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Utilizing Non-Pharmacological Interventions to Increase Resilience
Get PDFBACKGROUND: Low resilience increases mental illness symptoms (Llistosella et al., 2022). Brief Resilience Scale (BRS) measures resilience (Smith et al., 2008). Resilience assessment is important for identifying individuals at risk for ineffective coping, which increases mental illness severity (Llistosella et al., 2022; Nova, 2023).
LOCAL PROBLEM: A rural mobile mental healthcare clinic lacked resilience assessment. To address this, BRS was implemented. The purpose was to screen resilience, educate on non-pharmacological ways to increase resilience, and rescreen within three months to determine efficacy. The aim was: By January 12, 2025, 80% of individuals screened for resilience will have increased resilience score at the follow-up appointment at the project site. The goal was to influence providers to implement resilience screening and education by revealing benefit to patient outcomes.
METHODS: Iowa Model is an evidence-based practice framework for enhancing healthcare practice (Buckwalter et al., 2023). Iowa Model and Plan-Do-Study-Act (PDSA) cycles were used. BRS scores were evaluated initially, and the post-education scores were obtained at the next follow-up appointment, between 1-3 months later. After baseline screening, patients were educated on resilience, how to increase it, and given educational handouts. Specific/outcome measure variables were pre-/post- education scores. Paired sample T-test determined statistical significance.
INTERVENTIONS: BRS measured pre-/post-education resilience scores. Patients were educated on resilience, how to increase it, and given educational handouts. Non-pharmacological changes included yoga/exercise, volunteering, joining groups, journaling, sleep, eating healthily, meditation, guided imagery, and prayer (Mayo Clinic Staff, 2023; Arida & Teixeira-Machado, 2021).
RESULTS: There were 48 participants, with 39 participants aged 19-75 included in analysis. Mean pre-education BRS of 2.98 increased to 3.08 within three months post-education.
CONCLUSIONS: There was statistically/clinically significant difference in BRS scores post-education implementation. Psychoeducation improves outcomes. Providers should consider integrating resiliency education. Next steps are continued patient education via provider and advocacy for further research on resilience patient education
Book Review of Human Factor in Nuclear Security: Establishing and Optimizing Security Culture
Get PDFIn today’s interconnected world, where the specter of nuclear proliferation casts a long shadow over global security, fortifying nuclear safeguards has become an imperative of paramount importance. At the heart of this endeavor lies the intricate interplay of human behavior within nuclear organizations—a dynamic landscape meticulously explored in Igor Khripunov’s seminal work, Human Factor in Nuclear Security: Establishing and Optimizing Security Culture. This comprehensive analysis delves into the multifaceted realm of nuclear security culture (NSC), shedding light on its evolution, application, and ongoing challenges. By elucidating the pivotal role of the human factor in shaping security outcomes, Khripunov offers invaluable insights into fortifying global nuclear safeguards and fostering a culture of vigilance and compliance
A “Memorable Occasion:” Remembering the Convention and Interpreting the Constitution, 1787—1861
No full textThe United States Constitutional Convention of 1787 met behind closed doors. Therefore, Americans only learned the details of the proceedings gradually, as members of the Convention disclosed their recollections of the events and as accounts of the proceedings fitfully entered public conversation in the decades after 1787. Between the Constitution’s genesis in 1787 and its greatest test of endurance, the outbreak of civil war in 1861, Americans debated the Convention’s intentions, personalities, and deliberations as they developed various methods for interpreting the Constitution—methods which remained relatively consistent across the nineteenth century.
This dissertation analyzes the impact of successive revelations about the Convention and traces how various memories of the event shaped the development of American constitutionalism. In turn, we learn more about the meaning of the Founding era for subsequent generations and its role in shaping national identity, while we simultaneously witness the burgeoning popular constitutionalism of the antebellum United States and the ways in which divergences in constitutional exegesis contributed to the sectional fissures that resulted in civil war. This study speaks to questions about cultural memories of the Founding era, political manipulation of historical records, and debates about constitutional interpretation—all of which testify to perennial questions inherent in republican self-government. Americans’ varied responses to these questions in the nineteenth century affected the contours of political, cultural, and intellectual debate no less than our responses today affect contemporary political controversies about history and the Constitution. This work contributes critical analysis of historical precedents to inform our understanding of constitutional debates, past and present
A dynamic complementary relative phase heating for increasing microwave heating performance in dual-port solid-state based microwave heating
Get PDFSolid-state microwave heating, particularly dual-port systems, presents a promising alternative to conventional magnetron-based microwave ovens by offering dynamic control over relative phases to enhance heating uniformity. This study explores the evolution of a predictive complementary relative phase shifting strategy through three stages: development, optimization, and validation in real food products.
In the initial phase, a combined-sweeping and complementary relative phase shifting strategy was introduced to improve heating uniformity. By sweeping relative phases between two microwave ports from 0° to 315° in 45° [degree] intervals, relative phase-dependent thermal contributions were identified. Complementary relative phases were then selectively applied during heating, achieving superior uniformity in a model food, gellan gel, compared to fixed or sweeping-only strategies.
Building on these findings, the second phase aimed to optimize the approach by reducing the extensive sweeping process, which previously occupied about 44% of the heating time. A Predictive-Complementary relative phase strategy was developed, leveraging the sinusoidal variation of spatial microwave power dissipation. Instead of full-phase sweeping, only three relative phases (0°, 90°, and 180° [degree]) were measured, and a predictive model was used to estimate all other thermal contributions. This approach increased the complementary shifting duration to about 83% of the heating time, significantly improving heating uniformity and energy absorption efficiency. The model was validated with high correlation (R² [R-squared] = 0.91-0.97) and low RMSE [Root Mean Square Error] (0.17-1.02°C).
Finally, the strategy was tested in real commercial food products to assess their effectiveness beyond model foods. Five food products, ranging from single components to multicompartment meals, were evaluated. The strategy consistently improved heating uniformity and energy absorption efficiency in most cases, with the highest gains in single-compartment foods like lasagna and beef in gravy. However, uniformity improvements varied with food structure, as steam-venting packages and food weight influenced heating performance.
Overall, the predictive complementary relative phase shifting strategy demonstrates a robust and efficient approach for next-generation solid-state microwave ovens, enabling more precise and uniform heating across diverse food matrices. This study highlights its potential for practical applications in commercial food reheating, paving the way for intelligent microwave heating solutions
Neural Dynamics of Label Understanding and Their Role in Attentional Selection and Distractor Inhibition in Children
Get PDFPrevious research focuses on the ability of label representations to guide attention towards relevant features. However, the relationship between label representation and the ability to inhibit irrelevant information is less understood. The current study aims to assess how label representations can not only guide attention but suppress irrelevant distractors. To accomplish this, 36 8- to 10-year-olds performed the Dimensional Label Understanding (DLU) tasks and the additional singleton (AS) paradigm while fNIRS and EEG were measured. The AS paradigm allows for control of the saliency of distractors in the tasks by modifying the salience of distractors in different conditions. The current study seeks to analyze whether neural correlates of DLU predict individual differences in children’s reaction time during the AS paradigm. It was found that participants who were not distracted by irrelevant information in the AS paradigm activated the inferior frontal cortex and angular gyrus during the AS paradigm. In addition, these participants activated the right lateralized frontal-parietal network involved in bottom-up attentional networks during the DLU tasks, suggesting that children who have bottom-up label processing are better able to suppress irrelevant information. These results highlight the effectiveness of the attentional strategy of having a strong representation of features in order to better find targets and inhibit irrelevant information
The reassembly of faunal communities and species interactions in regenerating tropical forests and their potential applications to restoration initiatives
No full textAs deforestation and biodiversity loss continue to damage tropical forests around the globe, these ecosystems become less capable of maintaining organismal communities and ecological functions essential to their persistence. Ecosystem resilience, or the ability for forests to retain or reestablish their structure, functions, and interactions after a disturbance, depends on remnant biodiversity and species functional roles within the ecosystem. Therefore, restoring functional communities and strengthening ecological interactions is essential for promoting tropical forest regeneration and ensuring the long-term persistence of tropical ecosystems. In this dissertation, we explore the dynamics of assessing faunal community assembly and interaction recovery in regenerating tropical forests and their implications for forest restoration. First, we conducted a systematic review of studies utilizing seed dispersal networks in conservation and restoration, identifying major findings, methodological biases, and suggestions for future research directions. In this review, we highlight the importance of targeting species-rich, generalist interactions in early stages of forest regeneration while promoting stable and resilient interaction networks as forests mature. Second, we used functional diversity measures to test their applicability in assessing understory bird community responses to forest regeneration in the Chocó Rainforest of Northwest Ecuador. We found that not all functional diversity indices are indicative of community responses to forest regeneration, as only functional dispersion, functional divergence, and Rao’s quadratic entropy were associated with closed-canopy habitats and later stages of regeneration. Furthermore, we found that sampling for functional diversity requires less effort than traditional taxonomic surveys, which could be useful when utilizing these tools in restoration assessments. Lastly, we used ecological network analysis to investigate the reassembly of seed dispersal interactions across regenerating forests. Seed dispersal networks transitioned from loosely connected, generalist systems in early successional habitats to more specialized, modular, and nested networks in later stages of forest succession. Early stages of regeneration that promote a high number of diverse interactions may be pivotal in shaping successional trajectories for stable ecosystems. Together, our findings have implications for tropical forest restoration by emphasizing elucidating the importance of select functional measures and assessing species interaction recovery to achieve long-term ecological resilience for regenerating tropical forests
AI-Enhanced ERP Systems: An Investigation into the Impact on Organizational Processes and Employee Behavior in Large Enterprises
Get PDFThis dissertation examines the integration of artificial intelligence (AI) within enterprise resource planning (ERP) systems, addressing key challenges in adoption and optimization to enhance operational efficiency, decision making, and competitive advantage in large-scale enterprises. By exploring AI-driven improvements in knowledge management, business process re-engineering, and organizational alignment, this study highlights AI’s role in transforming ERP systems into more adaptive, user-centric, and strategically valuable tools.
Using a mixed-methods approach, the research combines qualitative insights from interviews with business executives experienced in AI-ERP implementations, and quantitative data from employee surveys. This dual approach provides a comprehensive understanding of organizational processes, employee perceptions, and behavioral dynamics throughout the visioning, planning, and implementation stages of AI-integrated ERP systems.
Findings indicate that AI enhances ERP efficiency by automating tasks, optimizing workflows, and leveraging advanced analytics for predictive decision-making. Additionally, AI-driven ERP systems enable personalized user experiences, improve supply chain management, facilitate natural language interactions, ensure continuous system adaptation, and enhance security. A key insight underscores the critical role of communication and stakeholder engagement in fostering a supportive environment for successful technological change.
These results suggest that while AI integration significantly optimizes ERP functionality, its success depends on strategic change management and a strong organizational culture to ensure user acceptance. The research emphasizes the importance of comprehensive communication strategies and employee involvement throughout the AI-ERP implementation process, offering insights to drive innovation and long-term business success.
Keywords: Enterprise Resource Planning (ERP), Artificial Intelligence (AI), Business Process Reengineering (BPR), Supply Chain Management, Organizational Culture, Implementation Strategies, Change Management
Power Outage Vulnerability During Winter Storms: A Comprehensive Assessment of Infrastructure, Society and Storm Dynamics
Get PDFExtreme winter storms have increasingly been experienced in the southern U.S., where unwinterized infrastructure and vulnerable populations have been subjected to widespread power outages, resulting in significant financial losses and health risks. In this dissertation, power outage vulnerability during extreme winter storms has been examined, with a focus on Winter Storm Uri and the Houston, Texas metropolitan area. Three interconnected dimensions were addressed: (1) the influence of infrastructure conditions on outage susceptibility, (2) the socioeconomic characteristics commonly associated with at-risk populations, and (3) the use of radar-derived storm characteristics for outage prediction.
A machine learning model, MaxEnt, was utilized to identify key predictors of outage vulnerability. Variables such as power line density, tree coverage, and proximity to schools were found to be most influential. Census blocks in Central and South-Central Houston were identified by the model as having the highest likelihood of experiencing outages.
To evaluate how socioeconomic conditions contribute to vulnerability, a synthetic population of Houston was developed. Self-Organizing Maps (SOMs) were employed to cluster socioeconomic characteristics alongside outage likelihood. It was found that individuals with overlapping vulnerabilities were concentrated in the areas most likely to experience outages. Planning and policy recommendations were informed by these SOM results to support more equitable outage response strategies.
A novel set of “stormscape metrics” was developed based on principles of landscape ecology to track spatial-temporal storm patterns using near real-time radar data from Winter Storm Uri. A negative binomial regression model was applied to examine the relationship between stormscape metrics and county-level outages. It was revealed that increased storm complexity and spatial dispersion were associated with higher outage counts. The approach was designed to enhance existing outage awareness platforms by enabling the generation of outage estimates directly from radar imagery before storms arrive.
Through this research, a holistic understanding of power outage vulnerability during winter storms was developed by integrating infrastructure characteristics, social vulnerability, and storm dynamics. It is expected that these findings will support more equitable preparedness, improved outage response, and enhanced early-warning systems in the context of increasingly severe winter weather events
Design and Development of A CMOS Integrated Multimodal Sensor System with Carbon Nano-electrodes for Biosensor Applications
No full textThis dissertation outlines the design and development of a CMOS integrated multimodal sensor system that leverages advanced carbon nanoelectrodes for innovative biosensor applications. The proposed system incorporates five synergistic sensors: temperature, impedance, pH, electrochemical, and optical sensors, which collectively enable the comprehensive analysis of biological phenomena. By integrating these modalities into a lab-on-CMOS platform, the system aims to capture in situ real-time physiological data from live biological cells, specifically genetically enhanced autobioluminescent glioblastoma cells.
A key innovation of this research is the fabrication of biocompatible nanoelectrodes using a novel approach that combines 3D printed polymer structures with Ti/Pt alloy metal electrodes. Polymer electrodes undergo a pyrolysis process to convert them into carbonized nanoelectrodes, optimizing their electrical properties to enhance interaction with biological samples. This design not only facilitates the precise monitoring of cellular behavior in response to various stimuli, but also enhances the sensitivity and specificity of the biosensing platform.
This novel design is the first example of a multimodal system with five sensors in one integrated chip in CMOS technology. The system also includes carbonized polymer nanoelectrodes on top metal pads. The technique of pyrolysis of polymers in CMOS-compatible temperatures is also relatively new, and this technique has been incorporated here in CMOS technology for the first time. The integration of multimodal sensing capabilities allows for a holistic understanding of cellular dynamics and biochemical interactions, addressing current limitations in biosensor technology. This research aims to contribute to the fields of biomedical engineering and nanotechnology by providing a robust framework for the real-time assessment of cellular responses, with potential applications in cancer research, drug development, and personalized medicine. Ultimately, this dissertation will advance the state-of-the-art in biosensing technologies by establishing a powerful tool for investigating complex biological systems