Search Results (44,810)

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

Soil salinization is increasingly affecting agricultural areas worldwide, reducing soil quality and crop yields. Surface salinization evidences present complex spectral features, increasing in depth with increasing salt concentrations. For this reason, low salinization detection provides a complex challenge to test the capabilities of new-generation hyperspectral satellites. The aim of this study is to test the capability of the new generation of hyperspectral satellites (EnMAP) in detecting early stages and low levels of topsoil salinization and to investigate the differences between laboratory and image spectra to take into account their influence on model performance. The area of study, the Grosseto plain, located in central Italy, presented heterogeneous salinity levels ($E{C}_{max}$= 11.7 dS/m, $E{C}_{mean}$= 0.99 dS/m). We investigated the salt-affected soil spectral behaviour with both laboratory-acquired spectra ($n_{obs}$= 60) and nMAP-derived spectra ($n_{obs}$= 20). Both datasets were pre-processed with multiple data transformation algorithms and 2D correlograms, PLSR and the Random Forest regressor were tested to identify the best model for salinity detection. Two-dimensional correlograms resulted in an $R^2$ of 0.88 for laboratory data and 0.63 for EnMAP data. PLSR proved to have the worst performance. The Random Forest regressor proved its capability in detecting complex spectral features, with $R^2$ scores of 0.72 for laboratory data and 0.60 for EnMAP. The Random Forest model provides very satisfactory mapping capabilities when tested on the whole study area. The results highlight that the EnMAP-derived dataset produces similar results to those of ASD laboratory spectra, providing evidences regarding EnMAP’s predictive capability to detect early stages of topsoil salinization. Full article

As an emerging energy-saving approach, bio-inspired drag reduction technology has become a key research direction for reducing energy consumption and greenhouse gas emissions. This study introduces the latest research progress on bio-inspired microstructured surfaces in the field of underwater drag reduction, focusing on analyzing the drag reduction mechanism, preparation process, and application effect of the three major technological paths; namely, bio-inspired non-smooth surfaces, bio-inspired superhydrophobic surfaces, and bio-inspired modified coatings. Bio-inspired non-smooth surfaces can significantly reduce the wall shear stress by regulating the flow characteristics of the turbulent boundary layer through microstructure design. Bio-inspired superhydrophobic surfaces form stable gas–liquid interfaces through the construction of micro-nanostructures and reduce frictional resistance by utilizing the slip boundary effect. Bio-inspired modified coatings, on the other hand, realize the synergistic function of drag reduction and antifouling through targeted chemical modification of materials and design of micro-nanostructures. Although these technologies have made significant progress in drag reduction performance, their engineering applications still face bottlenecks such as manufacturing process complexity, gas layer stability, and durability. Future research should focus on the analysis of drag reduction mechanisms and optimization of material properties under multi-physical field coupling conditions, the development of efficient and low-cost manufacturing processes, and the enhancement of surface stability and adaptability through dynamic self-healing coatings and smart response materials. It is hoped that the latest research status of bio-inspired drag reduction technology reviewed in this study provides a theoretical basis and technical reference for the sustainable development and energy-saving design of ships and underwater vehicles. Full article

Silicon nitride (Si₃N₄) ceramic is widely used in the production of structural components. The surface wettability is closely related to the service life of materials. Laser surface texturing is considered an effective method for controlling surface wettability by processing specific patterns. This research focused on the laser surface texturing of a Si₃N₄ ceramic, employing rectangular patterns instead of the typical dimple designs, as these had promising applications in heat transfer and hydrodynamic lubrication. The effects of scanning speed and number of scans on the change of the morphologies and dimensions of the grooves were investigated. The results indicated that the higher scanning speed and fewer number of scans resulted in less damage to the textured surface. As the scanning speed increased, the width and depth of the grooves decreased significantly first, and then fluctuated. Conversely, increasing the number of scans led to an increase in the width and depth of the grooves, eventually stabilizing. The analysis of the elemental composition of different areas on the textured surface presented a notable increase in oxygen content at the grooves, while Si and N levels decreased. It was mainly caused by the chemical reaction between Si₃N₄ ceramic and oxygen during laser surface texturing in an air environment. This study also assessed the wettability of the textured surface, finding that the contact angle of the water droplet was significantly affected by the groove dimensions. After laser surface texturing, the contact angle increased from 35.51 ± 0.33° to 57.52 ± 1.83°. Improved wettability was associated with smaller groove volume, indicating better hydrophilicity at lower scanning speed and enhanced hydrophobicity with a fewer number of scans. Full article

In this study, we tested a flexible polyurethane (PU) foam, synthesized from bio-based components, for the removal of petroleum-derived fuels from water samples. The PU was synthesized via the prepolymer method through the reaction of PEG 400 with L-lysine ethyl ester diisocyanate (L-LDI), followed by chain extension with 2,5-bis(hydroxymethyl)furan (BHMF), a renewable platform molecule derived from carbohydrates. Freshwater and seawater samples were artificially contaminated with commercial diesel, gasoline, and kerosene. Batch adsorption experiments revealed that the total sorption capacity (S, g/g) of the PU was slightly higher for diesel in both water types, with values of 67 g/g in freshwater and 70 g/g in seawater. Sorption kinetic analysis indicated that the process follows a pseudo-second-order kinetic model, suggesting strong chemical interactions. Equilibrium data were fitted using Langmuir and Freundlich isotherm models, with the best fit achieved by the Langmuir model, supporting a monolayer adsorption mechanism on homogeneous surfaces. The PU foam can be regenerated up to 50 times by centrifugation, maintaining excellent performance. This study demonstrates a promising application of this sustainable and bio-based polyurethane foam for environmental remediation. Full article

Natural gas storage is an effective solution to address the energy supply–demand imbalance, and underground gas storage (UGS) is a primary method for storing natural gas. The overarching goal of this study is to monitor and analyze surface deformation at the Hutubi underground gas storage facility in Xinjiang, China, which is the largest gas storage facility in the country. This research aims to ensure the stable and efficient operation of the facility through long-term monitoring, using remote sensing data and advanced modeling techniques. The study employs the SBAS-InSAR method, leveraging Synthetic Aperture Radar (SAR) data from the TerraSAR and Sentinel-1 sensors to observe displacement time series from 2013 to 2024. The data is processed through wavelet transformation for denoising, followed by the application of a Gray Wolf Optimization (GWO) algorithm combined with Variational Mode Decomposition (VMD) to decompose both surface deformation and gas pressure data. The key focus is the development of a high-precision predictive model using a Gated Recurrent Unit (GRU) network, referred to as GWO-VMD-GRU, to accurately predict surface deformation. The results show periodic surface uplift and subsidence at the facility, with a notable net uplift. During the period from August 2013 to March 2015, the maximum uplift rate was 6 mm/year, while from January 2015 to December 2024, it increased to 12 mm/year. The surface deformation correlates with gas injection and extraction periods, indicating periodic variations. The accuracy of the InSAR-derived displacement data is validated through high-precision GNSS data. The GWO-VMD-GRU model demonstrates strong predictive performance with a coefficient of determination (R²) greater than 0.98 for the gas well test points. This study provides a valuable reference for the future safe operation and management of underground gas storage facilities, demonstrating significant contributions to both scientific understanding and practical applications in underground gas storage management. Full article

Fermented plant-based beverages represent promising functional foods due to their content of bioactive compounds (polyphenols, prebiotics) and viable probiotic microorganisms. Sprouted buckwheat is a rich source of bioactives and nutrients, which makes it a promising ingredient for the development of synbiotic formulations. This study aimed to optimize the fermentation process of a plant-based beverage composed of germinated buckwheat, honey, inulin, and Lactiplantibacillus plantarum (Lpb. plantarum), using Box–Behnken experimental design (BBD) and Response Surface Methodology (RSM) tools. The influence of three independent variables (inulin, honey, and inoculum concentration) was evaluated on five key response variables: total polyphenol content, flavonoid content, antioxidant activity (RSA%), pH, and starter culture viability. The optimal formulation—comprising 3% inulin, 10% honey, and 6.97 mg/100 mL inoculum—demonstrated functional stability over 21 days of refrigerated storage (4 °C), maintaining high levels of antioxidants and probiotic viability in the fermented beverage. Kinetic analysis of the fermentation process confirmed the intense metabolic activity of Lpb. plantarum, as evidenced by a decrease in pH, active consumption of reducing sugars, and organic acids accumulation. Full article

The manufacture of lightweight components is one of the most important requirements in the automotive and aerospace industries. Gears, on the other hand, are among the heaviest parts in terms of their total weight. Accordingly, a spur gear was considered, the body of which was configured as a lattice structure to make it lightweight. In addition, the structure was optimized by topology optimization using ProTOP software. Subsequently, the gear was manufactured by a selective laser melting process by using a strong and lightweight material, namely Ti-6Al-4V. This study defeated the problems of manufacturing orientation, surface roughness, support structure, and bending due to the high thermal gradient in the selective laser melting process. To experimentally investigate the benefits of such a lightweight gear body structure, a new test rig with a closed loop was developed. This rig enabled measurements of strains in the gear ring, hub, and tooth root. The experimental results confirmed that a specifically designed and selectively laser-melted, lightweight cellular lattice structure in the gear body can significantly influence strain. This is especially significant with respect to strain levels and their time-dependent variations in the hub section of the gear body. Full article

Alkalihalophilus marmarensis is an obligate alkaliphile with exceptional tolerance to high-pH environments, making it a promising candidate for industrial bioprocesses that require contamination-resistant and extremophilic production platforms. However, its practical deployment is hindered by limited biomass formation under extreme conditions, which constrains overall productivity. This study presents a model-driven investigation of how pH (8.8 and 10.5), culture duration (24 and 48 h), and nitrogen source composition (peptone and meat extract) affect cell dry mass, lactate, and protease synthesis. Using the response surface methodology and multi-objective optimization, we established predictive models (R² up to 0.92) and uncovered key trade-offs in biomass and metabolite yields. Our findings reveal that peptone concentration critically shapes the metabolic output, with low levels inhibiting growth and high levels suppressing protease activity. Maximum cell dry mass (4.5 g/L), lactate (19.3 g/L), and protease activity (43.5 U/mL) were achieved under distinct conditions, highlighting the potential for targeted process tuning. While the model validation confirmed predictions for lactate, deviations in cell dry mass and protease outputs underscore the complexity of growth–product interdependencies under nutrient-limited regimes. This work delivers a foundational framework for developing fermentations with A. marmarensis and advancing its application in sustainable, high-pH industrial bioprocesses. The insights gained here can be further leveraged through synthetic biology and bioprocess engineering to fully exploit the metabolic potential of obligate alkaliphiles like A. marmarensis. Full article

In this study, a methacrylic derivative of xylan (XYLMA) was synthesized through transesterification reactions, with the aim of evaluating its physicochemical behavior and its interaction with kaolinite particles. Structural characterization by FT-IR and NMR spectroscopy confirmed the incorporation of methacrylic groups into the xylan (XYL) structure, with a degree of substitution of 0.67. Thermal analyses (TGA and DSC) showed a decrease in melting temperature and enthalpy in XYLMA compared to XYL, attributed to a loss of structural rigidity. Thermal analyses (TGA and DSC) revealed a decrease in the melting temperature and enthalpy of XYLMA compared to XYL, which is attributed to a loss of structural rigidity and a reduction in the crystalline order of the biopolymer. Aggregation tests in solution revealed that XYLMA exhibits amphiphilic behavior, forming micellar structures at a critical aggregation concentration (CAC) of 62 mg L⁻¹. In adsorption studies on kaolinite, XYL showed greater affinity than XYLMA, especially at acidic pH, due to reduced electrostatic forces and a greater number of hydroxyl groups capable of forming hydrogen bonds with the mineral surface. In contrast, modification with methacrylic groups in XYLMA reduced its adsorption capacity, probably due to the formation of supramolecular aggregates. These results suggest that interactions between xylan and kaolinite clay are key to understanding the role that hemicelluloses play in increasing copper recovery when added to flotation cells during the processing of copper sulfide ores with high clay content. Full article

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T_C) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T_C. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T_r-3T_C), derived from T_C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T_r-3T_C. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H₂O m⁻² s⁻¹ and 0.555 mmol H₂O m⁻² s⁻¹ for the respective years, indicating strong interannual stability. Full article

Enhanced oil recovery (EOR) via CO₂ flooding is a promising strategy for improving hydrocarbon recovery and carbon sequestration, yet the influence of pH on solid–liquid interfacial interactions in quartz-dominated reservoirs remains poorly understood. This study employs molecular dynamics (MD) simulations to investigate the pH-dependent adsorption behavior of crude oil components on quartz surfaces and its impact on CO₂ displacement mechanisms. Three quartz surface models with varying ionization degrees (0%, 9%, 18%, corresponding to pH 2–4, 5–7, and 7–9) were constructed to simulate different pH environments. The MD results reveal that aromatic hydrocarbons exhibit significantly stronger adsorption on quartz surfaces at high pH, with their maximum adsorption peak increasing from 398 kg/m³ (pH 2–4) to 778 kg/m³ (pH 7–9), while their alkane adsorption peaks decrease from 764 kg/m³ to 460 kg/m³. This pH-dependent behavior is attributed to enhanced cation–π interactions that are facilitated by Na⁺ ion aggregation on negatively charged quartz surfaces at high pH, which form stable tetrahedral configurations with aromatic molecules and surface oxygen ions. During CO₂ displacement, an adsorption–stripping–displacement mechanism was observed: CO₂ first forms an adsorption layer on the quartz surface, then penetrates the oil phase to induce the detachment of crude oil components, which are subsequently displaced by pressure. Although high pH enhances the Na⁺-mediated weakening of oil-surface interactions, which leads to a 37% higher diffusion coefficient (8.5 × 10⁻⁵ cm²/s vs. 6.2 × 10⁻⁵ cm²/s at low pH), the tighter packing of aromatic molecules at high pH slows down the displacement rate. This study provides molecular-level insights into pH-regulated adsorption and CO₂ displacement processes, highlighting the critical role of the surface charge and cation–π interactions in optimizing CO₂-EOR strategies for quartz-rich reservoirs. Full article

Surface, terrain, or even atmosphere analysis using images or their fragments is important due to the possibilities of further processing. In particular, attention is necessary for satellite and/or drone images. Analyzing image elements by classifying the given classes is important for obtaining information about space for autonomous systems, identifying landscape elements, or monitoring and maintaining the infrastructure and environment. Hence, in this paper, we propose a neural classifier architecture that analyzes different features by the parallel processing of information in the network and combines them with a feature fusion mechanism. The neural architecture model takes into account different types of features by extracting them by focusing on spatial, local patterns and multi-scale representation. In addition, the classifier is guided by an attention mechanism for focusing more on different channels, spatial information, and even feature pyramid mechanisms. Atrous convolutional operators were also used in such an architecture as better context feature extractors. The proposed classifier architecture is the main element of the modeled framework for satellite data analysis, which is based on the possibility of training depending on the client’s desire. The proposed methodology was evaluated on three publicly available classification datasets for remote sensing: satellite images, Visual Terrain Recognition, and USTC SmokeRS, where the proposed model achieved accuracy scores of 97.8%, 100.0%, and 92.4%, respectively. The obtained results indicate the effectiveness of the proposed attention mechanisms across different remote sensing challenges. Full article

Ceramic dental prosthetics with internal metal structures are made from a cobalt–chromium alloy that is coated with ceramic. This study aims to validate surface treatments for the metal that enhance the adhesion of the ceramic coating under masticatory forces. Surface conditioning is performed using mechanical methods, like sandblasting (SB), and thermal methods, such as oxidation (O). The ceramic coating is applied to the metal component following the conditioning process, which can be conducted using either a single method or a combination of methods. Each conditioned sample undergoes characterization through various techniques, including drop shape analysis (DSA), scanning electron microscopy (SEM), X-ray diffraction (EDX), and atomic force microscopy (AFM). After the ceramic coating is applied and subjected to thermal sintering, the metal–ceramic samples are mechanically tested to assess the adhesion of the ceramic layer. The research findings, illustrated by scanning electron microscopy (SEM) images of the metal structures’ surfaces, indicate that alloy powder particles ranging from 10 to 50 µm were either adhered to the surfaces or present as discrete dots. Particles that exceed the initial design specifications of the structure can be smoothed out using sandblasting or mechanical finishing techniques. The energy-dispersive spectroscopy (EDS) results show that, after sandblasting, fragments of aluminum oxide remain trapped on the surface of the metal structures. These remnants are considered impurities, which can negatively impact the adhesion of the ceramic to the metal substrate. The analysis focuses on the exfoliation of the ceramic material from the deformed metal surfaces. The results emphasize the significant role of the sandblasting method and the micro-topography it creates, as well as the importance of the oxidation temperature in the treatment process. Drawing on 25 years of experience in dental prosthetics and the findings from this study, this publication aims to serve as a guide for applying the ceramic bonding layer to metal surfaces and for conditioning methods. These practices are essential for enhancing the adhesion of ceramic materials to metal substrates. Full article

In high-elevation mining areas, the roadbeds of certain surface ore haul roads are predominantly composed of sandstone. These sandstones are exposed to cold climatic conditions for long periods and are highly susceptible to erosion by the effects of freeze–thaw, which can degrade their support properties. This paper investigates the mechanism of strength deterioration of sandstone containing prefabricated cracks under cyclic loading and unloading after experiencing freeze–thaw. Sandstone specimens containing prefabricated cracks were prepared and subjected to 0, 20, 40, 60, and 80 freeze–thaw cycle tests. The strength changes were tested, and the crack extension process was analyzed using numerical simulation techniques. The study results show the following: 1. The wave propagation speed within the sandstone is more sensitive to changes in the number of freeze–thaw cycles. In contrast, mass damage shows significant changes only when more freeze–thaw cycles are experienced. 2. As the number of freeze–thaw cycles increases, the frequency of energy release from the numerical model accelerates. 3. The trend of the Cumulative Strain Difference (${\epsilon }_c$) reflects that the plastic strain difference between numerical simulation and actual measurement gradually decreases with increasing stress cycle level. 4. With the increase in freeze–thaw cycles, the damage morphology of the specimen undergoes a noticeable change, which is gradually transformed from monoclinic shear damage to X-shaped conjugate surface shear damage. 5. The number of tensile cracks dominated throughout the cyclic loading and unloading process, but with the increase in freeze–thaw cycles, the percentage of shear cracks increased. As the freeze–thaw cycles increase, sandstones are more inclined to undergo shear damage. These findings are important guidelines for road design and maintenance in alpine mining areas. Full article