Search Results (609)

Landslides constitute a globally pervasive and highly destructive natural hazard. Although artificial intelligence (AI)-driven landslide susceptibility mapping has emerged as an effective tool for delineating high-risk zones, its predictive performance is frequently constrained by inherent data noise and insufficient characterization of landslide triggering factors, restricting the credibility of the mapping results. In this study, to remedy this limitation, we adopt fractal analysis to extract latent inherent information from topographic features. Specifically, the box-counting method and multifractal analysis are applied to excavate the intrinsic nonlinear characteristics embedded in eight topographic factors, and an improved K-means algorithm is utilized to perform feature selection and construct a dedicated fractal feature dataset, which is fed to advanced AI models. Our results indicate that the information dimension ($D_1$) of the slope gradient, the correlation dimension ($D_2$) of aspect, land relief, the $D_2$ of roughness, the $D_2$ of plan curvature, the multifractal spectrum width ($∆\mathsf{\alpha }$) of profile curvature, the $D_2$ of elevation, and the surface cutting depth were the most effective features, demonstrating superior performance in capturing landslide targets. Comparative performance evaluations reveal that AI models trained on fractal features demonstrate substantially superior predictive capabilities compared to AI models trained on raw features. This superiority is consistently evidenced across key evaluation metrics, including overall accuracy, kappa coefficient, F1-score, and predictive efficiency, demonstrating that the integration of fractal characteristics significantly augments model robustness and predictive efficacy. To mitigate the ‘black-box’ problem of AI modeling, Shapley additive explanations were employed to quantify individual feature contributions and elucidate the underlying predictive mechanisms. Our findings indicate that the integration of fractal analysis yields highly discriminative and robust feature representations, thereby expanding the representational capacity of the models and improving predictive accuracy. Furthermore, a joint assessment of spatial uncertainty and susceptibility maps demonstrates that these models exhibit low predictive variance and high spatial stability when delineating high-susceptibility zones. Notably, models utilizing fractal-derived features achieve superior spatial capture efficiency. The resultant topographic features characterized by fractal representation and selected via the improved K-means algorithm can significantly improve the predictive performance of trained AI models in landslide susceptibility mapping tasks, offering a scientific and viable technical approach for future landslide prediction and prevention. Full article

Polyurethane synthetic leather is a widely used covering material in automotive interiors, and its surface coating characteristics directly determine the occupant experience. However, the underlying mechanisms by which these characteristics influence visuo-tactile perception in the context of new energy vehicles (NEVs) require further investigation. In this study, a composite experimental matrix was constructed by combining surface textures with distinct roughness gradients and representative colors extracted via data mining within the HSV color space. Targeting these two surface coating characteristics—color and texture—systematic evaluations were conducted across three independent perception stages: purely visual, purely tactile, and combined visuo-tactile. Eye-tracking metrics, specifically pupil diameter and total fixation duration, were extracted and cross-analyzed alongside multidimensional subjective evaluations. The results indicate that surface texture exerts a significant main effect on both perceived tactile softness and pleasantness, whereas the impact of color variation is remarkably weak. Furthermore, highly complex surface textures lead to prolonged fixation durations, reflecting increased exploratory interest and the high perceptual salience of intricate details rather than mere cognitive workload. Moreover, significant differences in pupil diameter were observed across texture conditions, potentially reflecting the combined influence of low-level image properties and higher-order texture perception. Concurrently, an interference effect of visual features on tactile perception was observed; specifically, the introduction of visual cues (encompassing color and texture) significantly diminished the pleasantness experienced during tactile interaction. These findings elucidate the intrinsic connections between surface coating characteristics and users’ visuo-tactile perception, offering important theoretical guidance and practical implications for optimizing the surface design of automotive polyurethane synthetic leather and enhancing the overall occupant experience. Full article

In this work, the effect of chitosan concentration in chitosan/nickel composite coatings on their morphology and electrocatalytic activity in hydrogen evolution reaction (HER) was investigated. A series of Chitosan/Ni coatings with chitosan content from 0 to 0.7 wt.% was obtained by nickel electrodeposition onto a preformed biopolymer matrix, enabling targeted control of the roughness and specific surface area of the nickel layers. Morphology and roughness parameters were studied using atomic force microscopy and confocal microscopy. Electrochemical activity in the HER was examined by linear sweep voltammetry. Among the studied electrocatalysts, the Chitosan(0.6)/Ni system showed the best HER efficiency, with an overpotential of −200 mV at a current density of 10 mA/cm². Electrochemical impedance spectroscopy was used to determine the real surface area of the coatings. The Chitosan(0.6)/Ni sample exhibited the largest surface area, explaining its high HER activity. The obtained data revealed a correlation between chitosan concentration, composite morphology, and electrochemical activity, and allowed determination of the optimal composite composition. The results demonstrate the potential of chitosan as an effective structural modifier of nickel coatings and open up possibilities for the targeted design of composite materials with tailored electrochemical properties. Full article

Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article

Microbial fuel cells (MFCs) are promising bioelectrochemical systems for simultaneous wastewater treatment and energy recovery. However, their practical application is still limited by insufficient power output and weak transient energy-supply capability under fluctuating operational conditions. Herein, a bifunctional CF/MXene/FeS composite anode was fabricated through a one-step hydrothermal strategy to simultaneously enhance electricity generation and capacitive charge storage in MFCs. Unlike conventional bioanode modifications that primarily target conductivity enhancement alone, the constructed hierarchical composite integrates conductive MXene nanosheets and electroactive FeS phases to synergistically improve extracellular electron transfer and interfacial charge-storage behavior. The modified electrode exhibited enhanced surface roughness, abundant electroactive sites, and improved biofilm-supporting interfaces. Benefiting from the integrated conductive and electroactive composite framework, the CF/MXene/FeS anode achieved a maximum power density of 1.69 W/m², which was 70.7% higher than that of pristine CF, together with an increased open-circuit voltage of 0.711 V. In addition, the composite electrode delivered a high total charge density of 13,192.09 C/m² under the C900/D900 condition. Microbial community analysis further revealed substantial enrichment of electroactive bacteria, with the relative abundance of Geobacter increasing from 0.0058% to 22.84%. This work provides a promising strategy for integrating electricity generation and transient energy storage in bioelectrochemical systems, offering potential applications for energy-buffered MFCs under fluctuating power-demand conditions. Full article

To address the lack of long-term, wide-area surface deformation observations along the geologically complex Dangxiong–Yangbajing section of the G6 Expressway in the frozen-ground region of the Qinghai–Tibet Plateau, where conventional monitoring is insufficient, we applied Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to retrieve surface deformation within a 2.0 km corridor on both sides of the highway from 24 November 2021 to 26 December 2024, and to characterize the spatiotemporal evolution of deformation. We then integrated eight explanatory factors (slope, surface roughness, distance to rivers, distance to faults, surface soil moisture, precipitation, land surface temperature (LST), and fractional vegetation cover (FVC)). Geodetector was used to quantify their explanatory power and spatial heterogeneity with respect to deformation. The results show pronounced spatially uneven settlement along this highway segment, with maximum annual settlement rates exceeding −45 mm/a. Five settlement centers were identified, including two major pavement subsidence zones. Distance to faults and soil moisture showed higher single-factor explanatory power, whereas FVC, precipitation, and LST also contributed to deformation heterogeneity. Interaction detection further indicated that the interactions between fault-related conditions with vegetation, soil moisture, precipitation, and LST substantially enhanced the explanatory power, suggesting that the deformation pattern was associated with multi-factor coupling rather than a single dominant environmental factor. These findings demonstrate the utility of integrating SBAS-InSAR with Geodetector analysis for corridor-scale highway deformation assessment and provide a remote sensing basis for targeted hazard assessment and risk mitigation for highways in frozen-ground environments. Full article

Urban pluvial flood risk in industrial zones is intensifying under climate change, yet the joint influence of digital elevation model (DEM) resolution, surface roughness heterogeneity, and infiltration capacity on simulation accuracy remains insufficiently characterized. This study presents a comprehensive sensitivity analysis combining five DEM resolutions (0.5, 1, 2, 5, and 10 m), six rainfall scenarios (10- to 200-year return periods plus the observed event of 10 July 2024), and three infiltration rates (5, 10, and 20 mm h⁻¹), yielding 90 simulation cases executed with the open-source GPU solver SynxFlow on an NVIDIA A100 80GB GPU. A spatially distributed Manning’s roughness field (n_M = 0.013–0.100 s m^−1/3) was derived from the Ministry of Environment land cover product, replacing the conventional uniform-roughness assumption. Model performance was assessed against seven validation gauges (five flooded, two no-flood controls) compiled from contemporaneous news reports, using the 25 m × 25 m patch-maximum simulated depth at each gauge and probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The 0.5 m baseline achieved POD = 0.80, FAR = 0.20, and CSI = 0.67 at the 5 cm depth threshold. Coarsening the grid reduced peak depth by up to 37% and flooded area by 5%, with the most rapid degradation occurring between 2 m and 5 m. A 2 m grid retained area error within 2% and volume error within 1% while delivering an approximately 33-fold runtime reduction relative to the 0.5 m baseline; the 10 m grid achieved up to ~1400× speedup, spanning three orders of magnitude across the resolution range. Resolution sensitivity intensified under higher rainfall and lower infiltration, confirming that “adequate” resolution is conditional on event severity. A tiered resolution selection matrix linking application scale, target accuracy, and computational cost is proposed to support evidence-based flood adaptation planning for industrial zones. Full article

Rough-walled fractures in conglomerate reservoirs promote near-wellbore proppant deposition, nonuniform flow, and insufficient distal support, making proppant-schedule screening difficult using small-scale smooth-slot tests alone. This study develops a benchmark-constrained and cost-aware hierarchical screening workflow by integrating a 20 m rough-wall physical experiment, transient Fluent simulations, and archived short-time EDEM sensitivity records. The benchmark experiment used a 20 m × 4.5 m × 10 mm artificial rough-wall fracture and ten operating conditions involving pumping rate, fluid viscosity, proppant size, and sand concentration. In the Fluent model, wall roughness was treated as a regularized roughness representation, and the carrier fluids were modeled using Newtonian constant viscosities measured from laboratory calibration. The experimental effective propped area ranged from 25.5% to 65.1%. Within single-factor comparison subsets, medium viscosity improved support continuity, pumping-rate gains became limited near 0.20 m³/min, particle size affected the balance between distal coverage and bed stability, and 300 kg/m³ sand concentration caused blockage. Image-segmentation-based comparison showed that Fluent captured the main wedge-shaped deposition morphology and screening-level geometric trends. The archived EDEM records indicated that grid-resolution refinement and mixed particle-size representation substantially increased computational cost. A Case 10 mesh-sensitivity check further confirmed that mesh refinement did not alter the first-order deposition morphology. The proposed workflow uses Fluent for whole-domain rapid screening and reserves EDEM/CFD-DEM for targeted short-time sensitivity checks. Full article

To further improve the high-temperature dry sliding performance of Si₃N₄ ceramics, a CrAlN transition layer was introduced to improve interfacial stability, while Ag was incorporated as a solid lubricant into the CrAlN matrix. The effects of Ag content on the microstructure and mechanical properties of the coatings were systematically examined, and the tribological performance was evaluated from 25 °C to 550 °C under dry sliding conditions. The Ag concentration increased with increasing Ag target power and affected the morphology, nanoparticle distribution, surface roughness, and mechanical properties of the coatings. Among the tested samples, the coating containing 9.6 at.% Ag exhibited a comparatively favorable combination of mechanical properties within the investigated composition range, with a hardness of 11.5 GPa, an H/E ratio of 0.0913, and an H³/E² value of 0.096 GPa. Tribological tests showed that the average coefficient of friction decreased from 0.32 at 25 °C to 0.12 at 550 °C. This reduction may be associated with temperature-assisted Ag redistribution toward the worn surface and the possible development of Ag-rich surface features at elevated temperatures. However, the wear rate increased with temperature, reaching 3.6 × 10⁻⁵ mm³/(N·m) at 550 °C, suggesting that friction reduction was accompanied by increased material removal and possible near-surface weakening. These results indicate that controlling Ag content is important for balancing friction reduction and wear resistance in ceramic-based self-lubricating coatings. Full article

This comprehensive review aims to cover the surface tribology and triboelectric properties of additively manufactured (AM) natural biopolymers, including cellulose, chitosan (CS) and silk fibroin (SF), in biomedical interface engineering. While these sustainable materials exhibit innate biocompatibility and tribopositivity, their baseline triboelectric performance demands targeted surface engineering. We synthesize key physical mechanisms governing charge generation, emphasizing how controlled surface roughness, hierarchical porosity and nanoscale architectures maximize contact electrification. Furthermore, distinct dielectric and polarity modulation strategies are evaluated across the biopolymer families: cellulose relies heavily on chemical functionalization to overcome weak native polarity; chitosan utilizes ionic coordination and fillers to elevate its relatively low charge density; and silk fibroin achieves exceptional power outputs via highly porous three-dimensional nanocomposite aerogels. AM technologies afford unprecedented spatial control over these biointerfaces but introduce severe processing constraints. Techniques such as those based on extrusion impose strict shear-thinning rheology and rapid crosslinking for cellulose and chitosan, while SF frequently suffers from crystallization-induced nozzle clogging, necessitating photocurable derivatives. Full article

The target price for naval equipment orders is driven by the coupling of multidimensional technical and economic factors, exhibiting typical characteristics such as high dimensionality, strong nonlinearity, multicollinearity, and small-sample fluctuations. Traditional cost estimation methods struggle to achieve high-precision fitting and interpretable decision support. To address these issues, this paper constructs an integrated prediction model that combines Boruta–Lasso two-stage feature selection, grid search-optimized CatBoost, and SHAP interpretability analysis. First, the Boruta algorithm is used for rough screening of feature significance, then Lasso regression is applied for sparse fine screening, effectively eliminating redundant features and significantly mitigating multicollinearity; grid search and five-fold repeated cross-validation are employed to optimize CatBoost hyperparameters, while 10 repeated experiments with random seeds are conducted to verify model generalization robustness. SHAP is used to quantify the marginal contribution of features, revealing nonlinear associations and statistical response transition points between core features and price. This study is based on 33 publicly available real data from main combat vessels, from which 198 modeling samples were generated through interpolation-based small-sample data augmentation. The interpolated samples were only used for data augmentation and were not considered independent empirical samples. All core conclusions were validated on the 33 original real samples, and there are no missing values in the dataset. Experimental results show that the proposed model achieved the best individual results on the test set, with a coefficient of determination of R² = 0.8949, root mean square error RMSE = 0.0554, and mean absolute error MAE = 0.0476. Across 10 repeated robustness experiments, the average results were R² = 0.8828, RMSE = 0.0586, and MAE = 0.0529, with overall performance better than comparison models such as XGBoost, random forest, and standard CatBoost. Ablation experiments validated the effectiveness of the two-stage Boruta–Lasso selection strategy in improving model accuracy and stability. SHAP attribution analysis shows that full-load displacement, number of vertical missile launch cells, number of phased array radars, and combat capability are core features highly correlated with price, all showing significant nonlinear positive correlations and clear statistical response transition points. The dataset in this study has no missing values, is entirely constructed based on publicly traceable data, and does not include confidential information such as internal shipyard costs. The findings reflect statistical associations rather than causal effects. However, the sample size and ship-type coverage are limited, so the model’s applicability is somewhat constrained, and its generalization ability needs to be further verified on larger-scale, multi-ship-type independent datasets. This model combines high prediction accuracy, strong robustness, and good interpretability, providing reliable technical support for ship equipment procurement pricing demonstration, full lifecycle cost management, and scientific procurement decision-making. Full article

To enhance immersion in virtual reality (VR) environments and improve the fidelity of virtual tactile interaction, this study proposes a perceptually grounded haptic-rendering framework for fine surface-texture simulation. The framework is centred on a Perceptual Haptic Spectrum Model (PHSM), which maps virtual surface attributes, including hardness, elasticity, roughness, friction, and microtexture periodicity, to multi-band tactile targets in perceptual frequency space. A Just Noticeable Difference (JND)-inspired parameterisation strategy is used as a design guideline to avoid imperceptible or redundant actuation, while region-specific response functions adapt the output to the fingertip centre, finger pad, and lateral edge. To improve reproducibility, the revised manuscript now specifies the flexible thin-film force/strain-sensor cell, array quantity, 320 Hz per-cell acquisition setting, signal-conditioning pipeline, contact-state classification rules, delay budget, and dual-actuation scheduling logic. The sensing design is based on a commercial flexible piezoresistive force-sensor cell with microsecond-level response time and a 12-bit ADC acquisition chain that provides a sufficient aggregate sampling margin for a 7–21 cell array. Manufacturer-supported sensor performance and prototype-level acceptance criteria are reported for response time, linearity, repeatability, hysteresis, drift, SNR, contact-state detection, latency, and durability. The system remains a proof-of-concept platform rather than a completed large-scale psychophysical validation. Within these boundaries, the results show coherent integration of perceptual modelling, multi-rate sensing, state monitoring, predictive feedforward control, and coordinated haptic actuation for fine VR texture rendering. Full article

Being capable of high-resolution imaging and non-contact measurement, Ground Penetrating Radar (GPR) is a promising technology for the detection of unexploded ordnance (UXO). However, UXO detection is severely hindered by clutter, particularly in environments with significant surface roughness where conventional suppression methods prove ineffective. To address this, we propose a polarimetric GPR clutter suppression method based on an improved non-convex Tensor Robust Principal Component Analysis (TRPCA) framework. Specifically, a polarization-aware tensor construction scheme is designed by stacking the HH and VV channel data. This approach exploits the strong inter-channel correlation of clutter to enhance its low-rank property, while highlighting the distinct sparse signatures of targets derived from their polarimetric responses. To further optimize tensor decomposition, we introduce a non-convex Tensor Adjustable Logarithmic Norm (TALN) to overcome the estimation bias inherent in the conventional Tensor Nuclear Norm (TNN). Serving as a tighter surrogate for tensor rank, the proposed TALN regularizer improves the approximation accuracy of the low-rank component, thereby ensuring a clearer separation between clutter and targets. The resulting non-convex optimization problem is efficiently solved using Alternating Direction Method of Multipliers (ADMM). Numerical simulations and laboratory experiments demonstrate that the proposed method suppresses strong clutter stemming from rough-surface reflections more effectively than existing methods, achieving a Signal-to-Clutter Ratio (SCR) improvement of over 20 dB. Full article

A globally applicable and fully automated simulation method based on satellite-derived Earth Observation (EO) data and global circulation models was developed and validated. Inputs to the simulation are DSM/DTM layers, surface roughness layer, forest canopy layer, and single-level point data from the European Centre for Medium-Range Weather Forecasts fifth-generation ECMWF reanalysis (ECMWF ERA5, a global circulation model produced by the Copernicus Climate Change Service (C3S)). High-resolution roughness length maps are produced by deep learning from optical satellite data. Velocity fields are predicted by fluid dynamics simulations in OpenFOAM using the IDDES turbulence model, a 3D resolved tree canopy implemented as isotropic momentum sinks, and a corrector step based on sub-grid-scale dynamic downscaling of ERA5 data. No calibration data from wind measurements close to the target are necessary to achieve results accurate enough for site assessments and wind park planning. The presented method is suitable for the prediction of average wind speeds and average power densities in complex terrain with high ruggedness indices for WEC (wind energy converter) installations closer to the ground and at hub heights of typical large-scale WECs. Full article

Additive manufacturing (AM) enables the fabrication of complex three-dimensional components with embedded internal flow channels, but the as-built inner surfaces often exhibit high roughness and poor surface-quality uniformity, particularly at non-coplanar corner regions such as sharp bends and junctions. Conventional abrasive flow machining (AFM) can improve the overall surface finish of such channels; however, corner regions commonly remain weak-removal zones because of local flow stagnation and insufficient abrasive action. To address this limitation, this study proposes a six-degree-of-freedom (6-DOF) robotic-arm-assisted liquid metal-driven abrasive flow (LM-AF) polishing strategy in which robotic pose regulation is used to guide the liquid metal droplet to designated corner regions while preserving its responsiveness to the electric field. Numerical simulations and conventional AFM experiments on S-shaped and M-shaped spatial channels were first conducted to identify the corner regions as the primary sources of polishing non-uniformity. A robotic posture-control framework was then established through manipulator kinematics, point-cloud-based flow-direction identification, and Rodrigues-matrix-based pose transformation. On this basis, localized secondary polishing was experimentally performed on an S-shaped channel using an AC electric-field-driven liquid-metal abrasive system. The results show that corner-region roughness was significantly reduced and approached the straight-channel benchmark after secondary polishing, demonstrating a marked improvement in inner-surface uniformity. This study provides a practical route for targeted compensation polishing in complex three-dimensional internal channels and offers a new framework for robotic-assisted post-processing of AM-fabricated flow paths. Full article