Search Results (1,517)

The design of near-surface crown pillars involves the interaction between excavation geometry, rock mass quality, and the in situ stress state. This study proposes a formulation that integrates numerical modeling and empirical criteria to construct a unified framework for stability interpretation. The Modeled Span index is introduced as a hybrid measure that incorporates geometric relationships and the pre-mining stress state, which, when compared with rock mass quality, allows the definition of a probabilistic stability boundary. The results show that the design tool enables the establishment of a consistent classification within the domain represented by the dataset. The Modeled Span chart complements traditional stability assessment approaches and is intended for application in conceptual and prefeasibility studies of crown pillars in Sublevel Open Stoping mining. Full article

Traditional wood identification models are vulnerable to out-of-distribution (OOD) substitution in the global timber trade. In response to this issue, this study presents a dual-stage cascade authentication architecture using in situ Raman spectroscopy and machine learning. First, a physically informed preprocessing strategy, integrating adaptive truncation (>1749 cm⁻¹) and first-derivative filtering, is developed to extract a 1309-dimensional composite feature matrix. This step effectively decouples non-linear fluorescence and converts physical detector saturation into highly discriminative features. To mitigate data leakage, the system utilizes a cross-validated Random Forest engine for Stage-1 closed-set discriminative screening. Subsequently, it cascades a high-dimensional One-Class Support Vector Machine (OCSVM) for Stage-2 open-set non-linear boundary verification in the Reproducing Kernel Hilbert Space. This design avoids the “variance trap” of traditional linear dimensionality reduction (e.g., PCA), preserving weak but critical secondary metabolite signals. Under a controlled OOD benchmarking scenario involving three taxonomically and chemically similar substitute species, the optimized Stage-1 engine maintains a 91.67% closed-set accuracy on known species. Crucially, Stage-2 verification achieves an open-set detection AUROC of 0.9722 and limits the FPR95 to 3.33%. Feature importance mapping indicates that the model effectively incorporates macroscopicoptical surrogate features (e.g., fluorescence decay boundaries) for decision-making. Overall, this study offers a robust, controlled non-destructive approach for real-world wood authenticity verification. Full article

Accurately mapping agricultural land use in fragmented hilly and mountainous areas is crucial for resource management but is severely challenged by spatial heterogeneity. While high-resolution (HR) images excel at delineating fine parcel boundaries, their limited spectral and temporal information often leads to spectral confusion among diverse agricultural types. To address this limitation, this study proposes a novel spatiotemporal feature-driven geographical zoning method integrating vegetation phenology, topography, and human activity. This zoning strategy decouples the complex global classification task into relatively simple local problems, providing explicit geoscientific constraints for subsequent classification. The proposed method was validated by classifying plain open-field croplands, sloping croplands, terraces, and greenhouses in the hilly and mountainous areas of Beijing using 2 m resolution satellite images. Compared to traditional global classification methods, the proposed zoning-based method increased the overall accuracy from 84.81% to 90.81%, the Kappa coefficient from 0.74 to 0.85, and the Intersection over Union (IoU) from 77.85% to 90.85%. The advantages of geographic zoning were particularly evident in mitigating spatial heterogeneity and enhancing boundary precision. These findings indicate that integrating dynamic geographical zoning as a priori knowledge successfully bridges the gap between HR spatial details and environmental contexts, offering a robust solution for mapping fragmented agricultural landscapes. Full article

Fishways play a critical role in restoring river connectivity and conserving fishery resources, yet their efficiency is often limited by mismatches between hydraulic conditions and species-specific behavioral traits. To quantify the upstream migration behavior of fish under the combined influence of flow heterogeneity and schooling effects, this study examined the endangered species L. elongata in the Yangtze River Basin. Volitional swimming behavior was tested in an open-channel flume under three spatially heterogeneous flow regimes (I: Low–Moderate–High; II: High–Moderate–Low; III: Moderate–High–Low). A video monitoring system recorded the upstream movement of solitary fish and three-individual schools. Swimming trajectories, upstream migration time, preferred flow velocities, and schooling metrics—including nearest neighbor distance (NND) and mean pairwise distance (MPD)—were analyzed. Linear mixed-effects models were employed to account for repeated measures and individual variability. Results showed that schooling behavior significantly enhanced upstream migration efficiency: schooling fish arrived at the target area on average 8.93 s earlier than solitary individuals (p < 0.01), while flow condition alone had no detectable effect on arrival time. L. elongata consistently preferred low-velocity zones (0.20–0.50 m/s) and avoided high-velocity regions (0.75–1.25 m/s), with meandering upstream trajectories predominating. NND showed no significant differences across flow conditions (p > 0.05), indicating stable schooling cohesion. However, MPD increased significantly under Flow III compared to Flows I and II (p < 0.01), suggesting that higher flow heterogeneity leads to more dispersed group spacing while overall cohesion is maintained. Distinct movement strategies were observed: solitary fish predominantly utilized boundary regions as hydraulic refuges (wall-following: 63.8–80.5%), whereas schools exhibited greater spatial exploration and reduced wall-following. These findings demonstrate that schooling enhances migration efficiency while preserving a cohesive group structure and that flow heterogeneity influences within-group spatial organization. To optimize fishway performance for L. elongata, we recommend maintaining flow velocities within 0.20–0.50 m/s. This study provides scientific guidance for hydraulic regulation in fishway design and habitat restoration, emphasizing the combined effects of flow heterogeneity and schooling behavior on migration performance. Full article

The building façade serves as the primary interface between the built environment and external climate, marking the transition from static regulation to dynamic response in climate-adaptive design. While existing research predominantly addresses periodic climatic elements such as temperature and solar radiation, the highly stochastic wind environment and its potential for internal acoustic problems remain systematically unexplored. This study investigates the acoustic modulation mechanism of building façades under dynamic wind conditions through a simulation-based methodology. The primary aim is to demonstrate the use of active control to mitigate the influence of fluctuating wind on the internal acoustic environment of buildings with open windows or semi-open boundaries, focusing on the coupling between stochastic wind fields and architectural acoustics in humid subtropical climates. We propose a wind-responsive adaptive acoustic façade system employing fractal geometry and configurable delay strategies, and develop a high-fidelity simulation framework to quantify how façade geometry and activation logic regulate acoustic parameters under varying wind conditions (1–8 m/s). Results indicate that: (1) support vector regression-based mapping of wind speed to delay strategies maintains key sound-field parameters (Lateral Fraction (LF), Speech Clarity (C₅₀), and Early Decay Time to Reverberation Time ratio (EDT/RT₃₀)) within 10% fluctuation across wind regimes; (2) fractal configurations achieve balanced wide-band (125 Hz–8 kHz) performance, with SPL fluctuation <3 dB, spectral tilt (+0.3 dB), and reverberation time slope <0.3; (3) configurational switching between column (high LF) and row (high C₅₀) arrangements enables dynamic trade-off between spatial impression and speech clarity. This work establishes an integrated framework coupling wind dynamics, façade morphology, and acoustic modulation to regulate objective indoor acoustic parameters. Based on the simulated omnidirectional point-source model, the results show that key acoustic indicators remain stable across varying wind conditions, providing a theoretical and quantifiable basis for climate-responsive acoustic envelope design. Future work will include empirical prototype testing and listening tests to determine whether these simulated acoustic parameters translate into improved comfort and well-being for occupants. Full article

The use of millimeter-wave spectrum in fifth-generation (5G) systems is increasing the need for accurate prediction of received power and coverage in real deployment scenarios. In this context, ray tracing (RT) is a promising approach for site-specific analysis, although its reliability depends on how accurately different tools reproduce measurements in complex urban environments. This work presents a comparative assessment at 27 GHz of three RT tools: in-house Exact tool based on Vertical Plane Launching (VPL), Matlab 5G and open-source Sionna RT based on Shooting and Bouncing Rays (SBR). The comparison relies on a large outdoor walk-test campaign, including about 14,725 measurement points collected in a real urban area around a 27 GHz mMIMO base station, using real operator-provided antenna radiation patterns. Measured and simulated power levels are compared using statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and a planning-oriented coverage-rate metric. The results show a reasonable agreement between simulations and measurements, with RMSE and MAE values around 10–12 dB, highlighting tool-specific behaviors related to boundary effects, interaction modeling, and high-power overestimation. This work confirms that RT is a flexible support for 5G preliminary network design, reducing the need for extensive drive tests. Full article

This research develops a new low-cost method for continuous flow monitoring in open channels. Flow is calculated using a standard 1D hydraulic model that integrates surveyed cross-sections and water level measurements at the boundaries of a studied reach, from which the name Boundary Water Level Method (BWLM) is derived. By implementing low-cost ultrasonic sensors for water level measurement, the method gains advantage for application on smaller channels, which are often not included in national hydrological monitoring networks due to limited budgets. New and innovative monitoring methods in hydrology are a necessary alternative to increasing the monitoring budgets, especially for continuous, real-time flow monitoring. Like any novel method, it requires validation under the intended environmental conditions, especially when designed primarily for ungauged channels. Validation was conducted on two test-sites by comparing the BWLM discharge and the discharge from official hydrological stations, with an error of up to 15%. BWLM provides reliable discharges using estimated hydraulic roughness values based on the literature and experience. Sensitivity analysis of the estimated hydraulic roughness coefficient demonstrated a substantial influence on the resulting discharge values. This has to be considered when implementing the method in unstudied basins. Full article

This paper presents a theoretical study on the electromechanical coupling response of piezoelectric–flexoelectric–semiconductor (PFS) nanocantilevers by adopting flexoelectric elasticity and semiconductor theory. A unified mechanical–electrical model is established to incorporate a strain gradient, the piezoelectric effect, semiconducting characteristics, and flexoelectricity at micro-/nanoscales. Analytical solutions for deflection, electric potential, and electron concentration are obtained under three types of electrical boundary conditions. Numerical results show that flexoelectricity significantly enhances the effective bending stiffness of the beam under open-circuit conditions with or without surface electrodes, especially in thinner structures. With a fixed external electric potential condition, the applied potential can effectively modulate the deflection by adjusting the polarization field. The induced electric potential, under the open-circuit condition with surface electrodes, exhibits a peak value at a critical thickness and flexoelectric coefficient due to the synergistic effect of the strain gradient and flexoelectricity. The electron screening effect induced by the high doping concentration is found to suppress the induced potential considerably. The present work provides a fundamental understanding of PFS coupling and provides guidance for the design of high-sensitivity micro–nano-electromechanical systems/devices. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

Hybrid multi-backbone architectures and the utilization of edge cues for auxiliary training have become two major research trends in salient object detection (SOD). It is widely acknowledged that CNNs can effectively model local spatial structures, while Transformers can capture long-range global dependencies. However, the representation discrepancy between CNN and Transformer features, together with boundary-detail degradation during multi-scale fusion, remains a major challenge. In addition, how to effectively leverage edge cues as reliable structural guidance without introducing texture-induced false boundaries or boundary leakages remains an open issue. In this paper, we present SECA-Net, a unified framework that establishes a profound synergy between CNN and Transformer representations. It explicitly bridges their inherent discrepancies through level-dependent interaction strategies, while resolving structural degradation via a sequential “purify-and-guide” mechanism. This approach enables the network to extract and utilize edge cues effectively, thereby alleviating boundary degradation and texture-induced false contours. Specifically, we design a dual-encoder structure to extract features. A level-wise feature interaction (LFI) module is introduced to perform discrepancy-aware fusion across feature levels, stabilizing CNN–Transformer aggregation. Meanwhile, the features extracted from the CNN branch are projected into a semantic-aware edge refinement (SAER) module to produce clean multi-scale edge priors under high-level semantic guidance, suppressing texture-induced spurious edges. Finally, we design an edge-guided cross-attention feature aggregation (ECFA) module, which progressively injects refined edge priors as structural constraints into multi-scale saliency decoding via cascaded cross-attention, enabling effective structural refinement. Overall, LFI reduces cross-branch discrepancy, SAER purifies boundary priors, and ECFA integrates semantics and structure in a progressive decoding manner, forming a unified SECA-Net framework. Extensive experimental results on five benchmark SOD datasets show that SECA-Net outperforms 19 state-of-the-art methods, demonstrating its effectiveness. Specifically, our proposed method ranks first in $F_{\beta }$ and $BDE$ across all datasets, notably improving $F_{\beta }$ by $1.54\%$ on the challenging DUTS-TE dataset. Full article

This research experiment aimed to transform multicomponent Ni-based superalloys produced with e-waste additives into corrosion-resistant materials via heat treatment. The experiment involved a two-hour heat treatment of as-cast samples at 1000 °C in an argon atmosphere, followed by quenching in water and characterization by scanning electron microscopy coupled to energy-dispersive spectroscopy (SEM-EDS). Thereafter, the corrosion characteristics of the heat-treated and non-heat-treated samples were studied in 0.5 M sulfuric acid using open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP). Results showed that the FCC gamma solid-solution matrix in the microstructure was homogenized by heat treatment. A continuous grain boundary M₂₃C₆ and interdendritic M₆C were redistributed into discrete particles after the heat treatment, which facilitated the reduction in galvanic pathways and boosted corrosion resistance. The heat-treated samples exhibited nobler OCP, increased low-frequency impedance, reduced corrosion current density, a broader passive range, and increased breakdown potential. These findings have proved that it is feasible to convert scrap to service affordably. Full article

Water-based lubricants (WBLs) are increasingly being considered for electrified drivetrain applications; however, their electrochemical stability toward bearing steels remains insufficiently understood. This study evaluated the corrosion behavior of through-hardened AISI 52100 bearing steel in novel WBLs to elucidate the corrosion kinetics and surface degradation mechanisms. Round steel disks were cleaned and tested in 50 wt% aqueous dilutions of glycerol, ethylene glycol (MEG), polyethylene glycol (PEG), and polyalkylene glycol (PAG). Electrochemical measurements were conducted using a three-electrode cell in accordance with ASTM G3-14, employing open circuit potential (OCP), linear polarization resistance (LPR), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization curves. Among the uninhibited fluids, DI water exhibited the highest corrosion current density (19.85 µA/cm²), while glycerol- and PEG-based systems showed the lowest values (0.79 and 0.85 µA/cm², respectively), attributed to organic adsorption at the steel/electrolyte interface. EIS analysis revealed a single charge-transfer-controlled process across all fluids, consistent with a weak, non-passive interfacial oxide whose protective character is modulated by organic adsorption. The addition of NaNO₃ produced divergent effects depending on the base fluid chemistry: the corrosion activity was reduced in DI water and glycerol systems through enhanced passivation, while PEG- and PAG-based formulations showed increased corrosion current densities and reduced charge transfer resistance, attributed to competitive disruption of the polymer boundary layer by nitrate ions. Surface characterization by SEM/EDAX and white-light interferometry corroborated the electrochemical findings, revealing fluid-dependent corrosion morphologies ranging from uniform attack in DI water to localized pitting in polymer-based systems, with NaNO₃ shifting the corrosion mode in PEG/PAG systems from localized to combined localized and uniform attack. These findings highlight the critical role of fluid chemistry in controlling corrosion processes in water-based lubricants and provide mechanistic insight for the development of corrosion-stable formulations for high-performance electrified drivetrain applications. Full article

Laser-induced microjet-assisted ablation is an emerging technology in the field of laser processing. However, the influence of solid boundaries on jet behavior and the associated material removal mechanism remains unclear after observing and analyzing the ablation process. To address this, the present study systematically investigates the effect of the incidence angle on the processing efficiency and material removal mechanism in laser-induced microjet ablation. By controlling the laser power and liquid layer thickness, the dynamic behavior of the microjet, material removal performance, and surface morphology evolution under different inclination angles were explored. Based on video analysis and OpenCV processing, the regulation of jet morphology and impact mode by the incidence angle was revealed. Combined with white light interferometry and ultra-depth-of-field three-dimensional microscopy, the ablation depth and material removal rate were quantitatively characterized. The results showed that under normal incidence, the maximum material removal rate of 0.092 mm³/s was achieved at 9 W, while further increases in power led to a decrease in removal rate due to bubble aggregation. When the sample was tilted to 15°, the material removal rate reached 0.163 mm³/s, representing a 106.30% improvement compared to that at 0°, and the ablation depth also peaked with an average maximum depth of 12.32 ± 0.58 μm and a single-point maximum of 54.36 μm. Furthermore, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) were employed to elucidate the microstructural features and elemental distribution under different process parameters. Through multi-parameter experiments, this study achieved process parameter optimization and clarified the material removal mechanism influenced by different incidence angles, providing both a process reference and theoretical basis for efficient micro-machining of aerospace materials. Full article

Urban rooftop photovoltaic systems represent a substantial yet still underutilized renewable energy resource, particularly in high-density residential environments. Accurate large-scale assessment of rooftop solar potential, however, remains challenging due to the complex geometry of urban morphology and the limited availability of high-resolution geospatial data. This study presents a large-scale methodological framework for estimating the theoretical photovoltaic potential of urban rooftop spaces using Unmanned Aerial System (UAS)-based digital photogrammetry and GIS-based spatial analysis. The approach integrates centimeter-resolution Digital Surface Models (DSMs) and orthophotos derived from fixed-wing UAS surveys with detailed rooftop vectorization and solar radiation modeling implemented in a GIS environment. The methodology accounts for rooftop geometry, surface orientation, slope, shading effects, and rooftop-mounted obstacles. The methodology consists of data collection of high-resolution RGB imagery suitable for detailed three-dimensional reconstruction. The images are captured with a UAS equipped with a S.O.D.A. 3D photogrammetric camera, creating a dense, georeferenced three-dimensional point cloud based on UAS imagery. Based on the point cloud, a high-resolution Digital Surface Model (DSM) was produced. Rooftop boundaries and rooftop-mounted structures were digitized on the basis of an orthophoto created from UAS imagery. The analysis workflow consists of solar modeling using ArcGIS Pro, including calculating the solar radiation. The next methodological step is to filter low radiation rooftops, steep slopes, and northern-oriented rooftops. Finally, we calculate the potential electricity production. The framework was applied to high-density residential districts in Sofia, Bulgaria, dominated by prefabricated panel buildings with predominantly flat rooftops. Drone applications in such studies are typically restricted to modeling individual roofs, which severely limits their scalability for district-wide evaluations. To overcome this, the study employs a specialized fixed-wing UAS uniquely certified for legal operations over densely populated urban environments. This platform rapidly maps large territories, ensuring consistent lighting and shading conditions that significantly enhance the accuracy of subsequent rooftop digitization. Furthermore, the resulting centimeter-level precision enables the exact vectorization of micro-rooftop obstacles. Capturing these intricate details is a critical innovation that effectively prevents the overestimation of solar energy potential commonly observed in conventional large-scale models. Solar radiation was modeled at the pixel level for a full annual cycle and filtered using photovoltaic suitability criteria, including minimum annual radiation thresholds, slope, and aspect constraints. Theoretical electricity production was subsequently estimated using zonal statistics and system performance parameters representative of contemporary photovoltaic installations. The results indicate a total theoretical annual electricity potential of approximately 76.7 GWh for the analyzed rooftop spaces, with an average production of about 34 MWh per rooftop and pronounced spatial variability driven by rooftop geometry and exposure conditions. The findings demonstrate the significant renewable energy potential embedded in existing urban rooftop infrastructure and highlight the applicability of UAS-based photogrammetry for high-resolution, large-area solar potential assessments. The proposed framework provides actionable information for urban energy planning, municipal solar cadaster development, and the strategic integration of photovoltaic systems into dense urban environments, particularly in regions lacking open-access high-resolution geospatial datasets. Full article

Understanding how users perceive and attend to the anthropomorphic appearance and empathic responses of artificial intelligence medical conversational agents (AIMCAs) can help reveal the key judgment cues underlying trust formation and use decisions, while also informing interface and dialog design. To this end, this study employs a 3 (appearance anthropomorphism: high, medium, low) × 2 (empathic response: present, absent) within-subject eye-tracking experiment, combined with subjective scales and brief post-task open-ended feedback. During a static prototype viewing task based on hypothetical consultation scenarios, we concurrently recorded trust, behavioral intention, and visual measures for key areas of interest (AOIs; appearance area, conversational content area, and overall interface area). Eye-tracking measures were normalized by AOI coverage proportion to improve cross-AOI comparability. The results show that both anthropomorphic appearance and empathic response significantly increased users’ trust in AIMCAs and their behavioral intention. An interaction between these two types of social cues was also observed, suggesting that when visual embodiment and linguistic style are aligned at the social level, users are more likely to form favorable overall judgments. At the level of visual processing, however, no interaction effect was found, and the eye-tracking measures showed only partial main effects, indicating that subjective synergy does not necessarily correspond to synergistic changes in attentional allocation. Overall, anthropomorphic appearance and empathic response exerted consistent facilitating effects on outcome variables, but displayed different patterns of attentional allocation and information prioritization at the visual level. Accordingly, AIMCA design should emphasize consistency between appearance cues and conversational strategies, optimize users’ initial judgments and interface comprehension, and use intention through verifiable information organization and clear boundary cues. Full article