Search Results (18,302)

Diverging from conventional dephosphorization approaches, this study employs a novel pre-reduction and smelting separation (PR-SS) to efficiently co-recover iron and phosphorus from high-phosphorus oolitic iron ore, directly yielding Fe–P alloy, and the Fe–P alloy shows potential as feedstock for high-phosphorus weathering steel or wear-resistant cast iron, indicating promising application prospects. Using oolitic magnetite concentrate (52.06% Fe, 0.37% P) as feedstock, optimized conditions including pre-reduction at 1050 °C for 2 h with C/Fe mass ratio of 2, followed by smelting separation at 1550 °C for 20 min with 5% coke, produced a metallic phase containing 99.24% Fe and 0.73% P. Iron and phosphorus recoveries reached 99.73% and 99.15%, respectively. EPMA microanalysis confirmed spatial correlation between iron and phosphorus in the metallic phase, with undetectable phosphorus signals in vitreous slag. This evidence suggests preferential phosphorus enrichment through interfacial mass transfer along the pathway of the slag phase to the metal interface and finally the iron matrix, forming homogeneous Fe–P solid solutions. The phosphorus migration mechanism involves sequential stages: apatite lattice decomposition liberates reactive P₂O₅ under SiO₂/Al₂O₃ influence; slag–iron interfacial co-reduction generates Fe₃P intermediates; Fe₃P incorporation into the iron matrix establishes stable solid solutions. Full article

Multi-focus image fusion (MFIF) is an image-processing method that aims to generate fully focused images by integrating source images from different focal planes. However, the defocus spread effect (DSE) often leads to blurred or jagged focus/defocus boundaries in fused images, which affects the quality of the image. To address this issue, this paper proposes a novel model that embeds the Kolmogorov–Arnold network with convolutional layers in parallel within the U-Net architecture (KCUNet). This model keeps the spatial dimensions of the feature map constant to maintain high-resolution details while progressively increasing the number of channels to capture multi-level features at the encoding stage. In addition, KCUNet incorporates a content-guided attention mechanism to enhance edge information processing, which is crucial for DSE reduction and edge preservation. The model’s performance is optimized through a hybrid loss function that evaluates in several aspects, including edge alignment, mask prediction, and image quality. Finally, comparative evaluations against 15 state-of-the-art methods demonstrate KCUNet’s superior performance in both qualitative and quantitative analyses. Full article

The design of steel connections presents considerable complexity due to their inherently nonlinear behavior, cost constraints, and the necessity to comply with structural design codes. These factors highlight the need for advanced computational algorithms to identify optimal solutions. In this study, a comprehensive computational framework is presented in which the finite element method (FEM) is integrated with a genetic algorithm (GA) to optimize material usage in bolted steel end-plate joints, while structural safety is ensured based on multiple performance criteria. By incorporating both material and geometric nonlinearities, the mechanical response of the connections is accurately captured. The proposed approach is applied to a representative beam-to-column assembly, with numerical results verified against experimental data. By employing the framework, an optimized layout is obtained, yielding a $10.4\%$ improvement in the overall performance objective compared to the best-performing validated model and a $39.3\%$ reduction in material volume relative to the most efficient feasible alternative. Furthermore, a $53.6\%$ decrease in equivalent plastic strain is achieved compared to the configuration exhibiting the highest level of inelastic deformation. These findings demonstrate that the developed method is capable of enhancing design efficiency and precision, underscoring the potential of advanced computational tools in structural engineering applications. Full article

In this study, a Ag/ZrO₂ hybrid coating prepared by the sol–gel method on a β-type Ti45Nb alloy was applied by the spin coating technique, and the microstructural, mechanical, electrochemical, and tribological properties of the surface were evaluated in a multi-dimensional manner. The hybrid solution was prepared using zirconium propoxide and silver nitrate and stabilized through a low-temperature two-stage annealing protocol. The crystal structure of the coating was determined by XRD, and the presence of dense tetragonal ZrO₂ phase and crystalline Ag phases was confirmed. SEM-EDS analyses revealed a compact coating structure of approximately 1.8 µm thickness with homogeneously distributed Ag nanoparticles on the surface. As a result of the electrochemical corrosion tests, it was determined that the open circuit potential shifted to more noble values, the corrosion current density decreased, and the corrosion rate decreased by more than 70% on the surfaces where the Ag/ZrO₂ coating was applied. In the tribological tests, a decrease in the coefficient of friction, narrowing of wear marks, and significant reduction in surface damage were observed in dry and physiological (HBSS) environments. The findings revealed that the Ag/ZrO₂ hybrid coating significantly improved the surface performance of the Ti45Nb alloy both mechanically and electrochemically and offers high potential for biomedical implant applications. Full article

There are currently no approved therapeutic treatments targeting metabolic dysfunction-associated steatotic liver disease (MASLD). Albumin, a liver-produced plasma protein with anti-inflammatory and antioxidant properties, is reduced in advanced liver disease. Considering the role of chronic obesity-induced inflammation in MASLD pathogenesis, we investigated whether albumin administration could prevent disease progression to metabolic dysfunction-associated steatohepatitis (MASH). MASLD was induced in mice using a high-fat and high-cholesterol (PC) treatment for 8 weeks, followed by treatment with bovine serum albumin (BSA; 0.8 mg/kg) every three days for another 8 weeks. This regimen prevented time-dependent weight gain, regardless of diet, with 57% and 27% reductions in mice fed a standard chow (Std Chow) or PC diet, respectively. Further, supplementation reduced nuclear factor kappa B (NF-κB) activation by 2.8-fold (p = 0.0328) in PC-fed mice, consistent with albumin’s known anti-inflammatory properties. Unexpectedly, albumin also reduced hepatic neutral lipid accumulation and circulating non-esterified fatty acids. While PC-fed mice did not exhibit full progression to MASH, albumin treatment significantly increased hepatic matrix metalloproteinase-2 expression, suggesting the inhibition of early fibrotic signalling. While further studies are needed to elucidate the underlying mechanisms, these findings offer new insight into the potential of albumin, either alone or in combination with other therapies, to reduce hepatic steatosis in MASLD. Full article

Relaxation behavior of water confined in reverse micelles under temperature-induced micelle clustering is undertaken using broadband dielectric spectroscopy in frequency range 1 Hz–20 GHz. All microemulsion systems with sufficiently noticeable micelle water pool (water/surfactant molar ratio W > 10) depict three relaxation processes, in low, high and microwave frequencies, anchoring with relaxation of shell (bound) water, orientation of surfactant anions at water-surfactant interface and relaxation of bulk water confined in reverse micelles. The analysis of dielectric relaxation processes in AOT-based w/o microemulsions under temperature induced clustering of reverse micelles were made according to structural information obtained in NMR and conductometry experiments. The “wait and switch” relaxation mechanism was applied for the explanation of results for water in the bound and bulk states under spatial limitation in reverse micelles. It was shown that surfactant layer predominantly influences the bound water. The properties of water close to AOT interface are determined by strong interactions between water and ionic AOT molecules, which perturb water H-bonding network. The decrease in micelle size causes a weakening of hydrogen bonds, deformation of its steric network and reduction in co-operative relaxation effects. Full article

Honey bees (Apis mellifera ligustica) are essential pollinators in both ecosystems and agricultural production. However, their populations are declining due to various factors, including pesticide exposure. Despite their importance, the reproductive castes, particularly drones, remain understudied in terms of pesticide effects. To investigate the effects of azoxystrobin and picoxystrobin on honey bee drones, the drones were exposed to different concentrations of azoxystrobin and picoxystrobin for 14 days; the drone survival, body weight, nutrient content, reproductive organs, and sperm concentration were assessed. Results showed that exposure to both fungicides caused a significant reduction in drone survival rates, with survival rates decreasing progressively as the duration of exposure increased. Compared to the control group, the body weights of drones in all treatment groups were significantly lower on days 7 and 14. Nutrient analysis revealed that low concentrations of azoxystrobin and picoxystrobin increased protein levels, while free fatty acid content decreased significantly in all treatment groups. No significant changes were observed in the total carbohydrate content. Morphological examination of reproductive organs showed that the lengths of the mucus glands and seminal vesicles in drones were significantly shorter in the treatment groups compared to the control group. Furthermore, exposure to azoxystrobin and picoxystrobin resulted in a significant decline in sperm concentration in the drones. These findings indicate that azoxystrobin and picoxystrobin have adverse effects on the health and reproductive capacity of honey bee drones. The present study highlights the need to reassess the risks posed by these fungicides to pollinators, particularly given the critical role of drones in maintaining the genetic diversity and resilience of honey bee colonies. Further research is warranted to elucidate the underlying mechanisms of these effects and explore potential mitigation strategies. Full article

Marine biofouling significantly impacts the performance and longevity of polymer-based marine structures, particularly those designed for hydrodynamic applications such as Vortex-Induced Vibration suppression systems. Traditional antifouling solutions rely on copper-based multilayer coatings, which present challenges including mechanical vulnerability (e.g., chipping and scratching), high material and labor demands, and environmental concerns such as volatile organic compound emissions and copper leaching. Recent developments in material science have introduced an alternative system involving the direct incorporation of copper oxide (Cu₂O) into high-density polyethylene (HDPE) during the molding process. This study conducts a comparative life cycle assessment (LCA) of two antifouling integration methods—System 1 (traditional coating-based) and System 2 (Cu₂O-impregnated HDPE)—evaluating their environmental impact across production, application, use, and end-of-life stages. The functional unit used for this study was 1 square meter for a time period of five years. Using ISO 14040-compliant methodology and data from Ecoinvent and OpenLCA, three impact categories were assessed: global warming potential (GWP), cumulative energy demand (CED), and marine aquatic ecotoxicity Potential (MAETP). The results indicate that System 2 outperforms System 1 in GWP (4.42 vs. 5.65 kg CO₂-eq), CED (75.3 vs. 91.0 MJ-eq), and MAETP (327,002 vs. 469,929 kg 1,4-DCB-eq) per functional unit over a five-year lifespan, indicating a 21.8%, 17.3%, and 30.4% reduction in the key impact factors, respectively. These results suggest that direct Cu₂O incorporation offers a more environmentally sustainable and mechanically resilient antifouling strategy, supporting the potential of embedded antifouling systems to shift industry practices toward more sustainable marine infrastructure. Full article

The accurate detection of surface defects in bamboo is critical to maintaining product quality. Traditional inspection methods rely heavily on manual labor, making the manufacturing process labor-intensive and error-prone. To overcome these limitations, TEB-YOLO is introduced in this paper, a lightweight and efficient defect detection model based on YOLOv5s. Firstly, EfficientViT replaces the original YOLOv5s backbone, reducing the computational cost while improving feature extraction. Secondly, BiFPN is adopted in place of PANet to enhance multi-scale feature fusion and preserve detailed information. Thirdly, an Efficient Local Attention (ELA) mechanism is embedded in the backbone to strengthen local feature representation. Lastly, the original CIoU loss is replaced with EIoU loss to enhance localization precision. The proposed model achieves a precision of 91.7% with only 10.5 million parameters, marking a 5.4% accuracy improvement and a 22.9% reduction in parameters compared to YOLOv5s. Compared with other mainstream models including YOLOv5n, YOLOv7, YOLOv8n, YOLOv9t, and YOLOv9s, TEB-YOLO achieves precision improvements of 11.8%, 1.66%, 2.0%, 2.8%, and 1.1%, respectively. The experiment results show that TEB-YOLO significantly improves detection precision and model lightweighting, offering a practical and effective solution for real-time bamboo surface defect detection. Full article

Considering the importance of the automotive industry, reducing the environmental impact of automotive component manufacturing is crucial. Additionally, lightening of the latter promotes a reduction in fuel consumption throughout the vehicle’s life cycle, limiting emissions. This study presents a comprehensive approach to optimizing and manufacturing a MacPherson steering knuckle using topology optimization (TO), additive manufacturing, and rapid investment casting (RIC). Static structural simulations confirmed the mechanical integrity of the optimized design, with stress and strain values remaining within the elastic limits of the SG A536 iron alloy. The TO process achieved a 30% reduction in mass, resulting in lower material use and production costs. Additive manufacturing of optimized geometry reduced resin consumption by 27% and printing time by 9%. RIC simulations validated efficient mold filling and solidification, with porosity confined to removable riser regions. Life cycle assessment (LCA) demonstrated a 27% reduction in manufacturing environmental impact and a 31% decrease throughout the component life cycle, largely due to vehicle lightweighting. The findings highlight the potential of integrated TO and advanced manufacturing techniques to produce structurally efficient and environmentally sustainable automotive components. This workflow offers promising implications for broader industrial applications that aim to balance mechanical performance with ecological responsibility. Full article

This study investigates the mechanisms by which green finance mitigates non-point source pollution. Based on provincial panel data from China spanning 2005 to 2023, this study conducts an empirical analysis that yields several key findings: (1) The development of green finance significantly reduces the intensity of agricultural non-point source pollution. (2) Green finance indirectly contributes to pollution reduction by incentivizing farmers to adopt environmentally sustainable production practices. (3) The pollution control effects of green finance are amplified in regions with advanced digital infrastructure. (4) The impact of green finance on agricultural pollution demonstrates a threshold effect associated with regional innovation capacity—only when innovation capability exceeds a certain threshold does the emission reduction effect of green finance become evident. Theoretically, this study broadens the research dimensions of green finance by integrating farmer behavioral factors and revealing boundary conditions related to technology and innovation. Policy implications include the need to tailor green financial products for agriculture, accelerate the development of rural digital infrastructure, and implement innovation-driven differentiated policies to enhance precision. Full article

Long-term aging deteriorates asphalt’s self-healing capacity, yet the molecular mechanisms of bio-oil rejuvenation remain unclear. The fluidity and healing index of an asphalt binder were tested using a dynamic shear rheometer, and a healing model was established using molecular dynamics software to analyze the movement state. The results show that after adding the bio-oil, the healing index of aged asphalt increases significantly, lowering the optimal healing temperature by 10.1 °C. MD simulations demonstrate that bio-oil weakens van der Waals forces (with a 15.3% reduction in non-bonded energy) to enhance molecular diffusion, with a critical healing distance of 0.87 Å and aggregation at 1.11 Å. The bio-oil reduces the activation energy for healing from 4.97 kJ/mol (aged asphalt) to 3.75 kJ/mol. Molecular dynamics simulations can effectively aid scholars in understanding the asphalt healing process and movement patterns. Full article

Reconfigurable Intelligent Surface (RIS) has emerged as a promising enabling technology for wireless communications, which significantly enhances system performance through real-time manipulation of electromagnetic wave reflection characteristics. In RIS-assisted communication systems, existing deep learning-based channel state information (CSI) feedback methods often suffer from excessive parameter requirements and high computational complexity. To address this challenge, this paper proposes LwCSI-Net, a lightweight autoencoder network specifically designed for RIS-assisted multiple-input single-output (MISO) systems, aiming to achieve efficient and low-complexity CSI feedback. The core contribution of this work lies in an innovative lightweight feedback architecture that deeply integrates multi-layer convolutional neural networks (CNNs) with attention mechanisms. Specifically, the network employs 1D convolutional operations with unidirectional kernel sliding, which effectively reduces trainable parameters while maintaining robust feature-extraction capabilities. Furthermore, by incorporating an efficient channel attention (ECA) mechanism, the model dynamically allocates weights to different feature channels, thereby enhancing the capture of critical features. This approach not only improves network representational efficiency but also reduces redundant computations, leading to optimized computational complexity. Additionally, the proposed cross-channel residual block (CRBlock) establishes inter-channel information-exchange paths, strengthening feature fusion and ensuring outstanding stability and robustness under high compression ratio (CR) conditions. Our experimental results show that for CRs of 16, 32, and 64, LwCSI-Net significantly improves CSI reconstruction performance while maintaining fewer parameters and lower computational complexity, achieving an average complexity reduction of 35.63% compared to state-of-the-art (SOTA) CSI feedback autoencoder architectures. Full article

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article

The integration of renewable energy sources (RESs) into power systems, particularly in microgrids, is becoming a prominent trend aimed at reducing dependence on traditional energy sources. Replacing conventional synchronous generators with grid-connected RESs through power electronic converters has significantly reduced the inertia of microgrids. This reduction negatively impacts the dynamics and operational performance of microgrids when confronted with uncertainties, posing challenges to frequency and voltage stability, especially in a standalone operating mode. To address this issue, this research proposes enhancing microgrid stability through frequency control based on virtual inertia (VI). Additionally, the Iterative Learning Control (ILC) method is employed, leveraging iterative learning strategies to improve the quality of output response control. Accordingly, the ILC-VI control method is introduced, integrating the iterative learning mechanism into the virtual inertia controller to simultaneously enhance the system’s inertia and damping coefficient, thereby improving frequency stability under varying operating conditions. The effectiveness of the ILC-VI method is evaluated in comparison with the conventional VI (C-VI) control method through simulations conducted on the MATLAB/Simulink platform. Simulation results demonstrate that the ILC-VI method significantly reduces the frequency nadir, the rate of change of frequency (RoCoF), and steady-state error across iterations, while also enhancing the system’s robustness against substantial variations from renewable energy sources. Furthermore, this study analyzes the effects of varying virtual inertia values, shedding light on their role in influencing response quality and convergence speed. This research underscores the potential of the ILC-VI control method in providing effective support for low-inertia microgrids. Full article