Search Results (269)

Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 m, leveraging auxiliary variables including vegetation indices, terrain parameters, and land surface reflectance. By establishing non-linear relationships between LST and predictive variables through eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, the proposed framework was rigorously validated using in situ measurements across China’s Heihe River Basin. Comparative analyses demonstrated that integrating multiple vegetation indices (e.g., NDVI, SAVI) with terrain factors yielded superior accuracy compared to factors utilizing land surface reflectance or excessive variable combinations. While slope and aspect parameters marginally improved accuracy in mountainous regions, including them degraded performance in flat terrain. Notably, land surface reflectance proved to be ineffective in snow/ice-covered areas, highlighting the need for specialized treatment in cryospheric environments. This work provides a reference for LST downscaling, with significant implications for environmental monitoring and urban heat island investigations. Full article

(1) Background: Human walking involves adapting to diverse terrains, influencing gait biomechanics. This study examined how seven outdoor surfaces—flat–even, banked-right/-left, cobblestone, grass, sloped-down, and sloped-up—affect nonlinear gait dynamics in 30 healthy adults (14 females and 15 males). (2) Methods: Trunk and shank accelerations were analyzed for movement predictability (sample entropy, SE), smoothness (log dimensionless jerk, LDLJ), symmetry (step/stride regularity), and stability (short-/long-term Lyapunov exponents, LyE_s, LyE_l). (3) Results: Surface type significantly influenced all gait metrics, regardless of sex. Banked-right and sloped-down walking reduced SE, indicating less predictable movements. All surfaces except flat–even increased LDLJ, suggesting reduced smoothness. Cobblestone and sloped-down surfaces impaired step symmetry, while banked surfaces enhanced stride symmetry. LyE_s decreased on cobblestones (lower variability), while sloped-up increased it. LyE_l rose on all surfaces except cobblestones, indicating a more chaotic gait. No significant sex differences were found, though males showed a non-significant trend toward lower LyE_s. Notably, sex–surface interactions emerged for SE and stride symmetry on banked-right surfaces, with females showing decreased SE and increased symmetry. (4) Conclusions: These findings underscore the importance of terrain and sex in gait dynamics research. Full article

The present work aimed to develop a simple simulation tool to support studies of slip and other non-traditional boundary conditions in solid–fluid interactions. A mesoscale particle model (movable automata) was chosen to enable performant simulation of all relevant aspects of the system, including phase changes, plastic deformation and flow, interface phenomena, turbulence, etc. The physical system under study comprised two atomically flat surfaces composed of particles of different sizes and separated by a model fluid formed by moving particles with repulsing cores of different sizes and long-range attraction. The resulting simulation method was tested under a variety of particle densities and conditions. It was shown that the particles can enter different (solid, liquid, and gaseous) states, depending on the effective temperature (kinetic energy caused by surface motion and random noise generated by spatially distributed Langevin sources). The local order parameter and formation of solid domains was studied for systems with varying density. Heating of the region close to one of the plates could change the density of the liquid in its proximity and resulted in chaotization (turbulence); it also dramatically changed the system configuration, the direction of the average flow, and reduced the effective friction force. Full article

This paper presents the design and validation of a novel specialized fixture device for machining inclined planes with barrel cutters on 3-axis CNC machine tools. Barrel milling, also known as Parabolic Performance Cutting (PPC), is extensively used on 5-axis machines to enhance the efficiency of machining complex surfaces. While significant research has focused on optimizing barrel milling for aspects such as surface roughness and cutting forces, implementing this technique on 3-axis machines poses a challenge due to limitations in tool orientation. To overcome this, an innovative adaptable device was designed, enabling precise workpiece orientation relative to the barrel cutter. To overcome this limitation, an adaptable device was designed that enables precise workpiece orientation relative to the barrel cutter. The device utilizes interchangeable locating elements for different cutter programming angles (such as 18°, 20°, and 42.5°), thereby ensuring correct workpiece positioning. Rigid workpiece clamping is provided by the device’s mechanism to maintain precise workpiece positioning during machining, and probing surfaces are integrated into the device to facilitate the definition of the coordinate system necessary for CNC machine programming. Device control was performed using a Hexagon RA-7312 3D measuring arm. Inspection results indicated minimal dimensional deviations (e.g., surface flatness between 0.002 mm and 0.012 mm) and high angular accuracy (e.g., angular non-closure of 0.006°). The designed device enables the effective and precise use of barrel cutters on 3-axis CNC machines, offering a previously unavailable practical and economical solution for cutting tool tests and cutting regime studies. Full article

This study focuses on the ancient buildings in Cicheng Old Town, a typical architectural heritage area in the Jiangnan region of China. These buildings are famous for their well-preserved Tang Dynasty urban layout and Ming and Qing Dynasty roof tiles. However, the natural aging, weathering, and biological erosion of the roof tiles seriously threaten the integrity of heritage protection. Given that current detection methods mostly depend on manual checks, which are slow and cover only a small area, this study suggests using deep learning technology for heritage protection and creating a smart model to identify damage in flat tiles using the YOLOv8 architecture. During this research, the team used drone aerial photography to collect images of typical building roofs in Cicheng Old Town. Through preprocessing, unified annotation, and system training, a damage dataset containing 351 high-quality images was established, covering five types of damage: breakage, cracks, the accumulation of fallen leaves, lichen growth, and vegetation growth. The results show that (1) the model has an overall mAP of 73.44%, an F1 value of 0.75 in the vegetation growth category, and a recall rate of 0.70, showing stable and balanced detection performance for various damage types; (2) the model performs well in comparisons using confusion matrices and multidimensional indicators (including precision, recall, and log-average miss rate) and can effectively reduce the false detection and missed detection rates; and (3) the research team applied the model to drone images of the roof of Fengyue Painted Terrace Gate in Cicheng Old Town, Jiangbei District, Ningbo City, Zhejiang Province, and automatically detected and located multiple tile damage areas. The prediction results are highly consistent with field observations, verifying the feasibility and application potential of the model in actual heritage protection scenarios. Full article

Magnetorheological finishing is widely used in the high-precision processing of optical components, but due to the influence of multi-source system errors, the convergence of single-pass magnetorheological finishing (MRF) is limited. Although iterative processing can improve the surface accuracy, repeated tool paths tend to deteriorate mid-spatial frequency textures, and for complex surfaces such as aspheres, traditional manual alignment is time-consuming and lacks repeatability, significantly restricting the processing efficiency. To address these issues, firstly, this study systematically analyzes the effect of six-degree-of-freedom positioning errors on convergence behavior, establishes a positioning error-normal contour error transmission model, and obtains a workpiece positioning error tolerance threshold that ensures that the relative convergence ratio is not less than 80%. Further, based on these thresholds, a hybrid self-positioning method combining machine vision and a probing module is proposed. A composite data acquisition method using both a camera and probe is designed, and a stepwise global optimization model is constructed by integrating a synchronous iterative localization algorithm with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The experimental results show that, compared with the traditional alignment, the proposed method improves the convergence ratio of flat workpieces by 41.9% and reduces the alignment time by 66.7%. For the curved workpiece, the convergence ratio is improved by 25.7%, with an 80% reduction in the alignment time. The proposed method offers both theoretical and practical support for high-precision, high-efficiency MRF and intelligent optical manufacturing. Full article

This study analyses the dynamic impact between winter wheat grains (‘Memory’ cultivar) and a flat metal surface under normal collisions. Four moisture levels (7%, 10%, 13% and 16%) and impact velocities from 1.0 to 4.5 m·s⁻¹ were chosen to reflect conditions in agricultural machinery. A custom test rig—comprising a transparent drop guide, a high-sensitivity piezoelectric force sensor and a high-speed camera—recorded grain velocity by vision techniques and contact force at 1 MHz. Force–time curves were examined to evaluate restitution velocity, the coefficient of restitution (CoR) and the effect of moisture on elastic–plastic deformation. CoR decreased non-linearly as impact velocity rose from 1.0 to 5.0 m·s⁻¹, and moisture content increased from 7% to 16%, falling from ≈ 0.60 to 0.40–0.50. Grains with higher moisture struck at higher velocities showed greater plastic deformation, longer contact times and intensified energy dissipation, making them more susceptible to internal damage. The data provide validated reference values for discrete element method (DEM) calibration and will assist engineers in designing grain-handling equipment that minimises mechanical damage during harvesting, conveying and processing. Full article

A very fundamental property of both weakly and strongly interacting materials is the nature of their magnetic response. In this work, we detail the growth of crystals of the quasicrystal approximant Fe₄Al₁₃ with an Al flux solvent method. We characterize our samples using electrical transport and heat capacity, yielding results consistent with a simple non-magnetic metal. However, magnetization measurements portray an extremely unusual response for a dilute paramagnet and do not exhibit the characteristic Curie behavior expected for a weakly interacting material at high temperature. Electronic structure calculations confirm metallic behavior but also indicate that each isolated band near the Fermi energy hosts non-trivial topologies, including strong, weak, and nodal components, with resultant topological surface states distinguishable from bulk states on the (001) surface. With half-filled flat bands apparent in the calculation, but an absence of long-range magnetic order, the unusual quasi-paramagnetic response suggests the dilute paramagnetic behavior in this quasicrystal approximant is surprising and may serve as a test of the fundamental assumptions that are taken for granted for the magnetic response of weakly interacting systems. Full article

Motion control of droplets undergoing collisions with solid surface is required in a number of technological and industrial situations. Droplet dynamics after lifting off is often unpredictable, leading to a major problem in many technologies that droplets move in uncontrolled and potentially undesirable ways. Herein, this work shows that well-designed surface chemistry can produce an accurate control of force transmission to impinging droplets, permitting precise controlled droplet rebounce. The non-wetting surfaces (superhydrophobic), which mimics the water-repellent mechanism of lotus leaves via micro-to-nanoscale hierarchical morphology, with patterned “defect” of extreme wettability (hydrophilic), are synthesized by photolithography using only one inexpensive fluorine-free reagent (methyltrichlorosilane). The contact line of impinging droplet during flatting and receding is free to move on the superhydrophobic region and pinned as it meets with the hydrophilic defect, which introduces a net surface tension force allowing patterned droplet deposition, controlled droplet splitting, and directed droplet rebound. The work also achieves controlled vertical rebound of impinging droplets on inclined surfaces by controlling defect’s size, impact position, and impact velocity. This research demonstrates pinning forces as a general strategy to attain sophisticated droplet motions, which opens an avenue in future explorations, such as matter transportation, energy transformation, and object actuation. Full article

This study explores the impact of a phosphonium-based IL (trihexyltetradecylphosphonium bis(2-ethylhexyl) phosphate, [P_6,6,6,14][BEHP])) on the tribological performance of an automatic transmission fluid (ATF) when used as an additive. Tests were carried out under both non-electrified and electrified conditions in a reciprocating ball-on-flat tribometer. After tribological tests, the worn surfaces were subjected to extensive structural and surface analyses to understand the underlying friction and wear mechanisms. The addition of this ionic liquid improved the anti-wear protection of the ATF, although the wear rates were consistently higher than in non-electrified conditions. The tribofilm formed by the IL-containing ATF augmented the electrical resistance at the contact interface, thereby reducing the likelihood of electrification-induced wear. Our results point to the need for further improvements in the chemical formulation of the ionic liquids, like the one used in the present study, to enhance the protection of sliding surfaces against wear in future electric vehicle applications. Full article

This work aims to explore the magnetohydrodynamic mixed convection boundary layer flow (MHD-MCBLF) on a slanted extending cylinder using Eyring–Powell fluid in combination with Levenberg–Marquardt algorithm–artificial neural networks (LMA-ANNs). The thermal properties include thermal stratification, which has a higher temperature surface on the cylinder than on the surrounding fluid. The mathematical model incorporates essential factors involving mixed conventions, thermal layers, heat absorption/generation, geometry curvature, fluid properties, magnetic field intensity, and Prandtl number. Partial differential equations govern the process and are transformed into coupled nonlinear ordinary differential equations with proper changes of variables. Datasets are generated for two cases: a flat plate (zero curving) and a cylinder (non-zero curving). The applicability of the LMA-ANN solver is presented by solving the MHD-MCBLF problem using regression analysis, mean squared error evaluation, histograms, and gradient analysis. It presents an affordable computational tool for predicting multicomponent reactive and non-reactive thermofluid phase interactions. This study introduces an application of Levenberg–Marquardt algorithm-based artificial neural networks (LMA-ANNs) to solve complex magnetohydrodynamic mixed convection boundary layer flows of Eyring–Powell fluids over inclined stretching cylinders. This approach efficiently approximates solutions to the transformed nonlinear differential equations, demonstrating high accuracy and reduced computational effort. Such advancements are particularly beneficial in industries like polymer processing, biomedical engineering, and thermal management systems, where modeling non-Newtonian fluid behaviors is crucial. Full article

Marine antifouling coatings that rely on the release of antifouling agents are the most prevalent and effective strategy for combating fouling. However, the environmental concerns arising from the widespread discharge of these agents into marine ecosystems cannot be overlooked. An innovative and promising alternative involves incorporating antimicrobial groups into polymers to create coatings endowed with intrinsic antimicrobial properties. In this study, we reported an urushiol-based benzoxazine (URB) monomer, synthesized from natural urushiol and antibacterial rosin amine. The URB monomer was subsequently polymerized through thermal curing ring-opening polymerization, resulting in the formation of a urushiol-based benzoxazine polymer (URHP) coating with inherent antimicrobial properties. The surface of the URHP coating is smooth, flat, and non-permeable. Contact angle and surface energy measurements confirm that the URHP coating is hydrophobic with low surface energy. In the absence of antimicrobial agent release, the intrinsic properties of the URHP coating can effectively kill or repel fouling organisms. Furthermore, with bare glass slides serving as the control sample, the coating demonstrates outstanding anti-adhesion capabilities against four types of bacteria (E. coli, S. aureus, V. alginolyticus, and Bacillus sp.), and three marine microalgae (N. closterium, P. tricornutum, and D. zhan-jiangensis), proving its efficacy in preventing fouling organisms from settling and adhering to the surface. Thus, the combined antibacterial and anti-adhesion properties endow the URHP coating with superior antifouling performance. This non-release antifouling coating represents a green and environmentally sustainable strategy for antifouling. Full article

In this paper, we propose a novel deep dilated convolutional neural network (DDCNN) architecture to reconstruct periodic rough surfaces, including their periodic length, dielectric constant, and shape. Historically, rough surface problems were addressed through optimization algorithms. However, these algorithms are computationally intensive, making the process very time-consuming. To resolve this issue, we provide measured scattered fields as training data for the DDCNN to reconstruct the periodic length, dielectric constant, and shape. The numerical results demonstrate that DDCNN can accurately reconstruct rough surface images under high noise levels. In addition, we also discuss the impacts of the periodic length and dielectric constant of the rough surface on the shape reconstruction. Notably, our method achieves excellent reconstruction results compared to DCNN even when the period and dielectric coefficient are unknown. Finally, it is worth mentioning that the trained network model completes the reconstruction process in less than one second, realizing efficient real-time imaging. Full article

Electrolytic plasma polishing technology is widely used in medical devices, aerospace, nuclear industry, marine engineering, and other equipment manufacturing fields, owing to its advantages of shape adaptability, high efficiency, good precision, environmental protection, and non-contact polishing. However, the lack of in-depth research on the material removal mechanism of the electrolytic plasma polishing process severely restricts the regulation of the process parameters and polishing effect, leading to optimization and improvement by experimental methods. Firstly, the formation mechanism of passivation film was revealed based on an analysis of the surface morphology and chemical composition of stainless steel. Subsequently, the dissolution mechanism of the passivation film was proposed by analyzing the change in the valence state of the main metal elements on the surface. In addition, the surface enclosure leveling mechanism of electrolytic plasma polishing (EPP) for stainless steel was proposed based on a material removal mechanism model combined with experimental test methods. The results show that EPP significantly reduces the surface roughness of stainless steel, with Ra being reduced from 0.445 µm to 0.070 µm. Metal elements on the anode surface undergo electrochemical oxidation reactions with reactive substances generated by the gas layer discharge, resulting in the formation of passivation layers of metal oxides and hydroxides. The passivation layer complexes with solvent molecules in the energetic plasma state of the gas layer with SO₄²⁻ ions, forming complexes that enter the electrolyte. The dynamic balance between the formation and dissolution of the passivation film is the key to achieving a flat surface. This study provides theoretical guidance and technical support for the EPP of stainless steel. Full article

Chemical mechanical polishing (CMP) is a widely used technique in semiconductor manufacturing to achieve a flat and smooth surface on silicon wafers. A key challenge in CMP is enhancing the material removal rate (MRR) while reducing within-wafer non-uniformity (WIWNU). A computational fluid dynamics (CFD) model is employed to analyze the slurry flow between the wafer and the polishing pad. Several factors influence the CMP process, including the type of abrasives, slurry flow rate, pad patterns, and contact pressure distribution. In this study, two polishing pad patterns with concentric and radial grooves are proposed to address how morphology variations influence wafer removal rate and consistency. Under the same operating conditions, the CFD simulations show that (i) the radial grooves have higher wall shear stress, a more significant negative pressure region, and a more evenly distributed mass on the wafer surface than the concentric grooves, and (ii) the radial grooves exhibit superior slurry mass distribution. It is noted that reducing the negative pressure differential field area results in a less pronounced back-mixing effect. A comparison of radial and concentric polishing pad grooves reveals that radial grooves improve slurry distribution, reduce the slurry saturation time (SST), and increase wall shear stress, leading to higher MRR and improved non-uniformity (NU). Precisely, the errors between the experimental SST values of 21.52 s and 16.06 s for concentric circular and radial groove pads, respectively, and the simulated SST values of 22.23 s and 15.73 s are minimal, at 3.33% and 3.35%. Full article