Search Results (60,506)

Magnetorheological fluids (MRFs) are multiphase materials whose viscosity can be controlled via magnetic fields. However, particle sedimentation undermines their long-term stability. This review examines stabilization strategies based on the interaction between ultrasonic waves and time-varying magnetic fields, analyzed through advanced mathematical models. The propagation of acoustic waves in spherical and cylindrical domains is studied, including effects such as cavitation, acoustic radiation forces, and viscous attenuation. The Biot–Stoll poroelastic model is employed to describe saturated granular media, while magnetic field modulation is investigated as a means to balance gravitational settling. The analysis highlights how acousto-magnetic coupling supports the design of programmable and self-stabilizing intelligent fluids for complex applications. Full article

Nonlinear wind-induced vibrations and coupled static–dynamic instabilities pose significant challenges for long-span suspension bridges, especially under large-amplitude and high-angle-of-attack conditions. However, existing studies have yet to fully capture the mechanisms behind large-amplitude torsional flutter. To address this, wind tunnel experiments were performed on H-shaped bluff sections and closed box girders using a high-precision five-component piezoelectric balance combined with a custom support system. Complementing these experiments, a finite element time-domain simulation framework was developed, incorporating experimentally derived nonlinear flutter derivatives. Validation was achieved through aeroelastic testing of a 1:110-scale model of the original Tacoma Narrows Bridge and corresponding numerical simulations. The results revealed Hopf bifurcation phenomena in H-shaped bluff sections, indicated by amplitude-dependent flutter derivatives and equivalent damping coefficients. The simulation results showed less than a 10% deviation from experimental and historical wind speed–amplitude data, confirming the model’s accuracy. Failure analysis identified suspenders as the critical failure components in the Tacoma collapse. This work develops a comprehensive performance-based design framework that improves the safety, robustness, and resilience of long-span suspension bridges against complex nonlinear aerodynamic effects while enabling cost-effective, targeted reinforcement strategies to advance modern bridge engineering. Full article

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article

Electromigration (EM), current-driven atomic diffusion in interconnect metals, critically threatens integrated circuit (IC) reliability via void-induced open circuits and hillock-induced short circuits. This review examines EM’s physical mechanisms, influencing factors, and advanced models, synthesizing seven primary determinants: current density, temperature, material properties, microstructure, geometry, pulsed current, and mechanical stress. It dissects the coupled contributions of electron wind force (dominant EM driver), thermomigration (TM), and stress migration (SM). The review assesses four foundational modeling frameworks: Black’s model, Blech’s criterion, atomic flux divergence (AFD), and Korhonen’s theory. Despite advances in multi-physics simulation and statistical EM analysis, achieving predictive full-chip assessment remains computationally challenging. Emerging research prioritizes the following: (i) model order reduction methods and machine-learning solvers for verification of EM in billion-scale interconnect networks; and (ii) physics-informed routing optimization to inherently eliminate EM violations during physical design. Both are crucial for addressing reliability barriers in IC technologies and 3D heterogeneous integration. Full article

The bridge deck is more vulnerable to deterioration than other structural components. This is due to its direct exposure to environmental factors such as vehicular loads, chloride ingress, and freeze–thaw cycles. The resulting accelerated degradation often results in a serviceability life that is shorter than the intended design life. However, the absence of standardized condition assessment methods coupled with clear definitions of remaining service life has limited the establishment of rational guidelines for repair and strengthening. In a bid to address this lack, this study focuses on PSC-I type bridges in South Korea, utilizing long-term field inspection data to analyze environmental, structural, and material factors—including reinforcement corrosion, chloride diffusion, and freeze–thaw actions. Environmental zoning was applied based on regional conditions, while structural zoning was performed according to load characteristics, thereby allowing the classification of deck regions into moment zones and cantilever sections. Machine learning models were employed to identify dominant deterioration mechanisms, with the validity of the zoning classification being evaluated via model accuracy and SHAP value analysis. Additionally, a regression-based approach was proposed to estimate the remaining service life of the bridge deck for each corrosion phase, thereby providing a quantitative framework for durability assessment and maintenance planning. Full article

Soil environmental quality in arid oases is crucial for regional ecological security but faces multi-source heavy metal (HM) contamination risks. This study aimed to (1) characterize the spatial distribution of soil HMs (As, Cd, Cr, Cu, Hg, and Zn) in the Ka Shi gar oasis, Xinjiang, (2) quantify the driving effect of irrigation water, and (3) elucidate interactions between HMs, soil properties, and land use types. Using 591 soil and 12 irrigation water samples, spatial patterns were mapped via inverse distance weighting interpolation, with drivers and interactions analyzed through correlation and land use comparisons. Results revealed significant spatial heterogeneity in HMs with no consistent regional trend: As peaked in arable land (5.27–40.20 μg/g) influenced by parent material and agriculture, Cd posed high ecological risk in gardens (max 0.29 μg/g), and Zn reached exceptional levels (412.00 μg/g) in gardens linked to industry/fertilizers. Irrigation water impacts were HM-specific: water contributed to soil As enrichment, whereas high water Cr did not elevate soil Cr (indicating industrial dominance), and Cd/Cu showed no significant link. Interactions with soil properties were regulated by land use: in arable land, As correlated positively with EC/TN and negatively with pH; in gardens, HMs generally decreased with pH, enhancing mobility risk; in forests, SOM adsorption immobilized HMs; in construction land, Hg correlated with SOM/TP, suggesting industrial-organic synergy. This study advances understanding by demonstrating that HM enrichment arises from natural and anthropogenic factors, with the spatial heterogeneity of irrigation water’s driving effect critically regulated by land use type, providing a spatially explicit basis for targeted pollution control and sustainable oasis management. Full article

The Amazon Basin plays a crucial role in the global hydrological cycle, where seasonal and interannual variations in terrestrial water storage (TWS) are essential for understanding climate–hydrology coupling mechanisms. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow-on mission (GRACE-FO, collectively referred to as GRACE) to investigate the spatiotemporal dynamics of hydrological mass changes in the Amazon Basin from 2002 to 2021. Results reveal pronounced spatial heterogeneity in the annual amplitude of TWS, exceeding 65 cm near the Amazon River and decreasing to less than 25 cm in peripheral mountainous regions. This distribution likely reflects the interplay between precipitation and topography. Vertical displacement measurements from the Global Navigation Satellite System (GNSS) show strong correlations with GRACE-derived hydrological load deformation (mean Pearson correlation coefficient = 0.72) and reduce its root mean square (RMS) by 35%. Furthermore, the study demonstrates that existing hydrological models, which neglect groundwater dynamics, underestimate hydrological load deformation. Principal component analysis (PCA) of the Amazon GNSS network demonstrates that the first principal component (PC) of GNSS vertical displacement aligns with abrupt interannual TWS fluctuations identified by GRACE during 2010–2011, 2011–2012, 2013–2014, 2015–2016, and 2020–2021. These fluctuations coincide with extreme precipitation events associated with the El Niño–Southern Oscillation (ENSO), confirming that ENSO modulates basin-scale interannual hydrological variability primarily through precipitation anomalies. This study provides new insights for predicting extreme hydrological events under climate warming and offers a methodological framework applicable to other critical global hydrological regions. Full article

Apple-detection performance in orchards degrades markedly under low-light conditions, where intensified noise and non-uniform exposure blur edge cues critical for precise localisation. We propose Knowledge Distillation with Geometry-Consistent Feature Alignment (KDFA), a compact end-to-end framework that couples image enhancement and detection through the following two complementary components: (i) Cross-Domain Mutual-Information-Bound Knowledge Distillation, which maximises an InfoNCE lower bound between daylight-teacher and low-light-student region embeddings; (ii) Geometry-Consistent Feature Alignment, which imposes Laplacian smoothness and bipartite graph correspondences across multiscale feature lattices. Trained on 1200 pixel-aligned bright/low-light image pairs, KDFA achieves 51.3% mean Average Precision (mAPQ [0.50:0.95]) on a challenging low-light apple-detection benchmark, setting a new state of the art by simultaneously bridging the illumination-domain gap and preserving geometric consistency. Full article

The increasing shortage of drinking water, driven by reduced rainfall and the intensification of industrial and agricultural activities, has raised justified concerns about the quantity and quality of available water resources. These sectors not only demand high water consumption but also discharge large amounts of toxic substances such as organic matter, metal ions and inorganic anions, posing risks to both public health and the environment. This study evaluated the effectiveness of clay-based nanomaterials in the treatment of contaminated industrial wastewater from the mining sector. The materials tested included montmorillonite, high-loading expandable synthetic mica, and their organically functionalized forms (MMT, Mica-Na-4, C18-MMT, and C18-Mica-4). The experimental results show that these clays had minimal impact on the pH of the water, while a notable decrease in the chemical oxygen demand (COD) was observed. Ion chromatography indicated an increase in nitrogen and sulfur compounds with higher oxidation states. Inductively coupled plasma analysis revealed a significant reduction in the calcium concentration and an increase in the sodium concentration, likely due to cation exchange mechanisms. However, the removal of copper and iron was ineffective, possibly due to competitive interactions with other cations in the solution. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) confirmed the structural modifications and interlayer spacing changes in the clay materials upon exposure to contaminated water. These findings demonstrate the potential of clay minerals as effective and low-cost materials for the remediation of industrial wastewater. Full article

This research contributes to advance the sustainable production of biofuels and provides insights into the energy and exergy assessment of bio-oil, which is essential for developing environmentally friendly energy production solutions. Energy and exergy analyses were performed to evaluate the pyrolysis of beech wood biomass at 500 °C in an Auger reactor. To improve the quality of the obtained bio-oil, its catalytic deoxygenation was performed within an in-line fluidized catalytic bed reactor using a catalyst based on HZSM5 zeolite modified with 5 wt.% Iron (5%FeHZSM-5). A thermodynamic analysis of the catalytic and non-catalytic pyrolysis system was carried out, as well as a comparative study of the calculation methods for the energy and exergy evaluation for bio-oil. The required heat for pyrolysis was found to be 1.2 MJ/kg_biomass in the case of non-catalytic treatment and 3.46 MJ/kg_biomass in the presence of the zeolite-based catalyst. The exergy efficiency in the Auger reactor was 90.3%. Using the catalytic system coupled to the Auger reactor, this efficiency increased to 91.6%, leading to less energy degradation. Calculating the total energy and total exergy of the bio-oil using two different methods showed a difference of 6%. In the first method, only the energy contributions of the model compounds, corresponding to the major compounds of each chemical family of bio-oil, were considered. In contrast, in the second method, all molecules identified in the bio-oil were considered for the calculation. The second method proved to be more suitable for thermodynamic analysis. The novelties of this work concern the thermodynamic analysis of a coupled system of an Auger biomass pyrolysis reactor and a fluidized bed catalytic deoxygenation reactor on the one hand, and the use of all the molecules identified in the oily phase for the evaluation of energy and exergy on the other hand. Full article

The anisotropic nature of cup ears formed during the deep drawing of sheet metals is governed by the distribution of crystallographic orientation in interaction between earing. In this study, we examined the orientation development of a cube-oriented aluminum single crystal to couple the deep drawing kinematics with the formation of anisotropic orientations. A quarter model of a circular deep-drawn blank was simulated in the finite element software using a user-defined material subroutine. A cube-oriented aluminum single crystal was designed to serve as a reference and trace the orientation evolution in the deep drawing process. After the deep drawing, the bottom, wall, and flange of the drawn cup were investigated at azimuthal angles ($\alpha$ ) of 0° and 45° with respect to the radial direction (RD) in terms of the orientation. Our findings show that the change in the lattice orientation could be attributed to the rotation induced by drawing and bending processes under kinematic constraints. Thus, the initial cube orientation developed into different orientations during the deep drawing. The type-A slip system mainly contributed to the radial strain at α = 0°, and type-B and C slip systems accounted for the longitudinal and circumferential strains at α = 45°. Full article

Satellite sea surface salinity (SSS) products suffer from coarse spatiotemporal resolution, limiting their utility for mesoscale ocean monitoring. To address this, we proposed the Transformer-based satellite SSS super-resolution (SR) model (TSR) coupled with a progressive transfer learning (PTL) strategy. TSR improved the resolution of the salinity satellite SMOS from 1/4° and 10 days to 1/12° and daily. Leveraging Transformer, TSR captured long-range dependencies critical for reconstructing fine-scale structures. PTL effectively balanced structural detail acquisition and local accuracy correction by combining the gridded reanalysis products with scattered in situ observations as training labels. Validated against independent in situ measurements, TSR outperformed existing L3 salinity satellite products, as well as convolutional neural network and generative adversarial network-based SR models, particularly reducing the root mean square error (RMSE) by 33% and the mean bias (MB) by 81% compared to the SMOS input. More importantly, TSR demonstrated an enhanced capability in resolving mesoscale eddies, which were previously obscured by noise in salinity satellite products. Compared to training with a single label type or switching label types non-progressively, PTL achieved a 3%–66% lower RMSE and a 73–92% lower MB. TSR enables higher-resolution satellite monitoring of SSS, contributing to the study of ocean dynamics and climate change. Full article

The switching characteristics of GaN HEMT devices exhibit a very complex dynamic nonlinear behavior and multi-physics coupling characteristics, and traditional switching current models based on physical mechanisms have significant limitations. This article adopts a hybrid architecture of convolutional neural network and long short-term memory network (CNN-LSTM). In the 1D-CNN layer, the one-dimensional convolutional neural network can automatically learn and extract local transient features of time series data by sliding convolution operations on time series data through its convolution kernel, making these local transient features present a specific form in the local time window. In the double-layer LSTM layer, the neural network model captures the transient characteristics of switch current through the gating mechanism and state transfer. The hybrid architecture of the constructed model has significant advantages in accuracy, with metrics such as root mean square error (RMSE) and mean absolute error (MAE) significantly reduced, compared to traditional switch current models, solving the problem of insufficient accuracy in traditional models. The neural network model has good fitting performance at both room and high temperatures, with an average coefficient close to 1. The new neural network hybrid architecture has short running time and low computational resource consumption, meeting the needs of practical applications. Full article

As coal mining progresses to greater depths, the complex geological conditions significantly increase the risk of compound disasters. With increasing mining depth, elevated ground stress and gas pressure exacerbate the coupling effects of rockburst and gas outburst. This study employs laboratory tests and theoretical analysis to investigate gas disasters under varying gas and confining pressures. The experimental results are analyzed in terms of mechanical parameters, crack propagation, and acoustic emission (AE) time–frequency evolution. Under conventional compression, coal failure exhibits shear damage with axial splitting or debris ejection. The peak strength demonstrates a clear confining pressure strengthening effect and gas pressure weakening effect. At constant gas pressure, the elastic modulus increases with confining pressure, whereas at constant confining pressure, it decreases with rising gas pressure. Full article

Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of built-up areas to heavy-day rainfall (HDR) across Jammu, Kashmir, and Ladakh and the adjoining areas by integrating daily Climate Hazards Group InfraRed Precipitation with Stations product (CHIRPS) precipitation (0.05°) with Global Human Settlement Layer (GHSL) built-up fractions within the Google Earth Engine (GEE). Given the limited sub-hourly observations, a daily threshold of ≥100 mm was adopted as a proxy for HDR, with sensitivity evaluated at alternative thresholds. The results showed that HDR is strongly clustered along the Kashmir Valley and the Pir Panjal flank, as demonstrated by the mean annual count of threshold-exceeding pixels increasing from 12 yr⁻¹ (2000–2010) to 18 yr⁻¹ (2011–2020), with two pixel-scale hotspots recurring southwest of Srinagar and near Baramulla regions. The cumulative high-intensity areas covered 31,555.26 km², whereas 37,897.04 km² of adjacent terrain registered no HDR events. Within this hazard belt, the exposed built-up area increased from 45 km² in 2000 to 72 km² in 2020, totaling 828 km². The years with the most expansive rainfall footprints, 344 km² (2010), 520 km² (2012), and 650 km² (2014), coincided with heavy Western Disturbances (WDs) and locally vigorous convection, producing the largest exposure increments. We also performed a forecast using a univariate long short-term memory (LSTM), outperforming Autoregressive Integrated Moving Average (ARIMA) and linear baselines on a 2017–2020 holdout (Root Mean Square Error, RMSE 0.82 km²; measure of errors, MAE 0.65 km²; R² 0.89), projecting the annual built-up area intersecting HDR to increase from ~320 km² (2021) to ~420 km² (2030); 95% prediction intervals widened from ±6 to ±11 km² and remained above the historical median (~70 km²). In the absence of a long-term increase in total annual precipitation, the projected rise most likely reflects continued urban encroachment into recurrent high-intensity zones. The resulting spatial masks and exposure trajectories provide operational evidence to guide zoning, drainage design, and early warning protocols in the region. Full article