Search Results (1,372)

Background: Healthcare professionals experience continuous biological and psychosocial stressors that may disturb oral and systemic homeostasis. Alterations in salivary secretion, mucosal immunity, and microbiome composition reflect adaptive cellular responses to chronic occupational stress. Understanding these mechanisms may provide a biological framework for resilience and wellbeing in everyday clinical practice. Objective: To narratively review the evidence linking oral cellular and molecular mechanisms—salivary biomarkers, epithelial and immune cell activity, and microbiome dynamics—with stress, fatigue, burnout, and wellbeing outcomes among healthcare professionals. Methods: This narrative review employed a PRISMA-guided literature search of PubMed, Scopus, Web of Science, and Cochrane Oral Health to enhance transparency and coverage across databases. Given the heterogeneity of study designs and outcomes, data were synthesized thematically without quantitative pooling or formal meta-analysis. Methodological strength was evaluated qualitatively, focusing on biomarker validity, sampling conditions, and conceptual relevance. Eligible designs included observational, experimental, and interventional studies. Results: Evidence from 99 studies suggests that chronic occupational stress elevates salivary cortisol, oxidative stress markers, and pro-inflammatory cytokines (IL-6, TNF-α), while reducing protective salivary immunoglobulin A and microbiome diversity. Balanced oral immune and microbial profiles were associated with better psychological adaptation and lower fatigue indices. Conclusions: Oral cellular homeostasis offers a promising window into the biological underpinnings of occupational stress and resilience in healthcare professionals. Systematic integration of salivary and mucosal biomarkers into workplace wellbeing programs could enhance early detection of dysregulated stress physiology. Future interdisciplinary research should bridge oral biology, occupational medicine, and mental health to strengthen sustainable wellbeing strategies across the health workforce. Full article

Under the dual pressures of climate change and intensive urban expansion, which jointly exacerbate urban heat risks, optimizing the urban thermal environment through vegetation has become a core pathway for climate adaptation. However, accurately quantifying the nonlinear cooling responses of vegetation under complex urban morphologies and diverse geomorphic conditions remains a major scientific challenge in achieving efficient heat-resilient urban planning. This study takes three representative megacities in China—Beijing, Shanghai, and Shenzhen—as case studies. By integrating multi-source datasets, an urban spatial morphology indicator system was constructed that encompasses key dimensions of the natural environment, urban morphology, and socioeconomic factors. Eleven machine learning models were applied to model and compare urban land surface temperature (LST). The results demonstrate that the CatBoost model exhibited superior performance in simulating complex urban thermal environments (R² = 0.683–0.873), effectively capturing the interactive effects among multidimensional factors. The findings reveal a dual differentiation pattern of “topographic constraint–morphological dominance” in urban thermal environments: in mountainous cities, elevation and mountain forests act as rigid cooling barriers that restrict the spread of heat islands; whereas in plain cities, thermal conditions are primarily governed by the synergistic warming effects of impervious surface expansion and intensive human–economic activities. More importantly, the study identifies a significant nonlinear threshold effect of vegetation cover (NDVI) on LST reduction—only when vegetation coverage exceeds a critical threshold can large-scale cooling benefits be activated to effectively offset the thermal accumulation associated with high GDP intensity. Based on these insights, the study proposes differentiated climate-adaptive spatial planning strategies: mountainous cities should strictly maintain ecological redlines at mountain fronts to safeguard macro-scale cooling sources, while high-density plain cities should focus on integrating green space patches to surpass the “cooling threshold” and enhance vertical greening systems. These findings provide a quantitative scientific basis for improving urban thermal resilience. Full article

Dengue remains a significant health burden in Thailand, with over 160,000 cases reported in 2023. Although two dengue vaccines are approved, uptake remains limited. This study assessed Knowledge, Attitudes, and Practices (KAP) toward dengue and behavioural drivers of vaccine willingness using the Capability, Opportunity, Motivation–Behaviour (COM-B) framework, which posits that health behaviours arise from capability (knowledge/skills), opportunity (environmental/social enablers), and motivation (beliefs/drivers). A cross-sectional online survey was conducted in September 2024 among 600 Thai adults aged 20–60 years. The questionnaire, adapted from the GEMKAP study, generated composite KAP and COM-B scores (0–100%). Willingness to vaccinate was measured on a 0–10 Juster scale, with multivariable regression identifying behavioural predictors. Of 600 respondents, 40% were male, with a median age of 40 years, and 23% were in high-dengue-burden areas. Knowledge scores were moderate (51%), and dengue prevention practices were low (40%). The proportion of respondents with high willingness to vaccinate (score 8–10) was 68%, which was positively associated with Reflective Motivation and Physical Opportunity. Hesitancy centred on vaccine side effects (29%) and cost concerns (13%). These findings suggest that despite generally favourable attitudes, vaccine uptake is hindered by safety, cost, and awareness gaps. Physician communication and the integration of vaccines into schools, workplaces, and primary care, along with education and vector control, are key for sustainable national coverage. Full article

Background: We aimed to evaluate the impact of a Private Provider Engagement (PPE) model that integrated neighborhood private health providers into the immunization system to improve vaccine uptake and reduce coverage disparities among marginalized communities in Karachi, Pakistan, where health inequities and the risk of vaccine-preventable diseases remain high. Methods: Routine immunization service corners were established at nine private clinics in urban settlements of eight high-risk union councils (HRUCs) in Karachi. A quasi-experimental before-and-after study design was used with a baseline survey conducted in May–July 2022 and an end-line survey in April–June 2024. Households were selected using a multistage cluster sampling approach, and data were collected from parents or primary caregivers of children aged 4–11 months residing in the catchment areas for >3 months, using an adapted WHO immunization coverage questionnaire. The primary outcome was child immunization status for BCG, Polio, Pentavalent (DTP-3), Rotavirus, PCV, TCV, and MR vaccines, categorized as fully vaccinated, partially vaccinated, or unvaccinated, and verified through vaccination cards or caregiver recall. Multinomial and binary logistic regression were used to investigate factors associated with immunization coverage. Results: A total of 2167 children were surveyed (1141 children at baseline; 1026 children at end-line). The proportion of fully immunized children more than doubled across sexes, with significantly higher adjusted odds at endline (aOR: 6.34, 95%CI: 2.45–16.21). Age-appropriate uptake of all antigens improved, with over fourfold odds for receiving the Penta-3 vaccine (aOR 4.55, 95%CI: 3.55–5.82) and more than threefold odds for receiving the MR-1 Vaccine (aOR 3.67, 95%CI: 2.37–5.67). Parental education strongly predicted immunization, with the highest odds among children of fathers with secondary or higher education or skilled labor. Fully immunized Pashto-speaking children increased at endline but had the lowest odds compared to Urdu-speaking children. Conclusion: The PPE model increased vaccination coverage and reduced disparities in Karachi’s urban settlements, demonstrating potential for scale-up to strengthen routine immunization and reduce the number of zero-dose children. Full article

Unmanned aerial vehicles (UAVs) have revolutionized plant protection spraying due to their high efficiency and adaptability. However, the lack of rapid, portable tools for assessing droplet deposition remains a bottleneck for optimizing spray quality and improving pesticide utilization. The main purpose of this study is to develop a portable, image-based detection device with improved algorithms for real-time analysis (<3 s per card) of droplet deposition on spray cards during UAV plant protection spraying, addressing the limitations of existing methods in portability, real-time capability, and field robustness. This study presents a portable detection device integrated with advanced image processing algorithms for real-time analysis of droplet deposition on copperplate paper cards during UAV operations. The device employs a Raspberry Pi 5 as the core processor, coupled with a high-resolution camera and a standard chessboard calibration board for field-portable image acquisition. Key innovations include an adaptive background subtraction and local contrast enhancement method to address variable field lighting conditions, and an improved adhesion droplet segmentation algorithm combining iterative morphological opening operations with refined distance transform-based concave point matching. Validation on 21 field-collected cards using ImageJ as reference demonstrated a droplet extraction accuracy of 89.4%, with coverage rate improvements of 25.4% and 15.2% compared to OTSU and block thresholding methods, respectively. The adhesion segmentation relative error averaged 6.3%. This low-cost, lightweight device provides farmers and researchers with an effective tool for on-site spray quality evaluation, contributing to precision agriculture and reduced pesticide waste. Full article

Despite recent advances in women’s sport in Portugal, coaching remains a highly gendered domain in which women continue to be underrepresented in leadership positions. Understanding the barriers that constrain women’s coaching careers is therefore essential for advancing gender equality in sport. This study aimed to validate the Barriers to Sports Coaching Questionnaire for Women (BSCQW) for the Portuguese sport context. Following a translation and cultural adaptation process, data were collected from a sample of 660 Portuguese women coaches representing a wide range of sports and competitive levels. A formative measurement approach was adopted and assessed using Partial Least Squares Structural Equation Modeling. The results demonstrated acceptable collinearity among indicators and meaningful contributions of organizational, sociocultural, intrapersonal, and interpersonal barriers to their respective constructs. The sociocultural barriers construct was refined by removing one item due to limited relevance in the Portuguese context, resulting in a more parsimonious model while preserving theoretical coverage. Overall, the findings supported the validity and contextual adequacy of the BSCQW Portuguese version. This instrument provides a robust and practical tool for researchers, sport organizations, coach education programs, and policymakers to identify the barriers faced by women coaches, thereby informing initiatives to support women’s coaching careers in Portugal. Full article

The high energy consumption and spatiotemporal thermal asymmetry of data center cooling systems have become critical bottlenecks constraining their green and sustainable development. Traditional point-type temperature sensors suffer from insufficient spatial coverage, while conventional feedback control strategies exhibit delayed responses and limited adaptability under dynamic workloads. To address these challenges, this study proposes a real-time thermal symmetry management framework for data centers based on distributed fiber optic temperature sensing and model predictive control (MPC). The proposed system employs Brillouin scattering-based distributed sensing to continuously acquire high-density temperature measurements from thousands of points along a single optical fiber, enabling fine-grained perception of the three-dimensional thermal field. On this basis, a hybrid prediction model integrating thermodynamic physical equations with a Temporal Convolutional Network–Bidirectional Gated Recurrent Unit (TCN–BiGRU) deep neural network is developed to achieve accurate and stable spatiotemporal temperature forecasting. Furthermore, a symmetry-aware MPC controller is designed with the dual objectives of minimizing cooling energy consumption and suppressing thermal field deviations, thereby restoring temperature uniformity through rolling-horizon optimization. Experimental validation in a production data center demonstrates that the distributed sensing system achieves a measurement deviation of 0.12 °C, while the hybrid prediction model attains a root mean square error of 0.41 °C, representing a 26.8% improvement over baseline methods. The MPC-based control strategy reduces daily cooling energy consumption by 14.4%, improves the power usage effectiveness (PUE) from 1.58 to 1.47, and significantly enhances both thermal symmetry and operational safety. The Thermal Symmetry Index (TSI) decreased from 0.060 to 0.035, indicating a 41.7% improvement in spatial temperature distribution uniformity. The TSI is defined as the ratio of spatial temperature standard deviation to mean temperature, where lower values indicate better thermal uniformity; TSI < 0.03 represents excellent symmetry, 0.03–0.05 indicates good symmetry, and TSI > 0.08 suggests significant asymmetry requiring intervention. These results provide an effective and practical solution for intelligent operation, energy-efficient control, and low-carbon transformation of next-generation green data centers. Full article

Introduction: Stage three osteonecrosis of the jaw (ONJ), whether medication-related (MRONJ) or osteoradionecrosis (ORN), often necessitates aggressive surgical management due to extensive necrosis, infection, and risk of pathologic fracture. While free flap reconstruction remains the gold standard post-segmental mandibulectomy, it may not be feasible for elderly or systemically compromised patients. Objective: The presentation of our own experience with advanced mandibular ONJ on patients managed exclusively with a contemporary titanium reconstruction plate system and to evaluate the clinical outcomes of this approach in the context of the current literature. Methods: From a group of 21 patients treated for ONJ, just four patients with Stage 3 MRONJ or Grade III ORN, unfit for microvascular surgery, underwent segmental mandibulectomy followed by alloplastic reconstruction using standard titanium plating. Outcomes were assessed clinically and radiographically over a follow-up period ranging from 3 to 20 months. A focused literature review was conducted to contextualize results. Results: All patients demonstrated stable reconstruction without plate exposure, fracture, or intraoral bone exposure during follow-up. Esthetic and functional outcomes are reported. No hardware complications were reported. The review of the literature supports plate-only reconstruction as a valid alternative for patients unsuitable for free flap surgery, especially when using rigid, anatomically adaptive systems with robust soft tissue coverage. Conclusions: Titanium plate–only reconstruction following segmental mandibulectomy can provide reliable short- to mid-term outcomes in selected patients with advanced ONJ. Used titanium plating systems appears to be a promising option. Full article

The rapid development of low earth orbit (LEO) satellite constellations for navigation augmentation represents significant challenges in optimizing coverage performance while minimizing system complexity. A hybrid optimization algorithm based on pelican optimization algorithm and quantum particle swarm optimization (POA-QPSO) is proposed in this paper for multi-objective optimization design of dual-layer Walker constellations. The algorithm integrates the global search capability of the POA and the local exploitation ability of QPSO, effectively balancing exploration and exploitation through a probability-driven dual-phase search mechanism, a three-tier adaptive parameter adjustment strategy, and a pareto frontier maintenance mechanism. Probability factor and quantum tunneling facilitate low-cost deep search in complex non-convex environments. Experiments demonstrate that the algorithm outperforms MOPOA and MOPSO on ZDT test functions, with an 18.5% improvement in IGD metrics. In LEO constellation optimization, the designed dual-layer configuration (800 km/144 satellites in the first layer and 1426 km/56 satellites in the second layer) achieves a 92.7% global coverage, with an average PDOP of 1.78 and 5.8 visible satellites in polar regions. Furthermore, comparative benchmark tests show that the proposed solution outperforms most mainstream algorithms and performs better than traditional medium Earth orbit satellite systems in mid-to-high latitude regions. This research provides an efficient solution for LEO navigation augmentation system design. Full article

Precise Estimated Time of Arrival (ETA) prediction in Terminal Maneuvering Areas (TMA) constitutes a prerequisite for efficient arrival sequencing and airspace capacity management. While data-driven approaches outperform kinematic models, conventional Recurrent Neural Networks (RNNs) exhibit limitations in modeling complex multi-aircraft spatial interactions and lack the capability to quantify predictive uncertainty. Conversely, Spiking Neural Networks (SNNs) enable energy-efficient event-driven computation, yet their applicability to continuous trajectory regression is hindered by “input starvation,” where normalized state vectors fail to induce sufficient neural firing rates. This study proposes a Probabilistic Spiking Transformer (PST) architecture to integrate neuromorphic sparsity with global attention mechanisms. An Adaptive Spiking Temporal Encoding mechanism incorporating learnable linear projections is introduced to resolve the regression-spiking incompatibility, facilitating the autonomous mapping of continuous trajectory dynamics into sparse spike trains without heuristic scaling. Concurrently, a Distance-Biased Multi-Aircraft Cross-Attention (MACA) module models air traffic conflicts by weighting spatial interactions according to physical proximity, thereby embedding separation constraints into the feature extraction process. Evaluation on large-scale real-world ADS-B datasets demonstrates that the PST yields a Mean Absolute Error (MAE) of 49.27 s, representing a 60% error reduction relative to standard LSTM baselines. Furthermore, the model generates well-calibrated probabilistic distributions (Prediction Interval Coverage Probability > 94%), offering quantifiable uncertainty metrics for risk-based decision support while ensuring real-time inference suitable for operational deployment. Full article

Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period. Full article

Understanding how the built environment relates to urban ecological resilience is essential for resilience-oriented planning in high-density cities. Using Wuhan, China, as a case study, we constructed a 1 km grid-based Ecological Resilience Index (ERI) by integrating ecosystem resistance, adaptability, and recovery, and we confirmed significant spatial autocorrelation in ERI. We then applied a Bayesian-optimized XGBoost model (v2.0.3) with block-based spatial cross-validation to improve robustness under spatial dependence, and used SHAP to interpret nonlinear, threshold-like patterns and interactions among predictors. The results indicate that building coverage ratio (BCR), nighttime light intensity (NTL), elevation (ELE), mean building height (MBH), and precipitation (PRE) were the most influential predictors of ERI. SHAP main effects indicate clear non-monotonic and threshold-like response patterns across key predictors. SHAP interaction analysis further suggests that, under high BCR, the SHAP interaction term tends to be positive when MBH is below approximately 10 m, whereas the interaction between high NTL and low MBV is predominantly negative. This study provides fine-scale empirical evidence to inform the optimization of three-dimensional urban morphology to support urban ecological resilience. Full article

Large language models (LLMs) with the Transformer architecture as the core have made significant progress in the field of natural language processing, and their application value in English teaching has also attracted much attention. In tasks such as text generation, question-answering systems, and translation, the processing capabilities of LLMs have significantly improved. However, existing LLMs have problems such as insufficient coverage of professional knowledge, rough semantic parsing, and weak personalized services. To address the aforementioned issues, this study proposes a dual-path retrieval-enhanced generation scheme that integrates vector databases and intelligent agents, aiming to improve the application of large models in English language teaching. Semantic retrieval of unstructured data in English teaching is realized through vector databases, knowledge is dynamically acquired by combining agents, and the accuracy is improved by using Bloom filters to fuse dual-path retrieval. At the same time, the retrieval efficiency is optimized by an importance-oriented algorithm, and user profiles are constructed based on multi-dimensional data to achieve personalized adaptation. Experiments show that the maximum optimization of the retrieval time of this scheme can reach 26.32%, and the highest retrieval accuracy can reach 86%. The key indicators and scores in tasks such as English knowledge retrieval and question-answering reasoning are better than those of the comparative schemes, providing an effective technical path for intelligent English teaching. Full article

Low-Power Wide-Area Network (LPWAN) technologies play a central role in large-scale wireless sensor network (WSN) deployments, where energy efficiency, coverage and reliability dominate over throughput. Among them, Long Range (LoRa) technology has emerged as a widely adopted physical-layer solution due to its ability to operate at extremely low signal-to-noise ratios (SNRs). While multi-antenna techniques can potentially enhance link performance, their applicability in LoRa-type systems is constrained by low-SNR operation, strict energy budgets and the quality of channel state information (CSI). This paper presents a systematic and progressively structured evaluation of multiple-input multiple-output (MIMO) techniques in LoRa-type systems under representative operating conditions. A multi-stage simulation framework, implemented using the Vienna SLS v2.0 (Q3) simulator and adapted to LoRa-like waveforms, is employed to isolate the impact of large-scale propagation, small-scale fading, antenna configuration and CSI quality. The analysis starts from a system-level coverage baseline and advances to link-level evaluations of diversity-oriented MIMO schemes and spatial multiplexing configurations under both ideal and imperfect CSI. The results demonstrate that spatial diversity techniques are well aligned with the operational characteristics of LoRa links, offering robust performance in low-SNR regimes and under limited CSI accuracy. In contrast, spatial multiplexing exhibits higher sensitivity to channel estimation errors, with its practical benefits becoming apparent primarily when evaluated using throughput-oriented metrics such as packet error rate and normalized goodput. Overall, the study highlights the fundamental trade-off between reliability and capacity in LoRa MIMO systems and provides design-oriented insights for wireless sensor network deployments. Full article

Maritime dynamic observation missions, such as environmental monitoring, marine ranching inspection, and emergency response, typically require large-scale and high-efficiency operations in complex and variable maritime environments. Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) offer complementary advantages in such missions: USVs provide long endurance and stable platform support, while UAVs enable rapid, high-coverage aerial perception. However, limited UAV battery capacity and dynamic task environments pose significant challenges to autonomous collaborative operations. This study proposes a collaborative operation and energy replenishment strategy for USV–UAV systems in maritime dynamic observation missions. Under a unified framework, task allocation, collaborative path planning, and energy replenishment are jointly optimized, where the USV serves as a mobile replenishment platform to provide energy support for the UAV. The proposed method incorporates dynamic task updates, environmental disturbances, and energy constraints, achieving real-time adaptive collaboration between heterogeneous agents. Validation through both simulations and actual sea trials demonstrates that the proposed strategy significantly outperforms four baseline methods (greedy strategy, static planning, multi-objective genetic algorithm, and reinforcement learning scheduler) across five core metrics: task completion rate (91.74% in simulation/90.85% in sea trials), total energy consumption (1284.66 kJ/1298.42 kJ), mission completion time (40.28 min/41.12 min), average response time (10.21 s/10.35 s), and path redundancy (13.79%/14.03%). Furthermore, ablation experiments verify that the energy replenishment strategy enhances the task completion rate in both simulation and field tests. This method provides a feasible and scalable collaborative solution for autonomous multi-agent systems, offering significant guidance for the practical deployment of future maritime observation and monitoring missions. Full article