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CenTex FIRST Tech Challenge Conference 2023 Digest
(2023-08-19) Lu, James; Jin, Justin; Zhang, Angela; Olkowski, Parker; Bakri, Ilias; Liu, Vincent; Xu, Isabel; Rao, Aditya
The CenTex FTC Conference, held at the University of Texas at San Antonio on August 19, 2023, was a vibrant gathering focused on igniting interest in robotics among high school students in central Texas. The conference was organized by the FTC team 16458, TechnoWizards, and centered on the innovative application of the FIRST Tech Challenge (FTC) program to inspire and engage young minds. Eight FTC teams hailing from Austin, University, San Antonio, and Laredo showcased their experiences and insights gained from participating in the 2022-2023 Power Play competition. Through a series of 14 presentations, attendees gained valuable knowledge and perspectives on robotics, enhancing their understanding and enthusiasm for STEM education.
Physical activity and local blue/green space access during the COVID-19 pandemic
(SAGE Publications, 2024-04-05) Nicklett, Emily J.; Sharma, Bonita B.; Testa, Alexander
Purpose: To examine whether local blue and green space access was associated with weekly physical activity frequency during the COVID-19 pandemic.
Design: Cross-sectional.
Setting: Population-based, nationally representative sample of U.S. adults (May and June 2021).
Sample: Adults, ages 18-94 (N = 1,771).
Measures: Self-reported data included the presence of blue spaces (e.g., lakes, outdoor swimming pools, riverside trails) and green spaces (e.g., parks, forests, or natural trails) in their neighborhoods, and days of physical activity per week (e.g., running, swimming, bicycling, lifting weights, playing sports, or doing yoga).
Analysis: Multiple Poisson regression assessed relationships between blue and green spaces and physical activity, with coefficients transformed into incidence risk ratios (IRR).
Results: Among participants, 67.2% reported living near a blue space and 86.1% reported living near a green space. Racial/ethnic and socioeconomic disparities in access to blue and green spaces were observed, with less access among non-Hispanic Black participants and those with lower income and educational attainment. Living near blue (IRR = 1.23, 95% CI = 1.10, 1.39) or green space (IRR = 1.25, 95% CI = 1.02, 1.54) was significantly associated with more frequent weekly physical activity.
Conclusion: Proximity to blue or green spaces is associated with more frequent physical activity during the COVID-19 pandemic. Health promotion efforts should include equitable strategies to improve accessibility to blue and green spaces.
COVID-19 Pandemic Impact on Nursing Homes Financial Performance
(SAGE Publications, 2024-03-21) Orewa, Gregory N.; Weech-Maldonado, Robert; Lord, Justin; Davlyatov, Ganisher; Becker, David; Feldman, Sue S.
Nursing homes expressed concern about potential severe adverse financial outcomes of COVID-19, with worries extending to the possibility of some facilities facing closure. Maintaining a strong financial well-being is crucial, and there were concerns that the pandemic might have significantly impacted both expenses and income. This longitudinal study aimed to analyze the financial performance of nursing homes during COVID-19 pandemic. Specifically, we examined the impact of the pandemic on nursing home operating margins, operating revenue per resident day, and operating cost per resident day. The study utilized secondary data from various sources, including CMS Medicare cost reports, Brown University’s Long Term Care Focus (LTCFocus), CMS Payroll-Based Journal, CMS Care Compare, Area Health Resource File, Provider Relief Fund distribution data, and CDC’s NH COVID-19 public file. The sample consisted of 45 833 nursing home-year observations from 2018 to 2021. Fixed-effects regression analysis was employed to assess the impact of the pandemic on financial performance while controlling for various organizational and market characteristics. The study found that nursing homes’ financial performance deteriorated during the COVID-19 pandemic. Operating margins decreased by approximately 4.3%, while operating costs per resident day increased by $26.51, outweighing the increase in operating revenue per resident day by about $17. Occupancy rates, payer mix, and staffing intensity were found to impact financial performance. The study highlights the significant financial impact of the COVID-19 pandemic on nursing homes. While nursing homes faced substantial financial strains, the findings offered lessons for the future, underscoring the need for nursing homes to improve the accuracy of their cost reports and enhance financial transparency and accountability.
Combined Emission Economic Dispatch using Quantum-inspired Particle Swarm Optimization and its Variants
(SAGE Publications, 2024-03-19) Asif, Muhammad; Amin, Adil; Jamil, Umar; Mahmood, Anzar; Ahmed, Ubaid; Razzaq, Sohail; Mahdi, Fahad Parvez
The ever-increasing electricity demand, its dependency on fossil fuels, and the consequent environmental degradation are major concerns of this era. The worldwide domination of fossil fuels in bulk electricity generation is rapidly increasing the emissions of CO2 and other environmentally dangerous gases that are contributing to climate change. The economic and emission dispatch are two important problems in thermal power generation whose combination produces a complex highly constrained nonlinear optimization problem known as combined economic and emission dispatch. The optimization of combined economic and emission dispatch aims to allocate the generation of committed units to minimize fuel cost and emissions, simultaneously while honoring all equality and inequality constraints. Therefore, in this article, we investigate a solution of the combined economic and emission dispatch problem using quantum particle swarm optimization and its two modified versions, that is, enhanced quantum particle swarm optimization and quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor. The enhanced quantum particle swarm optimization algorithm achieves particles’ diversification at early stages and shows good performance in local search at later stages. The quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor boosts search performance of quantum particle swarm optimization and attains better global optimality. The suggested methods are employed to achieve solution for the combined economic and emission dispatch in four distinct systems, encompassing two scenarios with 6 units each, one with a 10-unit configuration, and another with an 11-unit setup. A comparative analysis with methodologies documented in existing literature reveals that the proposed approach outperforms others, demonstrating superior computational performance and robust efficiency.
Housing perceptions and code enforcement: An assessment of demolition orders using street view imagery and machine intelligence
(SAGE Publications, 2024-03-04) López Ochoa, Esteban A.; Zhai, Wei
The rapid growth of U.S. Sunbelt cities has intensified urban development pressures. Low-income housing demolitions are a result of such pressures as they are “low hanging fruit” for urban renewal, which can be further intensified by housing quality perceptions. By combining deep learning on Street View images (STV) with machine learning, we provide a model that accurately predicts demolition orders and allows assessing the heterogeneity of these predictions depending on the evaluator’s perceptions. Based on fast-growing San Antonio (TX) data, our results show that automated models can be useful to assess human perception biases of code enforcers.