Search Results (162)

The bond strength of corroded reinforced concrete (CRC) structures is critical for structural safety and long-term durability. However, the corrosion-induced bond degradation process is influenced by multiple, coupled factors and exhibits complex, nonlinear behavior, making it difficult for traditional theoretical models to provide accurate predictions. To address this challenge, this study proposes a novel, unified prediction framework based on machine learning techniques. A total of 391 experimental datasets were collected and compiled, covering key parameters including bond strength, reinforcing bar diameter, yield strength, concrete cover thickness, concrete compressive strength, mass loss rate due to corrosion, and the presence of stirrups. Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms were employed to develop predictive models for bond strength. Model training and testing were performed using 10-fold cross-validation. Furthermore, the SHapley Additive exPlanations (SHAP) approach was introduced to enhance model interpretability and quantitatively assess the influence of each input feature, revealing that mass loss rate and bar diameter are the dominant factors. This study effectively bridges the research gap between high-precision black-box algorithms and the need for physical interpretability in engineering. The results demonstrate that (1) the proposed XGBoost model significantly outperforms traditional empirical formulations, achieving a high coefficient of determination (R² = 0.893) and a much lower coefficient of variation (25.85%) on the testing set, and (2) the SHAP analysis reveals that the machine learning predictions are highly consistent with established physical mechanisms, successfully capturing the negative impact of splitting tensile stresses caused by rust expansion and the positive confinement effect of stirrups. Overall, the proposed models demonstrate superior accuracy, robustness, and generalization capability, providing an effective tool and theoretical basis for evaluating bond behavior and designing durable CRC structures with broad engineering applicability. Full article

The friction size effect in thin-sheet microforming constrains the attainable forming quality of microscale sheet components. In this study, T2 copper foils with thicknesses of 0.04, 0.08, 0.16, and 0.32 mm were investigated by comparative tensile testing, pin-on-disk testing, sliding-friction experiments, surface characterization, and reduced-order analysis under dry friction and three liquid-lubrication conditions. The results showed that, as the thickness decreased from 0.32 mm to 0.04 mm, elongation and tensile strength decreased by nearly 60% and 40%, respectively, whereas the direct contribution of the mechanical size effect to the friction coefficient remained limited. Under dry friction, the friction coefficient changed little with specimen size. Under soybean oil, castor oil, and Vaseline lubrication, however, the friction coefficient increased markedly as specimen size decreased and gradually approached the dry-friction value; the lowest-viscosity lubricant exhibited the greatest loss of effectiveness at small scales. This behavior was associated with the expansion of the edge non-lubricated region and the loss of closed lubricant pockets, both of which increased the real contact area. On this basis, a size-dependent friction model was established for the present material and surface conditions, and its prediction for the castor oil case was consistent with the experimental trend. Full article

Urbanisation reshapes Land Cover and Land Use (LCLU) by driving deforestation, wetland loss, and the conversion of natural and agricultural areas into built environments. However, integrated analyses of LCLU change in response to climate variability in topographically complex, rapidly urbanising African cities remain limited. Therefore, this study examined 2000–2024 LCLU changes in hilly Gasabo District (Kigali, Rwanda) using 30 m Landsat imagery and a Random Trees classifier (92.7% accuracy, 70/30 train-test split), with 2032 projections via a population-driven hybrid trend model. Population estimates/projections 320,516 in 2002 to 967,512 in 2024, 1.41 million by 2032, were derived from Rwanda’s census data and exponential growth modelling (calibrated to 5.05% annual growth). Rapid population growth has driven a 539% expansion of Built-up Areas, accompanied by notable declines in cropland and Forest. Local climate trends (Meteo Rwanda stations) aligned with global datasets (ERA5-Land and CHIRPS): rainfall fluctuation and temperature rose, with strong correlations between population-driven Built-up Areas expansion. From 2024 to 2032, LCLU projections indicate that Built-up Areas will continue to expand by 29.5%. Cropland was forecast to decline to 15.9%, while Forest loss slowed to 5.7%. MLR analysis revealed strong correlations between population-driven expansion of Built-up Areas, cropland/forest loss, warming, and rainfall fluctuations in Gasabo. An ARDL model was applied to address multicollinearity among LCLU predictors, which limited the interpretation of individual coefficients, and confirmed the core MLR correlation trends, with statistically significant (p < 0.05) coefficients. The results highlight the need for data-driven spatial planning in Gasabo (stricter zoning, high-rise buildings, targeted reforestation, climate-resilient green infrastructure) to mitigate population and urbanisation-driven environmental degradation. Full article

Rapid urban expansion and the growing spatial requirements of utility-scale photovoltaic (PV) deployment compete for the same category of land—flat, accessible, and high-insolation terrain—yet the scale, trajectory, and planning-sensitivity of this conflict remain poorly characterised at the regional level. This study quantifies the spatiotemporal competition between urban construction land and PV-suitable land in the Yangtze River Delta (YRD) from 2000 to 2020 and projects its evolution to 2030 under three development scenarios. Built-up areas were extracted for three epochs using a Random Forest (RF) classifier on the Google Earth Engine (GEE) platform, achieving overall accuracies of 87.7–94.5% and Kappa coefficients of 0.718–0.739. PV site suitability was evaluated through a hybrid Multi-Criteria Decision Analysis (MCDA) framework combining Boolean exclusion constraints with an Analytic Hierarchy Process (AHP)-based Weighted Linear Combination model; the weight structure was validated by a Consistency Ratio of 0.006, and a One-At-a-Time sensitivity analysis confirmed spatial robustness across threshold scenarios. Spatial overlay analysis reveals that the cumulative area of PV-suitable land occupied by urban built-up uses grew from 15,862 km² in 2000 to 23,872 km² in 2020, representing an incremental loss of 8010 km² over two decades. Future conflict was projected using the PLUS model, calibrated on 2010–2020 observed expansion and validated against the 2020 classified map (OA = 93.99%, Kappa = 0.91). Under the Business-as-Usual (BAU) scenario, 33,368 km² of currently open PV-suitable land faces urban encroachment by 2030; the Ecological Conservation Priority (ECP) scenario reduces this figure to approximately 30,767 km², while the Economic Development (ED) scenario yields a near-identical outcome to BAU, indicating that development velocity alone does not determine the spatial extent of conflict—the allocation of growth does. These findings provide a quantitative basis for designating energy-strategic reserve zones within national spatial planning frameworks and demonstrate that targeted spatial governance, applied at high-pressure locations, can substantially slow the erosion of the region’s solar energy land base. Full article

To facilitate the development of low-cost LTCC substrate materials and the high-value utilization of industrial tailings, α-cordierite glass-ceramics with varying Na₂O additions were prepared from perlite tailings as the main raw material via the melt-quenching method followed by sintering-induced crystallization. The synergistic effects of sintering temperature and Na₂O addition on the parent glass structure, crystallization behavior, and properties were systematically investigated. The results demonstrated that the addition of Na₂O effectively depolymerized the degree of network polymerization of the parent glass, altered the crystallization pathway of cordierite crystal, and promoted the densification of glass-ceramics at lower sintering temperature. The calculations of crystallization kinetics revealed that the crystallization process of α-cordierite was mainly dominated by three-dimensional bulk growth, and its nucleation mechanism changed from “site saturation” to “continuous nucleation” with the increase of Na₂O addition. The α-cordierite glass-ceramics sintered at 850 °C with 0.6 wt.% Na₂O addition exhibited the optimal comprehensive properties, including low dielectric constant (5.82 @ 10 MHz) and dielectric loss (1.80 × 10⁻² @ 10 MHz), high flexural strength (147.3 MPa), a Vickers hardness (9.01 GPa), and suitable coefficient of thermal expansion (2.96 × 10⁻⁶ K⁻¹, close to Si). The glass-ceramics are expected to be an ideal candidate for low-cost LTCC substrate materials. Full article

Polymer–ceramic hybrid composites are emerging as attractive candidates for lightweight, corrosion-resistant absorber components in solar thermal collectors; however, their adoption is constrained by the intrinsically low thermal conductivity of polymers, processing-induced anisotropic heat transport, interfacial thermal resistance at tube/laminate joints, and durability challenges under outdoor exposure. This review provides a collector-centered synthesis of polymer–ceramic hybrid materials, emphasizing the translation of composite properties into collector-level outcomes rather than conductivity enhancement alone. A structure–property–performance mapping approach is presented to connect directional thermal conductivity ((k_in-plane), (k_perp)), thermal diffusivity, heat capacity, coefficient of thermal expansion, and service temperature with collector performance parameters such as heat removal effectiveness, overall heat losses, and stagnation behavior. Ceramic fillers (e.g., boron nitride, aluminum nitride, silicon carbide, alumina) are examined for stable conduction-network formation, coating compatibility, and long-term reliability, while carbon fillers (graphite, graphene nanoplatelets, carbon nanotubes) are evaluated for combined heat spreading and solar absorption benefits, with attention to emissivity penalties. Hybrid ceramic–carbon architectures and multilayer absorber designs are identified as the most promising routes to balance thermal transport, optical selectivity (high solar absorptance and low thermal emittance), manufacturability, and durability under UV, humidity, and thermal cycling. Full article

The main purpose of this study was to investigate the effects of 10, 20, and 40 wt% micron-sized particles (aluminum nitride, aluminum oxide, silicon nitride, and silicon oxide) on the thermal performance of an epoxy resin used in microelectronic devices. Specimens were produced via a solution mixing technique followed by molding and curing. Although there were slight differences between the particle types used, various thermal analyses revealed that increasing the amount of all particle types significantly improved the thermal performance of the epoxy resin. The property that influences the thermal performance of microelectronic devices the most is thermal conductivity (λ). Heat produced during operation should be released via heat diffusion, which requires a certain level of λ. In this study, the use of a 40 wt% particle content increased the thermal conductivity (λ) by more than 3 times compared to neat epoxy (0.15 W/m·K). Another significant problem during the operation of these devices is the formation of “thermal strain mismatch” due to the different thermal expansion coefficients (α) of the materials used in the device that might lead to a loss of dimensional stability and malfunctioning. In this study, a particle content of 40 wt% decreased the thermal expansion coefficient of epoxy (49 × 10⁻⁶/K) down to 28 × 10⁻⁶/K, a decrease of −43%. Thermal performance also depends on the Glass Transition Temperature (T_g) values. In this study, a particle content of 40 wt% increased the T_g from 51 °C (neat epoxy) to 68 °C, an increase of 17 °C, and increased the Thermal Degradation Temperature (T_d) from 324 °C (neat epoxy) to 356 °C, an increase of 32 °C. Moreover, it was also revealed that there was no decrease in the lap shear adhesion strength of the epoxy resin after incorporation of any of the particle types. Additionally, the particles also increased the mechanical rigidity of the epoxy in terms of Storage Modulus at 25 °C and 50 °C. Full article

Liquid crystalline poly(ester imide)s (LCPEIs) were synthesized by solution polymerization from 4-hydroxybenzoic acid (4-HBA), 6-hydroxy-2-naphthoic acid (HNA) and N-(3-carboxyphenyl)-4-hydroxyphthalimide (3-CHP), with the capping groups of benzocyclobutene (BCB)-containing compounds (BCB-HP for phenolic hydroxyl group and BCB-CP for aromatic carboxylic acid). Subsequent cross-linking of the BCB capping groups upon hot pressing afforded the cured LCPEI films. Optimal properties of these films were achieved by adjusting the capping BCB-HP/BCB-CP contents.These LCPEIs showed favorable thermal properties with a relatively high glass transition temperature (T_g, 137–167 °C) and low melting temperature (T_m, 186–194 °C). With the increase in BCB capping content, the tensile modulus, tensile strength, and coefficient of thermal expansion (CTE) exhibited a non-linear tendency of first decreasing and then increasing. LCPEI-3.0 (3 mol% BCB) showed optimal performance: a relatively low CTE (20 × 10⁻⁶ K⁻¹), a relatively high storage modulus (2.55 GPa), a moderate tensile modulus (2.65 GPa), a relatively low dielectric constant (Dk = 3.17) with low dielectric loss (Df = 0.0034) at 10 GHz, and excellent hydrophobicity (water contact angle = 133°). This improvement embodies an effective strategy to combine advantages of polyester, polyimide, and benzocyclobutene to achieve favorable and excellent comprehensive properties for convenient processability and practical application prospects. Full article

Age-related macular degeneration (AMD) is the leading cause of central vision loss in aging populations. Geographic atrophy (GA) is the advanced, non-neovascular form of AMD. Predicting the longitudinal progression of GA remains a critical challenge in ophthalmic clinical practice and clinical trial design. Forecasting the trajectory of GA is complicated by highly variable growth rates and the inherent scarcity of long-term, high-quality imaging data. To address these challenges, we introduce the Sliding Window Attention U-Net (SWAU-Net), a hybrid architecture that integrates Transformer-based temporal modeling of GA growth with precise spatial modeling of GA location with a U-Net convolutional neural network (CNN). To ensure generalization in the low-data regime, SWAU-Net embeds explicit temporal and geometric consistency priors via a weight-shared Sliding Window Attention core and feature-level regularization that preserves sparse, high-frequency lesion boundaries across frames. Experimental results demonstrate that these structural constraints prevent the model from overfitting to imaging noise, achieving a Growth Mask Dice Similarity Coefficient (DSC) of 0.66 (representing the spatial overlap between the predicted and ground truth lesion expansion regions), a significant improvement over unregularized Transformer and standard recurrent baseline models. Our framework provides a robust tool for predicting GA lesion trajectories, potentially supporting more efficient clinical trial designs and personalized patient monitoring. Full article

To address the high bending stresses and potential structural failure risks caused by differential settlement at expansion joints during bridge widening projects of straight bridges, this paper proposes an “Adaptive Rebound Displacement Compensation Device”. Existing research primarily focuses on analyzing settlement patterns and passive control standards, with limited attention to active dynamic regulation. Notably, the bending stress induced by new pier settlements can reach 3–5 times that of vehicle loads, posing serious safety concerns. Through theoretical derivation, this study clarifies the relationship between superstructure loss of strength and factors such as pier settlement, device stiffness, friction coefficient, and L-shaped baffle angle, and a comprehensive design framework is established accordingly. Combining numerical simulations, laboratory tests, and field measurements from engineering practices, multiple validation approaches are employed. The simulation results demonstrate that the proposed device can limit deck subsidence to 10–20% of pier settlement height, and experimental outcomes align closely with theoretical predictions. This device has been successfully implemented in a bridge widening project on a highway section in Jiangxi Province. It should be noted that all data presented in the paper are derived from finite element method (FEM) numerical simulations, and there are currently no on-site measurements of the device’s performance. FEM analysis indicates that the device demonstrates certain feasibility for practical engineering applications. Compared to scenarios without the installation of this device, bridge deck displacements can be reduced by approximately 16.5%. By enabling adaptive rebound through self-adjustment mechanisms for settlement compensation, this device significantly alleviates bending stresses at expansion joints, breaking through traditional passive control limitations. This study provides an innovative approach for actively controlling settlement differences in the widening of straight bridges, offering significant implications both at the theoretical and practical levels. Full article

Sabina vulgaris is a keystone shrub species endemic to arid northwestern China, renowned for its exceptional drought tolerance, sand fixation capabilities, and critical role in desert ecosystem stability. This study investigates the impact of coal mining activities on the spatiotemporal dynamics of S. vulgaris shrublands in the ecologically fragile Mu Us Sandy Land, focusing on the Longde Coal Mine adjacent to the Shenmu S. vulgaris Nature Reserve. Utilizing seven periods (2013–2025) of 2 m resolution Gaofen-1 (GF-1) satellite imagery spanning 12 years of mining operations, we implemented a deep learning approach combining UAV-derived hyperspectral ground truth data and the SegU-Net semantic segmentation model to map shrub distribution via GF-1 data with high precision. Classification accuracy was rigorously validated through confusion matrix analysis (incorporating the Kappa coefficient and overall accuracy metrics). Results reveal contrasting trends: while the S. vulgaris Protection Area exhibited substantial expansion (e.g., Southern Section coverage grew from 2.6 km² in 2013 to 7.88 km² in 2025), mining panels experienced significant degradation. Within Panel 202, coverage declined by 15.4% (58.4 km² to 49.5 km²), and Panel 203 showed a 18.5% decrease (3.16 km² to 2.57 km²) over the study period. These losses correlate spatially and temporally with mining-induced groundwater depletion and land subsidence, disrupting the shrub’s shallow-root water access strategy. The study demonstrates that coal mining drives fragmentation and coverage reduction in S. vulgaris communities through mechanisms including (1) direct vegetation destruction, (2) aquifer disruption impairing drought adaptation, and (3) habitat fragmentation. These findings underscore the necessity for targeted ecological restoration strategies integrating groundwater management and progressive reclamation in mining-affected arid regions. Full article

To meet the requirement of thermal management of modern electronic devices, polymer composites with high thermal conductivity (TC) and dielectric performance are nowadays in urgent demand. Herein, a highly ordered continuous network of boron nitride nano-sheet (BNNS) was constructed in cyclic olefin polymer (COP) films via the forced flow processing in the rubbery state (FFRS), melt-spinning, fiber-alignment, and hot-pressing procedures. The composites exhibited superior TC, low dielectric permittivity, and low dielectric loss simultaneously. The in-plane TC of the composites reached 3.92 W/(mK) when the content of BNNS was at 27 weight percentage (27 wt%), since the procedures improved the face-to-face contact between the BNNS (which was exfoliated, dispersed, and in-plane oriented during FFRS), enhancing the continuity of the BNNS thermally conductive network. Both the TC and the experimental results indicated the outstanding heat dissipation performance of the composites. Meanwhile, the dielectric permittivity and dielectric loss of the 27 wt% BNNS composites were 2.56 and 0.00085 at 10 GHz, respectively, lower than that of the COP-POE matrix. Moreover, the mechanical properties, water vapor permeability, and coefficient of thermal expansion of the composites were excellent. The composites with such highly ordered continuous networks are very promising in high-performance electronic devices. Full article

The phase-pure cubic pyrochlore of the Bi₂Cr_0.5Co_0.5Nb₂O_9+Δ composition can be successfully synthesized by a modified sol–gel method (Pecini method-PM) and a traditional solid-phase method (SPM). A feature of the solid-phase synthesis method is the formation of bismuth(VI) chromates as intermediate synthesis products, which is confirmed by X-ray spectroscopy data (NEXAFS). During the sol–gel synthesis method, bismuth chromates are not formed due to the formation of the Bi₂₈O₃₂(SO₄)₁₀ salt, which is thermally stable up to 880 °C, preventing the interaction of bismuth(III) and chromium(III) oxides. The temperature of the final pyrochlore calcination during sol–gel synthesis is reduced by 100 °C (950 °C) compared to the solid-phase method. The crystal structure of pyrochlore (sp. gr. Fd-3m, PM, a = 10.49360(5) Å, Z = 4) was refined by the Rietveld method based on X-ray powder diffraction (XRD) data. NEXAFS Cr2p and Co2p spectra of ceramics synthesized at 1050 °C correspond to the charge states of Cr(III), Co(II) and Co(III) ions. The thermal expansion coefficient of the cell was calculated from high-temperature X-ray diffraction measurements in the range of 20–1200 °C. The thermal expansion coefficient (TEC) monotonically increases from 3.92 × 10⁻⁶ °C⁻¹ (20 °C) to 9.89 × 10⁻⁶ °C⁻¹ (1020 °C). Above 1110 °C, TEC decreases due to thermal dissociation of Bi₂Cr_0.5Co_0.5Nb₂O_9+Δ with the formation of CoNb₂O₆, Bi₂O₃. The mixed pyrochlore (PM) exhibits a moderately high permittivity of ∼97, and low dielectric losses of ∼2 × 10⁻³ at 1 MHz and ∼30 °C. The activation energy of conductivity of the high-temperature region is 0.89 eV. The electrical properties of pyrochlore were synthesized by the ceramic (SPM) and Pechini methods (PM) were analyzed. The electrical properties of the samples up to 400 °C were modeled using equivalent electrical circuits Full article

With the rapid expansion of the global fish maw industry, the increasing prevalence of counterfeit products has made authenticity detection a critical challenge. Traditional detection methods rely on organoleptic assessment, chemical analysis, or molecular techniques, which limits their practical application. This paper treats fish maw authenticity detection as an object detection problem and proposes RDT-YOLO, a lightweight detection algorithm based on YOLO11n. Specifically, to address the challenges of insufficient fine texture feature extraction and computational redundancy in fish maw detection, we design hierarchical reparameterized feature extraction modules that utilize reparameterization technology to enhance texture feature extraction capability at different scales. To mitigate information loss during multi-scale feature fusion, we develop a Dynamic Adaptive Multi-Scale Pyramid Processing (DAMSPP) module that incorporates dynamic convolution mechanisms for adaptive feature aggregation. Additionally, we propose an Adaptive Task-Aligned Detection Head (ATADH) that combines task interaction and shared convolution to reduce model parameters while improving detection accuracy. Furthermore, a Wise-ShapeIoU loss function is introduced by incorporating a focusing coefficient into Shape-IoU, enhancing model detection performance through improved bounding box shape optimization. Experimental validation demonstrates that RDT-YOLO achieves 91.9% precision, 89.6% recall, and 94% mAP@0.5 while reducing parameters, model size, and computational complexity by 75.6%, 73.8%, and 63.8%, respectively, compared to YOLO11s. When evaluated against YOLOv10s and YOLOv12s, RDT-YOLO shows mAP@0.5 improvements of 0.8% and 0.5%, respectively. This work provides an automated solution for fish maw authenticity detection with potential for broader food safety applications. Full article

The increasing rate of land use change, particularly deforestation and agricultural expansion, has intensified soil degradation, leading to reduced sediment retention and accelerated soil erosion. This study aims to analyze soil erosion and sediment retention in the Lam Phra Phloeng (LPP) watershed, Thailand, using a coupled modelling approach integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) model from the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite. Six land use classes (forest, cropland, rangeland, flooded vegetation, built-up areas, and water bodies) were identified using Sentinel-2 MSI satellite data, with a Random Forest (RF) classification algorithm achieving an overall accuracy of 91.3% (Kappa coefficient = 0.89). The results indicate that forested areas exhibit the highest sediment retention, whereas croplands and rangelands experience the most significant soil loss due to erosion. The RUSLE model estimated an average annual soil loss ranging between 50 and 90 tons/ha/year, with the highest erosion rates observed in agricultural lands with steep slopes and minimal vegetation cover. The InVEST SDR model further corroborates these findings, showing that sediment retention is predominantly concentrated in densely vegetated areas, reinforcing the crucial role of natural forests in preventing soil displacement. This complementary modelling approach identifies priority areas for soil conservation practices. This study is the first study to integrate the RUSLE and InVEST models for the Lam Phra Phloeng watershed, providing a coupled assessment of erosion risk and sediment retention capacity and offering a novel and transferable framework for watershed-scale conservation planning and soil management in tropical monsoonal environments. Full article