Scientific Reports最新文献

Household air pollution (HAP) is a major environmental health risk globally and is strongly associated with adverse child health outcomes, including neonatal, infant, and under-five mortality. Household environmental conditions such as water source, sanitation, cooking fuel type, and housing materials (roofing and walling) influence exposure levels and subsequent health risks. While there is robust global evidence linking HAP to poor child health outcomes, evidence from Sub-Saharan Africa (SSA) remains comparatively limited, despite the region bearing a disproportionately high burden of solid fuel use and child mortality. This gap constrains a comprehensive understanding of the magnitude and contextual drivers of HAP-related risks among children in SSA. We investigated the association between HAP due to household environmental variables (source of water, sanitation, type of cooking fuel, roofing materials, walling material etc.) and selected child health outcomes (neonatal, infant, ARI and under five mortality) in 32 Sub-Saharan Africa (SSA) countries. In all we analyzed Demographic and Health Survey (DHS) data from 362,072 children under the age of five, applying complex survey design features including stratification, clustering, and sampling weights. HAP exposure was defined using quantile distribution and summarized into Household Air Pollution Index through principal component analysis, categorized as "unexposed", "moderate exposure" and "high exposure". Associations were estimated using Poisson regression models with a robust variance adjusting for confounding variables and survey design effects. Approximately two-thirds (65%) children under-five were exposed to HAP, with the highest exposures in Central (71%) and West Africa (67%). Over the study decade, mortality rates were under five (92 per 1000 live births), infants (52 per 1000 live births), and neonatal (28 per 1000 live births). Exposure to HAP was associated with increased risk of under-five mortality (aRR: 1.3; 95% CI: 1.19, 1.46; p = 0.001) and infant mortality (aRR: 1.4; 95% CI: 1.28, 1.60; p = 0.001). Children with high exposure to HAP had a higher mortality risk than the unexposed (aRR: 1.10; 95% CI: 0.91-1.33; p = 0.032). Our findings demonstrate that HAP significantly contributes to infants, ARI and under five mortalities in SSA. Given the heavy reliance on solid fuels in low-resource settings, urgent government action is needed to reduce solid fuel use, improve household sanitation, expand access to clean water, and reconsider housing materials to protect child health.

Utility poles are critical in supporting various electrical and communication infrastructure systems, including power transmission lines, streetlights, telephone networks, and cable services. Each type of pole whether steel, aluminum, or fiber-reinforced polymer (FRP) is designed with specific applications and performance characteristics in mind. This study presents a Quality Function Deployment (QFD) framework tailored for industrial applications, focusing on enhancing information integration to guide the selection of the most suitable pole type. The research examines advancements in utility pole technologies and management practices over the past two decades. Through market surveys, focus group discussions and individual interviews, ten KPIs were identified: service life, safety performance, overall cost, color retention, conductivity resistance, weight, production duration, transportability, installation approach, and wind resistance. Based on these KPIs, decision-makers outlined nine functional requirements that, when met, would enhance user satisfaction. The proposed framework was developed to support analytical evaluation and selection of the optimal pole type by aligning client needs with technical specifications. Using the QFD approach, the FRP pole emerged as the top-performing alternative, receiving a score of 4.12 out of 5. This framework provides a structured method for decision-makers to evaluate electrical pole options based on project-specific criteria, enabling informed and client-focused choices in early design phases.

Device-to-device (D2D) communication is used to frequently gather and exchange information in various domains. Millimeter-wave research has also incorporated D2D networks. The reliability of multiuser communication is more challenging because of the complex nature of wireless channels. In recent years, the supremacy of the D2D mm-wave communication model has been validated using the outage probability. Generally, the outage and minimize energy consumption to increase the robustness of the network coverage in the D2D mm-wave communication system. In this study, an optimization-enabled Deep Learning (DL) model is introduced to minimize the outage probability and energy consumption. Initially, the simulation of D2D communication was performed, and three types of D2D mm-wave communication coverage probability mechanisms, such as coherent, single-cluster approximation, and non-coherent lower bound, were considered. The minimization of the outage probability is performed using Flamingo Elk Herd Optimization (FEHO). Moreover, transit energy consumption is used to minimize the joint coverage probability by optimally devising a specific threshold. Here, a Deep Spiking Neural Network (DSNN) model is used to create a specific threshold for energy minimization. Furthermore, the performance of the FEHO+DSNN was evaluated by comparing it with existing techniques, where the proposed attained superior performance with 39.056 dBm, and 0.0015 for average transmit power and outage probability.

The rapid growth of short-video platforms has reshaped how individuals access health information, but it has also fueled the spread of misinformation and disinformation. Dry eye, a prevalent ocular surface disorder, provides a representative case for examining these challenges. Reliable and scalable methods are urgently needed to identify and mitigate misinformation risks in online health content. We proposed a framework employing Video Large Language Models (VideoLLMs) for automated evaluation of science popularization videos. Three representative VideoLLMs (VideoLLaMA3, QwenVL, and InternVL) were benchmarked using three established instruments: Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V), Global Quality Score (GQS), and Video Information and Quality Index (VIQI). A dataset of 185 Chinese-language videos on dry eye was collected from TikTok and independently annotated by two ophthalmologists. Agreement between VideoLLM-generated scores and expert ratings was quantified using the Intraclass Correlation Coefficient (ICC). Across most metrics, VideoLLMs demonstrated poor agreement with expert annotations (ICC < 0.40), except for the actionability dimension of PEMAT-A/V, where QwenVL and InternVL achieved ICCs of 0.50 and 0.43, respectively, with the experts. This work establishes the first benchmark of VideoLLMs for evaluating ophthalmic science popularization videos and reveals substantial limitations in the performance of current models, with agreement levels falling well short of practical acceptability. Rather than demonstrating readiness for deployment, our open-source framework serves as a reference tool for systematically assessing model behavior, highlighting existing gaps, and motivating further methodological improvements before VideoLLMs can be considered for automated evaluation or governance of medical video content.

Electric vehicle (EV) charging station load prediction is crucial for ensuring the stable operation of power grids and optimizing charging infrastructure. However, the stochastic nature of charging behaviors and the complex influence of external factors pose significant challenges to accurate prediction. To address these issues, this study proposes a novel Transformer-based architecture, the Multi-scale Fusion Transformer (MFT), which integrates a Multi-scale Modeling Mechanism (3M), a Feature-correlation Analysis Module (FAM), and a Multi-variable Fusion Module (MFM). The 3M enhances the model's ability to capture temporal dependency across varying granularities, while the FAM identifies key external features such as weather and traffic patterns. The MFM dynamically fuses these features based on their relevance to each sample using a cross-attention mechanism. Experimental evaluations using real-world data from Norway demonstrate that MFT significantly outperforms baseline models in both short-term and long-term forecasting horizons. Notably, MFT exhibits superior stability and accuracy, especially in long-term prediction tasks, with up to 25.59% average performance improvement over competitors. These results confirm the effectiveness of MFT in modeling complex, multi-scale, and externally influenced load patterns, offering a robust solution for intelligent grid scheduling and energy resource management in EV-dominated futures.

To examine the clinical attributes and likelihood of lymph node metastasis (LNM) in patients with differentiated thyroid carcinoma classified as clinically lymph node-negative (cN0), with a minimum tumor diameter > 0.5 cm and maximum tumor diameter < 3.0 cm. Clinical data of 232 patients who underwent radical thyroidectomy and satisfied the inclusion and exclusion criteria were collected, and we found that average age of the LNM-positive group was younger than that of the LNM-negative group (40.9 ± 10.8 vs. 45.3 ± 11.8, P = 0.0031); sex distribution also showed a statistically significant difference, with male patients being more prone to LNM (P = 0.0436). Patients with positive LNM exhibited higher ultrasound thyroid imaging reporting and data system (TI-RADS) scores for thyroid nodules (p < 0.001). In terms of maximum tumor diameter and RET fusion, the LNM-positive group was higher in LNM-negative group (1.11 ± 0.832 cm vs. 0.808 ± 0.616 cm, P = 0.0034 and 16.3% vs. 2.7%, P = 0.0026), showing a statistically significant difference, The proportion of multifocal lesions was also higher in the LNM-positive group (26.8% vs. 20.2%). Patients in the LNM-positive group had higher levels of peripheral blood thyroid stimulating hormone (2.68 ± 2.88 μIU/L vs. 2.12 ± 2.07 μIU/L). Notably, statistically significant differences were observed between the LNM-positive and negative groups in terms of prothrombin time activity (PT%) (110 ± 13.0% vs. 107 ± 11.5%, P = 0.034) and white blood cell (WBC) count (6.11 ± 1.76 × 10^9/L vs. 6.59 ± 1.85 × 10^9/L, P = 0.0495), and further investigations revealed that BMI (R = 0.19) and blood urea nitrogen (R = 0.17) were positively correlated with PT%, whereas PT% was negatively correlated with peripheral blood T3 (R = - 0.17) and T4 (R = - 0.13) levels, which has not been reported in previous studies. We observed that for patients with cN0 differentiated thyroid cancer, we should also pay attention to the influence of factors such as gender, age, tumor diameter, RET fusion, and even PT and WBC on lymph node metastasis.

Aflatoxin B1 (AFB1), a known mycotoxin and environmental hazard, has been linked to breast cancer, yet the exact biological pathways remain poorly characterized. We performed a comprehensive multi-omics assessment to investigate how AFB1 may influence breast tumor biology. This encompassed transcriptomic analysis, co-expression network modeling (WGCNA), immune landscape profiling, transcription factor regulatory mapping, and spatial plus single-cell transcriptomics. Predictive biomarkers were determined through a machine learning pipeline. Twenty-two genes were identified at the intersection of AFB1-predicted targets and disease-associated expression modules. A refined panel of seven biomarkers (EGFR, MIF, MET, PPARG, MME, NQO2, NR3C2) was established through model optimization. A composite classifier using glmBoost and StepGLM achieved high discriminative accuracy (area under the curve = 0.996). SHAP interpretability indicated PPARG may act protectively, while MIF showed risk-promoting characteristics. Expression heterogeneity was observed across cell populations and spatial regions. Our integrated analytical framework offers new insights into the oncogenic potential of AFB1 in breast cancer. The identified gene set may serve as both mechanistic mediators and diagnostic markers, underscoring the value of multi-omics and machine learning approaches in environmental carcinogenesis research.

Drying pomegranate peels, a by-product of juice production, preserves their beneficial properties and minimizes waste. Using optimal drying conditions, such as controlled temperatures and thin layers, improves efficiency and ensures high quality. These dried peels can then be utilized in various industries, including food, pharmaceuticals, and cosmetics. To our knowledge, there are no existing studies that detail the effects of hybrid solar drying, drying temperatures, and layer thickness on the drying kinetics, power consumption, and economic and environmental aspects. In this study, a hybrid indirect SD (HISD) with a temperature and humidity control unit was used to dry pomegranate peels at three different temperatures-50 °C, 60 °C, and 70 °C-and three different thicknesses-1, 2, and 3 cm. The HISD was then compared to a conventional oven drying system (CODS). The obtained results indicated that increasing the drying temperature increased the weight loss of pomegranate peels. Also, the average initial moisture content of pomegranate peels was 76.5% (w.b.). The final MC ranged from 2.67 to 2.10% and from 2.97 to 2.84% for the CODS and HISD, respectively. The higher drying rates of the pomegranate peels dried using CODS and HISD were 169.79 and 196 kg_{water/kgdrymatter}/h, respectively, at a layer thickness of 3 cm and a drying temperature of 70 °C. Additionally, using HISD led to a reduction in power consumption by about 64.28% to 75.75% compared to the CODS. Furthermore, the environmental analysis results showed that the embodied energy is about 1270.463 kW.h. The energy payback period for HISD ranges between 2.38 and 6.34 years. The earned carbon credit for drying pomegranate peels using the HISD ranged between 770.1 and 2207.2 USD. Based on economic analysis, the lowest drying cost using the HISD was 144.5 USD per ton of pomegranate peels, achieved at layer thicknesses of 1 cm and a drying temperature of 70 °C.