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Introducing statistical and machine learning-based methods of enhancing the resiliency and security of electrical-based critical infrastructure
No full textSince its introduction into society in the late 1800’s, electricity has become a critical component in how society has functioned. High-voltage electricity provides power for appliances that have become integral to daily living, such as lights, refrigeration, and Heating, Ventilation, And Cooling (HVAC). Low-voltage electricity is used to process and transmit information on and between computers. This information may pertain to medical, financial, and defense-related activities. Over the past hundred years, significant research has been performed to improve electrical-based technology’s capability, scale, resiliency, and security. This study focuses solely on resiliency and security, both of which require efficient data collection, storage, and processing. In the case of the resiliency of high-voltage electrical transmission, the continuous, 24-hour collection of transmission line activity generates data that exceeds the ability to store for long-term forensics. For the secure transmission of information, the information must be (i) hard to recover by an adversary and (ii) trusted by the recipient. This dissertation presents research aimed at (i) improving the reliability of High-voltage electrical transmission by statistically compressing data at the edge by up to 99.96% while still maintaining actionable information to enable real-time Incipient Fault Prediction (IFP), (ii) reducing an adversary’s ability to intercept information by introducing AI-based, session-based cryptographic scheme generation, and (iii) improving information’s trust by identifying the source of wireless transmission at the physical layer by enabling cross-collection Specific Emitter Identification (SEI) at up to 99.51% blind collection accuracy across eight commercial emitters
Escapism: The Presence of Escapist Tendencies in Depressed Gamers
Get PDFEscapism, a coping mechanism used to relieve stress, uniquely characterized by removing one’s conscious self from reality, can be utilized by video gamers with depression to escape from their own realities. The implications of this phenomenon could allow researchers to better understand how escapism affects this community. It was predicted that there would be a positive relationship between escapist gaming motivations and depressive symptoms. Using a combination of quantitative and qualitative survey methods drawing from undergraduate students at Austin Peay State University (n = 113) and correlational analyses, our hypotheses were supported. A significant positive relationship was found between escapist gaming motivations and depressive symptoms. For each gaming motivation measured, depressive symptoms were more likely to occur. These results could imply that video gamers with depression use video gaming as a coping mechanism for their mental health disorder. Understanding the motivations of video gamers with depression for gaming could allow researchers and practitioners to determine the best treatment and provide alternative coping mechanisms that suit their needs. This study sought to expand the knowledge on the relationship between video gaming, escapism, and depression in order to gain a better understanding of why individuals use gaming as an outlet
Beyond Buzzwords: How AI is Transforming Learning & Development
Get PDFArtificial Intelligence is rapidly revolutionizing Learning and Development (L&D), offering benefits such as personalized training, streamlined content creation, and enhanced accessibility. This session delves into the transformative power of AI in the design and delivery of training programs. By addressing common training hurdles, like outdated manuals and rigid materials, the presentation showcases how AI can significantly enhance both effectiveness and learner experience. It will also explore key theoretical frameworks, including Cognitive Load, Social Learning, Constructivist, and Self-Determination Theories, and how they underpin AI applications like immersive Virtual Reality training, adaptive learning platforms, and intelligent tutoring systems. This session will provide clear, actionable guidance on integrating AI into daily learning and development (L&D) practices. Participants will explore practical examples and case studies that demonstrate structured prompts, showcasing how AI can effectively summarize documents, create quizzes, craft engaging narratives, and simplify complex language. Drawing from the Technology Acceptance Model, straightforward strategies will be offered to encourage AI adoption in the workplace. By the end of the session, attendees will gain practical strategies and ready-to-use prompt guides that will allow them to immediately design and implement AI-enhanced training tailored to the needs of the modern workplace
Innovative Machine Learning Approach for Predicting the In Vitro MAO Coated Magnesium-Based Substrate
No full textThe increasing number of orthopedic surgeries due to various reasons has driven demand for improved implant materials beyond conventional Ti–Ni alloys, which can cause stress shielding and other complications. Magnesium-based implants have gained attention due to their biocompatibility, bone-like mechanical properties, and controllable degradation rates using different approaches. Micro-arc oxidation (MAO) offers an effective means to enhance corrosion resistance by forming a protective ceramic layer, with process parameters significantly influencing degradation behavior. This study presents a machine learning approach to predict the in vitro corrosion performance of MAO-coated magnesium implants, enabling efficient optimization of process parameters and improved implant design
The influence of trauma-informed self-care practices on compassion fatigue, secondary traumatic stress, and burnout among educators
Get PDFEducators play a pivotal role in supporting students who have experienced trauma, yet the emotional toll of this work is often underestimated. This mixed-methods study examined the influence of trauma-informed self-care (TISC) practices on compassion satisfaction, compassion fatigue, secondary traumatic stress (STS), and burnout among K–12 educators in a large southeastern Tennessee district, including Priority Schools serving high-poverty communities. A total of 157 educators were surveyed, while 32 educators participated in focus groups to provide deeper insight into self-care supports. Quantitative analyses revealed that higher levels of TISC engagement were significantly associated with increased compassion satisfaction and personal accomplishment, and reduced compassion fatigue and emotional exhaustion. However, TISC practices did not significantly reduce STS, suggesting limits to the effectiveness of individual self-care in addressing vicarious trauma. Qualitative findings underscored educators’ calls for systemic supports such as administrative prioritization of mental health, peer networks, and embedded professional learning to sustain their well-being and professional resilience. Together, the results highlight both the protective role of trauma-informed self-care and the need for organizational change to address educator burnout and retention, particularly in post-pandemic contexts. Implications include the integration of TISC into professional development and policy initiatives to enhance teacher well-being, strengthen student–teacher relationships, and ultimately improve student outcomes
Understanding and managing Frogeye Leaf Spot through network-based modeling in soybean
No full textFrogeye Leaf Spot (FLS), caused by Cercospora sojina, poses a significant threat to soybean production, with yield losses of 30 - 60%. Traditional mass-action models assume homogeneous mixing, which rarely holds in real fields and limits their ability to gain insights into FLS management. To address this, we developed a network-based model that incorporates real-field structure to improve FLS management in soybeans. Using Approximate Bayesian Computation, we estimated key epidemiological parameters and found that infection origin can shift the balance between transmission routes. Data analyses indicated that tillage and non-tillage plots did not differ significantly in fungal spread, decay, or disease severity. Finally, we show that early, targeted roguing is more effective than delayed or random removal. Together, these findings offer science-based guidance for FLS management and highlight the value of network-based models to inform agricultural disease control
Excerpt scenes from twin lilies of tirg
Get PDFExcerpts of Twin Lilies of Tirg: is a craft paper and three novel scenes. The craft paper focuses on the elements and structure of dialogue and how it is present in three scenes that follow the three elements in a novel: the ground situation, the conflict, and complication. The novel Twin Lilies of Tirg is a fantasy fiction that focuses on King Sebestian as he navigates running his kingdom while being blackmailed by another King
Understanding the positive and negative aspects of different calling intensities
Get PDFThis study examined the relationships among occupational calling intensity (OCI), personal calling intensity (PCI), occupational engagement (OE), personal engagement (PE), and need for resource recovery (NFRR). OCI, an all-consuming desire to have a significant positive impact through employment, is often associated with high OE but may also be linked to high NFRR, suggesting that OE mediates the relationship between OCI and NFRR. This study also explored whether PCI, which refers to a calling outside of employment, exhibits the same relationships with PE and NFRR as OCI. Data from 193 participants were analyzed using correlational and regression techniques. The findings revealed a negative relationship between OCI and NFRR, with work identity salience (WIS) serving as a significant mediator. Both engagement types were positively related to both calling intensities and negatively related to NFRR. These results deepen our understanding of calling intensities, engagement, and their complex links to WIS and NFRR
Modeling NatureServe subnational conservation status ranks for Tennessee vascular plants
No full textConservation status ranks measure the potential risk of extinction for species at the global, national, and subnational levels, taking into account rarity, threats, and population trends. This study used the NatureServe system to generate provisionary subnational conservation status ranks (s-ranks) for the vascular flora of Tennessee using five methods: (1) assigning an s-rank based on surrounding state s-ranks, (2) assigning an s-rank based on the percentage of counties with an occurrence record, (3) using FME to calculate criteria and assign an s-rank, (4) using RareCat to calculate criteria and assign an s-rank, and (5) using ArcGIS Pro to calculate criteria and assign an s-rank. These methods generated provisionary s-ranks for 99% of Tennessee’s flora (2,992 species). A manual was created on how ArcGIS Pro can be used to calculate criteria and assign provisionary s-ranks, which was shared with NatureServe and their state-level partners to further ranking efforts
A look into how inhibition and stress influence visual orienting
Get PDFSelective attention allows individuals to prioritize relevant stimuli while filtering out distractions. It consists of visual orienting, which shifts attention to a target, and inhibition, which suppresses irrelevant stimuli. The Posner paradigm (Posner, 1980) was one of the first tasks to measure both processes, while the Infant Orienting With Attention task (IOWA; Ross-Sheehy et al., 2015) was developed for infants. This study examines whether the IOWA task reliably measures visual orienting in adults, thereby bridging the gap between infant and adult research. Additionally, it explores how perceived stress influences visual orienting and inhibition. Results showed similar reaction time trends between the IOWA (adult) and Posner task, as well as accuracy trends between the IOWA (adult) and IOWA (infant). Stress was not significantly correlated with reaction time or accuracy, though cue condition significantly affected both measures. Findings supported the IOWA task’s validity for measuring orienting in adults