Search Results (770)

The article considers issues related to improving the surface characteristics of titanium Gr2 using one of the lightest, cheapest and most ecological methods—electrospark deposition with low pulse energy and with ultradisperse electrodes TiB₂-TiAl with nanosized additives of NbC and ZrO₂. Using profilometric, metallographic, XRD, SEM and EDS methods, the change in the geometric characteristics, composition, structure, micro and nanohardness of the coatings as a function of the electrical parameters of the ESD regime has been studied. The results show that the use of TiB₂-TiAl electrodes and low pulse energy allows the formation of dense, continuous and uniform coatings that demonstrate a significant reduction in roughness, inherent irregularities and structural defects of electrospark coatings. Coatings with minimal defects, with crystalline–amorphous structures, with newly formed intermetallic and wear-resistant double and triple phases of the type AlTi3, TiAl₃, TiB, TiN_0.3, Al₂O₃, AlB₂, TiC_0.3N_0.7, Ti_3.2B_1.6N_2.4, Al_2.86O_3.45N_0.55 have been obtained. Possibilities have been found for controlling and obtaining specific values for the roughness and thickness of coatings in the ranges Ra = 1.5–3.2 µm and δ = 8–19.5 µm, respectively. The electrical parameters of the modes ensure the production of coatings with previously known thickness and roughness, with increased microhardness up to 13 GPa, with the maximum possible content of deliberately synthesized high-hard phases and with ultra-fine-grained structures have been defined. Full article

Non-Pneumatic Tires (NPTs) have been recognized for their advantages, such as low rolling resistance, burst resistance, and lightweight design, which make them highly suitable for application in electric vehicles under complex conditions, including high-frequency starts and stops and high torque. However, the discontinuous spoke support structure has resulted in a significantly higher ground contact pressure distribution compared to traditional pneumatic tires, leading to more severe wear, especially in the contact area where complex stress concentrations have occurred. Currently, the wear behavior mechanisms of NPTs have not been fully clarified, and wear simulation methods that take temperature effects into account are lacking. In this study, a temperature-modified Archard wear equation was integrated into the UMESHMOTION subroutine to achieve real-time updates of the tire surface geometry and simulate the evolution of wear. The modeling approach was validated through experimental testing. The simulation results showed that as the load increased from 100 N to 700 N, the peak ground contact pressure significantly increased, and the contact area gradually expanded, resulting in a notable increase in wear. Additionally, as the slip ratio increased from 2% to 5%, the contact stress and wear area were significantly amplified, leading to an increase in surface roughness and evident local damage. Comparative results indicated that the slip ratio had a more significant impact on wear volume than the load. The study has been conducted from a physical mechanism perspective to verify the dominant role of the slip ratio in the short-term rolling distance of tires, providing a theoretical basis for the structural optimization and wear-resistant design of non-pneumatic tires under complex operating conditions. Full article

Surface roughness, critically measured by the Arithmetical Mean Roughness (Ra), is a vital determinant of workpiece functional performance. Traditional contact-based measurement methods are inefficient and unsuitable for online inspection. While machine vision offers a promising alternative, existing approaches lack robustness, and pure deep learning models suffer from poor interpretability. Therefore, MTFE-Net is proposed, which is a novel deep learning framework for surface roughness classification. The key innovation of MTFE-Net lies in its effective integration of traditional texture feature analysis with deep learning within a dual-branch architecture. The MTFE (Multi-dimensional Texture Feature Extraction) branch innovatively combines a comprehensive suite of texture descriptors including Gray-Level Co-occurrence Matrix (GLCM), gray-level difference statistic, first-order statistic, Tamura texture features, wavelet transform, and Local Binary Pattern (LBP). This multi-scale, multi-perspective feature extraction strategy overcomes the limitations of methods that focus on only specific texture aspects. These texture features are then refined using Multi-Head Self-Attention (MHA) mechanism and Mamba model. Experiments on a dataset of Q235 steel surfaces show that MTFE-Net achieves state-of-the-art performance with 95.23% accuracy, 94.89% precision, 94.67% recall and 94.74% F1-score, significantly outperforming comparable models. The results validate that the fusion strategy effectively enhances accuracy and robustness, providing a powerful solution for industrial non-contact roughness inspection. Full article

A Monte Carlo based fractal prediction model is proposed to describe the evolution of surface morphology during the running-in process. The model accounts for the random and fractal characteristics of worn surfaces. The Weierstrass–Mandelbrot function is employed to simulate rough surfaces and establish the correlation between fractal dimension and surface roughness. By integrating traditional sliding wear models with surface effect functions, a unified prediction framework is developed. Experiments are conducted to obtain worn surface parameters and calculate fractal dimensions at different running-in stages. Model parameters are optimized by minimizing the variance between experimental and predicted results. Monte Carlo simulations are then introduced to represent the stochastic nature of the friction system, thereby improving prediction accuracy and objectivity. The proposed model reveals locally random yet globally convergent patterns, which are consistent with experimental observations. It effectively captures the stochastic evolution of surface morphology and provides a reliable approach for predicting worn surface behavior during running-in. Full article

The kelp genus Eisenia Areschoug is represented by two species in the Southeast Pacific: Eisenia cokeri, distributed in Peru and Chile, and E. gracilis, endemic to Peru. However, the taxonomic distinction between these species has long been questioned, and it remains unclear whether E. cokeri is conspecific with the Northeast Pacific species E. arborea. To address these issues, we conducted an integrative taxonomic assessment combining morphological analyses, molecular phylogenetics, and molecular species delimitation approaches. Sampling for morphological and molecular analyses was carried out in subtidal zones at five localities along the Peruvian coast, and an additional locality in Chile was included for molecular analyses where E. cokeri was documented. Peruvian Eisenia species exhibited consistent morphological differentiation, including differences in thallus size, holdfast diameter, stipe modifications (e.g., longitudinal division and pseudostipe formation), frond division, frond margins, and surface roughness, as supported by morphometric analyses. Phylogenetic reconstructions based on mitochondrial and chloroplast markers, together with concordant results from multiple species delimitation methods (ABGD, GMYC, and bPTP), consistently recovered three independent evolutionary lineages corresponding to E. cokeri, E. gracilis, and a distinct lineage of Eisenia from the Desventuradas Islands (Chile). Our results confirm the taxonomic distinctiveness of E. cokeri and E. gracilis, demonstrate that E. cokeri is genetically distinct from E. arborea, and reveal the presence of a previously unrecognized lineage of Eisenia in the Southeast Pacific. These findings refine species boundaries within the genus and provide a robust framework for the management and conservation of these endemic, foundation, habitat-forming species. Full article

Surface roughness is a critical quality attribute in laser cutting, directly influencing edge integrity, dimensional accuracy, and post-processing requirements. While most studies address surface roughness through forward modeling and optimization, practical manufacturing tasks often require solving inverse parameter selection problems, where process parameters must be chosen to satisfy prescribed surface quality requirements. In this study, surface roughness control in laser cutting is formulated within an inverse target-tracking framework based on response surface methodology (RSM). A quadratic response surface model is established using a Box–Behnken experimental design, with cutting speed, laser power, and assist-gas pressure as input factors. The fitted response surface provides an explicit forward mapping within a bounded operating window and serves as a local surrogate for methodological demonstration of target-oriented parameter estimation. Based on this surrogate model, a model-predicted feasible roughness range within the investigated design space is identified as R_a = 1.952–4.212 μm. For prescribed roughness targets within this interval, an inverse least-squares target-tracking formulation is employed to compute model-based parameter estimates. The inverse results are presented as continuous set-point maps and tabulated operating conditions, accompanied by a target-versus-predicted consistency check performed at the model level. Owing to the statistically significant lack-of-fit of the forward response surface, the inverse results presented in this study should be interpreted as theoretical, model-based estimates intended to illustrate the proposed framework rather than as experimentally validated process set-points. The proposed approach highlights both the potential and the limitations of inverse target-tracking strategies based on response surface models and underscores the need for statistically adequate models and independent experimental validation for industrial application. Full article

To address the severe surface imperfections induced during ultrafast pulsed laser fabrication of fused silica microfluidic chips, a high-precision CO₂ laser polishing strategy based on shallow-layer melting and reflow was employed. This method enables localized melting within an extremely thin surface layer, effectively smoothing the topography without altering the original microstructure geometry. An L9(3³) orthogonal experimental design was conducted to systematically investigate the influence of key parameters on polishing quality, identifying defocus distance as the dominant factor affecting surface roughness, followed by scanning speed and laser power. The optimal parameter combination was determined to be a laser power of 8 W, a defocus distance of 6 mm, and a scanning speed of 5 mm/s. Furthermore, an overlap rate between 38% and 63% was found to ensure sufficient fusion without excessive remelting, with the minimum surface roughness of 0.157 µm achieved at a 50% overlap rate. Based on the optimized parameters, adaptive scanning paths were designed for different functional units of a fused silica microfluidic chip. Surface characterization demonstrated that the surface roughness was remarkably reduced from 303 nm to 0.33 nm, meeting optical-grade surface quality requirements. Full article

Brain electrical activity, as recorded through electroencephalography (EEG), displays scale-free temporal fluctuations indicative of fractal behavior and complex dynamics. This study explores the use of the Higuchi Fractal Dimension (HFD) as a proxy of two complementary aspects of EEG temporal organization: local signal irregularity, interpreted within a Kolmogorov-type framework, and persistence related to temporal structure, associated with statistical complexity. The latter can be used to evidence persistence in the EEG signal, serving as an alternative to previously used approaches for estimating the Hurst exponent. Thirty-eight healthy participants underwent resting-state EEG recordings in open- and closed-eyes conditions. HFD was computed for the original signals to assess Kolmogorov complexity and for the signals’ cumulative envelopes to evaluate statistical complexity and, consequently, persistence. The results confirmed that HFD values align with theoretical expectations: higher for random noise in the Kolmogorov model (~2) and lower in the statistical model (~1.5). EEG data showed condition-dependent and topographically specific variations in HFD, with parieto-occipital regions exhibiting greater complexity and persistence. The HFD values in the statistical model fall within the 1–1.5 range, indicating long-term correlation. These findings support HFD as a reliable tool for assessing both the local roughness and global temporal structure of brain activity, with implications for physiological modeling and clinical applications. Full article

As LED devices continue to advance toward miniaturization and higher power density, heat dissipation has become a critical factor constraining their reliability and service life. Molybdenum is widely employed as a substrate material in LED devices owing to its high thermal conductivity and low coefficient of thermal expansion. However, substrate applications impose stringent requirements on surface finish, flatness, and low-damage processing. Chemical mechanical polishing (CMP) can effectively balance global and local flatness and serves as the final step in producing high-quality molybdenum substrate surfaces. To enable efficient and precise processing of molybdenum substrates, this study adopts an orthogonal experimental design for double-sided CMP to systematically investigate the effects of polishing pressure, polishing slurry pH, additives in the polishing slurry, and abrasive particle size on the material removal rate (MRR) and surface roughness (Sa). An optimal parameter combination was identified via weight-matrix optimization: a polishing pressure of 115 kPa, pH 11, H₂O₂ (0.5%) and glycine (5 mg/L) as additives, and an abrasive particle size of 0.6 μm. Under these conditions, the MRR reached 80 nm·min⁻¹ and Sa decreased to 1.1 nm, yielding a smooth, mirror-like surface. The results indicate that multi-factor synergistic optimization can substantially enhance both surface quality and processing efficiency in double-sided CMP of molybdenum substrates, providing a process basis for applications in high-power LED devices. Full article

Seepage–dissolution in carbonate rock fractures serves as the core driver governing the evolution of key engineering projects, including reservoir dam stability, CO₂ geological sequestration, and unstable rock collapse mitigation strategies. While physical heterogeneity (e.g., fracture aperture, mineral distribution) is widely recognized as a critical factor regulating dissolution processes, the specific influence of mineral distribution heterogeneity on dissolution rates still lacks quantitative quantification. To address this gap, this study focuses on limestone fractures and employs multi-component reactive transport numerical simulations to model acidic fluid (pH = 5.0) seepage–dissolution under two Darcy flux conditions (37.8/378 m·yr⁻¹). It investigates the controlling mechanisms of fracture roughness (λ_b = 0.036~0.308) and calcite contents (55%, 75%, 95%) on dissolution dynamics, and analyzes spatial variations in local Darcy velocity, reaction rate, and effective dissolution rate (R_eff,i). Results demonstrate that mineral distribution heterogeneity directly induces pronounced spatial heterogeneity in dissolution behavior: diffusion dominates under low flux (simulation duration: 48.3 days), forming discrete reaction fronts (~15 mm) controlled by mineral clusters; advection prevails under high flux (simulation duration: 4.83 days), generating alternating dissolution–deposition zones (~7.5 mm) with R_eff,i one order of magnitude greater than that under low flux. Notably, 55% calcite content yields the highest R_eff,i (1.87 × 10⁻¹¹ mol·m⁻²·s⁻¹), 0.94 orders of magnitude greater than that at 95% calcite content. A strong linear correlation (R² > 0.98) exists between the Damköhler number (Da_I) and R_eff,i at the same calcite content. Furthermore, the synergistic interaction between fracture aperture and mineral heterogeneity amplifies dissolution complexity, with high roughness (λ_b = 0.308) coupled with 55% calcite content achieving the highest R_eff,i of 2.1 × 10⁻¹¹ mol·m⁻²·s⁻¹. This study provides critical theoretical insights and quantitative data support for fractured rock mass evolution prediction models, geological hazard prevention, and geological carbon sequestration optimization. Full article

The increase in population and demand for the various needs of citizens increases the interaction with the geo-environment. Thus, the rate of natural events affecting daily human life increases. Such an event occurred on a rock cliff in a densely populated area in İstanbul (Türkiye). More than four rock blocks (approximately 3–5 m³) belonging to the Paleozoic sequence of İstanbul, composed of nodular limestone with sandy-clay interlayers, detached and fell. The blocks traveled along a path of approximately 60 m and stopped by crushing a couple of buildings downslope. The path was rough and contained various surface conditions (e.g., bedrock, talus, and plants). This study was initiated by the examination of the dimensions of failed rock blocks, their paths, and topographic conditions. Unmanned vehicles (drones) facilitated the generation of 3D numerical models of topographic changes on the site. Quantifying discontinuity properties (such as persistence, spacing, roughness, etc.) and defining weathering properties comprises the second stage, along with sampling. Based on digital topographic data and field observations, cross-sections were defined by means of possible rockfall areas within the area of potentially unstable blocks. Numerical analysis and rockfall analysis were conducted along these critical sections. Interpretation of laboratory data and results obtained from numerical studies leads to an understanding of the mechanism of the recent rockfall event and demonstrates the most critical areas to be considered and reinforced. The research comprises proposing appropriate reinforcement techniques due to the strong Turkish regulations along the “Bosphorus Waterfront Protected Zone”. The study advises pre-cleaning of potentially unstable blocks after a fence production on paths where rocks could fall, and rock anchors in some localities with varying lengths. The latest part of the research covers the re-assessment of mitigation processes with numerical models, which shows that the factor of safety increased to the desired levels. The reinforcement applications at the site match well with the proposed prevention methods. Full article

Metal Injection Molding (MIM) enables complex orthodontic-bracket geometries but can introduce surface and geometric discontinuities that act as initiation sites for crevice and pitting corrosion. The effect of acidic, kombucha-like exposure on corrosion and repassivation was assessed for MIM-316L brackets relative to a commercial comparator, and the coupling between surface quality (roughness and wettability) and localized damage at scanning electron microscopy (SEM)-identified hot-spots was examined. Kombucha was characterized by pH and titratable acidity. Surfaces were characterized by SEM, areal roughness metrics (R_a, S_a, S_z, and A2), and wettability by sessile-drop goniometry. Electrochemical behavior in artificial saliva was measured using open-circuit potential and cyclic potentiodynamic polarization (ASTM F2129/G59), and a qualitative magnetic check was included as a pragmatic quality-assurance screen. Exposure in kombucha reduced breakdown and repassivation potentials and increased passive current density, with the strongest effects co-localizing geometric discontinuities. Commercial brackets exhibited markedly poorer surface quality (notably higher S_z), amplifying acidity-driven susceptibility. These findings indicate that, under acidic challenges, surface/geometry quality dominates corrosion behavior; non-magnetic-phase compliance and simple chairside screening (e.g., magnet test), alongside tighter manufacturing controls on roughness and edge finish, should be incorporated into clinical and industrial quality assurance (QA). Full article

The accurate evaluation of the Joint Roughness Coefficient (JRC) is crucial for rock mechanics engineering. Existing JRC prediction models based on a single fractal parameter often face limitations in physical consistency and predictive accuracy. This study proposes a novel two-parameter JRC prediction method based on fractal topology theory. The core innovation of this method lies in extracting two distinct types of information from a roughness profile: the scale-invariant characteristics of its frequency distribution, quantified by the Hurst exponent (H), and the amplitude-dependent scale effects, quantified by the coefficient (C). By integrating these two complementary aspects of roughness, a comprehensive predictive model is established: JRC = $100.014H^{-1.5491}{C}^{1.2681}$. The application of this model to Atomic Force Microscopy (AFM)-scanned coal rock surfaces indicates that JRC is primarily controlled macroscopically by amplitude-related information (reflected by C), while the scale-invariant frequency characteristics (reflected by H) significantly influence local prediction accuracy. By elucidating the distinct roles of scale-invariance and amplitude attributes in controlling JRC, this research provides a new theoretical framework and a practical analytical tool for the quantitative evaluation of JRC in engineering applications. Full article

Long-term wind speed changes over the Yangtze River waterway have critical implications for inland shipping efficiency, emission dispersion, and renewable energy potential. This study utilizes a high-resolution 5 km gridded reanalysis dataset spanning 1979–2018 to conduct a comprehensive spatiotemporal analysis of surface wind climatology, variability, and trends along China’s primary inland waterway. A pivotal regime shift was identified around 2000, marking a transition from terrestrial stilling to a recovery phase characterized by wind speed intensification. Multiple change-point detection algorithms consistently identify 2000 as a pivotal turning point, marking a transition from the late 20th century “terrestrial stilling” to a recovery phase characterized by wind speed intensification. Post-2000 trends reveal pronounced spatial heterogeneity: the upstream section exhibits sustained strengthening (+0.02 m/s per decade, p = 0.03), the midstream shows weak or non-significant trends with localized afternoon stilling in complex terrain (−0.08 m/s per decade), while the downstream coastal zone demonstrates robust intensification exceeding +0.10 m/s per decade during spring–autumn daytime hours. Three distinct wind regimes emerge along the 3000 km corridor: a high-energy maritime-influenced downstream sector (annual means > 3.9 m/s, diurnal peaks > 6.0 m/s) dominated by sea breeze circulation, a transitional midstream zone (2.3–2.7 m/s) exhibiting bimodal spatial structure and unique summer-afternoon thermal enhancement, and a topographically suppressed upstream region (<2.0 m/s) punctuated by pronounced channeling effects through the Three Gorges constriction. Critically, the observed recovery contradicts widespread basin greening (97.9% of points showing significant positive NDVI trends), which theoretically should enhance surface roughness and suppress wind speeds. Correlation analysis reveals that wind variability is systematically controlled by large-scale atmospheric circulation patterns, including the Northern Hemisphere Polar Vortex (r ≈ 0.35), Western Pacific Subtropical High (r ≈ 0.38), and East Asian monsoon systems (r > 0.60), with distinct seasonal phase-locking between baroclinic spring dynamics and monsoon-thermal summer forcing. These findings establish a comprehensive, fine-scale climatological baseline essential for optimizing pollutant dispersion modeling, and evaluating wind-assisted propulsion feasibility to support shipping decarbonization goals along the Yangtze Waterway. Full article

The thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject. Full article