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Background: Hip fractures represent a major public health challenge in aging populations and are associated with high morbidity, mortality, and healthcare costs. While osteoporosis is the main underlying cause, biochemical markers of bone metabolism may provide additional insight into skeletal remodeling processes. However, the relationship between bone turnover biomarkers and specific hip fracture patterns remains poorly understood. Methods: A retrospective observational study was conducted, including patients admitted with hip fractures between January 2022 and December 2023 at our institution. Serum levels of vitamin D, parathyroid hormone (PTH), N-terminal propeptide of type I collagen (PINP), and beta-C-terminal telopeptide of type I collagen (β-CTX) were analyzed. Fractures were classified as intracapsular or extracapsular. Continuous variables were compared using the Mann–Whitney U test. Multivariable logistic regression analysis was performed to identify factors independently associated with extracapsular fractures. Results: A total of 131 patients were included, comprising 57 intracapsular fractures and 74 extracapsular fractures. Patients with extracapsular fractures were significantly older (83 (75–89) vs. 80 (71–86) years; p = 0.0079). No significant differences were observed in vitamin D levels between fracture groups (p = 0.446). PTH levels were higher in extracapsular fractures (p = 0.030), while β-CTX levels tended to be lower (p = 0.080). In multivariable logistic regression analysis, age remained independently associated with extracapsular fracture pattern (OR 1.05, 95% CI 1.01–1.09; p = 0.03). Higher β-CTX levels were inversely associated with extracapsular fractures (OR 0.65, 95% CI 0.43–0.96; p = 0.03), whereas vitamin D levels were not independently associated with fracture type. Conclusions: Extracapsular hip fractures were primarily associated with older age in this cohort. Among bone metabolism biomarkers, β-CTX showed an inverse association with extracapsular fracture pattern after adjustment for confounding factors. These findings should be interpreted with caution and considered exploratory, highlighting the need for prospective studies to clarify their clinical significance. Full article

Diet quality, nutrition knowledge, and psychosomatic literacy—defined as the understanding of the interactions between diet, gut microbiota, and mental well-being—may shape weight-related behaviours in youth. This study used a cross-sectional design to integrate these domains with digital information pathways in Central–Eastern Europe. This study assessed diet quality, nutrition, and psychosomatic knowledge, supplement use, and health-information sources among Polish adolescents and young adults, with emphasis on age-related differences and the role of social media. A cross-sectional, anonymous online survey (October 2025–January 2026) was conducted in Poland (final analytical sample: n = 478; adolescents 15–19 years vs. young adults 20–30 years). Of 591 individuals who accessed the survey, 478 were included in the final analytical sample. Diet quality was estimated from FFQ data using KomPAN-derived indices (pHDI-10, nHDI-14, DQI). Nutrition knowledge (0–25 points), psychosomatic/gut–brain indicators, supplementation, and information sources were analysed using χ²/Fisher tests and Mann–Whitney U tests with effect sizes. The primary outcomes measured were dietary supplement use and excess body weight (BMI ≥ 25 kg/m²). Multivariable logistic regression examined predictors of supplement use and BMI ≥ 25 kg/m². Overall diet quality was low to moderate, with limited intake of whole grains, legumes, and fish, and common nutrition misconceptions. Social media was the most frequently indicated source of diet/supplement information and was independently associated with more frequent supplement use (OR = 2.29; 95% CI: 1.43–3.64). Adolescents reported lower whole-grain intake and more misconceptions than young adults. Predictors of BMI ≥ 25 kg/m² included male sex (OR = 2.46; 95% CI: 1.46–4.15), lower education, and lower nutrition knowledge, while age showed a non-linear positive association with excess body weight. Polish adolescents and young adults show gaps between declared pro-health attitudes and actual diet quality/competencies. Social media reliance appears particularly linked to product-oriented behaviours (supplementation). Prevention should strengthen nutrition and food safety education, digital health literacy, and professional guidance on supplementation, especially in adolescents. Our findings suggest that social media is a primary driver for dietary supplementation among Polish youth, more so than objective nutrition knowledge. While diet quality is linked to weight status, the relationship is complex. These results may inform future public health interventions targeting digital health literacy to promote balanced nutrition and safe supplementation practices. Full article

Subterranean termites, particularly Odontotermes obesus, cause severe damage to forest nurseries and plantations in arid and semi-arid ecosystems. This study demonstrates the dual functional role of endophytic entomopathogenic fungi, Metarhizium anisopliae and Beauveria bassiana, in termite suppression and induction of plant defense responses. Laboratory bioassays revealed significantly higher virulence of M. anisopliae, with a lower LT₅₀ (lethal time required to cause 50% mortality) of 33.1 h compared to B. bassiana (46.7 h), a steeper probit slope (5.4 ± 0.3), and strong model fit (R² = 0.95), indicating rapid and synchronized mortality. Endophytic colonization varied across host species and application methods, with soil incorporation consistently outperforming foliar inoculation. Maximum colonization (82.5%) was recorded in Tecomella undulata and exceeded 80% in Azadirachta indica under M. anisopliae. Biochemical analyses revealed significant increases in protein (up to 3.5 mg g⁻¹), phenols (3.7 mg g⁻¹), and tannins (2.7 mg g⁻¹). Activity of defense enzymes was significantly enhanced, with catalase reaching 263.5 U mL⁻¹, while Phenylalanine ammonia-lyase and Tyrosine ammonia-lyase exceeded 170 and 198 U mL⁻¹, respectively, indicating activation of antioxidant and phenylpropanoid pathways. Molecular docking analysis further revealed strong interactions between fungal metabolites and termite cellulase, with Bassianin (−8.4 kcal mol⁻¹) and Tenellin (−8.1 kcal mol⁻¹) showing the highest binding affinities. These findings highlight the combined biochemical and molecular mechanisms underlying fungal-mediated termite suppression and plant defense induction, and future research should prioritize transcriptomic validation, rhizosphere microbiome interactions, formulation optimization, and long-term multi-location field evaluation to support sustainable termite management strategies. Full article

Impact-damaged reinforced concrete (RC) columns often experience significant reductions in load-carrying capacity and ductility when subjected to subsequent axial loading. Carbon fiber-reinforced polymer (CFRP) sheets have been widely used to strengthen such damaged columns; however, the underlying strengthening mechanism remains insufficiently understood, largely due to the difficulty of experimentally capturing the evolution of internal damage. To address this issue, a three-dimensional (3D) meso-scale finite element (FE) model has been developed to investigate the mechanical behavior of CFRP-strengthened impact-damaged RC columns. The proposed model captures the evolution of micro-damage within concrete and provides a more realistic representation of impact-induced damage compared with conventional homogeneous models. The model was first validated against available experimental results, showing good agreement in both failure modes and responses. Based on the validated model, three typical strengthening schemes, including the longitudinally applied CFRP, U-shaped CFRP, and fully wrapped CFRP, are systematically examined in terms of failure patterns, load-carrying capacity, stiffness, ductility, and energy dissipation. The results indicate that the fully wrapped CFRP configuration most effectively mitigated damage in the impact-affected zone and increased the load-carrying capacity by up to 86%. Furthermore, a quantitative evaluation framework based on strengthening indices for axial capacity and energy dissipation is proposed, indicating that strengthening with two CFRP layers can lead to a desirable ductile failure mode within the scope of this numerical investigation. These findings provide useful mechanistic insights into the strengthening process and offer preliminary guidance for the rehabilitation of impact-damaged RC columns, though further validation is required before practical implementation. Full article

Volatile halogenated hydrocarbons (VHHs) are critical air toxic pollutants, with some ozone-depleting substances (ODSs) strictly regulated by the Montreal Protocol. However, current understanding of the pollution characteristics, sources, and health risks of atmospheric VHHs in Southwest China remains insufficient. This study performed field observations of atmospheric VHHs in summer in Mianyang, a medium-sized industrial city in the Sichuan Basin. Freon-12 (563 ± 20 ppt) and Freon-11 (264 ± 15 ppt) were the most abundant chlorofluorocarbons (CFCs); chloromethane (785 ± 261 ppt) and methylene chloride (563 ± 505 ppt) dominated among VSLSs. The mean concentration of regulated ODSs (1037 ± 33 pptv) was notably lower than unregulated very short-lived chlorinated substances (1887 ± 745 pptv), reflecting effective ODSs phase-out locally, yet enhancements relative to Northern Hemisphere background implied potential leakage from residual tanks. Methylene chloride and trichloroethylene concentrations exceeded global background levels by over 10 times, indicating strong anthropogenic industrial influences. Phased-out CFCs displayed negligible diurnal variation due to stringent emission controls, whereas unregulated VSLSs exhibited a distinct U-shaped diurnal cycle, with peaks driven by morning boundary layer dynamics and evening accumulation. Positive matrix factorization revealed that industrial sources, including electronic solvents (28.6%), industrial processes (27.8%), and solvent usage (23.7%), accounted for 80.1% of total VHHs. The total carcinogenic risk (2.3 × 10⁻⁵) surpassed the acceptable threshold (1 × 10⁻⁶), dominated by 1,2-dichloroethane, chloroform, carbon tetrachloride, and 1,2-dichloropropane. All individual compounds exhibited mean hazard quotients (HQs) below the non-carcinogenic risk threshold. The cumulative hazard index reached 1.5, suggesting combined non-carcinogenic risks to the local population. These results support VHHs health risk management and ODSs control in Southwest Chinese industrial cities. Full article

Aero-engine defects often exhibit micro-scale and high-frequency characteristics under complex metallic textures, which makes precise segmentation difficult. Most existing pixel-level methods rely on spatial-domain modeling and lack frequency-domain decoupling. As a result, high-frequency details are easily hidden by low-frequency background information. In addition, repeated downsampling weakens the representation of fine-grained structures, leading to inaccurate boundary localization and limited robustness. To address these issues, a spectrum-driven hierarchical learning network is proposed for aero-engine defect segmentation. First, a dual-band spectral module is constructed using the discrete cosine transform to separate high-frequency and low-frequency components, providing stable and physically meaningful frequency-domain priors for the network. Second, a detail-guided module is designed where high-frequency features adaptively guide skip connections, compensating information loss during encoding and improving boundary recovery. Furthermore, a low-frequency-driven region-aware modeling module is developed. The internal defect regions, boundary areas, and background regions are modeled hierarchically. A dynamic hyper-kernel generation mechanism performs region-sensitive convolutional modeling, improving adaptation to complex structural variations. Extensive experiments on the Turbo19 and NEU-Seg datasets demonstrate that the proposed method produces accurate defect boundaries and achieves mIoU scores of 89.82% and 91.44%, improving over the second-best method by 5.22% and 4.42%, respectively. Full article

Mangrove represent vital coastal ecosystems that contribute to shoreline stabilization, ecological balance, and environmental management. Nevertheless, the precise delineation of mangrove regions using remote sensing data is often impeded by spectral similarities with intertidal mudflats and aquatic features, alongside the irregular spatial patterns and intricate margins of mangrove stands. This research utilizes high-resolution Gaofen-6 (GF-6) satellite observations as the foundational data to develop Triplet Axial High-Resolution Network (TAHRNet), a semantic segmentation architecture derived from the High-Resolution Network with Object-Contextual Representations (HRNet-OCR) framework for mangrove identification. The model integrates a Triplet Attention module to facilitate cross-dimensional feature dependencies and an improved Multi-Head Sequential Axial Attention mechanism to capture long-range spatial context while maintaining structural consistency. Based on evaluations using the test dataset, TAHRNet yielded a Mean Intersection over Union (MIoU) of 92.01% and a Overall Accuracy of 96.38%. Relative to U-Net and SegFormer, the proposed approach showed MIoU improvements of 5.25% and 1.88%, with corresponding Accuracy gains of 2.68% and 0.94%. Further application to coastal mapping in Zhanjiang produced results that align with manual visual interpretation. These findings suggest that TAHRNet is a viable tool for mangrove extraction and can provide technical support for coastal monitoring and ecological analysis. Full article

Deepfake content forgery compromises the integrity of digital media and the protection of personal identity, making its detection essential for preserving trust and enabling effective forensic analysis. Most deepfake detection approaches focus on global classification with a binary decision, which is inadequate for precise localization of manipulated regions. This limitation becomes particularly evident under image processing distortions. This paper proposes a dual-decoder architecture for the detection and segmentation of original and deepfake facial manipulations. Unlike conventional single-decoder segmentation models, the proposed approach introduces two decoding branches that learn complementary feature representations of authentic and forgery facial textures. In addition, attention mechanism modules are incorporated to refine encoder features based on decoder context, introducing adaptive feature selection during reconstruction. This architectural design reduces feature interference during reconstruction and enhances the localization of subtle inconsistencies introduced by deepfake manipulations. This approach generates complementary masks for real and forged regions, providing more precise boundary delineation. Experimental results highlight the robustness of the proposed method under image processing distortions, achieving intersection over union (IoU) scores of 0.9387 for real faces and 0.9254 for deepfake segmentation. These results underscore the effectiveness of the dual-decoder architecture in accurately detecting and localizing deepfake facial manipulations. Full article

For power-grid applications such as transmission corridor inspection, substation asset inspection, and post-disaster emergency repair, reliable UAV self-localization under GNSS-degraded or GNSS-denied conditions is critical to ensuring operational safety and accurate defect geotagging. Due to substantial discrepancies in viewpoint, scale, and geometric structure between oblique UAV images and nadir satellite images, conventional RGB-based cross-view retrieval methods often suffer from unstable alignment and insufficient geometric modeling, particularly in scenarios with repetitive textures and partial overlap. To address these challenges, we propose a cross-view visual geo-localization model that integrates RGBD multimodal inputs with multi-scale attention enhancement. Specifically, MiDaS is used to estimate relative depth from UAV imagery, which is concatenated with RGB to form a four-channel input, while satellite images are padded with an additional zero channel to maintain dimensional consistency. A shared-weight ViTAdapter is adopted to learn joint semantic–geometric representations, and a lightweight Efficient Multi-scale Attention (EMA) module is adopted on spatial feature maps to strengthen multi-scale spatial consistency. In addition, an IoU-weighted InfoNCE loss is employed to accommodate partial matching during training, thereby improving the robustness of feature alignment. Experiments on the GTA-UAV dataset under the cross-area protocol show stable performance across both retrieval and localization metrics. Specifically, Recall@1, Recall@5, and Recall@10 reach 18.12%, 38.83%, and 49.47%, respectively; AP is 28.01 and SDM@3 is 0.53; meanwhile, the top-1 geodesic distance error Dis@1 is 1052.73 m. These results indicate that explicit geometric priors combined with multi-scale spatial enhancement can effectively improve cross-view feature alignment, leading to enhanced robustness and accuracy for localization in challenging power inspection scenarios. Full article

The interlayer oxidation zone-type Mengqiguer uranium deposit in the southern Yili Basin is a typical sandstone-hosted uranium deposit in northwest China, and the lower member of the Jurassic Xishanyao Formation is its main ore-hosting stratum. However, mineralogical and geochemical responses to redox evolution in the deposit have not been systematically constrained. In this study, we carried out detailed petrographic observation, X-ray diffraction analysis, electron probe microanalysis, and whole-rock geochemical analyses on samples from the interlayer oxidation zone in the lower member of the Xishanyao Formation. Kaolinite and illite are the dominant clay minerals in the deposit, with higher contents in oxidation zones than in transition and unaltered zones, while the illite–smectite mixed-layer content shows the opposite trend. The main uranium minerals are uranium oxides and coffinite. U, S and organic carbon are enriched in the transition zone, while the Fe³⁺/Fe²⁺ ratio increases with the oxidation degree. Comprehensive analysis on clay minerals shows that the ore-forming fluids evolved from acidic oxidized meteoric fluids to weakly alkaline reduced fluids; the uranium was mainly derived from the leaching of uraniferous sandstone. The formation of the deposit is controlled by sedimentary facies, tectonic uplift, organic–inorganic fluid interaction and redox reaction. This study provides detailed mineralogical and geochemical evidence for the metallogenic mechanism of interlayer oxidation zone-type uranium deposits, and has important guiding significance for uranium prospecting in the Yili Basin. Full article

Iodine is an essential trace element for thyroid hormone synthesis, metabolic homeostasis, and fetal neurodevelopment. During pregnancy, maternal iodine requirements increase substantially, yet global recommendations are primarily based on population-level biomarkers rather than individualized physiological data. In this review, we examine current international guidelines for iodine adequacy in pregnancy, evaluate the limitations of population-based metrics—such as urinary iodine concentration (UIC) and serum thyroglobulin (Tg)—and highlight emerging evidence on physiological adaptations, functional biomarkers, and individualized risk factors. We incorporated data from population surveillance studies, mechanistic investigations of thyroid adaptation, and clinical outcome research identified through a literature search of PubMed/MEDLINE and Scopus (2016–2025). Evidence indicates that the widely adopted WHO range for iodine intake in pregnant women may overestimate the actual needs of gestation. There is a U-shaped relationship between iodine intake and thyroid outcomes, meaning both low and high iodine exposure adversely affect maternal thyroid function and fetal neurodevelopment, highlighting the narrow optimal intake window. Individualized considerations—including autoimmune thyroid disease, supplementation practices, environmental exposures, and coexisting micronutrient deficiencies—further modulate iodine requirements. Functional indices, such as the Thyroid Feedback Quantile-based Index (TFQI), may offer complementary tools for assessing iodine adequacy beyond traditional biomarkers. Full article

Cyanotic congenital heart disease (CCHD) remains a major contributor to infant morbidity and mortality in the United States, yet the influence of maternal social determinants of health on risk is not fully understood. This study examined associations of maternal age, education, and race/ethnicity with the live birth prevalence of CCHD using recent national birth data from the Centers for Disease Control and Prevention (CDC) National Vital Statistics System (2022–2023). CCHD was identified from birth certificate records and analyzed as a binary outcome. Regression analyses were performed to evaluate relationships between maternal characteristics and CCHD occurrence. Overall, CCHD was a rare outcome with a modest decline in prevalence between the two years examined. Increasing maternal age was associated with higher odds of CCHD, while Latina ethnicity was associated with lower odds compared to the reference group. Other racial/ethnic categories and maternal education level were not significantly associated with CCHD risk in adjusted analyses. These findings suggest that certain maternal factors, particularly age and ethnicity, are associated with variation in the live birth prevalence of CCHD and underscore the need for further research into underlying environmental and structural contributors not captured in standard birth records. Full article

Cultivated land, as a vital resource for human sustenance, requires region-specific protection strategies worldwide. Semantic segmentation technology for agricultural land remote sensing imagery offers a scientific foundation and decision-making support for cultivated land protection through accurate identification and dynamic monitoring. In China, the fragmented distribution, small parcel sizes, complex terrain, and indistinct boundaries of cultivated land pose challenges to the intelligent interpretation of high-resolution remote sensing (HRRS) imagery. Conventional semantic segmentation methods often struggle to address these complexities. To address this issue, we propose a hybrid network called STAR-Net (Swin Transformer Auxiliary Residual Structure) for semantic segmentation of agricultural land in HRRS imagery whose encoder integrates a Global-Local Feature Fusion Module to effectively merge complementary information from both branches. A Multi-Scale Aggregation Module within the decoder facilitates the fusion of shallow spatial details and deep semantic cues, enhancing the model’s ability to discriminate objects at varying scales. Using the LoveDA dataset, we show that STAR-Net generates the highest Intersection over Union (IoU) on the “Barren” and “Forest”, achieving the improvement of 9.88% and 7.05% respectively, while delivering comparable IoU performance on other categories. Overall performance improved by 0.46% in mIoU compared to state-of-the-art models. Across all target categories, the method also achieves the greatest count of leading segmentation metrics. Full article

Sandstone-hosted uranium deposits in the western Qaidam Basin are spatially associated with hydrocarbon-bearing structures, yet the specific roles of different sulfur sources in uranium mineralization remain poorly constrained. This study aims to distinguish the contributions of bacterial sulfate reduction and hydrocarbon-associated sulfate reduction to uranium precipitation by integrating detailed petrography, in situ trace element analyses, and sulfur isotope measurements of chalcopyrite from the Yuejin Ⅱ deposit. Chalcopyrite is restricted to high-grade uranium ores and occurs intergrown with uranium minerals, pyrite, baryte, and carbonate cements. Trace element patterns indicate that oxidizing brines acted as the main transport medium for both uranium and copper, as evidenced by positive correlations between U and brine-related elements (Ba, Sr, Na, K). Positive U-Th correlations with relatively constant Th/U ratios (0.027–0.225) reflect a combination of source composition, fluid transport capacity, and limited thorium remobilization in this near-source, hydrocarbon-rich environment. Correlations between U and high field strength elements (Sn, W) point to a highly evolved granitic origin, with Altyn granitoids likely supplying the copper. Sulfur isotopes show a clear bimodal distribution: one group exhibits heavy δ³⁴S values (+6.9‰ to +18.5‰), while the other shows extremely light values (–36.0‰ to –44.6‰). The light group reflects bacterial sulfate reduction in shallow strata, supported by framboidal pyrite textures, whereas the heavy group corresponds to surface-derived sulfate reduced at hydrocarbon-associated redox fronts, rather than direct incorporation of deep H₂S. The lack of intermediate δ³⁴S values indicates that two discrete sulfur reduction mechanisms coexisted within the same deposit, refining genetic models for uranium mineralization in petroliferous basins and challenging frameworks that invoke a single dominant sulfur source. Full article

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings. Full article