Search Results (409)

The Surface Water and Ocean Topography (SWOT) mission provides unprecedented high-resolution observations of sea-surface height. However, their direct use in ocean circulation studies is complicated by the presence of small-scale unbalanced motion signals and instrumental noise, which hinder accurate estimation of geostrophic velocities. To address these limitations, we developed an adaptive convolutional neural network (CNN)-based filtering technique that refines SWOT-derived sea-level observations. The network includes multi-head attention layers to exploit information on concurrent wind fields and standard altimetry interpolation errors. We train the model with a custom loss function that accounts for the differences between geostrophic velocities computed from SWOT sea-surface topography and simultaneous in-situ drifter velocities. We compare our method to existing filtering techniques, including a U-Net-based model and a variational noise-reduction filter. Our adaptive-filtering CNN produces accurate velocity estimates while preserving small-scale features and achieving a substantial noise reduction in the spectral domain. By combining satellite and in-situ data with machine learning, this work demonstrates the potential of an adaptive CNN-based filtering approach to enhance the accuracy and reliability of SWOT-derived sea-level and velocity estimates, providing a valuable tool for global oceanographic applications. Full article

This study proposes a method for estimating the kinetic coefficient of friction (COF) for asphalt pavements by improving and applying Persson’s friction theory. The method utilizes the power spectral density (PSD) of the top surface topography instead of the full PSD to better reflect the actual contact conditions. This approach avoids including deeper roughness components that do not contribute to real rubber–pavement contact due to surface skewness. The key aspect of the method is determining an appropriate cutting plane to isolate the top surface. Four cutting strategies were evaluated. Results show that the cutting plane defined at 0.5 times the root mean square (RMS) height exhibits the highest robustness across all pavement types, with the estimated COF closely matching the measured values for all four tested surfaces. This study presents an improved method for estimating the kinetic coefficient of friction (COF) of asphalt pavements by employing the power spectral density (PSD) of the top surface roughness, rather than the total surface profile. This refinement is based on Persson’s friction theory and aims to exclude the influence of deep surface irregularities that do not make actual contact with the rubber interface. The core of the method lies in defining an appropriate cutting plane to isolate the topographical features that contribute most to frictional interactions. Four cutting strategies were investigated. Among them, the cutting plane positioned at 0.5 times the root mean square (RMS) height demonstrated the best overall applicability. COF estimates derived from this method showed strong consistency with experimentally measured values across all four tested asphalt pavement surfaces, indicating its robustness and practical potential. Full article

Mesoscale coastal eddies are key components of ocean circulation, mediating the transport of heat, nutrients, and marine debris. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution sea surface height data, offering a novel opportunity to improve the observation and characterization of these features, especially in coastal regions where conventional altimetry is limited. In this study, we investigate a mesoscale anticyclonic coastal eddy observed southwest of Mallorca Island, in the Balearic Sea, to assess the impact of SWOT-enhanced altimetry in resolving its structure and dynamics. Initial eddy identification is performed using satellite ocean color imagery, followed by a qualitative and quantitative comparison of multiple altimetric datasets, ranging from conventional nadir altimetry to wide-swath products derived from SWOT. We analyze multiple altimetric variables—Sea Level Anomaly, Absolute Dynamic Topography, Velocity Magnitude, Eddy Kinetic Energy, and Relative Vorticity—highlighting substantial differences in spatial detail and intensity. Our results show that SWOT-enhanced observations significantly improve the spatial characterization and dynamical depiction of the eddy. Furthermore, Lagrangian transport simulations reveal how altimetric resolution influences modeled transport pathways and retention patterns. These findings underline the critical role of SWOT in advancing the monitoring of coastal mesoscale processes and improving our ability to model oceanic transport mechanisms. Full article

The surface topography of the rolls used in skin-pass cold rolling determines the surface finish of rolled sheets. In this sense, work rolls can be intentionally textured to produce certain topographical features on the final sheet surface. The maskless electrochemical texturing method (MECT) is a potential candidate for industrial-scale application due to its reduced texturing cost and time when compared to traditional texturing methods. However, there are few studies in the literature that address the MECT method applied to the topography control of cold rolling work rolls. The present work aims to analyze the viability of surface texturing via MECT of work rolls used in skin-pass cold rolling. In this study, we first investigated how texturing occurs for tool steel using flat textured samples to facilitate the understanding of the dissolution mechanisms involved. In this case, a specially designed texturing chamber was built to texture flat samples extracted from an actual work roll. The results indicated that the anodic dissolution involved in tool steel texturing occurs preferentially in the metallic matrix around the primary carbides. Then, we textured a work roll used in pilot-scale rolling tests, which required the development of a special prototype to texture cylindrical surfaces. After texturing, the texture transfer from the work roll to the sheets was investigated. Rolling tests showed that the work roll surface textured with a dimple pattern generated a pillar-shaped texture pattern on the sheet surface, possibly due to a reverse extrusion mechanism. Full article

The present research is related to the application of artificial intelligence (AI) approaches for classifying surface textures, specifically regular reliefs patterns formed by ball burnishing operations. A two-stage methodology is employed, starting with the creation of regular reliefs (RRs) on test parts by ball burnishing, followed by 3D topography scanning with Alicona device and data preprocessing with Gwyddion, and Blender software, where the acquired 3D topographies are converted into a set of 2D images, using various virtual camera movements and lighting to simulate the symmetrical fluctuations around the tool-path of the real camera. Four pre-trained convolutional neural networks (DenseNet121, EfficientNetB0, MobileNetV2, and VGG16) are used as a base for transfer learning and tested for their generalization performance on different combinations of synthetic and real image datasets. The models were evaluated by using confusion matrices and four additional metrics. The results show that the pretrained VGG16 model generalizes the best regular reliefs textures (96%), in comparison with the other models, if it is subjected to transfer learning via feature extraction, using mixed dataset, which consist of 34,037 images in following proportions: non-textured synthetic (87%), textured synthetic (8%), and real captured (5%) images of such a regular relief. Full article

In response to the inability of traditional remote sensing technology to accurately capture the micro-topographic features of landslide surfaces in vegetated areas under complex terrain conditions, this paper proposes a method for enhanced landslide terrain display and trace recognition based on airborne LiDAR technology. Firstly, a high-precision LiDAR-DEM is constructed using preprocessed LiDAR point cloud data, and visual images are generated using visualization methods, including hillshade, slope, openness, and Sky View Factor (SVF). Secondly, pixel-level image fusion methods are applied to the visual images to obtain enhanced display images of the landslide terrain. Finally, a threshold is determined through a fractal model, and the Mean-Shift algorithm is utilized for clustering and denoising to extract landslide traces. The results indicate that employing pixel-level image fusion technology, which combines the advantageous features of multiple terrain visualization images, effectively enhances the display of landslide micro-topography. Moreover, based on the enhanced display images, the fractal model and the Mean-Shift algorithm are applied for denoising to extract landslide traces. Compared to orthophotos, this method can effectively and accurately extract landslide traces. The findings of this study provide valuable references for the enhanced display and trace recognition of landslide terrain in densely vegetated areas within complex mountainous areas, thereby providing technical support for emergency investigations of landslide disasters. Full article

One of the important parameters of the sheet metal forming process is the coefficient of friction (CoF). Therefore, monitoring the friction coefficient value is essential to ensure product quality, increase productivity, reduce environmental impact, and avoid product defects. Conventional CoF monitoring techniques pose a number of problems, including the difficulty in identifying the features of force signals that are sensitive to the variation in the coefficient of friction. To overcome these difficulties, this paper proposes a new approach to apply unsupervised artificial intelligence techniques with unbalanced data to classify the CoF of DP780 (HCT780X acc. to EN 10346:2015 standard) steel sheets in strip-drawing tests. During sheet metal forming (SMF), the CoF changes owing to the evolution of the contact conditions at the tool–sheet metal interface. The surface topography, the contact loads, and the material behaviour affect the phenomena in the contact zone. Therefore, classification is required to identify possible disturbances in the friction process causing the change in the CoF, based on the analysis of the friction process parameters and the change in the sheet metal’s surface roughness. The Kohonen self-organising map (SOM) was created based on the surface topography parameters collected and used for CoF classification. The CoF determinations were performed in the strip-drawing test under different lubrication conditions, contact pressures, and sliding speeds. The results showed that it is possible to classify the CoF using an SOM for unbalanced data, using only the surface roughness parameter Sq and selected friction test parameters, with a classification accuracy of up to 98%. Full article

Urban forests are very important for the environment and for people, especially in semi-arid cities where there is not much greenery. This makes heat stress worse and makes the city less livable. This paper presents a comprehensive geospatial methodology for selecting afforestation sites in the expanding semi-arid urban area of Şanlıurfa, Turkey, characterized by minimal forest cover, rapid urbanization, and extreme weather conditions. We identified nine ecological and infrastructure criteria using high-resolution Sentinel-2 images and features from the terrain. These criteria include slope, aspect, topography, land surface temperature (LST), solar radiation, flow accumulation, land cover, and proximity to roads and homes. After being normalized to make sure they were ecologically relevant and consistent, all of the datasets were put together into a GIS-based Multi-Criteria Decision Analysis (MCDA) tool. The Analytic Hierarchy Process (AHP) was then used to weight the criteria. A deep learning-based semantic segmentation model was used to create a thorough classification of land cover, primarily to exclude unsuitable areas such as dense urban fabric and water bodies. The final afforestation suitability map showed that 151.33 km² was very suitable and 192.06 km² was suitable, mostly in the northeastern and southeastern urban fringes. This was because the terrain and subclimatic conditions were good. The proposed methodology illustrates that urban green infrastructure planning can be effectively directed within climate adaptation frameworks through the integration of remote sensing and spatial decision-support tools, especially in ecologically sensitive and rapidly urbanizing areas. Full article

Orthopedic implant technology has historically seen difficulties in attaining long-term stability and biological integration, leading to complications such as implant loosening, wear debris production, and heightened infection risk. Nanotechnology provides a revolutionary method for addressing these constraints through the introduction of materials characterized by exceptional biocompatibility, durability, and integration potential. Nanomaterials (NMs), characterized by distinctive surface topographies and elevated surface area-to-volume ratios, facilitate improved osseointegration and provide regulated medication release, thereby creating a localized therapeutic milieu surrounding the implant site. To overcome the long-standing constraints of conventional implants, such as poor osseointegration, low mechanical fixation, immunological rejection, and implant-related infections, nanotechnology is causing a revolution in the field of orthopedic research. NMs are ideally suited for orthopedic applications due to their exceptional features, including increased tribology, wear resistance, prolonged drug administration, and excellent tissue regeneration. Because of their nanoscale size, they can imitate the hierarchical structure of real bone, which in turn encourages the proliferation of cells, lowers the risk of infection, and helps with the mending of bone fractures. This article will investigate the wide-ranging possibilities of nanostructured ceramics, polymers, metals, and carbon materials in bone tissue engineering, diagnostics, and the treatment of implant-related infections, bone malignancies, and bone healing. In addition, this paper will provide a basic overview of the most recent discoveries in nanotechnology driving the future of translational orthopedic research. It will also highlight safety evaluations and regulatory requirements for orthopedic devices. Full article

The desert-steppe transition zone at the southern edge of the Hobq Desert features complex topography and frequent wind/sand activities. To explore the impact of different underlying surfaces and topography on the wind-sand environment in this area, field measurements were conducted to analyze the temporal and spatial variations of sand-moving wind conditions and sand drift potential. The results indicate that the average wind speed, sand-moving wind frequency, sand drift potential and sand transport rate in this area were higher in spring and winter than in summer and fall temporally. Spatially, different underlying surfaces and topographic conditions, the characteristics of the average wind speed, sand-moving wind frequency, sand drift potential and sand transport rate were as follows: quicksand surface > grassland surface > shrub surface, and top of slope > quicksand surface > middle of slope. The predominant annual wind directions and sand-moving wind directions were W, WNW and NW. The sand drift direction was towards the E or ESE in winter and spring. This study provides a theoretical basis and scientific support for the development of targeted sand control measures in the desert-steppe transition zone at the southern edge of the Hobq Desert, thereby maintaining regional ecological sustainability. Full article

The study of impact craters is crucial for understanding planetary evolution and geological processes, particularly small craters, which are key to reconstructing the lunar impact history. Detecting small craters, with diameters ranging from 0.2 to 2 km, remains a challenge due to the power-law distribution of crater sizes and the complex topography of the lunar surface. This work uses high-resolution lunar imagery data from the Chang’E-2 mission, with a 7 m spatial resolution, to develop a deep learning framework for small crater detection, named YOLO-SCNet. The framework combines a high-quality, diversified sample dataset, generated through data augmentation techniques, with YOLO-SCNet, specifically designed for small target detection. Key challenges in lunar crater detection, such as varying lighting conditions and complex terrains, are addressed through the innovative model architecture, which incorporates a small object detection head, dynamic anchor boxes, and multi-scale feature fusion. Experimental results demonstrate that YOLO-SCNet achieves outstanding performance in detecting small craters across different lunar regions, with precision, recall, and F1 scores of 90.2%, 88.7%, and 89.4%, respectively. The framework offers a scalable solution for constructing a global lunar crater catalog (≥0.2 km) and can be extended to other planetary bodies like Mars and Mercury, significantly supporting future planetary exploration and mapping efforts. Full article

The use of composite materials within extreme environments is an exciting frontier in which a wealth of cutting-edge developments have taken place recently. Although there is vast knowledge of composites’ behaviour in standard room temperature and humidity, there is a great need to understand their performance in ‘hot/wet’ conditions, as these are the conditions of their envisaged applications. One of the key failure mechanisms within composites is interlaminar fracture, commonly referred to as delamination. The environmental effects of moisture and elevated temperatures on interlaminar fracture toughness are therefore essential design considerations for laminated aerospace-grade composite materials. IM7/8552, a toughened epoxy/carbon fibre reinforced polymer, was experimentally characterised in both ‘Dry’ and ‘Wet’ conditions at 23 °C and 90 °C. A moisture uptake study was conducted during the ‘Wet’ conditioning of the material in a 70 °C/85% relative humidity environment. Dynamic mechanical thermal analysis was carried out to determine the effect of moisture on the glass transition temperature of the material. Mode I initiation and propagation fracture properties were determined using double cantilevered beam specimens and Mode II initiation fracture properties were deduced using end-notched flexure specimens. The effects of precracking and the methodology of high-temperature testing are discussed in this report. Mode I interlaminar fracture toughness, $G_{IC}$, was found to increase with elevated temperatures and moisture content, with $G_{IC}=0.205{\mathrm{kJ}/\mathrm{m}}^2$ in ‘Dry 23 °C’ conditions increasing by 26% to $G_{IC}=0.259{\mathrm{kJ}/\mathrm{m}}^2$ in ‘Wet 90 °C’ conditions, demonstrating that the material exhibited its toughest behaviour in ‘hot/wet’ conditions. Increased ductility due to matrix softening and fibre bridging caused by temperature and moisture were key contributors to the elevated $G_{IC}$ values. Mode II interlaminar fracture toughness, $G_{IIC}$, was observed to decrease most significantly when moisture or elevated temperature was applied individually, with the combination of ‘hot/wet’ conditions resulting in an 8% drop in $G_{IIC}$, with $G_{IIC}=0.586{\mathrm{kJ}/\mathrm{m}}^2$ in ‘Dry 23 °C’ conditions and $G_{IIC}=0.541{\mathrm{kJ}/\mathrm{m}}^2$ in ‘Wet 90 °C’ conditions. The coupled effect of fibre-matrix interface degradation and increased plasticity due to moisture resulted in a relatively small knockdown on $G_{IIC}$ compared to $G_{IC}$ in ‘hot/wet’ conditions. Fractographic studies of the tested specimens were conducted using scanning electron microscopy. Noteworthy surface topography features were observed on specimens of different fracture modes, moisture saturation levels, and test temperature conditions, including scarps, cusps, broken fibres and river markings. The qualitative features identified during microscopy are critically examined to extrapolate the differences in quantitative results in the various environmental conditions. Full article

Rapid advancement in the nanotechnology and semiconductor industries has driven the demand for fast, precise measurement systems. Atomic force microscopy (AFM) is a standout metrology technique due to its high precision and wide applicability. However, when operated at high speeds, the quality of AFM images often deteriorates, especially in areas where sharp topographic features are present. This occurs because the feedback speed of the Z-scanner cannot keep up with the sample height changes during raster scanning. This study presents a simple variable scan speed control strategy for improving AFM imaging speed while maintaining the image quality obtained at low scan speeds. The proposed strategy aims to leverage the similarity in the height profiles between successive scan lines. The topographic information collected from the previous line scan is used to assess the surface complexity and to adjust the scan speed for the following line scan. The AFM system with this variable speed control algorithm was found to reduce the scan time needed for one AFM image by over 50% compared to the fixed-speed scanning while maintaining the similar level of accuracy. The calculated mean square errors (MSEs) show that the combination of speed adjustments and preemptive surface topography prediction has successfully allowed us to suppress the potential oscillations during the speed adjustment process, thereby enhancing the stability of the adaptive AFM system as well. Full article

Nanotopography refers to the intricate surface characteristics of materials at the sub-micron (<1000 nm) and nanometer (<100 nm) scales. These topographical surface features significantly influence the physical, chemical, and biological properties of biomaterials, affecting their interactions with cells and surrounding tissues. The development of nanostructured surfaces of polymeric nanocomposites has garnered increasing attention in the fields of tissue engineering and regenerative medicine due to their ability to modulate cellular responses and enhance tissue regeneration. Various top-down and bottom-up techniques, including nanolithography, etching, deposition, laser ablation, template-assisted synthesis, and nanografting techniques, are employed to create structured surfaces on biomaterials. Additionally, nanotopographies can be fabricated using polymeric nanocomposites, with or without the integration of organic and inorganic nanomaterials, through advanced methods such as using electrospinning, layer-by-layer (LbL) assembly, sol–gel processing, in situ polymerization, 3D printing, template-assisted methods, and spin coating. The surface topography of polymeric nanocomposite scaffolds can be tailored through the incorporation of organic nanomaterials (e.g., chitosan, dextran, alginate, collagen, polydopamine, cellulose, polypyrrole) and inorganic nanomaterials (e.g., silver, gold, titania, silica, zirconia, iron oxide). The choice of fabrication technique depends on the desired surface features, material properties, and specific biomedical applications. Nanotopographical modifications on biomaterials’ surface play a crucial role in regulating cell behavior, including adhesion, proliferation, differentiation, and migration, which are critical for tissue engineering and repair. For effective tissue regeneration, it is imperative that scaffolds closely mimic the native extracellular matrix (ECM), providing a mechanical framework and topographical cues that replicate matrix elasticity and nanoscale surface features. This ECM biomimicry is vital for responding to biochemical signaling cues, orchestrating cellular functions, metabolic processes, and subsequent tissue organization. The integration of nanotopography within scaffold matrices has emerged as a pivotal regulator in the development of next-generation biomaterials designed to regulate cellular responses for enhanced tissue repair and organization. Additionally, these scaffolds with specific surface topographies, such as grooves (linear channels that guide cell alignment), pillars (protrusions), holes/pits/dots (depressions), fibrous structures (mimicking ECM fibers), and tubular arrays (array of tubular structures), are crucial for regulating cell behavior and promoting tissue repair. This review presents recent advances in the fabrication methodologies used to engineer nanotopographical microenvironments in polymeric nanocomposite tissue scaffolds through the incorporation of nanomaterials and biomolecular functionalization. Furthermore, it discusses how these modifications influence cellular interactions and tissue regeneration. Finally, the review highlights the challenges and future perspectives in nanomaterial-mediated fabrication of nanotopographical polymeric scaffolds for tissue engineering and regenerative medicine. Full article

The Karak Wadi Al Fayha Fault (KWF) is a major NW-trending intraplate wrench fault system extending over 325 km from Western Karak in Jordan to Wadi Al Fayha in Saudi Arabia. Structurally linked to the Precambrian Najd Fault System, the KWF has been previously mapped using field observations, gravity, magnetic, and reflection seismic methods. However, these approaches lacked the vertical resolution necessary to characterize its shallow structure, leaving its influence on recent deposits and surface topography poorly understood. This study employs reflection seismic sections integrated with a Digital Elevation Model to refine terrain analysis and enhance fault mechanism solutions for determining the regional stress field pattern. Our results provide compelling evidence of the KWF’s upward propagation into the surface, as demonstrated by deformation of the uppermost Cretaceous and Cenozoic successions, distinct geomorphic features in the Digital Elevation Model, alignment of earthquake epicenters along the fault, and active landslides associated with its movement. We suggest that the reactivation of the KWF has been influenced by changing stress fields from the Late Cretaceous (Turonian) to the present. The Northwestern Arabian plate has undergone multiple tectonic stress transitions, including WNW–ESE compression associated with the Syrian Arc Fold-Belt system (Turonian–Plio-Pleistocene) and subsequent NNE–SSW extension linked to Red Sea rifting (Neogene–present). The analysis of fault mechanism solutions suggests that the latest fault movements result from the continued activity of the Irbid Rift event (Eocene) and the Dead Sea Transform Fault since the Miocene. Full article