Search Results (3,231)

High-resolution marine gravity fields are critical for interpreting seafloor structure, investigating marine geodynamics, and enabling gravity-aided navigation. However, sparse shipborne observations, heterogeneous multi-source geodetic datasets, and the inability of conventional methods to handle nonlinear inversion limit accurate gravity recovery. To overcome these limitations, we propose a spectral physics-informed constraint deep-learning framework based on a multi-channel Swin Transformer to reconstruct high-resolution marine gravity anomaly fields. The model ingests multi-source geodetic inputs organized as 64 × 64 grid patches centered near each computation point and fuses them to predict the target gravity anomaly. We adopt a remove–compute–restore (RCR) strategy that isolates residual gravity signals, which improves numerical stability and accelerates training. Inputs include satellite-altimetry-derived vertical gravity gradients, vertical deflections, mean sea surface height, and topography; the model is trained on over 430,000 shipborne gravity samples from the South China Sea (0–30° N, 105–125° E). To enforce physical consistency, we embed a spectral-domain physics constraint derived from potential-field theory into the loss function; this constraint helps recover short-wavelength gravity signals. We also introduce an adaptive multi-domain multi-scale feature fusion module (AMAMFF) to improve the integration of heterogeneous inputs, and we demonstrate its benefits in experiments across complex terrain. Validation against independent shipborne gravity checkpoints yields an RMS error of 3.09 mGal, indicating a substantial performance advantage over existing deep-learning approaches and conventional gravity-field models. Full article

Background: The structural integrity of the abdominal wall is critically dependent on the organization of aponeurotic tissue, a dense collagen-rich connective structure responsible for directional force transmission. While the clinical relevance of the aponeurosis is well recognized in abdominal wall reconstruction, its nano-scale structural organization remains insufficiently characterized. Atomic force microscopy (AFM) provides a suitable approach for investigating surface morphology and nano-architectural features of biological tissues. Methods: Human aponeurotic tissue samples were analyzed using atomic force microscopy operated in contact-mode deflection and topography imaging. Two-dimensional and three-dimensional surface topographies were acquired at the micrometer scale to assess nano-architectural organization. Areal surface roughness parameters (Sa, Sq, Sp, Sv, Sy) were calculated to quantify morphological heterogeneity. AFM deflection imaging was used to evaluate relative spatial variations in deflection imaging contrast under the applied scanning conditions across collagen-dense and interfibrillar regions. Results: AFM analysis revealed a well-organized fibrillar architecture with preferential orientation, consistent with the anisotropic organization of aponeurotic connective tissue. Deflection images demonstrated spatial heterogeneity in deflection contrast at the scanned scale, reflecting variations in the tip–sample interaction signal between collagen-dense and interfibrillar regions. Surface topography showed a continuous morphology with moderate height variations and smooth transitions between structural elements. Roughness parameters reflected a compact extracellular matrix organization within the scanned areas, without features suggestive of surface disruption. Conclusions: Atomic force microscopy enables detailed nano-scale structural characterization of human aponeurotic tissue and reveals spatial heterogeneity in deflection imaging contrast under specific contact-mode scanning conditions. These findings provide a baseline nano-scale descriptive reference dataset for macroscopically normal aponeurotic tissue, supporting future comparative investigations without implying validated mechanical differences or direct tissue–implant interaction analysis within the present study. Full article

The design of multifunctional biomaterials that offer both structural support and biochemical cues is essential for enhancing tissue regeneration. In this study, hybrid nanofibrous scaffolds composed of poly(L-lactide-co-ε-caprolactone) (PLCL) and bioactive factors secreted by Wharton’s jelly-derived mesenchymal stem cells (WJ-MSCs) were fabricated via co-electrospinning. Nanofibers were produced in aligned and random configurations following an optimized protocol developed at the Institute for Bioengineering of Catalonia (IBEC). Their morphology and topography were characterized by light microscopy, scanning electron microscopy (SEM), and atomic force microscopy (AFM), and fiber orientation was quantified via Fast Fourier Transform (FFT) analysis. The scaffolds showed fiber diameters of 542.9 ± 62.3 nm, with aligned fibers predominantly oriented within 20° of the principal axis. Human AD-MSCs were used to assess biocompatibility and cell–material interactions. Aligned and random nanofiber architectures elicited distinct cellular responses. AD-MSCs on aligned fibers exhibited smaller spreading areas (~320 μm²) vs. on random nanofibers (~500 μm²) and substantially higher proliferation, resulting in a shorter cell-doubling time (~25 h) than those on random nanofibers (~130 h) or control substrates (~70 h). In addition, aligned nanofibers promoted markedly faster migration, reaching rates of ~5000 μm²/h surface coverage, compared with random nanofibers (~770 μm²/h) and controls (~1800 μm²/h). Together, the results show that nanofiber alignment and biochemical functionalization jointly influence MSC behavior and improve regeneration, highlighting the potential of these PLCL-based hybrid secretome/PLCL nanofibers for advanced wound healing. Full article

Electrical Discharge Machining (EDM) enables precision post-weld finishing of AISI 304 stainless steel, but stochastic spark overlaps make the fatigue-critical maximum peak-to-valley height (R_max) difficult to predict. This study develops a validated physics-based framework quantifying how crater overlap governs R_max evolution. Experiments on unwelded AISI 304 cylinders—proxying weld metal while excluding heat-affected zone (HAZ) effects—used Central Composite Design (20 trials, 900–9380 μJ discharge energies). Profilometry and scanning electron microscopy (SEM) correlated the crater size, overlap intensity, micro-cracking, and R_max escalation from 18 to 85 μm. Primary and secondary crater formation under minimum and maximum overlap configurations were simulated using a 2D axisymmetric finite element model with Gaussian heat flux and temperature-dependent thermophysical properties. The predictive metric R_max,num = (d_initial + d_secondary)/2 achieved 11–19% average error against the experimental R_max,exp, with complementary valley depth (R_v) validation at 13% error. The Specimen 7 outlier (~50% error) reveals the limitations of deterministic modelling under stochastic debris accumulation and plasma instability at intermediate energies. Crater overlap generates secondary dimples, sharp inter-crater peaks, and rim micro-crack networks, driving the 4.7-fold R_max increase—approaching International Institute of Welding (IIW) fatigue thresholds (<25 μm for high-cycle categories). The framework explicitly links the discharge energy, plasma channel radius (R_pc), and overlap geometry to surface topography, enabling process optimization (I·t_on < 60 A·s maintains R_max < 25 μm). Mesh independence (<2.5% convergence) and six centre-point replicates (CV = 4.2%) confirm robustness. This validated upper-bound R_max predictor supports the digital co-optimization of welding and EDM parameters for aerospace/energy applications, with planned extensions to stochastic 3D models incorporating adaptive remeshing and real weld topographies. Full article

Extreme rainfall events often trigger landslides, debris flows, and sediment-laden floods that cause severe damage in built-up areas, yet sediment deposition is rarely quantified in hazard assessments. This study evaluates the capability of the physically based catchment model LISEMHazard to reconstruct sediment generation, transport, and deposition during Hurricane Maria (2017) in two catchments in Dominica (Coulibistrie and Grand Bay). Simulations were performed at 10 m resolution using rainfall, topography, soil, and land-use data. Model calibration and validation used mapped landslides and debris flows, field measurements of deposition height, and DEMs of Difference (DoDs). LISEMHazard reproduced the general magnitude of sediment volumes and the frequency–area distribution of medium and large landslides but showed poor ability to predict their exact locations and overestimated landslide depth and deposition height. Agreement between modeled and observed debris-flow patterns was good in major channels but weak in minor ones. Sensitivity analysis indicated that soil depth and cohesion dominate uncertainties, whereas saturated hydraulic conductivity and surface roughness exert minimal influence. Despite substantial data and model limitations, physically based modeling remains a practical approach for spatial estimation of sediment deposition needed for risk assessment, structural damage evaluation, and cleanup cost estimation. Full article

Accurate estimation of topsoil moisture (TSM) is essential for optimizing agricultural practices, particularly in the context of precision farming. This study evaluates the use of high-resolution synthetic aperture radar (SAR) imagery from TerraSAR-X (X-band) and Radarsat-2 (C-band) for estimating TSM over bare agricultural soils, at both plot and intra-plot spatial scales. The experiment was conducted over a 420 km² area in southwest France, comprising 29 agricultural plots with varying topography, soil texture, and land management practices. Extensive in situ measurements of TSM, soil texture, and surface roughness were collected over multiple dates. A random forest regression model was developed to estimate soil moisture, using radar backscatter coefficients, incidence angles, soil texture components (clay, silt, sand), and roughness parameters (Hrms, correlation length) as input features. The modeling approach was applied at multiple spatial scales by extracting satellite signals within circular buffers of varying radius (5 to 30 m), as well as at the plot scale. Results indicate that estimation performance improves with increasing buffer size, with the best results achieved at the 30 m intra-plot scale (R² > 0.8, RMSE < 4 m³·m⁻³), outperforming plot-scale estimates. Both C-band and X-band data provided reliable results, with a slight advantage when combining data from multiple incidence angles. The inclusion of surface roughness and soil texture significantly improved model accuracy, underlining the importance of accounting for local soil properties in radar-based moisture retrieval. The intra-plot variability of TSM was found to be substantial, often exceeding inter-plot differences, highlighting the necessity for high spatial resolution in moisture monitoring. This study demonstrates the value of combining ground observations with multi-frequency SAR data and machine learning for high-resolution soil moisture mapping. The approach supports more precise water management strategies and contributes to sustainable agricultural development through informed decision-making. Full article

High-power clutches operating under high-frequency engagement–disengagement cycles demand piston ring seals with exceptional leakage control and tribological reliability. Conventional architectures often experience lubrication failure and severe adhesive wear during transient pressure fluctuations. This research proposes an autonomous intelligent sealing strategy leveraging fluid pressure-induced morphological evolution. By strategically integrating periodic macroscopic structural relief features on the non-sealing surface, the sealing interface transforms into a micron-scale wavy topography in response to hydraulic loading. This structurally embedded intelligence significantly improves fluid pressure distribution, facilitating a transition toward a more favorable lubrication regime. Furthermore, a “self-healing and positional stagnation” logic is elucidated: upon pressure dissipation, the induced waviness elastically recovers to a planar state to ensure sealing integrity, while the ring maintains its axial position due to the predominant frictional resistance of the secondary seal. This synergistic mechanism effectively precludes deleterious dry friction during the clutch disengagement phase. High-fidelity numerical investigations, benchmarked against established experimental data, identify the rectangular groove configuration as the optimal geometry for maximizing waviness amplitude (≈1.5 µm). This research provides a robust framework for developing responsive, zero-wear intelligent seals in advanced power transmissions. Full article

Machined surfaces contain rich information about machining conditions and system behavior and are typically assessed using off-line, small-area metrology. This study developed and validated an image-based methodology for process-oriented surface texture analysis of end-milled Spheroidal Graphite Iron (SGI), enabling scalable, non-contact monitoring suitable for in-line deployment. End milling trials were conducted under optimized and aggressive cutting conditions and in two orthogonal feed directions (X,Y). Surface topography from White Light Interferometry (WLI) was complemented by Charge-Coupled Device (CCD) microscope imaging. Image processing comprised automatic orientation correction, intensity profile extraction, and frequency-domain analysis via Fast Fourier Transform and power spectral density estimation. Texture metrics (RMS amplitude, skewness, kurtosis, dominant wavelength) were derived from intensity profiles, and two spectral indices were introduced: a Change Index (CI), capturing high-frequency content linked to process disturbances, and a Surface Anisotropy Metric (SAM), quantifying texture directionality. Aggressive cutting increased RMS by 28.5% and shifted skewness by 274% with strong statistical significance. Directional analysis showed 22% higher texture amplitude in Y than X, indicating axis-dependent machine behavior. CI correlated with the machining parameters and stability, while SAM reflected the machine and setup characteristics. Trends were consistent with WLI, supporting the method as a rapid, complementary tool for surface quality and machine condition monitoring. Full article

Laser surface hardening (LSH) is an efficient and flexible technique for improving the surface integrity of steel components used in high-load automotive applications. In this study, the surface changes occurring during laser hardening of 42CrMo4 steel were systematically investigated using a 4 kW high-power diode laser. The influence of laser power and scanning speed on surface roughness, hardness distribution, hardened layer depth, tribological behavior, and phase composition was analyzed. Surface topography was evaluated using three-dimensional laser scanning microscopy, while mechanical performance was assessed through microhardness and scratch testing. Phase transformations and residual structural changes were examined by X-ray diffraction (XRD) at different depths beneath the treated surface. The results demonstrate that laser processing parameters strongly affect surface integrity through competing mechanisms of surface melting, oxidation, and self-quenching. High laser power combined with low scanning speed produced deep hardened layers but promoted surface melting and retained austenite formation, whereas lower power and higher scanning speed yielded a stable martensitic surface with reduced roughness and a steep hardness gradient. XRD analysis confirmed that oxide formation was limited to the near-surface region, while the subsurface hardened zone consisted predominantly of martensitic/bainitic phases. An optimal processing window was identified that balances surface hardness, roughness, and microstructural stability without compromising surface integrity. These findings provide practical guidelines for optimizing diode laser hardening of 42CrMo4 steel gears in industrial automotive applications. Full article

Copper (Cu) is widely used in electrical and thermal management systems; however, its low hardness and limited dry sliding wear resistance reduce long-term reliability in friction-loaded conductive components. In this study, Cu–Hf and Cu–Hf–rGO hybrid composites were fabricated by powder metallurgy using 1.0–5.0 wt.% Hf and 1.0–2.0 wt.% reduced graphene oxide (rGO). The microstructure and phase evolution were characterized by SEM/EDS and XRD. Electrical conductivity and hardness were measured, while tribological performance was evaluated by dry sliding wear tests based on mass loss. Post-wear surface characteristics were analyzed by AFM and LFM to assess nanoscale topography and frictional behavior. The hybrid composites exhibited composition-dependent multifunctional enhancements. Electrical conductivity increased from approximately 3.0 × 10⁶ S/m (~5.2% IACS) for pristine Cu to about 2.0 × 10⁷ S/m (~34.5% IACS) for the composite reinforced with 3.0 wt.% Hf and 2.0 wt.% rGO, indicating an optimum Hf–rGO combination that preserves continuous conductive pathways. Hardness increased from 60 ± 3 HV_0.30 to 159 ± 12 HV_0.30 for the composite containing 5.0 wt.% Hf and 2.0 wt.% rGO, demonstrating the dominant contribution of Hf to matrix strengthening and load-bearing capacity. The mass loss after 1000 m of sliding distance decreased from about 0.12 g for Cu to approximately 0.01 g for the 5.0 wt.% Hf–2.0 wt.% rGO hybrid composite, consistent with the concurrent increase in hardness and reduction in frictional shear during sliding. Nanoscale surface analyses revealed reduced surface roughness and frictional response, supporting the formation of a smoother and lower-friction sliding interface in rGO-containing composites. Overall, Hf enhanced load-bearing capacity through matrix strengthening, while rGO contributed to stabilizing conductive pathways and solid lubrication. Full article

Electroless Ni–P coatings are widely used for corrosion and wear protection, yet their ability to deliver water-based superlubricity and the role of phosphorus content remain insufficiently understood. Here, electroless Ni–P coatings with four P contents (3.4, 6.4, 9.0, and 12.4 wt%) were deposited on GCr15 steel with nearly constant thickness and comparable initial roughness, and were tested against Si₃N₄ balls in neutral 0.5 M NaH₂PO₂ solution. Friction measurements, together with surface topography characterization and tribofilm analysis, were used to link P content with tribofilm chemistry and superlubricity. All coatings achieved macroscale superlubricity, exhibiting steady-state friction coefficients below 0.01, while the running-in time decreased markedly as P content increased. During sliding, the wear tracks underwent mechano-chemical polishing to Sa ≈ 11–12 nm and formed phosphate–silicate tribofilms enriched in P–O and Si–O species on both the coating and the counterface. These findings establish a composition–tribofilm–superlubricity relationship in the Ni–P/NaH₂PO₂ system and demonstrate that P-content optimization is an effective internal design lever to accelerate running-in, mitigate wear, and achieve robust superlubricity under neutral aqueous lubrication. Full article

The surface properties and electrical behavior of carbon-based materials can be effectively modified by energetic ion irradiation. In the present study, graphene oxide (GO) and cyclic olefin copolymer foils (COC, Topas 112 and 011, respectively) were irradiated with 1 MeV Au ions using a 3 MV Tandetron accelerator at fluences of 1 × 10¹⁴, 1 × 10¹⁵, and 2.5 × 10¹⁵ cm⁻². The irradiation induced systematic modifications in surface chemistry, morphology, wettability, and electrical properties. Composition changes were investigated using Rutherford backscattering spectrometry (RBS) and elastic recoil detection analysis (ERDA), while surface morphology and roughness were characterized by atomic force microscopy (AFM). This revealed a clear fluence-dependent evolution of nanoscale topography. The vibrational characteristics were assessed through Raman spectroscopy, and the chemical composition of the surface layers was analyzed by X-ray photoelectron spectroscopy (XPS). The surface wettability was evaluated by static contact angle measurements, and surface free energy was determined using the Owens–Wendt–Rabel–Kaelble (OWRK) method. These measurements showed a consistent decrease in water contact angle and an increase in surface free energy with increasing ion fluence in the COC substrates, whereas GO exhibited a distinct response. Electrical characterization demonstrated a pronounced fluence-dependent decrease in sheet resistivity in polymers. The results show that 1 MeV Au ion irradiation enables systematic and fluence-dependent modification of both surface and electrical properties. Full article

Focusing on the digital preservation and management of Lingnan modern historical gardens, this study proposes and practices a full-process framework of landscape information modeling (LIM), integrating multi-source data collection, information integration and business collaboration in view of the three major challenges of insufficient overall records, regional information integration difficulties, and disconnection between digitalization and management practice. Its innovation lies in the fusion of ground/handheld laser scanning and 3D Gaussian splash technology to cope with the complex environment of buildings, vegetation and topography, and achieve high-precision interpretation of modern historical garden elements in Lingnan for the first time. On this basis, The study established the first regional heritage information platform integrating a cloud-based information management system with a game engine, incorporating local protection rules. In this study, application modules such as preventive preservation, emergency response, and assessment and repair for daily management are further developed, and the synergy between technical capabilities and management needs is initially realized. On the practical surface, the framework achievements realize the analysis of complex historical garden elements and control the accuracy within 4 mm, and the platform effectively integrates 5 types of multi-source data and connects the link from data to management. This study provides a set of reusable digital preservation and management methodologies for the sustainable protection and refined management of Lingnan and even similar historical gardens. Full article

A combined microanalysis and optical study of β-(Al_xGa_1−x)₂O₃ films grown on sapphire via metalorganic chemical vapour deposition, with thickness 350–1000 nm and Al fraction (x) from 0% to 45%, is presented. Al incorporation in the films showed a linear relation with nominal Al composition calculated from precursor flow rate, and the optical bandgap increased from 4.96 eV to 5.44 eV with a bowing parameter of 1.7 ± 0.5 eV. A high Al fraction led to reduced crystallinity, increased surface roughness, and diminished cathodoluminescence intensity. The topography revealed elongated surface features that evolved with Al content, and luminescence spectra exhibited a blueshift in peak emission attributed to the widening of the bandgap. These findings highlight the trade-off between bandgap tuning and material quality, informing future growth strategies for future electronic and optical devices. Full article

This study investigates two measurement campaigns: extended time and short time, to determine the stability of roughness measurements, focusing on the Sdr parameter. Extended-time measurements were conducted using the most sensitive instrument available to follow metrological fluctuations. The results revealed that Sdr exhibits the clearest trend and the highest dispersion among all roughness parameters, making it the most relevant indicator for tracking temporal deviations. Other parameters, such as Sa, Sq, and Sds, also emerged as potential candidates. These results were validated through a stability analysis (SI), showing that Sdr is the worst stable roughness parameter. To ensure the robustness of the findings and be closer to the real conditions, a short-time assessment was performed using a dedicated measurement plan performed on multiple instruments. The results confirmed that measurement fluctuations are instrument-dependent, but similar results are found across the same technologies (CSI(S) and CSI(B)). The short-time study included a quality inspection, a drift/stability analysis employing AR (2) models on the time series data systematically and a relevance measurement assessment using ANOVA. The study was conducted using a full-scale roughness analysis and could potentially be applied to a multiscale approach. These findings highlight the ability of Sdr to monitor metrological fluctuation during a long-time acquisition and according to a dedicated measurement plan. Full article