Search Results (18,063)

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

In indoor thermal analyses, the effect of humid air as a radiatively participating medium that absorbs and emits energy is often neglected. This simplification can underestimate important values in the results. This study presents a numerical investigation of the humid air that participates radiatively in the conjugate heat and mass transfer convection into a room modeled as a two-dimensional square cavity with a semitransparent wall (glass). The governing equations for mass, momentum, energy, species transport, turbulence, and radiative heat transfer were solved using the Finite Volume Method and coupled with the SIMPLEC algorithm. Two scenarios were analyzed: a radiatively participating medium (RPM) and a non-participating medium (NPM), under two climatic conditions (hot and cold). Results show that considering the radiatively participating medium breaks the symmetric patterns observed in the case of NPM. The energy absorbed by humid air enhances turbulent viscosity, buoyant forces, and indoor temperature. Humid air absorbs approximately 30–32% of the incident energy entering the enclosure. Finally, a correlation for the average temperature is proposed. The results provide insight into the influence of radiatively participating humid air on indoor-like thermal behavior. The study focuses on the analysis of fundamental transport mechanisms. Full article

Fluid–structure interaction (FSI) research is crucial for applications in fields such as naval engineering, geological hazards, and biomechanics. Traditional grid-based methods (such as CFD) often face challenges in simulating large-deformation flow fields and complex boundary conditions, where mesh distortion can compromise simulation accuracy. Building upon the DualSPHysics5.2 framework, this study leverages the strengths of weakly compressible SPH (WCSPH) in modeling free surface flows and large-deformation fluids, as well as the discrete element method (DEM), for accurately describing particle collisions and fragmentation behaviors. We propose an improved MSPH-DEM coupling algorithm that incorporates moving least squares (MLS) correction for kernel function gradient optimization. This algorithm utilizes MLS-based gradient correction to achieve smoother fluid surfaces as well as bidirectional coupling between fluids and particles. Experimental validation demonstrates that in dam break simulations, this method reduces pressure errors. In the dam break impacting a cube experiment, it enhances accuracy, while in the dam break impacting a baffle experiment, the horizontal displacement of marker points closely aligns with the experimental values from Liao et al. This approach effectively improves the accuracy of the simulations of FSI problems, offering a more reliable numerical simulation methodology for engineering applications such as geological hazard prevention. Full article

While multilane roundabouts follow geometric design standards, they often overlook motorcycle-dominated traffic behavior. This study evaluates lane-reduction strategies to create safer and more inclusive urban corridors in mixed-traffic conditions, focusing on a case study in Southern Thailand. High-resolution unmanned aerial vehicle (UAV) trajectory data were analyzed using the Macroscopic Fundamental Diagram (MFD), Cell Transmission Model (CTM), and Time-To-Collision (TTC) frameworks under three configurations: full lane availability, partial inner-lane closure, and full inner-lane closure. Results indicate progressive deterioration in performance under restricted-lane conditions. Under full closure, total flow decreased by 31%, and average travel time increased by 43%. The MFD curve shifted toward higher critical densities, indicating earlier congestion onset, while CTM results revealed longer discharge times, queue spillback, and increased merging friction. Conversely, safety outcomes (TTC) improved significantly: extreme rear-end conflicts were reduced by 48%, and severe lane-change conflicts were nearly eliminated (99%). Behavioral evidence suggests that full closure constrains motorcycles to a single circulating path, reducing erratic filtering and promoting more stable interactions. Overall, this study identifies a systemic trade-off between safety and efficiency, highlighting how geometric interventions catalyze behavioral adaptation. The findings highlight how geometric constraints shape collective behavior in motorcycle-dominated roundabouts and demonstrate the value of an integrated UAV-based framework as a vital tool for inclusive urban management, providing the granular data needed to balance safety and mobility in complex traffic landscapes. Full article

Non-consumptive wildlife tourism serves as a vital vehicle for promoting ecological conservation and nature education. Understanding visitors’ perceptions of wildlife behavior and how these perceptions translate into long-term pro-environmental behavioral intentions is crucial for balancing visitor recreational experiences with ecological management in nature reserves. This study developed a hybrid analytical method integrating Latent Dirichlet Allocation (LDA) and Structural Equation Modeling (SEM). Based on 62,557 online reviews and 351 questionnaires collected from 33 macaque tourism sites in China, we identified three dimensions of perceived macaque behavior: food-driven approach (FDA), co-presence experience (CPE), and natural habitat-based behavior (NHB). SEM results revealed that all three dimensions significantly influenced Perceived Ecological Value (PEV) and Positive Emotional Arousal (PEA). NHB and FDA exert a stronger influence on PEV, while CPE primarily drives PEA. Furthermore, both PEV and PEA significantly promote PEBI, with PEV having a stronger effect. These findings indicate that PEBI formation relies more heavily on understanding ecological significance than on immediate positive emotions alone. These findings refine the “experience-to-conservation support” mechanism and suggest that managers should optimize ecological interpretation and regulate food interactions to foster sustainable wildlife tourism. Full article

We construct a connection-theoretic framework for parallel transport of spectral components along parameter families of signals on the similarity group $\tilde{G}=\mathbb{R}\times \mathrm{SO}\left(2\right)$. Let ${\left\{f_t\right\}}_{t\in I}$ be a signal family that evolves under a $C^1$ group trajectory. The frequency support of the associated scale-rotation transforms produces three Hilbert subbundles over the parameter interval, and the trajectory velocity induces a covariant derivative on each subbundle. The standard spectral viewpoint treats transformation behavior at individual parameter values. Our formulation instead organizes the propagation of spectral components along the entire parameter path and provides closed-form transport operators together with error bounds on each subbundle. We derive three explicit parallel transport formulas. On the equivariant subbundle the transport is an exact isometric translation. On the coupled subbundle, the transport combines log-scale translation with a phase factor $e^{i{n}_0\Delta \theta }$. On the invariant subbundle, the transport is approximate, with the quantitative bound $\parallel {\Pi }^{\mathrm{inv}}F-F\parallel \le \epsilon \left|\Delta \tau \right|\parallel F\parallel$, where ${\Pi }^{\mathrm{inv}}$ denotes the parallel transport operator on that subbundle. We introduce the notion of non-parallelism rate as a pointwise measure of deviation from parallel evolution, and we prove that cumulative deviation along the path is bounded by the path integral of this quantity. The bound separates into two parts. One part is controlled by trajectory estimation error and reflects geometric mismatch. The other part is controlled by intrinsic appearance variation and reflects non-geometric drift. We also show that regularity transfers from the signal family to the spectral sections, and we establish a discrete transport theorem whose finite-sum error bounds recover the continuous estimates in the small-step limit. The framework provides a quantitative geometric tool for multi-scale feature evolution under continuous scale-rotation transformations. Full article

Limit state analysis provides building designers with a better understanding of fundamental structural resistance and deformation requirements, resulting in an overall material economy and offering clear safety boundary conditions for intelligent structural design. Cold-formed high-strength steel has extensive application prospects in structural engineering due to its excellent mechanical properties and flexible cross-sectional options. However, most existing research focuses on its ultimate strength-related behavior, lacking sufficient investigation into deformation properties. This study aims to comprehensively reveal the continuous variation laws of structural resistance and ductility of cold-formed high-strength CHSs (circular hollow sections) with different cross-sectional selections under axial load. Through reliable finite element analysis, the effects of yield strength (f_sy) of cold-formed CHSs, diameter-to-thickness ratio (D/t), and cross-sectional slenderness (λ) on compressive performance in the limit state, including failure mode, axial load-end shortening curve, ultimate-to-yield strength ratio (N_u/N_y), and ductility indicators (displacement ductility coefficient (μ) corresponding to the ascending stage and post-buckling ductility degradation coefficient (R_0.85)), were systematically investigated. The results indicate that the dominant failure mode of high-strength CHSs exhibits outward deformation. With an increase of f_sy and D/t, the value of N_u/N_y decreases, and the development of multiple compression performance exhibits significant nonlinearity, which indicates that blindly improving material strength may not necessarily be conducive to developing structural compressive performance or achieving efficient and economical design solutions. To better serve the ductile limit design of high-strength CHSs, combined with available experimental data and simulation results, the upper limit of λ is proposed to be 0.22, and the predictive models of μ and R_0.85 are established, respectively. Full article

The increasing prevalence of insecticide resistance in Aedes aegypti threatens the effectiveness of chemical vector control and highlights the need for alternative approaches targeting mosquito behavior. This study evaluated the oviposition deterrent and larvicidal activity of Salvia munzii essential oil against insecticide-susceptible (New Orleans; NO) and insecticide-resistant (Escobedo) Ae. aegypti strains. The essential oil, dominated by camphor (29.6%), 1,8-cineole (20.8%), and limonene (16.7%), was assessed through laboratory and semi-field bioassays. Larvicidal activity yielded LC₅₀ values of 184.38 µg mL⁻¹ for the susceptible strain and 305.04 µg mL⁻¹ for the resistant strain, with a resistance ratio of 1.65, indicating susceptibility. Oviposition deterrence was quantified using the Oviposition Activity Index (OAI), and median repellent concentrations (RC₅₀) were estimated. Under laboratory conditions, RC₅₀ values were 1.65 µg mL⁻¹ for the NO strain and 1.73 µg mL⁻¹ for the Escobedo strain. Under semi-field conditions, the RC₅₀ for the Escobedo strain decreased to 0.62 µg mL⁻¹. Deterrent activity increased with concentration and persisted for up to 40 days, particularly at higher doses. These results demonstrate that S. munzii essential oil exhibits both larvicidal and oviposition deterrent activity against Ae. aegypti, including a pyrethroid-resistant population, under laboratory and semi-field conditions. The findings support further evaluation of S. munzii essential oil as a potential complementary tool for integrated vector management strategies. Full article

With the rapid expansion of wind energy infrastructure, there is an increasing accumulation of wind turbine blade waste (WTBW), which is mainly composed of glass fiber-reinforced thermosetting composites. Due to the irreversible nature of polymer crosslinking, conventional recycling methods remain limited. In this study, plasma vitrification was employed to convert WTBW into a reactive calcium-aluminum-silicate slag suitable for use in geopolymer materials. Plasma treatment at a temperature of approximately 2750 K resulted in the formation of predominantly amorphous vitrified slag (VS). Structural characterization using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) revealed the spatial heterogeneity of the VS. This heterogeneity was influenced by thermal gradients and varied between samples collected from different slag discharge zones, both vertically and horizontally from the reactor. All VS samples contained between 30 and 89% amorphous phase and 10–55% anorthite, with the proportions varying by sampling location. Chemical stability tests showed the dissolution of calcium and aluminum in acidic media, resulting in a silica-enriched residual structure in which the Ca and Al content decreased to less than 0.5 at.% after 100 days. In contrast, exposure to alkaline media caused only minimal surface reorganization—the addition of 5 wt.% VS to acid-based geopolymers made with two metakaolin precursors resulted in a 35% decrease in the mechanical strength of pure metakaolin-based systems. In contrast, when metakaolin containing illite impurities was used, strength values were similar to those of the reference geopolymer. The results quantitatively demonstrate that plasma-derived slag exhibits composition-dependent reactivity, directly linked to its amorphous content and dissolution behavior. Full article

ARQ2 is introduced as a next-generation extension of the ARQ (Archive-guided Roulette-based with Quarantine) optimization framework, which was originally established as a cohesive strategy for achieving stable performance on difficult and noisy search landscapes. While rooted in the core principles of the original design, ARQ2 advances this foundation through a more refined integration of adaptive search, archive-guided exploration, controlled replacement, and robustness-aware population management. In doing so, it moves beyond the level of a simple algorithmic modification and emerges as a distinct methodological development within hybrid continuous optimization. Its significance lies in shaping a more mature and experimentally substantiated variant that promotes dependable behavior, consistent search quality, and balanced exploration–exploitation dynamics across diverse optimization environments. Empirical evaluation against nine established optimizers on 36 continuous optimization problems yields an overall average rank of 1.958, with 25 rank-1 placements and a mean-value improvement over ARQ on 21 out of 36 problems, confirming the stronger robustness and repeated-run reliability of the proposed design. As such, ARQ2 contributes to the ongoing development of stability-oriented optimization methodologies and reinforces the scientific relevance of this design line in the contemporary literature. Full article

Background/Objectives: This study assessed the diagnostic accuracy of two commonly used diagnostic instruments for autism spectrum disorder (ASD), the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) and the Autism Diagnostic Interview-Revised (ADI-R), in comparison to a best-estimate (BE) diagnosis made by a research psychologist. Methods: Two hundred and thirteen children aged 5 years 0 months to 7 years 11 months completed a comprehensive research assessment that included multiple diagnostic measures. Once each research assessment was complete, a research psychologist gave each participant an overall BE research diagnosis of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) ASD based on all available information from diagnostic testing and behavioral observations during testing. We assessed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both the ADOS-2 and ADI-R separately and in combination and used receiver operating characteristic (ROC) curves to compare the areas under the curve (AUCs) of these instruments. Results: Both the ADOS-2 Spectrum Criterion scoring (sensitivity = 96.2%; specificity = 97.5%) and ADOS-2 Autism Criterion scoring (sensitivity = 82.0%; specificity = 100%) had excellent accuracy in comparison to the BE ASD diagnosis. The ADI-R had good accuracy (sensitivity = 78.6%; specificity = 83.5%) compared to BE ASD diagnosis. In receiver operating curve analyses, both scoring criteria for ADOS-2 were significantly more accurate than the ADI-R. Conclusions: Overall, both instruments provide good, if not excellent, classification accuracies when used individually, as well as in combination. Thus, when deciding which measures to use for ASD research, other factors should also be considered. Full article

Amid rapid industrialization and urbanization, air pollution has emerged as a major public health concern linked to non-communicable diseases (NCDs), with older adults particularly vulnerable. Beyond its direct physiological effects, social participation could buffer environmental health risks by enhancing resilience, encouraging healthy behaviors, and reducing stress. Using data from the 2020 China Longitudinal Aging Social Survey (CLASS; 11,398 respondents aged 60 and above), linked with county-level air pollution indicators (PM_2.5, O₃, SO₂, NO₂, and CO), this study applied multilevel models to examine the association between air pollution and NCD prevalence among older adults, as well as the mediating role of social participation. Results show that higher NO₂ concentrations significantly increased NCD risk (OR = 1.27, 95% CI: 0.87–1.73), whereas higher SO₂ concentrations (mean = 9.96 µg/m³, ranged from 5.69 to 19.99 µg/m³) were unexpectedly associated with reduced risk (OR = 0.68, 95% CI: 0.58–0.8). This finding should be interpreted with caution and warrants further investigation; notably, the observed SO₂ levels were well below the World Health Organization air quality guideline values. CO exhibited an inverted U-shaped relationship with disease prevalence. Social participation functioned as a protective factor, lowering NCD risk (OR = 0.75, 95% CI: 0.66–0.84) and may partly explain the association between NO₂ exposure and NCDs. These findings highlight the complex and sometimes counterintuitive pathways through which air pollution and social participation jointly shape NCDs in later life. Policy interventions should integrate air quality improvements with initiatives that promote social participation to enhance resilience, reduce disparities, and foster healthy aging in polluted urban environments. For example, establishing well-ventilated indoor community centers equipped with air filtration systems in high-pollution areas could provide safer spaces for older adults to participate in social activities while minimizing exposure to harmful pollutants. Such interventions could simultaneously reduce environmental health risks and strengthen social participation, thereby offering a practical pathway for promoting healthy aging. Full article

Evaluations of medical artificial intelligence (AI) explanations often rely on self-reported trust, perceived usefulness, acceptance, or final decision outcomes, while less directly characterizing whether users check evidence around AI outputs during decision making. In AI-assisted chest radiograph interpretation, a critical process-level question is whether users return from the AI output to the original image evidence when further scrutiny is needed. To address this question, we examined whether verifiable explanations—explanations designed to make AI recommendations checkable against the original image evidence—are associated with process markers of visual evidence checking in AI-assisted chest radiograph interpretation using eye-tracking and human-factors process measures. A 2 × 2 between-subjects experiment manipulated verifiable explanations (present vs. absent) and risk context (high vs. low), with AI recommendation correctness embedded at the trial level. Fifty-six clinically trained participants each completed 24 interpretation trials. Analyses focused primarily on gaze transitions between the AI output and the original image and dwell time on the original image, with response time and exploratory verification-related behavioral states used as auxiliary process measures. Verifiable explanations did not simply increase acceptance of AI recommendations. Instead, when AI recommendations were incorrect, they were most clearly associated with more frequent AI–image transitions and longer absolute dwell time on the original image evidence. Exploratory state-based analyses further suggested a lower tendency toward no-verify adopt under incorrect AI recommendations, but these findings were treated as complementary rather than primary evidence. Overall, the value of verifiable explanations lies not only in final decisions but in whether they make AI recommendations more inspectable against the original evidence. These findings provide eye-tracking evidence consistent with visual evidence checking in AI-assisted diagnostic interfaces and underscore the value of process-sensitive human-factors measures in medical AI evaluation. Full article

The diminished Sombor index is a degree-based topological index that normalizes the Sombor contribution of each edge (defined as the Euclidean norm of the endpoint degrees) by the sum of those degrees, thereby making the index independent of graph size and ensuring a more balanced reflection of the relative degree contributions of adjacent vertices. In this paper, we investigate the extremal behavior of the diminished Sombor index over the class of connected bipartite graphs with fixed order and diameter. We establish a sharp upper bound for this index within the family of all bipartite graphs on a given number of vertices and with a prescribed diameter, and we completely characterize the extremal graphs attaining this bound. Furthermore, we prove that the maximum diminished Sombor index strictly decreases as the diameter increases. As a consequence, we determine the connected bipartite graphs of fixed order that achieve the three largest values of the diminished Sombor index. Full article

The tectonic setting of Taiwan and its surrounding regions is characterized by the complex interaction between the northwest-oriented Ryukyu subduction zone and the east-oriented Manila subduction zone. Within this subduction framework, the elastic thickness of the lithosphere (Te) serves as a critical parameter for elucidating the mechanical behavior of the area. In this study, we employed the admittance–correlation method to estimate Te values across Taiwan and adjacent territories. The findings indicate that sedimentary loading results in an overestimation of the maximum Te by approximately 50 km; after adjustment, the Te values range from 0 to 60 km throughout the study area. On Taiwan, Te values predominantly lie between 20 and 30 km, decreasing to 10–20 km near the margins adjacent to the Ryukyu and Manila subduction fronts. The Philippine Sea Plate exhibits comparatively higher Te values, ranging from 40 to 65 km. The spatial distribution of Te broadly corresponds with major tectonic subdivisions. Statistical analyses reveal a weak negative correlation between Te and surface heat flow (r = −0.44) and a weak positive correlation with shear-wave velocity anomalies at a depth of 100 km (r = 0.22), suggesting that the thermal structure exerts only a moderate influence on lithospheric strength in this region. Nonetheless, within oceanic crustal domains, the relationship between Te and oceanic crustal age largely adheres to models of crustal cooling and lithospheric thickening, consistent with isotherm depths of approximately 200–400 °C. Additionally, dynamic topography associated with slab subduction may locally diminish Te by up to 25 km. Cross-sectional profiles through northern Taiwan and the Philippine Sea block reveal pronounced coupling between subduction geometry and Te distribution. The observed spatial patterns of Te reflect the mechanical imprint of prolonged tectonic evolution, with the orientation of Te gradients generally aligned with the direction of maximum principal compressive stress. Collectively, these results suggest that subduction geometry and tectonic processes are important factors influencing the spatial variability and evolutionary trajectory of lithospheric strength in Taiwan and its environs. Full article