Search Results (9,056)

This study systematically investigates the influence of short carbon-fiber (SCF) content on the mechanical, thermal, and tribological properties of self-lubricating polyphenylene sulfide (PPS) composites filled with PTFE and MoS₂, addressing the critical need for high-wear resistance in Carbon-Fiber-Reinforced Thermoplastic (CFRTP) structural applications. The results identified 10 wt% SCF as the optimal content that achieved the best balance between load-bearing capacity and friction performance. The coefficient of friction μ and wear amount were reduced by 29.28% and 29.29%, respectively, compared to the PPS/PTFE/MoS₂ composite material without SCF, and by 14.67% and 20.75%, respectively, compared to the material with excessive SCF filling (20 wt%). Finite-Element Analysis-Representative Volume Element (FEA-RVE) reveals the mechanism by which excessive content of SCF at the microscopic level leads to a slight decrease in mechanical properties. Critically, the tribological performance exhibited a discrepancy with bulk mechanical properties: above 15 wt% SCF, the wear rate worsened despite high mechanical strength, revealing that increased fiber agglomeration and micro-abrasion effects were the primary causes of performance deterioration. Further in-depth XPS analysis revealed a synergistic lubrication mechanism: In the optimal sample, an ultra-dense PTFE transfer film was formed to mask the underlying MoS₂. This masking, coupled with the high surface activity of MoO₃ particles leads to stronger physicochemical interactions with the polymer matrix, ensures the exceptional durability and stability of the tribo-film. This research establishes a complete structure–performance relationship by integrating mechanical, thermal, and tribo–chemical mechanisms, offering critical theoretical guidance for the design of next-generation high-performance self-lubricating CFRTPs. Full article

To address the inherent defects in the fabrication of AlCrN titanium alloy coatings and enhance interfacial bonding strength as well as tribological performance, an AlCrN coating was employed as an absorption layer and subjected to laser shock processing to form an AlCrN/TC4 transition layer. Subsequently, a secondary AlCrN coating was deposited to construct a gradient coating architecture. The surface and cross-sectional morphologies and elemental distributions under varying laser energies were systematically investigated, and the influence of laser energy on the adhesion and wear resistance of the gradient coatings was analyzed. The results demonstrate that with increasing laser impact energy, the thickness of the AlCrN/TC4 transition layer gradually decreases from 3.75 μm to 1.32 μm, accompanied by significant changes in elemental distribution across the surface and cross-section. The interfacial bonding strength of the gradient coating increases substantially from 13.6 N to 43.3 N, while the average friction coefficient rises from 0.436 to 0.507. Concurrently, the wear track depth is reduced, and the wear rate decreases from 86.46 × 10⁻⁵ mm³/(N·m) to 7.67 × 10⁻⁵ mm³/(N·m). Laser shock peening promotes elemental diffusion, enabling the formation of a diffusion-aided interlayer. The incorporation of this diffused zone facilitates the successful construction of a high-quality TC4 titanium alloy gradient coating, effectively broadening the film–substrate interface, enhancing surface hardness, and significantly improving both interfacial adhesion and wear resistance. Full article

Titanium alloys are widely used as bone graft materials due to their excellent corrosion resistance and biocompatibility. Implant failure can result from long-term exposure to body fluids and inflammation-induced pH decreases, both of which compromise the material’s corrosion resistance and mechanical stability. To address this issue, porous Ti-6Al-4V alloy was selected in this work. Immersion tests were conducted in Hank’s solution with different pH values (3, 5, and 7) for 90 days to simulate the in vivo microenvironment under various physiological conditions. The degradation behavior of porous Ti-6Al-4V alloy during the 90-day immersion period was systematically investigated using a combination of characterization techniques. The results indicated that TiO₂, Ca₃(PO₄)₂, and Ca(H₂PO₄)₂ phases were formed on the surface of the after 90 days of immersion. Massive dissolution of TiO₂ was observed in solutions with high H⁺ concentration (low pH). Ion release tests revealed that the concentration of titanium ions released was significantly higher in acidic solutions, suggesting that the passive film formed on porous Ti-6Al-4V alloy was unstable and prone to dissolution under acidic conditions. Consequently, a large amount of corrosion products accumulated on the specimen surfaces immersed in acidic solutions for a long duration. Moreover, the compression properties of the samples deteriorated after immersion. Specifically, the compressive strength decreased by 12.68 MPa, 11.67 MPa, and 5.84 MPa for sample immersed in solutions with pH = 3, 5, and 7, respectively. The significant reduction in compressive performance of the alloy in high H⁺ concentration solutions was attributed to the decreased compactness caused by ion release. The fracture mode of the porous Ti-6Al-4V alloy after immersion was identified as a mixed mode of ductile and brittle fracture. Full article

Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain–Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant’s subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting. Full article

Two-dimensional (2D) correlation functions are central to understanding structural crossovers in soft-core fluids; however, their asymptotic analysis is hindered by the Hankel-transform kernel, whose asymptotic representation introduces a term that breaks the natural conjugate symmetry of the poles. To address this, we present a symmetric contour integration scheme that restores symmetry at the level of the integration path. By employing quarter-circle contours in the first and fourth quadrants, the method captures conjugate pole pairs simultaneously and evaluates the sine term from the Bessel-function asymptotic without variable transformation or real-part extraction, yielding closed-form analytic expressions for the long-range decay of the density–density correlation function. The approach is demonstrated for a 2D Gaussian-charge one-component plasma under the random phase approximation at intermediate coupling, where the pole analysis provides direct access to the oscillation wavelength and decay length. In the high-density regime, the pole equations simplify to a form amenable to a Lambert W-function approximation, revealing a logarithmic scaling of correlation lengths even at moderate coupling. These findings establish symmetric contour integration as a transparent and versatile framework for pole-resolved asymptotics in 2D liquids. Full article

Low-voltage AC series arc faults are a leading cause of electrical fires, posing significant risks to life and property. While artificial intelligence-based detection methods have achieved high accuracy, they often suffer from limited interpretability and are typically tailored to specific loads, thus struggling to adapt to the diverse and dynamic load conditions in residential environments. To address these limitations, this paper proposes a novel interpretable arc fault detection model based on prototype learning with a hybrid attention mechanism. Specifically, we design a Tri-Domain Dynamic Attention (TDDA) module that integrates time-domain, frequency-domain, and temporal derivative information, and embed it into a Convolutional Neural Network (CNN) for enhanced feature extraction. Visual prototypes are constructed from sample characteristics, forming a tri-domain arc fault prototype set. A dedicated non-arc prototype set is further introduced to refine the decision boundary and improve accuracy. The proposed model is validated through comprehensive experiments and hardware implementation on a dedicated test platform. Results demonstrate that our model achieves an accuracy of 99.65%, maintains over 99% accuracy across various single-load conditions, and exhibits high detection performance under complex multi-load scenarios. Full article

To address surface defects and enhance the wear resistance of 316L stainless steel parts fabricated by Selective Laser Melting (SLM), this study applied shot peening (SP) surface treatment to the SLM-processed samples. Ball-on-disk tribological tests were systematically conducted under water-lubricated conditions to investigate the evolution of surface morphology, microstructure, microhardness, and tribological performance before and after SP. The results indicate that SP induced severe plastic deformation in the surface layer, effectively refining the coarse columnar crystals and melt pool structures characteristic of SLM, and forming a crystalline hardened layer with a depth of 70–80 μm. Consequently, the surface microhardness increased by 21.97% compared to the un-peened samples. Under loads of 20 N and 30 N, the coefficient of friction (COF) of the SP-treated samples decreased by 16.36% and 12.4%, while the wear rate was reduced by 17.09% and 14.9%, respectively. In this load range, the samples primarily exhibited uniform plowing and localized adhesive wear, demonstrating significantly improved resistance to plastic deformation and crack initiation. However, when the load increased to 40 N, intense stress and thermal effects diminished the strengthening benefits of SP, resulting in no significant difference in tribological performance between the SP-treated and untreated samples. At this stage, the dominant wear mechanism transitioned to severe plastic deformation, extensive delamination, and thermally induced adhesion. Full article

This study addresses the intensifying conflict between conservation and tourism development in global natural World Heritage sites by exploring how cognitive misalignments among stakeholders obstruct scene–village symbiosis and by proposing governance strategies grounded in cognitive coordination to enhance sustainable governance effectiveness. Focusing on three representative villages located in the overlapping area of the Li River World Heritage protection zone and the scenic tourism area, which represent the consolidation/maturity, emerging incubation, and potential cultivation stages of tourism development, the study employs Q methodology to identify stakeholder cognitive clusters and their interactive logics. Four cognitive clusters are revealed: utilitarian landscape instrumentalism, livelihood entitlement-oriented, nostalgic disciplinary gaze, and institutional risk aversion. Their presence and combinations vary across different development stages, forming distinct cognitive configurations. These clusters exhibit both couplings and tensions in value preferences, benefit claims, and action logic, which shape rule acceptance and willingness to collaborate. By overcoming the limitations of conventional surveys in capturing latent perceptions, this study proposes an integrated “cognitive differences—strategic interactions—policy mechanisms” framework. The findings offer transferable insights for managing multi-stakeholder heritage destinations, particularly in ecologically fragile areas facing overtourism pressures and sustainability challenges. Full article

Small molecules either derived from cell metabolic reactions or produced by gut bacterial flora have shown the potential of affecting gene expression, which suggests the possibility of interactions able to modulate cellular functions. In this context, the reported experiments were aimed at verifying a possible interplay between lactate and butyrate in modulating the efficacy of antineoplastic drugs. Butyrate is a product of gut bacterial flora, shown to be endowed with anticancer properties; conversely, increased lactate levels in cancer cells were found to be associated with higher proliferation and drug resistance. For the reported experiments, we adopted two cell lines from clinically relevant, but different cancer forms: pancreatic and triple-negative mammary adenocarcinomas. In spite of their different tissue origin, the two cell lines appeared to similarly respond to the effects of the two metabolites, which were found to modulate in opposite ways the expression of key genes involved in DNA repair by homologous recombination. As a consequence, changed efficacy of this repair pathway and modified response to PARP inhibitors were observed. Notably, our results also suggest that the counteracting effect between these two metabolites may be leveraged to address additional challenges limiting the success of anticancer therapies. Full article

Yunnan Jiama (paper horse prints), a representative form of intangible cultural heritage in southwest China, is characterized by subtle inter-class differences, complex woodblock textures, and heterogeneous preservation conditions, which collectively pose significant challenges for digital preservation and automatic image classification. To address these challenges and improve the computational analysis of Jiama images, this study proposes an enhanced object detection framework based on YOLOv8 integrated with a Global Attention Mechanism (GAM), referred to as YOLOv8-GAM. In the proposed framework, the GAM module is embedded into the high-level semantic feature extraction and multi-scale feature fusion stages of YOLOv8, thereby strengthening global channel–spatial interactions and improving the representation of discriminative cultural visual features. In addition, image augmentation strategies, including brightness adjustment, salt-and-pepper noise, and Gaussian noise, are employed to simulate real-world image acquisition and degradation conditions, which enhances the robustness of the model. Experiments conducted on a manually annotated Yunnan Jiama image dataset demonstrate that the proposed model achieves a mean average precision (mAP) of 96.5% at an IoU threshold of 0.5 and 82.13% under the mAP@0.5:0.95 metric, with an F1-score of 94.0%, outperforming the baseline YOLOv8 model. These results indicate that incorporating global attention mechanisms into object detection networks can effectively enhance fine-grained classification performance for traditional folk print images, thereby providing a practical and scalable technical solution for the digital preservation and computational analysis of intangible cultural heritage. Full article

To address the bottleneck issues of traditional ultrasonic polishing—such as unclear material removal mechanisms for ductile metals and difficulties in controlling machining outcomes—this paper employs a combined approach of computational fluid dynamics (CFD) simulation and non-contact fixed-point polishing experiments to systematically reveal the intrinsic relationship between the dynamic characteristics of the polishing flow field and the evolution of the material surface. Numerical simulations demonstrate that the cavitation effect significantly regulates the flow field structure: it not only confines the minimum pressure near the saturated vapor pressure but also markedly reduces the pressure peak while concurrently causing an overall decrease in flow velocity, forming a strongly coupled multi-parameter system of pressure, cavitation, and flow velocity. Experimental results indicate a clear spatial differentiation in the material removal mechanism: the central region is dominated by cavitation erosion, resulting in numerous pits and a 33.6% increase in residual compressive stress; the edge region is primarily governed by fluid-mechanical scraping, effectively improving surface finish and increasing residual stress by 22.3%; the transition zone, influenced by synergistic mechanisms, shows the smallest stress increase (19.7%). The enhancement of residual compressive stress can significantly improve the fatigue resistance of materials and prolong their fatigue life. This study comprehensively elucidates the multi-mechanism synergistic material removal process involving “cavitation impact, mechanical scraping, and fatigue spallation” in ultrasonic polishing, providing a key theoretical basis and process optimization direction for sub-micrometer ultra-precision machining. Full article

Inclusive AI-mediated mathematics education for students with learning difficulties refers to a human-centered approach to mathematics teaching and learning that uses artificial intelligence (AI), adaptive technologies, and data-rich environments to support learners who experience persistent challenges in mathematics. These challenges may take the form of a formally identified developmental learning disorder with impairment in mathematics, broader learning difficulties, low and unstable achievement, irregular engagement, or heightened mathematics anxiety that places students at risk of disengagement and poor long-term outcomes. This approach integrates early screening, personalized instruction, and affect-aware support to address both cognitive difficulties and the emotional burden associated with mathematics anxiety. Situated within digitally augmented schools, homes, and community spaces typical of smart cities, it seeks to reduce stress and anxiety, prevent the reproduction of educational inequalities, and promote equitable participation in science, technology, engineering, and mathematics (STEM) pathways. It emphasizes Universal Design for Learning (UDL), ethical and transparent use of learner data, and sustained collaboration among teachers, families, technologists, urban planners, and policy-makers across micro (individual), meso (school and community), and macro (urban and policy) levels. Crucially, AI functions as decision support rather than replacement of pedagogical judgment, with teachers maintaining human-in-the-loop oversight and responsibility for inclusive instructional decisions. Where learner data include fine-grained logs or affect-related indicators, data minimization, clear purpose limitation, and child- and family-friendly transparency are essential. Implementation should also consider feasibility and sustainability, including staff capacity and resource constraints, so that inclusive benefits do not depend on high-cost infrastructures. Full article

This study investigates the influence of toxic language in hate speech targeting immigrants, particularly through narrative formats like first-person X (Twitter) threads. Hate speech, defined as promotion of hatred based on personal or group characteristics, increasingly escalates on social media, impacting public attitudes and behaviors. While previous research has primarily focused on measuring the scope of hate speech through content analysis and computational methods, there has been limited attention to its effects on audiences. This study presents the results of an online experiment (N = 339) with a 2 × 2 between-subjects design that manipulates the presence of toxic language and message popularity. Results indicate that hate messages lacking toxic language promote greater identity fusion with the author of the message, which in turn increases the intention to share the message, reinforces negative attitudes toward immigrants, and increases support for harsh policies against irregular immigration. Moreover, non-toxic hate messages significantly enhance narrative transportation exclusively for individuals with conservative political views, thereby further increasing their intention to share the message. These findings highlight that subtler forms of hate speech can create strong audience connections with hostile perspectives, emphasizing the need for anti-hate campaigns to address both overt and subtle hate content. Full article

Multilayered geological structures are common in geotechnical engineering, where cooling shrinkage induces polygonal cracks in interlayers, compromising rock mass strength, permeability, and long-term stability. Existing thermo-mechanical studies on layered rock cracking insufficiently address the combined effects of weak interlayer geometry or interface-regulated mechanisms. To address this gap, based on meso-damage mechanics and thermodynamics, this study adopts a thermo-mechanical coupling simulation method considering rock heterogeneity, innovatively focusing on the understudied stress transfer effect at strong–weak interlayer interfaces. Systematic investigations were conducted on the initiation, propagation, and saturation of polygonal cracks in plate-like layered rocks under surface cooling, analyzing the influences of weak interlayer thickness, number, and position, and comparing surface vs. inner interlayer behaviors. Results showed that stress transfer interruption at weak–strong layer interfaces can inhibit crack propagation. Inter weak interlayers produce significantly more cracks and fragments than surface weak interlayers, with a stratified crack length distribution, and the maximum fragment area increases exponentially with weak interlayer thickness. Crack development is strongly influenced by weak interlayer thickness, with thinner layers dominated by non-penetrating cracks and thicker layers tending to develop penetrating lattice-like cracks. The inter layer stress and crack distribution exhibit fractal characteristics, with crack density decreasing layer by layer and no new cracks forming after saturation. This study clarifies the regulatory mechanism of weak interlayer features and surface cooling on crack evolution, quantifies interface effects and fractal characteristics, and provides a theoretical basis for instability prediction of layered rock structures in low-temperature geotechnical engineering. Full article

Background: Sleep quality (SQ) and physical activity (PA) are among the strongest behavioral determinants of healthy aging, while dietary behavior and psychological factors act as complementary modulators of these relationships. Although each domain has been studied extensively, their combined influence on subjective life expectancy (SLE)—an individual’s perceived likelihood of living to an advanced age—remains largely unexplored. Methods: This narrative review synthesizes evidence from sleep science, exercise physiology, behavioral medicine, and psychological aging. Literature published between January 2015 and 15 December 2025 was examined across PubMed, Scopus, and Web of Science using integrative keyword strategies. Studies addressing SQ, PA, circadian rhythms, psychological health, SLE, or aging-related outcomes were included. Results: The review identifies several converging pathways linking sleep and PA to aging trajectories. Sleep architecture, circadian stability, metabolic regulation, inflammatory balance, and autonomic function represent key biological mechanisms. PA contributes through improvements in mitochondrial efficiency, VO₂max, muscle metabolism, and anti-inflammatory signaling (IL-6 as a myokine). Across studies, both sleep and PA strongly influence psychological health, health perception, and future-oriented expectations, within a broader lifestyle context supported by nutritional status and dietary quality. SLE emerges as a central psychological mediator that shapes motivation, adherence to health behaviors, and long-term health outcomes. Contextual moderators—including age, gender, socioeconomic status, cultural norms, and wearable technology engagement—further influence these relationships. Conclusions: SQ and PA form the core behavioral components of a dynamic system that is further shaped by dietary behavior and psychological well-being and centered on SLE. Our proposed integrative model positions SLE as a key psychological link between lifestyle behaviors and longevity. This framework is hypothesis-generating and requires empirical validation through future longitudinal and interventional studies, underscoring the need for multidomain research integrating behavioral, biological, nutritional and psychological indicators of aging. Full article