St. Cloud State University

Abstract Abstract People with disabilities derive multiple benefits from social support. Being interconnected with friends, family, and the broader disability community is associated with a multitude of positive outcomes, although factors such as ableism and structural barriers prevent many people with disabilities from receiving adequate and satisfactory support. This chapter explores the benefits of social support for people with disabilities and the challenges they face in accessing it through a biopsychosocial lens. It discusses key theories surrounding social support and interventions to increase support, ending with future directions for studying social support in disabled populations, including more precise conceptualizations of support, applying current social support theories to disability, and drawing from more diverse samples.

This analysis investigates the key factors driving total program payments in healthcare, focusing on the impact of coinsurance payments and visit frequency per enrollee. Using a combination of linear regression modeling and scenario analysis, we explored how changes in these factors affect overall program costs. The goal was to provide actionable insights for effective cost management in the healthcare program. The analysis proves that Coinsurance payments are a significant driver of total program costs. With each dollar increase in coinsurance payments correlate with an increase in total program payments. In Scenario 1, where coinsurance payments increased by 10% with no change in utilization, total program payments rose significantly to $1.22 billion. This finding underscores the cost sensitivity of the program to changes in out-of-pocket coinsurance amounts. Visit frequency per enrollee also plays a critical role in cost dynamics, though it has a complex relationship with total payments. In Scenario 2, a 5% reduction in coinsurance payments accompanied by a 10% increase in visit frequency led to a decrease in total program payments to $1.01 billion. This result suggests that higher utilization may help in reducing overall costs if it aligns with efficient or preventive care. Conversely, in Scenario 3, a 5% increase in coinsurance payments with a 10% decrease in visits led to a moderate increase in total program payments to $1.17 billion, indicating that lower utilization can reduce costs but may depend on the care's effectiveness. Based on our findings we recommend the following prescriptive analysis. Managing visit frequency per enrollee through preventive care programs or other efficient measures can significantly impact on total program costs, potentially reducing the need for frequent high-cost interventions. Adjusting coinsurance rates offers a lever for managing program costs. Lowering coinsurance might encourage utilization but could increase total program expenses. Conversely, increasing coinsurance payments could offset costs but may raise financial burdens for enrollees. A balanced approach is recommended. Leveraging scenario analysis as shown in this study can support proactive policymaking. This analysis could be relevant to policy makers to evaluate the financial implication of proposed changes to cost sharing mechanisms or programs affecting public health and utilization management.

AI data centers have fundamentally shifted from north-south transactional traffic to east-west GPU cluster communication, rendering traditional spreadsheet-driven network design inadequate. While commercial DCIM tools manage physical connectivity, no system can algorithmically generate complete network Bills-of-Materials (Net-BOMs) from workload, topology, and reliability constraints. We present NetBOM-Synth, an algorithmic framework that synthesizes deployment-ready network BOMs for AI data halls through four integrated components: (1) multi-dimensional bin-packing for Top-of-Rack switch allocation, (2) graph-based spine fabric synthesis with oversub-scription optimization, (3) Monte Carlo reliability evaluation achieving 99.999% availability, and (4) NSGA-II multi-objective optimization exploring cost-performance-reliability trade-offs. Evaluation on a representative 48-rack AI facility demonstrates that NetBOM-Synth produces designs using 73% fewer spine switches and 59% lower cost compared to manual approaches, while delivering 53 % better performance through validated 1.4: 1 oversubscription versus industry-standard 3:1 ratios. Monte Carlo simulation across 50,000 trials confirms the design exceeds 99.99% SLA requirements with empirical reliability validation. NetBOM-Synth represents the first end-to-end system bridging abstract requirements to concrete, SKU -accurate deployment specifications, enabling reproducible, workload-aware infrastructure design for modern AI training clusters.

The rapid adoption of video conference systems has created a demand to automate the meeting summary process, aiming to boost productivity and reduce memory consumption. This paper presents a meeting summarization framework based on an Artificial Neural Network (ANN) that can develop concise and rich context summaries for real-time or recorded meetings. The collection of data is carried out with the help of audio, video, and text reports from a public dataset and live meetings of based cloud database. Preprocessing involves converting speech into text, filtering out noise, performing speaker diarization, and removing irrelevant content store in the cloud of the network. The Natural Language Processing (NLP) technique of feature extraction employs TF-IDF, word embeddings, and other topic modelling to capture semantics in the cloud of the network in performance. An ANN is used to classify and prioritize important discussion points, decisions, and action items in the cloud. The measures of performance, being ROUGE score 83%, precision 77%, recall 77%, and F1-score 85%, are used. The experimental evidence indicates that the ANN model offers high summarization quality and relevance, allowing for efficient review of meetings and avoiding the need to spend time writing notes.

Abstract The need for advancements in geriatric and gerontology education has become increasingly vital. The current workforce has not kept pace with aging demographics. There is a low percentage of students choosing geriatrics as a specialty, with fewer than 5% of physicians specializing in this field (Kusmaul, 2023). Approximately 25% of BSW and 20% of MSW students have taken a course in gerontology. Adding to the issue is that nearly 50% of MSW students state that they have little or no interest in working with older adults after graduation (Cummings & Galambos, 2002). This poster examined the current landscape and future directions of medical and social service education regarding older adults. It stresses the importance of a critical look at gaps to equip future providers with the necessary skills and knowledge to address the complex needs of an aging population. This poster seeks to identify significant barriers, such as limited exposure to geriatric and gerontology training, underrepresentation of geriatric specialists, and a lack of funding and incentives for professionals working with an aging population. Proposed recommendations include all future practitioners having a baseline education in working with older adults, advocating for policy reforms to prioritize funding incentives (e.g.; scholarships, assistantships, internships) for students in the field. Overall, addressing these issues and implementing the recommended strategies, gerontology and geriatrics education can better prepare professionals not only to deliver high quality services for older adults but rather connect passion with their WHY of ultimately improving the lives of older adults.

The relationship between denture retention and phonetic performance in 60 edentulous patients is of interest. Phonetic assessmentsand retention measurements were conducted before and after denture placement. Maxillary and mandibular retention forces averaged6.8± 1.2 N and 5.4± 1.0 N, respectively. A strong positive correlation (r = 0.82, p < 0.001) was found between retention andphonetic accuracy, with speech scores improving by 15.3% post-insertion. Thus, the importance of phonetic evaluation in optimizingcomplete denture fit and function is shown.

The collection status of rubber latex is a vital indicator for evaluating both yield efficiency and tapping management performance in rubber plantations. To automate the classification of latex levels in tapping cups, this study defines three distinct states: underfilled, spilled, and filled. Based on the YOLOv11 architecture, we propose an improved detection model incorporating dynamic snake convolution and a content-guided attention mechanism, enhancing the network’s capability to recognize fine object contours and critical regions. Experimental validation on standardized datasets confirms the model’s competitive performance across all categories, achieving $88.2 \%$ precision, $87.6 \%$ recall, $85.0 \% \mathrm{mAP} 50$, and $76.2 \% \mathrm{mAP} 50-95$. Compared with existing object detectors (YOLOv5 to YOLOv12), our method maintains high accuracy while striking an optimal balance between recall and localization precision, thereby facilitating intelligent monitoring of latex collection.

In health emergencies such as pandemics, nurses are on the front lines, thus increasing their risk of psychological distress. The mental health of nursing students may also deteriorate as a result of changes in learning and clinical practice environments. We measured the psychological effects of the COVID-19 pandemic and electronic health (eHealth) literacy among nursing students and identified associated factors. We used a cross-sectional design to analyze students studying at 2 nursing schools in the United States and Türkiye (N = 887 nursing students). We used the Fear of COVID-19 Scale (range, 7-35) and the Coronavirus Anxiety Scale (range, 5-20) to measure fear and anxiety of the COVID-19 pandemic, and we used the Electronic Health Literacy Scale (range, 8-40) to measure eHealth literacy among students from April through June 2022. We conducted 1-way multivariate analysis of variance (F) to examine the relationships among variables, with P ≤ .05 considered as significant. Students had mean scores of 30.7 for eHealth literacy, 14.1 for Fear of COVID-19 Scale, and 6.2 for Coronavirus Anxiety Scale. Scores for eHealth literacy varied according to the students' school, academic level, and employment but were generally high. Sex (Wilks λ = 0.952; F = 14.787; P < .001) and the frequency of following news related to COVID-19 (Wilks λ = 0.927; F = 11.424; P < .001) influenced COVID-19-related fear and anxiety. eHealth literacy and fear of COVID-19 differed significantly by students' vaccine dose (λ = 0.983; F = 5.081; P = .002). Increasing the level of eHealth literacy can contribute to reducing the psychological effects of health emergencies, such as the COVID-19 pandemic, among nursing students.