resilient, human-focused workplaces
Resilience as a coming-of-age model in young adult literature
Get PDFI argue that resilience should be recognized as a coming-of-age model because it offers support and empowerment while being inclusive to all comings of age. I have identified three types of resilience and analyzed them in three novels: non-guardian resilience in Rainbow Rowell’s Eleanor and Park, peer resilience in Matthew Hubbard’s The Last Boyfriends Rules for Revenge, and creative resilience in Isabel Quintero Gabi, A Girl in Pieces
Hive city
No full textThis thesis contains two parts: a craft paper on interiority and four chapters of a new adult dystopian fiction novel. The craft paper focuses on interiority by conveying the protagonist’s unique perspective of the novel’s world by analyzing two dystopian fiction novels, No Coincidence by Rafał Kosik and Thin Air by Richard K. Morgan. Hive City focuses on Monica, a gun-for-hire working to live up to the dream of the brother she killed, as she accepts a job to steal medical equipment from an intermediary that she doesn’t quite trust. Things go awry though when the equipment she’s expected to steal turns out to be a young girl named Lysander. Monica’s decision to help her draws the attention of Gentech Industrials, the largest corporation in Hive City, executive board, more specifically its CEO, Gideon Almasa, as the company mobilizes to take Lysander back by any means necessary. The chapters included are the first meeting between Monica and Lysander
Boxed in: prose poetry and transgender poetics
Get PDFThough lived experience is essential to every poet\u27s work, transgender poetry especially must be filtered through the poet\u27s diet. The manifestation of this experience often results in cross-genre works, such as the prose poem. This thesis consists of a craft essay, in which contemporary queer poets and their genre-bending works are examined, and a creative portion, which emphasizes transgender experience and the prose poem
A quantitative evaluation of environmental enrichment for stream fish at the Tennessee Aquarium
No full textEnvironmental enrichment is utilized to elevate welfare of animals under human care; it has been shown to increase well-being, promote natural behaviors, and reduce maladaptive behaviors in fishes. I evaluated seasonal mimicking as environmental enrichment in the Tennessee Aquarium’s Ridges to Rivers stream tank. I quantified behaviors of tangerine darters and Tennessee dace in response to increasing temperature. If seasonal mimicking functions as environmental enrichment, I expected both species to exhibit behaviors consistent with those observed in wild populations. Tennessee dace became more active. Tangerine darters became more active, increased their space use, were more likely to be adjacent to another fish, and spent more time begging. Both species did not exhibit maladaptive behaviors. My results show that both species exhibit behaviors expected of wild populations, demonstrate that maladaptive behaviors do not arise in response to seasonal mimicking, and thus indicate that seasonal mimicking is effective enrichment for both species
A multiscale approach to controlling corrosion in Magnesium-based materials: nanoparticle alloying, hybrid coatings, and predictive modeling
No full textBiodegradable materials have garnered significant interest over the past decades as alternatives to non-degradable implant materials used in osteosynthesis surgeries. Among these, magnesium and its alloys have emerged as promising candidates due to their favorable mechanical properties and biocompatibility. However, the high degradation rate of magnesium in aqueous environments remains a major limitation to its broader application. To address this challenge, recent research has focused on tailoring the corrosion behavior of magnesium using alloying, coatings, and various fabrication processes to suit specific clinical needs. In this work, we explore two distinct approaches to enhance the performance of magnesium-based implants. The first approach involves the use of powder metallurgy to incorporate nanoparticles into magnesium alloy powders and investigate their impact on key performance properties. The second novel approach focuses on systematically examining the influence of different coating parameters for a hybrid micro-arc oxidation (MAO) and sol-gel coating process to gain a deeper understanding of the factors’ role in achieving desirable implant characteristics. Additionally, we present an innovative machine learning (ML) model developed to predict the corrosion behavior of MAO-coated magnesium. This model, applied for the first time to deal with the very complex corrosion behavior of magnesium-based systems, utilizes key process parameters as predictors. Such an ML approach is expected to minimize material usage and reduce experimental time when predicting the corrosion behavior of MAO-coated magnesium materials
Decision-making methods under Uncertainty in Discrete Multi-objective Optimization
No full textThis dissertation investigates decision-making methods in Uncertain Discrete Multi-objective Optimization Problems (UDMOPs), where uncertainty arises in both objective function and constraint coefficients. The study pursues three main goals: (1) constructing sensitivity regions in the objective space to handle objective-wise uncertainty, (2) constructing sensitivity regions in the decision space to handle feasibility uncertainties, and (3) developing methods to sort, group, and prune uncertain solutions based on their similarity. Each goal proposes a method to explore uncertain solutions and quantify their level of uncertainty. Based on this, solutions are classified as low and high-risk solutions, according to the Decision-Maker (DM)\u27s preferences and risk tolerance. The proposed approaches employ stochastic optimization techniques to identify low and high-risk solutions, enabling risk-averse decision-making. Numerical experiments, including a real-world application, and benchmark comparisons, show that low or high-risk solutions under uncertainty can outperform the efficient solutions from deterministic model. Overall, the methods provide a more consistent and informative decision support system for DMs under uncertainty