Search Results (992)

School burnout is an increasing concern in highly competitive educational contexts. As artificial intelligence (AI) becomes embedded in classrooms, it shapes both learning processes and students’ stress experiences. Grounded in Mindset Theory and Conservation of Resources framework, this longitudinal study examined whether AI learning support moderates the link between stress mindset and school burnout. A sample of 850 Chinese middle school students (Mage = 15.09, 41% boys) completed two waves of surveys one year apart. Regression results showed that viewing stress as enhancing predicted lower subsequent burnout after controlling for baseline burnout and demographics. Although AI learning support did not directly predict burnout, its interaction with stress mindset was significant: the negative association between a positive stress mindset and burnout was observed when AI learning support was high. These findings suggest that AI can function as an external resource that amplifies adaptive beliefs, offering new pathways for fostering resilience in digital learning environments. Full article

Shearers and roadheaders are critical equipment in coal mining and roadway excavation, where the rock-breaking performance of cutting picks directly influences operational efficiency and economic outcomes. Complex geological conditions, such as hard coal seams and embedded inclusions like gangue or pyrite nodules, pose significant challenges to cutting efficiency and tool wear. This study presents a numerical investigation into the rotational cutting process of a single pick in heterogeneous coal seams using the Smoothed Particle Hydrodynamics (SPH) method integrated with a mixed failure model. The model combines the Drucker–Prager criterion for shear failure and the Grady–Kipp damage model for tensile failure, enabling accurate simulation of crack initiation, propagation, and coalescence without requiring explicit fracture treatments. Simulations reveal that cutting depth significantly influences the failure mode: shallow depths promote tensile crack-induced spallation of hard nodules under compressive stress, while deeper cuts lead to shear-dominated failure. The cutting pick exhibits periodic force fluctuations corresponding to stages of compressive-shear crack initiation, propagation, and spallation. The results provide deep insights into pick–rock interaction mechanisms and offer a reliable computational tool for optimizing cutting parameters and improving mining equipment design under complex geological conditions. A key finding is the identification of a critical transition in failure mechanism from tensile-dominated spallation to shear-driven fragmentation with increasing cutting depth, which provides a theoretical basis for practitioners to select optimal cutting parameters that minimize tool wear and energy consumption in field operations. Full article

This paper conducts a PRISMA-guided systematic review of the empirical literature on environmental, social, and governance (ESG) risk integration in European banking. Using evidence systematically retrieved from Scopus, ScienceDirect, IDEAS/RePEc, and SSRN, the review synthesizes 51 peer-reviewed and working studies published between 2020 and 2025, reflecting the recent and rapidly evolving nature of this research field. The analysis classifies the literature into three domains—pricing and allocation, monitoring and stress testing, and governance and management control systems—and evaluates whether ESG variables operate as first-order drivers within production credit-risk models. The results indicate that while ESG signals are increasingly incorporated into pricing decisions, stress-testing exercises, and governance frameworks, no study provides verifiable evidence of full model-level integration within Probability of Default (PD) or Loss Given Default (LGD) models. The contribution of this review lies in systematically identifying the structural, data-related, and supervisory constraints that sustain this integration gap and in proposing a roadmap that distinguishes incremental ESG sensitivity from genuine prudential model embedding. Overall, the findings clarify that ESG responsiveness in European banking is substantial, yet integration into core risk models remains limited. Full article

Ceramic Matrix Composites (CMCs) have emerged as a structural material alternative to nickel superalloys for high-pressure turbines (HPT) components operating at high temperature, like shrouds. Despite the outstanding thermal stability of the CMCs, limited cooling is still necessary due to the extreme thermal operating conditions necessary to maximize engine performance and minimize fuel consumption. The design of CMC components, indeed, must consider a maximum service temperature that should not be exceeded to avoid damage and accelerated oxidation. The cooling, on the other hand, may induce the formation of thermal gradients and thermal stresses. In this work, different design options for the cooling system are investigated to minimize the thermal stresses of an HPT shroud-like geometry subjected to maximum temperature constraints on the material. Cooling is obtained via colder air jet streams (air taken from the compressor), whose impact position (the surface where the cold air impacts the component) has a different effect on the temperature field and on the induced stress field. Besides stress evaluation with different cooling systems, an ONERA damage model is investigated at a key location to potentially take into account stress components acting simultaneously and potential stiffness degradation of the CMC. Finally, the design evaluation of potential discrete crack propagation is discussed. A standard cohesive elements approach has been compared with a brittle element death approach. The results showed that the cohesive element approach resulted in shorter crack propagation, underestimating the actual crack behavior due to the embedded stiffness degradation method, while the element death returned encouraging results as a quicker, less complex, but still accurate design evaluation. Full article

Unmanned aerial vehicles (UAVs) have become essential tools for monitoring crop condition, detecting early signs of plant stress, and supporting timely interventions in modern precision agriculture. However, real-time onboard image analysis remains challenging due to the limited computational and energy resources of small embedded UAV platforms. This work presents a deployment-aware neural architecture search (NAS) framework for discovering lightweight object detection networks explicitly optimized for edge hardware constraints. Building on the YOLOv8n baseline, the proposed NAS procedure yields detector architectures that substantially reduce computational load while preserving high detection accuracy for agricultural field monitoring tasks. The best-discovered model reduces GFLOPs by 37.0% and parameters by 61.3% compared to YOLOv8n, with only a 1.96% decrease in mAP@50. When deployed on an NVIDIA Jetson Nano, it achieves a 28.1% increase in inference speed and an 18.5% improvement in energy efficiency under ONNX Runtime, with additional gains using TensorRT FP16. Evaluation on wheat head and cotton seedling datasets demonstrates strong generalization across crop types and varying imaging conditions. By enabling highly efficient onboard inference, the proposed NAS framework supports practical UAV-based crop monitoring workflows and contributes to the development of responsive, field-ready remote sensing systems in resource-limited environments. Full article

The combined finite–discrete element method (FDEM) is an advanced numerical calculation method that is highly suitable for simulating the entire rock blasting process. Considering that rock mass contains many joints, the present study introduces a rock joint constitutive model to capture the transmission and reflection phenomena of blasting stress waves when they reach the joint. At the same time, based on the original FDEM code, an optimized blasting calculation model is proposed. This model considers the effect of explosive gas and accurately describes the physical relationship between the explosive gas pressure and the change in the blasting chamber area caused by crack propagation. To overcome the limitation of previous blasting models that only apply the pressure of the explosive gas to the borehole wall, the present study optimizes the determination conditions for crack penetration and the calculation method for the blasting chamber area as well as further considered the influence of the embedding effect of explosive gas on crack propagation. Finally, through three examples, the transmission and reflection laws of stress waves at the joints and the entire process of rock mass throw blasting are simulated. The results illustrate that this model can capture the propagation of stress waves, the gas wedge effect of explosive gas, and the entire process of crack initiation, propagation, and penetration in the rock mass during the explosion, demonstrating the potential of FDEM in blasting simulation. Full article

Cancer health disparities represent profound inequalities in incidence, outcomes, and survivorship across populations. While traditionally examined through distinct lenses of either molecular biology or social epidemiology, these disparities arise from the complex interplay of genetic susceptibility, epigenetic dysregulation, and social determinants of health (SDoH). This review proposes that DNA damage and genomic instability serve as a critical mechanistic bridge, integrating exposures from the societal level to cellular dysfunction. We synthesize evidence demonstrating how SDoH—such as systemic inequities, environmental exposures, and chronic stress—converge with genetic and epigenetic factors to disproportionately increase DNA damage burden, impair repair mechanisms, and accelerate tumorigenesis in marginalized communities. Using the elevated gastrointestinal cancer rates among Native Hawaiians and Pacific Islanders (NH/PI) as a case study, we illustrate how historical, environmental, and socioeconomic factors interact with biological pathways to drive disparities. The review highlights key advances in DNA damage research—from somatic mutation theory to the modern understanding of chronic genomic stress—and explores how innovations in single-cell genomics, biomarker discovery, and computational modeling can unravel disparity etiologies. We argue that a translational framework linking social exposure data to molecular biomarkers of DNA damage is essential for moving beyond descriptive disparities to mechanistic understanding. Ultimately, addressing cancer equity requires interdisciplinary strategies that bridge molecular oncology, public health, and community-engaged research, targeting the root causes where social inequities become biologically embedded as genomic instability. Full article

Water scarcity in arid/semi-arid regions restricts agricultural sustainability systems and hinders the achievement of regional sustainable development goals, especially in northwest China’s extremely arid areas, where acute water supply–demand conflicts and inefficient traditional practices intensify competition for water between agricultural and ecological sectors. This study aims to verify the effectiveness of an intelligent automatic irrigation system in mitigating water scarcity pressures and enhancing agricultural sustainability in the Shule River Basin of northwestern China, a region where traditional irrigation methods not only yield suboptimal crop outputs but also undermine long-term water resource sustainability. A smart irrigation module, integrating “sensing–decision–execution” processes, was embedded within a digital twin platform to enable precise, resource-efficient water management that aligns with sustainable development principles. Sunflower (Helianthus annuus L.), the most popular cash crop in the area, was used as the test crop, with three soil moisture-based irrigation levels compared against traditional farmer practices. Key indicators including leaf area index (LAI), dry biomass, grain yield, and irrigation water use efficiency (IWUE) were systematically evaluated. The results showed that (1) LAI increased from the seedling to flowering stage, with smart irrigation treatments significantly outperforming farmer practices in both crop growth and water-saving effects, laying a foundation for sustainable yield improvement; (2) total dry biomass at maturity was positively correlated with irrigation amount but smart irrigation optimized the allocation of water resources to avoid waste, balancing productivity and sustainability; (3) grain yield peaked within 70–89% field capacity (fc), with further increases leading to diminishing returns and unnecessary water consumption that impairs sustainable water use; (4) IWUE followed a parabolic trend, reaching its maximum under the same optimal irrigation range, indicating that smart irrigation can maximize water productivity while preserving water resources for ecological and future agricultural needs. The digital twin-driven smart irrigation system enhances both crop yield and water productivity in arid regions, providing a scalable model for precision water management in water-stressed agricultural zones. The results provide a key empirical basis and technical approach for sustainably using irrigation water, optimizing water–energy–food–ecology synergy, and advancing sustainable agriculture in arid regions of Northwest China, which is crucial for achieving regional sustainable development objectives amid worsening water scarcity. Full article

Gender incongruence significantly impacts the family system, yet the subjective experiences of caregivers remain relatively underexplored. This narrative review synthesizes contemporary evidence regarding psychological distress, emotional burden, and quality of life among caregivers of transgender and gender-diverse individuals. A targeted literature search of PubMed, Scopus, PsycInfo, and Google Scholar (2015–2025) was conducted, identifying 16 studies for thematic synthesis. Results indicate that caregivers consistently report elevated emotional distress, characterized by chronic anxiety, hypervigilance, and ambiguous loss. This burden is primarily driven by prolonged exposure to uncertainty, the weight of complex medical decision-making—particularly regarding fertility and hormone therapy—and vicarious minority stress stemming from social stigma and systemic barriers. Notably, distress is often intensified by sociopolitical climates rather than the transition process itself. Conversely, access to peer support networks, healthcare relationships, and engagement in advocacy emerged as vital protective factors facilitating resilience and adaptive meaning-making. We can conclude that caregiver well-being is a multifaceted process deeply embedded in social and institutional contexts. These findings underscore the necessity of integrated, family-centered medical-psychological models that explicitly support caregivers to ensure more equitable and effective gender-affirming care pathways. Full article

Background/Objectives: Early childhood caries (ECC) and saliva have been studied across disparate domains, including microbiome, fluoride, immune, oxidative-stress, and neuroendocrine research. However, the ECC–saliva literature has not previously been mapped as a connected system using modern natural language processing (NLP). This study treats PubMed titles and abstracts as data to identify major themes, emerging topics, and candidate neuroimmune axes in ECC–saliva research. Methods: Using the NCBI E-utilities API, we retrieved 298 PubMed records (2000–2025) matching (“early childhood caries” [Title/Abstract]) AND saliva [Title/Abstract]. Text was cleaned with spaCy and embedded using a transformer encoder; BERTopic combined UMAP dimensionality reduction and HDBSCAN clustering to derive thematic topics. We summarised topics with class-based TF–IDF, constructed keyword co-occurrence networks, defined an internal topic-level Novelty Index (semantic distance plus temporal dispersion), and mapped high-novelty topics to gene ontology and Reactome pathways using g:Profiler. Prophet was used to model temporal trends and forecast topic-level publication trajectories. Finally, we generated a fully synthetic neuroimmune salivary dataset, based on realistic ranges from the literature, to illustrate how the identified axes could be operationalised in future ECC cohorts. Results: Seven coherent ECC–saliva topics were identified, including classical microbiome and fluoride domains as well as antioxidant/redox, proteomic, peptide immunity, and Candida–biofilm themes. High-novelty topics clustered around total antioxidant capacity, glutathione peroxidase, superoxide dismutase, and peptide-based host defence. Keyword networks and ontology enrichment highlighted “Detoxification of Reactive Oxygen Species”, “cellular oxidant detoxification”, and cytokine-mediated signalling as central processes. Temporal forecasting suggested plateauing growth for classical epidemiology and fluoride topics, with steeper projected increases for antioxidant and peptide-immunity themes. A co-mention heatmap revealed a literature-level Candida–cytokine–neuroendocrine triad (e.g., Candida albicans, IL-6/TNF, cortisol), which we propose as a testable neuro-immunometabolic hypothesis rather than a confirmed mechanism. Conclusions: AI-assisted topic modelling and network analysis provide a reproducible, bibliometric map of ECC–saliva research that highlights underexplored antioxidant/redox and neuroimmune salivary axes. The synthetic neuroimmune dataset and modelling pipeline are illustrative only, but together with the literature map, they offer a structured agenda for future ECC cohorts and mechanistic studies. Full article

Urban microgrids are evolving into socially coupled energy systems in which prosumer decisions are shaped by both market incentives and peer influence. Conventional optimization approaches overlook this behavioral interdependence and offer limited adaptability under environmental disturbances. This study develops a behaviorally embedded multi-agent optimization framework that integrates social influence propagation with physical power network coordination. Each prosumer’s decision process incorporates economic, comfort, and behavioral components, while a community operator enforces system-wide feasibility. The resulting bilevel structure is formulated as an equilibrium problem with equilibrium constraints (EPEC) and solved using an iterative hierarchical algorithm. A modified 33-bus urban microgrid with 40 socially connected agents is assessed under stochastic wildfire ignition and propagation scenarios to evaluate resilience under hazard-driven uncertainty. Incorporating behavioral responses increases welfare by 11.8%, reduces cost variance by 9.1%, and improves voltage stability by 23% compared with conventional models. Under wildfire stress, socially cohesive agents converge more rapidly and maintain more stable dispatch patterns. The findings highlight the critical role of social topology in shaping both equilibrium behavior and resilience. The framework provides a foundation for socially responsive and hazard-adaptive optimization in next-generation human-centric energy systems. Full article

Modern battlefield operations require wearable technologies to operate reliably under harsh physical, environmental, and security conditions. This review looks at today and tomorrow’s potential for ready field-grade wearables embedded with biometric authentication systems. It details physiological, kinematic, and multimodal sensor platforms built to withstand rugged, high-stress environments, and reviews biometric modalities like ECG, PPG, EEG, gait, and voice for continuous or on-demand identity confirmation. Accuracy, latency, energy efficiency, and tolerance to motion artifacts, environmental extremes, and physiological variability are critical performance drivers. Security threats, such as spoofing and data tapping, and techniques for template protection, liveness assurance, and protected on-device processing also come under review. Emerging trends in low-power edge AI, multimodal integration, adaptive learning from field experience, and privacy-preserving analytics in terms of defense readiness, and ongoing challenges, such as gear interoperability, long-term stability of templates, and common stress-testing protocols, are assessed. In conclusion, an R&D plan to lead the development of rugged, trustworthy, and operationally validated wearable authentication systems for the current and future militaries is proposed. Full article

(1) Background: The evolving demands of general practice have increased stress, workload, and fatigue among patients and doctors. In 2022, the European Young Family Doctors Movement (EYFDM) identified wellbeing as a key competency for future GPs. This study primarily explored the perspectives of early-career GPs on integrating wellbeing in general practice training. (2) Methods: A concurrent mixed-methods approach combined a quantitative survey with a town hall discussion at the EYFDM workshop during WONCA Europe 2023 in Brussels. The meeting included brainstorming, subgroup discussions, and synthesis of findings. Subgroup discussions among young GPs and GP trainees were recorded, analyzed using content analysis, and validated through two rounds of stakeholder consultation. (3) Results: Participants advocated for mandatory wellbeing-focused timeslots during training with flexible, self-selected learning activities. Proposals included a toolbox with individual, group, and supervised options. A cultural shift towards prioritizing wellbeing as part of professional development was unanimously supported. Senior GP involvement was seen as crucial for driving this change, alongside wellbeing training for coaches and role models. (4) Conclusions: GP trainees across Europe emphasize the need for greater focus on wellbeing in training, supported by a generational cultural shift. Voluntary, diverse learning activities (toolbox) and role-modeling activities with experienced GPs may support wellbeing to be embedded as a core competency in general practice. Full article

Wearable sensors generate continuous physiological and behavioral data at a population scale, yet wellness prediction remains limited by noisy measurements, irregular sampling, and subjective outcomes. We introduce HybridSense, a unified framework that integrates raw wearable signals and their statistical descriptors with large language model–based reasoning to produce accurate and interpretable estimates of stress, fatigue, readiness, and sleep quality. Using the PMData dataset, minute-level heart rate and activity logs are transformed into daily statistical features, whose relevance is ranked using a Random Forest model. These features, together with short waveform segments, are embedded into structured prompts and evaluated across seven prompting strategies using three large language model families: OpenAI 4o-mini, Gemini 2.0 Flash, and DeepSeek Chat. Bootstrap analyses demonstrate robust, task-dependent performance. Zero-shot prompting performs best for fatigue and stress, while few-shot prompting improves sleep-quality estimation. HybridSense further enhances readiness prediction by combining high-level descriptors with waveform context, and self-consistency and tree-of-thought prompting stabilize predictions for highly variable targets. All evaluated models exhibit low inference cost and practical latency. These results suggest that prompt-driven large language model reasoning, when paired with interpretable signal features, offers a scalable and transparent approach to wellness prediction from consumer wearable data. Full article

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper presents a novel, bioinspired, and integrated prosthetic system as an advancement in bionic technology. The design incorporates a shoulder joint based on an asymmetric 3-RRR spherical parallel mechanism (SPM) with actuators embedded within the moving platform, and an elbow joint actuated by low-voltage Shape Memory Alloy (SMA) springs. The inverse kinematics of the shoulder mechanism was established, revealing the existence of up to eight configurations. We employed Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously maximize workspace coverage, enhance dexterity, and minimize joint torque. The optimized design achieves remarkable performance: (1) 85% coverage of the natural shoulder’s workspace; (2) a maximum von Mises stress of merely 3.4 MPa under a 40 N load, ensuring structural integrity; and (3) a sub-0.2 s response time for the SMA-driven elbow under low-voltage conditions (6 V) at a motion velocity of 6°/s. Both motion simulation and prototype testing validated smooth and anthropomorphic motion trajectories. This work provides a comprehensive framework for developing lightweight, high-performance prosthetic limbs, establishing a solid foundation for next-generation wearable robotics and bionic devices. Future research will focus on the integration of neural interfaces for intuitive control. Full article