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Background: Particulate matter consisting of fine particles measuring 2.5 microns or less in diameter (PM2.5), a component of air pollution, has been linked to adverse health outcomes. The objective of this study was to assess the association between ambient PM2.5 exposure and survival in children with cancer in the United States. Methods: Individuals aged birth to 19 years who were diagnosed with cancer between January 1, 2004, and December 31, 2019, were selected from the National Cancer Database. The association between the annual PM2.5 level at the patient's zip code of residence at the time of diagnosis and overall survival was evaluated using time‐varying Cox proportional hazards models (crude and adjusted for diagnosis year and age). To address concerns that exposure to air pollution is correlated with other social determinants of health, the authors tested the association between PM2.5 levels and survival among sociodemographic subgroups. Results: Of the 172,550 patients included, 27,456 (15.9%) resided in areas with annual PM2.5 concentrations above the US Environmental Protection Agency (EPA) annual PM2.5 standard of 12 μg/m3. Residing in these high‐pollution areas was associated with worse overall survival (adjusted hazard ratio [aHR], 1.06; 95% confidence interval [CI], 1.012–1.10). Similarly, when PM2.5 was evaluated as a linear measure, each unit increase in PM2.5 exposure was associated with worse survival (aHR, 1.011; CI, 1.005–1.017). Exposure to PM2.5 at levels above the EPA standards was also significantly associated with worse overall survival among sociodemographic subgroups. Conclusions: Exposure to PM2.5 was significantly associated with worse overall survival among children with cancer, even at levels below EPA air quality standards. These results underscore the importance of setting appropriate air quality standards to protect the health of this sensitive population. Plain Language Summary: The authors investigated how living in areas with high air pollution (defined as particulate matter consisting of fine particles measuring 2.5 microns or less in diameter; PM2.5) affects the overall survival of children with cancer in the United States.The results indicated that children living in areas with higher PM2.5 levels, and even at levels below prior and current US Environmental Protection Agency standards, had lower survival rates than children living in areas with lower levels of PM2.5.This finding emphasizes the need for stricter air quality standards to better protect children, particularly those with serious health conditions like childhood cancer. Exposure to particulate matter consisting of fine particles less than or equal to 2.5 microns in diameter was significantly associated with worse overall survival in children with cancer, even at levels below US Environmental Protection Agency air quality standards. These findings highlight the need for stricter air quality regulations to protect vulnerable populations. [ABSTRACT FROM AUTHOR]

Typically, it is challenging to incorporate near-surface soils into 3D physics-based numerical simulations (PBSs) for ground-motion prediction. The low shear wave speed of near-surface soils, coupled with the complexity of the soil seismic response, poses significant difficulties. To overcome these limitations, a hybrid approach was proposed in this study, combining PBSs with artificial neural networks (ANNs). The essence of the hybrid method can be summarized as follows: (1) development of ANN models, establishing a strong-motion database, training the ANNs on it to predict the ground-motion parameters for East–West (EW), North–South (NS), and Vertical (UD) directions afterward; (2) establishment of 3D PBS model, obtaining the ground-motion parameters of the bedrock face corresponding to a certain shear wave speed; (3) application of the trained ANNs to predict the ground-motion parameters on the ground surface, taking the simulated results and related site parameters as inputs, and the outputs are peak ground acceleration (PGA) and 5% damped spectral accelerations (Sa) at different periods on the ground surface. In this study, ANN models were trained on a strong-motion database based on Kiban–Kyoshin Network (KiK-net). After several verifications of the ANN predictions, a case study of the 21 October 2016 Mw6.2 Central Tottori earthquake was conducted. In addition to the comparison with observations, the broadband (0.1–10 Hz) results of the hybrid method were also compared with the results that obtained by transfer function based on recorded data and Next Generation Attenuation (NGA)-West2 ground-motion prediction equations (GMPEs) to demonstrate the effectiveness and applicability of the proposed method. In addition, the distribution of Sa for four periods in simulated area was presented. The performance of the hybrid method for predicting broadband ground-motion characteristics was generally satisfactory. [ABSTRACT FROM AUTHOR]

Background: This study examines patients' understanding of health insurance terms and concepts and quantifies health insurance literacy (HIL) levels by key sociodemographic factors. Methods: This study included 393 adult patients with cancer (>18 years old) receiving treatment in two ambulatory infusion centers: Mayo Clinic in Phoenix, Arizona and the University of Mississippi Medical Center in Jackson, Mississippi. Respondents' perceptions of their HIL were assessed using the Health Insurance Literacy Measure (HILM), a validated 21‐item measure of a consumer's ability to select and use health insurance (HIL self‐efficacy). Respondents' knowledge of health insurance concepts (HIL knowledge) was measured using 10 items created by the Kaiser Family Foundation. The number of correct answers was categorized into three levels: 0–4 (low knowledge), 5–6 (moderate knowledge), and 7–10 (high knowledge). Multivariable logistic regressions were used to compare correct answers to HIL knowledge questions by HIL self‐efficacy. Results: Nearly three‐quarters of patients had high HIL self‐efficacy and high HIL knowledge (70.5%), understanding basic insurance terms, such as premiums and deductibles. Relatively low percentages of patients correctly answered questions about the meaning of provider networks, health insurance formularies, and calculating out‐of‐pocket spending in scenarios when insurers pay a portion of allowed charges. Lower HIL knowledge was more common among patients with less educational attainment (

Wearing safety helmets at construction sites is a major measure to prevent safety accidents, so it is essential to supervise and ensure that workers wear safety helmets. This requires a high degree of real-time performance. We improved the network structure based on YOLOv7. To enhance real-time performance, we introduced GhostModule after comparing various modules to create a new efficient structure that generates more feature mappings with fewer linear operations. SE blocks were introduced after comparing several attention mechanisms to highlight important information in the image. The EIOU loss function was introduced to speed up the convergence of the model. Eventually, we constructed the efficient model EGS-YOLO. EGS-YOLO achieves a mAP of 91.1%, 0.2% higher than YOLOv7, and the inference time is 13.3% faster than YOLOv7 at 3.9 ms (RTX 3090). The parameters and computational complexity are reduced by 37.3% and 33.8%, respectively. The enhanced real-time performance while maintaining the original high precision can meet actual detection requirements. [ABSTRACT FROM AUTHOR]

Background: Cancer survivors may face challenges affording food, housing, and other living necessities, which are known as health‐related social needs (HRSNs). However, little is known about the associations of HRSNs and mortality risk among adult cancer survivors. Methods: Adult cancer survivors were identified from the 2013–2018 National Health Interview Survey (NHIS) and linked with the NHIS Mortality File with vital status through December 31, 2019. HRSNs, measured by food insecurity, and nonmedical financial worries (e.g., housing costs), was categorized as severe, moderate, and minor/none. Medical financial hardship, including material, psychological, and behavioral domains, was categorized as 2‐3, 1, or 0 domains. Using age as the time scale, the associations of HRSNs and medical financial hardship and mortality risk were assessed with weighted adjusted Cox proportional hazards models. Results: Among cancer survivors 18–64 years old (n = 5855), 25.5% and 18.3% reported moderate and severe levels of HRSNs, respectively; among survivors 65–79 years old (n = 5918), 15.6% and 6.6% reported moderate and severe levels of HRSNs, respectively. Among cancer survivors 18–64 years old, severe HRSNs was associated with increased mortality risk (hazards ratio [HR], 2.00; 95% confidence interval [CI], 1.36–2.93, p <.001; reference = minor/none) in adjusted analyses. Among cancer survivors 65–79 years old, 2‐3 domains of medical financial hardship was associated with increased mortality risk (HR, 1.58; 95% CI, 1.13–2.20, p =.007; reference = 0 domain). Conclusions: HSRNs and financial hardship are associated with increased mortality risk among cancer survivors; comprehensive assessment of HRSN and financial hardship connecting patients with relevant services can inform efforts to mitigate adverse consequences of cancer. Health‐related social needs (HRSNs), including food insecurity and nonmedical financial worries, are associated with higher mortality risk among younger adult cancer survivors. Standardized and comprehensive assessment of HRSNs and medical financial hardship is essential to identify and mitigate adverse economic impacts of cancer. [ABSTRACT FROM AUTHOR]