Search Results (14,306)

This study investigates whether long-term global warming is associated with fertility decline across 195 countries from 1960 to 2023, and whether this relationship varies by economic development and adaptive capacity. We analyze Total Fertility Rate (TFR) data from the World Bank alongside temperature anomaly measures from NOAA and NASA using Pearson correlations and ordinary least squares (OLS) regression models. Regional analyses include Africa, Asia, Europe, the Middle East, and the Arctic, with GDP per capita serving as a proxy for economic development and adaptive capacity. Globally, temperature anomalies and fertility exhibit a strong negative correlation ($r\approx -0.90$, $p<0.001$). However, substantial regional heterogeneity emerges after controlling for GDP. In Africa ($r=-0.89$) and the Middle East, temperature anomalies remain statistically significant predictors of fertility decline even after GDP adjustment ($\beta =-0.99$, $p<0.001$; $\beta =-1.27$, $p<0.001$, respectively). In contrast, temperature effects become statistically insignificant in South Asia, East Asia, Europe, and the Arctic once GDP is controlled, indicating that fertility decline in these regions is driven primarily by socioeconomic modernization rather than climatic stress. These findings suggest that global warming functions as a conditional demographic stressor whose impact depends critically on adaptive capacity. In regions with limited infrastructure, including constrained access to air conditioning, healthcare, and occupational heat protection, rising temperatures remain significant predictors of fertility decline, potentially mediated through heat-sensitive biological mechanisms such as impaired spermatogenesis. By contrast, in higher-income regions, high adaptive capacity appears to buffer reproductive systems from thermal stress, allowing socioeconomic factors to dominate fertility dynamics. Full article

4D millimeter-wave radar provides a promising solution for robust perception in adverse weather. Existing detectors still struggle with sparse and noisy point clouds, and maintaining real-time inference while achieving competitive accuracy remains challenging. We propose SGE-Flow, a streamlined PointPillars-based 4D radar 3D detector that embeds lightweight spatiotemporal geometric enhancements into the voxelization front-end. Velocity Displacement Compensation (VDC) leverages compensated radial velocity to align accumulated points in physical space and improve geometric consistency. Distribution-Aware Density (DAD) enables fast density feature extraction by estimating per-pillar density from simple statistical moments, which also restores vertical distribution cues lost during pillarization. To compensate for the absence of tangential velocity measurements, a Transformer-based Inter-frame Flow (IFF) module infers latent motion from frame-to-frame pillar occupancy changes. Evaluations on the View-of-Delft (VoD) dataset show that SGE-Flow achieves 53.23% 3D mean Average Precision (mAP) while running at 72 frames per second (FPS) on an NVIDIA RTX 3090. The proposed modules are plug-and-play and can also improve strong baselines such as MAFF-Net. Full article

Ubiquitous aerial sensing with unmanned aerial vehicles (UAVs) is becoming an essential component of AI-native perception systems, motivated by the trend toward edge deployment and potential integration with future sixth-generation (6G)-connected aerial networks. In this work, we focus on improving the perception-side accuracy and computational efficiency of small-object detection in UAV imagery. However, small object detection in high-altitude UAV imagery remains highly challenging due to the extremely low pixel occupancy of targets and the severe multi-scale interference introduced by complex backgrounds. To address these limitations, we propose a Multi-scale Attention Fusion Network (MAF-Net), an AI-native paradigm for real-time small object detection in UAV imagery. The proposed approach enhances small-target representation and robustness through three key designs. First, a density-adaptive anchor optimization strategy is developed by combining K-means++ clustering with an IoU-based distance metric, enabling anchors to better match scale variation under diverse object densities. Second, a multi-scale feature reinforcement module is introduced to strengthen fine-grained detail preservation by integrating shallow feature maps via skip connections and hierarchical aggregation. Third, a dual-path attention mechanism is employed to jointly model channel importance and spatial localization, improving discriminative feature calibration in cluttered aerial scenes. Extensive experiments on three public benchmarks (AI-TOD, DOTA, and RSOD) demonstrate that MAF-Net consistently outperforms the baseline detector, achieving mAP@0.5 gains of 14.1%, 11.28%, and 22.09%, respectively. These results confirm that MAF-Net provides an effective and deployment-friendly solution for robust small object detection, supporting real-time UAV-based inspection and AI-native ubiquitous aerial sensing applications. Full article

Soil erosion is a critical issue on the Loess Plateau due to weak soil and intense summer rainfall. Plant roots provide essential soil stabilization. A split-plot field experiment was conducted in Liulin County, Shanxi Province, to evaluate the effects of biodegradable mulch and organic amendments on maize root development and soil stabilization. The main plots included no mulch (N) and biodegradable mulch (M). The subplots comprised five treatments: control (CK, no amendment), peat (PT), biochar (BC), fermented pig manure (PM), and corn stover (CS). Correlation and principal component analyses were used to elucidate the underlying mechanisms. The results showed that organic amendments were the primary factor influencing the root and soil properties. Peat and biochar significantly raised the root surface area density (RSAD, p < 0.05) and root–soil composite cohesion (with increases of 122.56% and 109.06% for NPT and NBC compared to NCK, respectively). Biodegradable mulch, and its interaction with the organic amendments, had no statistically significant effect on either the root–soil composite cohesion or root system parameters. The strong positive correlations of cohesion with the root length density (RLD, r = 0.80) and root volume density (RVD, r = 0.81) highlight that root occupancy is the key mechanism for enhanced shear resistance. Therefore, biochar is recommended for its effectiveness in enhancing soil retention and its potential co-benefits for carbon sequestration. This study provides a technical reference for sustainable agriculture on the Loess Plateau, while also acknowledging the need for further research on long-term carbon dynamics. Full article

Indoor environmental quality significantly affects occupant health, making stress-reduction interventions in built environments important. Aroma, a key sensory element, requires validation for stress relief. This study induced stress in 24 participants using a difficult arithmetic task in a controlled lab. EEG was recorded during exposure to lavender essential oil, a blended essential oil, or no aroma. Analyses focused on EEG microstates and functional connectivity. Stress reduced microstate C frequency, increased A-D transition probability, and weakened functional connectivity. Both aromas reversed these neural abnormalities toward baseline. The blended essential oil significantly enhanced theta-band functional connectivity, indicating a superior stress-relief effect compared with lavender. Integrating specific aromas indoors can thus promote user stress recovery, supporting aroma-based biophilic design for mental health. Full article

Intelligent Transportation Systems (ITS) primarily rely on flow rate and occupancy to estimate traffic states. However, in heterogeneous traffic conditions characterized by weak lane discipline and diverse vehicle classes, these conventional metrics fail to capture the true operational efficiency of signalized intersections. High flow rates can mask underlying inefficiencies, while low flow rates do not necessarily indicate free-flow conditions. This paper introduces a novel computer vision-based metric, the Effective Flow Ratio (EFR), designed to quantify the actual discharge efficiency of mixed traffic. By leveraging Bird’s-Eye View (BEV) vehicle tracking using You Only Look Once version 11 (YOLOv11) and ByteTrack, EFR distinguishes between kinematic movement and effective discharge, resolving the ambiguity of “moving but not clearing” states. We analyze 21 days of continuous footage from a rooftop-mounted camera overlooking a congested intersection in Dhaka, Bangladesh, exhibiting distinct non-linear behaviors compared to raw flow counts. Our results demonstrate that: (i) Flow rate and discharge efficiency are dynamically decoupled, evidenced by significant variance in EFR within identical flow bins; (ii) Temporal rolling correlations reveal transient regimes where traditional signal control logic would misinterpret congestion severity; and (iii) EFR provides a more robust proxy for intersection performance than occupancy or volume alone. The proposed metric offers a granular, physics-informed input for next-generation adaptive traffic signal control in developing urban environments. Full article

Machine learning (ML) models can complement traditional measurement-based approaches by supporting large-scale screening, spatial analysis, and prioritization of buildings for testing of indoor radon, a leading cause of lung cancer among non-smokers. Originating from uranium decay in soil and rock, radon enters homes via foundation cracks and accumulates indoors, influenced by building characteristics, ventilation, urbanization, and geogenic factors. As part of the Zagreb pilot within the “Evidence Driven Indoor Air Quality Improvement” (EDIAQI) project, this is the first ML application for indoor radon analysis in Croatia. This research evaluates residential indoor radon concentrations in Zagreb using ML applied to a dataset of 80 households. Several linear regression and tree-based ensemble methods were tested. The best-performing model (GBR) achieved an R² of 0.99 on the training set and 0.57 on the test set, with an RMSE of 33 Bq/m³ and MAE of 26 Bq/m³. Although predictive performance was moderate and generalization limited, key building characteristics such as construction year, dwelling type, occupancy details, and floor level were identified as relevant variables. The results suggest that machine learning may support radon risk prioritization in urban environments, but cannot replace direct measurements for regulatory purposes. Full article

Background: Return to work (RTW) after acute coronary syndrome (ACS) or acute heart failure (HF) is a pivotal outcome reflecting functional recovery and quality of life (QoL). While survival after cardiac events has improved through reperfusion and guideline-directed pharmacotherapy, sustainable RTW depends on an integrated set of clinical, psychological, social, and occupational determinants. Objective: This study aimed to synthesize and expand the evidence on predictors of RTW, delineate practical workload-matching rules using METs and CPET, and position multidisciplinary cardiac rehabilitation (CR) as the bridge from clinical recovery to durable vocational reintegration. Key findings: Beyond left ventricular ejection fraction (LVEF), depression, anxiety, illness perceptions, and RTW self-efficacy are robust predictors of vocational outcomes. CPET-guided exercise prescriptions and MET-based job matching ensure adequate metabolic reserve; sustained task demand should remain at ≤35–40% of maximal capacity, with peak capacity ≥2× average job demand. CR (Class IA in the 2023 ESC ACS Guidelines) improves exercise tolerance, medication adherence, psychosocial well-being, and deployment of vocational support, including stepwise reintegration plans and ergonomic adaptations. Telerehabilitation extends monitoring and counseling into the workplace and maintains adherence after RTW. Conclusions: Comprehensive CR that integrates exercise training, psychosocial counseling, lifestyle modification, and vocational interventions offers the most effective pathway to stable RTW, improved QoL, and reduced socio-economic burden. Early identification of vulnerable subgroups and personalized, digitally supported follow-up are essential for long-term job retention. Full article

Burnout represents a critical occupational health issue within the maritime sector, where demanding work schedules, prolonged periods at sea, and safety-critical responsibilities expose seafarers to significant psychological strain. This study investigates how burnout influences safety behaviors among maritime workers, adopting a multilevel framework that incorporates work engagement, quality of life, and work–family conflict as key factors shaping this relationship. Data was collected through a structured questionnaire administered to 216 seafarers distributed across 36 commercial vessels, representing a diverse range of onboard roles and operational contexts. The multilevel design allows for simultaneous examination of individual-level experiences and ship-level dynamics, offering a more nuanced understanding of how psychosocial risks translate into safety-relevant outcomes in maritime environments. Data were analyzed using multilevel structural equation modeling (MSEM), including multilevel confirmatory factor analysis (ML-CFA) and multilevel path analysis, implemented in Mplus version 8.10. The findings reveal that burnout undermines seafarers’ safe behaviors through diminished work engagement and a worsened quality of life. Furthermore, high levels of interference between work and family life amplify the negative effect of burnout on safe behaviors. This study contributes to the limited empirical literature on maritime behavioral health and provides implications for strengthening safety culture and crew well-being in the global shipping industry. Full article

Aviation maintenance is a high-risk work environment in which worker safety and operational safety must be managed simultaneously. This study develops and validates a dual-domain safety culture framework for aviation maintenance technicians (AMTs) employed by U.S. Part 121 airlines. The framework distinguishes between two complementary dimensions of safety culture: Maintenance Occupational Safety Culture (MOSC), which emphasizes AMTs’ physical safety and protection from workplace hazards, and Maintenance-Based Aviation Safety Culture (MASC), which focuses on organizational practices that prevent maintenance errors and support overall aviation safety. A quantitative survey of AMTs (n = 240) was administered, and data were analyzed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). The measurement model demonstrated acceptable fit indices and reliability. SEM findings indicate that trust has a significant positive effect on both MOSC and MASC, whereas fear exerts a negative effect, though statistically non-significant. These results highlight the central role of trust in strengthening safety culture in aviation maintenance, while suggesting that fear may only marginally undermine safety-related perceptions. The validated framework further emphasizes the importance of psychological safety in enhancing both AMT well-being and operational safety. Overall, this research advances understanding of safety culture in aviation maintenance by treating occupational and aviation safety as distinct yet interrelated domains, offering practical guidance for industry leaders, safety managers, and regulators seeking to improve safety outcomes by cultivating trust. Full article

Pulmonary histoplasmosis is often misdiagnosed as or coinfected with pulmonary tuberculosis (TB). This study aims to analyze the misdiagnosis or co-occurrence of published cases of pulmonary TB and pulmonary histoplasmosis. Cases of histoplasmosis with dissemination were excluded, as it affects other organs. Systematic research was conducted using PubMed, EBSCOhost, ProQuest, BioRxiv, and MedRxiv databases. Twenty-seven articles were included, covering a total of 51 cases. Males were predominantly affected, with a median age of 54 years. Exposure to caves and farming occupations were identified as the primary sources of infection (61.9%). The most common clinical symptoms were fever (80%) and cough (82.5%). Laboratory tests revealed culture positivity in 77.1% of cases, with sputum being the most frequently used specimens. In proven pulmonary histoplasmosis, antibody tests were positive in 18 out of 24 cases. Chest X-rays commonly showed cavities, infiltrates, and nodules, with an increase in nodular pattern in recent cases. The number of pulmonary nodules detected was higher on chest computed tomography (CT). Radiologic abnormality could occur in any lung region. This review suggests the potential for misdiagnosis and/or coinfection of pulmonary histoplasmosis and pulmonary TB. The combination of clinical suspicion, radiological findings, antibody and/or antigen testing could improve the diagnosis of pulmonary histoplasmosis. Full article

Background: Cubital Tunnel Syndrome (CuTS) is the second-most common compressive neuropathy of the upper limb, traditionally associated with prolonged elbow flexion, trauma, or anatomical constraints. With the widespread adoption of smartphones, sustained upper-limb postures have emerged as potential novel risk factors for ulnar nerve compression. This retrospective study aimed to investigate the potential correlation between smartphone use patterns and the development of CuTS. Methods: A retrospective observational study was conducted on 100 subjects recruited between 2021 and 2024, including 50 patients with EMG-confirmed CuTS who underwent surgical decompression and 50 matched controls without clinical or electrophysiological evidence of ulnar neuropathy. Demographic variables, daily smartphone use (h/day), predominant activity type, and habitual posture during device handling were collected through clinical records and questionnaires. Group comparisons were performed using t-tests and Chi-square analyses, with significance set at p < 0.05. Results: Daily smartphone use was higher in the CuTS group compared with controls (4.94 ± 1.8 vs. 4.04 ± 1.5 h/day), although the difference did not reach statistical significance (p = 0.0716). Posture during device use showed a significant association with CuTS: 82% of affected patients reported using smartphones with the elbow flexed, compared with 56% of controls, whereas supportive postures were less frequent among CuTS patients (16% vs. 38%) (p = 0.019). No significant differences were found between groups regarding smartphone activity type (p = 0.858). Conclusions: Smartphone use may contribute to ulnar nerve compression primarily through ergonomically disadvantageous postures, particularly sustained elbow flexion, rather than total usage time. These findings highlight a modifiable behavioral risk factor relevant to the rising prevalence of CuTS in the digital era. Increased clinical attention to device-handling habits and public-health strategies promoting ergonomic posture may support CuTS prevention. Prospective and biomechanically informed studies are warranted to further elucidate causal mechanisms. Unmeasured confounders (e.g., occupational and sleep-related elbow flexion) may influence these associations. Full article

Occupant presence is a primary driver of Heating, Ventilation, and Air Conditioning (HVAC) and lighting energy consumption in office environments. Existing occupancy-sensing solutions often rely on privacy-sensitive modalities or require costly infrastructure, limiting their applicability in Small and Medium Offices (SMOs). To address these limitations, this study proposes a lightweight CSI-based occupancy-sensing framework based on a dual-core ESP32-S3 architecture, enabling concurrent CSI processing, environmental sensing, and cloud communication. A multi-stage signal preprocessing pipeline compresses raw CSI streams into a compact $56\times 8$ statistical feature matrix, achieving 98.86% classification accuracy for multi-level occupancy estimation. Compared with image-based baselines such as DenseNet121, the proposed approach reduces input data size to 24 kB and model parameters to 138 K, yielding over 129× reduction in transmission volume without sacrificing performance. These results demonstrate that the proposed framework provides a practical, privacy-preserving, and edge-deployable solution for occupancy-aware energy management in SMOs. Full article

Work-related wellbeing research is increasingly constrained by conceptual and terminological clutter. Labels such as worker wellbeing, employee wellness, wellbeing at work, occupational health, quality of working life, and other terms are often treated as synonyms, defined at different levels of abstraction, or operationalised through proxies, limiting cumulative theory and cross-study comparability. This conceptual, semantically-focused, critical paper demonstrates the ways in which legacy terminology, interdisciplinary language gaps, issues with the overarching concept of wellbeing and category errors contributed to the current conceptual and definitional disarray. It then proposes a conceptual map to help resolve this state of affairs. The framework distinguishes overall wellbeing from work-related wellbeing, separates life-domain labels from wellbeing components, clarifies population segmentations, and offers unified set of definitions to these wellbeing constructs. This conceptual work is intended to improve construct selection, specification, and measurement in both research and applied settings. This paper concludes with a future agenda. Full article