Search Results (2,281)

To address the growing problem of electromagnetic pollution, the development of intelligent, multifunctional electromagnetic shielding materials is essential. The objective of this work is to fabricate an intelligent, low-reflection and high-absorption electromagnetic shielding composite via direct ink writing. In this study, epoxy resin (EP) was employed as the matrix, with nickel powder (Ni), multi-walled carbon nanotubes (MWCNTs), and silver powder (Ag) serving as functional fillers. Direct-ink printing enabled the fabrication of uniformly structured composites and layered gradient-structured composites. By precisely varying the filler content through layer-by-layer printing, the gradient-structured composite exhibited an increasing electrical conductivity gradient and a decreasing magnetic permeability gradient along the direction of electromagnetic wave incidence. Comprehensive characterization of microstructure, electrical, magnetic, and dielectric properties, and electromagnetic shielding effectiveness revealed that the uniformly structured composites exhibited higher total shielding effectiveness (SE_T) and reflection coefficient (R) with increased electrical conductivity. The layered gradient-structured composite achieved an electrical conductivity of 5.44 S/m and an SE_T of 17.74 dB, with the R value reduced to 0.53. Compared to the highly conductive homogeneous composite used in the bottom layer (R = 0.87), this represents a reduction in reflectivity of approximately 39.1%, thereby mitigating secondary pollution from excessive reflection. Under a DC voltage of 200 V, all composites recovered their original shape within 63 s, with shape fixity (R_f) and recovery (R_r) ratios exceeding 92%. This strong shape memory capability supports conformal coating on complex devices and facilitates material recycling, offering a practical foundation for next-generation multifunctional electromagnetic shielding materials. Full article

The characterization and prediction of seasonal variations in river water quality are essential for maintaining control of aquatic ecosystems and resource management. This study aims to develop predictive models using Artificial Intelligence (AI) techniques, particularly Machine Learning (ML) algorithms, to classify seasonal patterns in three major rivers in Bangladesh: Buriganga, Shitalakhya, and Turag. This study considered 15 of the most significant water quality parameters, including pH, alkalinity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDSs), and electrical conductivity (EC). A total of 476 samples were gathered on a monthly basis at 17 monitoring points in the three rivers, covering all months between January and December from 2021 to 2023. With K-fold cross-validation and hyperparameter optimization, three ML models, like Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision Tree (DT), were employed for predicting seasonal variation in river water quality. The models were assessed based on accuracy, precision, recall, F1, and ROC–AUC scores. Partial Dependence Plot (PDP) analysis was applied to explore the marginal effects of key water quality features on seasonal prediction while keeping other variables constant. RF achieved the highest accuracy of 79%, and XGBoost was about 77% among the models. The achieved prediction accuracies indicate a robust capability to capture key seasonal and spatial changes in river water quality. At this performance level, the models are effective in identifying conditions associated with deteriorated water quality and potential exceedances of guideline-based thresholds established by the World Health Organization (WHO) and Bangladesh water quality standards, supporting timely assessment and management interventions. The SHAP analysis demonstrated TDS, alkalinity, and EC as the top feature drivers of seasonal differences, providing insight into the interplay between chemical composition and climate. The results of the study have the potential to accurately depict the seasonal patterns in river water quality using AI approaches. Full article

Urbanization in mountainous valley regions is constrained by rigid topography, generating complex correlations between spatial growth and ecological security. The coupling between urban expansion and landscape ecological risk (ERI) was evaluated for six representative valley cities in western China from 1985 to 2023. Annual land-cover data (CLCD) and fine-scale terrain models were integrated with expansion metrics, slope gradient analysis, and spatial statistics to identify growth trajectories and risk reorganization. Urban growth shifted from edge expansion to leapfrog development as valley floors became saturated. Two vertical trajectories emerged: a low-slope lock-in pattern (e.g., Lanzhou) where development remains largely on slopes < 6° and an uplift towards mid-slopes pattern (e.g., Chongqing), where expansion increasingly occurs on 6–25° terrain. ERI correspondingly showed three spatial typologies: valley contrast, heterogeneous mosaic, and high-risk background dominance. Although ERI generally declined, reflecting structural hardening with rising built-up land shares, the spatial clustering of risk remained stable. GeoDetector results indicate that terrain sets a baseline for ERI differentiation, but its explanatory power varies across cities and is often surpassed by land-cover composition. These findings support differentiated governance, requiring strict controls on slope disturbance in uplift cities and prioritizing corridor connectivity in lock-in cities. Full article

Effective clash management is critical to the success of complex construction projects, yet BIM coordinators face severe information overload when modern detection tools generate thousands or even millions of collision reports, making interdisciplinary coordination increasingly difficult. This article presents a conceptual framework for using AI for collision triage in a Building Information Modeling (BIM) environment. Previous approaches have focused mainly on collision detection itself and simple, rule-based prioritization, rarely exploiting the potential of Artificial Intelligence (AI) methods for post-processing of results, which constitutes the main innovation of this work. The proposed framework describes a modular system in which collision detection results and data from BIM models, schedules (4D), and cost estimates (5D) are processed by a set of AI components, offering adaptive, data-driven decision support unlike static rule-based methods. These include: a classifier that filters out irrelevant collisions (noise), algorithms that group recurring collisions into single design problems, a model that assesses the significance of collisions by determining a composite ‘AI Triage Score’ indicator, and a module that assigns responsibility to the appropriate trades and process participants. The framework leverages supervised machine learning methods (gradient boosting algorithms, selected for their effectiveness with tabular data) for noise filtering, density-based clustering (HDBSCAN, chosen for its ability to detect clusters of varying densities without predefined cluster count) for clash aggregation, and multi-criteria scoring models for priority assessment. The article also discusses a potential way to integrate the framework into the existing BIM workflow and possible scenarios for its validation based on case studies and expert evaluation. The proposed conceptual framework represents a step towards moving from manual, intuitive collision triage to a data- and AI-based approach, which can contribute to increased coordination efficiency, reduced risk of errors, and better use of design resources. As a conceptual study, the framework provides a foundation for future empirical validation and its limitations include dependency on historical training data availability and the need for calibration to project-specific contexts. Full article

Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates a multisource data-fusion approach for improving cropland SOM prediction in Yucheng City, Shandong Province, China. We applied a Local Energy Weighted Discrete Wavelet Transform (LEW-DWT) to fuse multi-temporal Landsat 8 imagery (2014–2023). Quantitative analysis (e.g., Information Entropy and Average Gradient) demonstrated that LEW-DWT effectively preserved high-frequency spatial details and texture features of fragmented croplands better than traditional DWT and simple splicing methods. These were combined with 41 environmental predictors to construct composite Ev–Tn–Mm features (environmental variables, temporal NDVI features, and multi-temporal multispectral information). Random Forest (RF) and Convolutional Neural Network (CNN) models were trained and compared to assess the contribution of the fused data to SOM mapping. Key findings are: (1) Comparative analysis showed that the LEW-DWT fusion strategy achieved the lowest spectral distortion and highest spatial fidelity. Using the fused multitemporal dataset, the CNN attained the highest predictive performance for SOM (R² = 0.49). (2) Using the Ev–Tn–Mm features, the CNN achieved R² = 0.62, outperforming the RF model (R² = 0.53). Despite the limited sample size, the optimized shallow CNN architecture effectively extracted local spatial features while mitigating overfitting. (3) Variable importance analysis based on the RF model reveals that mean soil moisture is the primary single variable influencing the SOM, (relative importance 15.22%), with the NDVI phase among time-series features (1.80%) and the SWIR1 band among fused multispectral bands (1.38%). (4) By category, soil moisture-related variables contributed 45.84% of total importance, followed by climatic factors. The proposed multisource fusion framework offers a practical solution for regional SOM digital monitoring and can support precision agriculture and soil carbon management. Full article

Transformer-based deep learning techniques have recently shown outstanding potential in remote sensing scene classification (RSSC), benefiting from their ability to capture global semantic relationships and contextual dependencies. However, effectively utilizing the raw image and global semantic information while simultaneously taking into account detailed features and multi-scale spatial relationships remains a major challenge. Therefore, this paper proposes a novel FG-Swin KANsformer model that integrates frequency domain and gradient prior information from raw images with the Kolmogorov–Arnold Network (KAN) to enhance nonlinear feature modeling. The FG-Swin KANsformer consists of three key components: the Discrete Cosine Transform (DCT) module, the gradient-spatial feature extraction (GSFE) module, and the Swin Transformer module integrated with KAN. In the feature embedding phase, the DCT module extracts frequency domain features, while the GSFE module uses multi-scale convolutions and Sobel operators to extract spatial structures and gradient information at different scales, thereby enhancing the utilization of the original image’s frequency domain and gradient prior information. In the Swin Transformer feature modeling phase, the conventional multilayer perceptron (MLP) in Swin Transformer Blocks is replaced by KAN, which decomposes complex multivariate functions into univariate compositions, thereby improving nonlinear representation capacity and enhancing feature discrimination. The thorough experiments on three distinct public remote sensing (RS) datasets demonstrate that FG-Swin KANsformer exhibits outstanding performance. Full article

This paper reviews constitutive models used to predict concrete spalling under elevated temperatures, with emphasis on fire exposure and concrete linings in deep geological repositories for spent fuel and nuclear waste. The review synthesizes (1) how material composition (ordinary Portland cement concrete, geopolymer concrete, and fiber-reinforced systems using polypropylene and steel fibers) affects spalling resistance; (2) how coupled environmental and mechanical actions (temperature, moisture, stress state, chloride ingress, and radiation) drive damage initiation and spalling; and (3) how constituent-scale characteristics (microstructure, porosity, permeability, elastic modulus, and water content) govern thermal–hydro–mechanical–chemical (THMC) transport and damage evolution. We compare major constitutive modeling frameworks, including plasticity–damage models (e.g., concrete damage plasticity), statistical damage approaches, and fully coupled THM/THMC formulations, and highlight how key parameters (e.g., water-to-binder ratio, temperature-driven pore-pressure gradients, and crack evolution laws) control predicted spalling onset, depth, and timing. Several overarching challenges emerge: lack of standardized experimental protocols for spalling tests and assessments, which limits cross-study benchmarking; continued debate on whether spalling is dominated by pore pressure, thermo-mechanical stress, or their interaction; limited integration of multiscale and constituent-level material characteristics; and high data and computational demands associated with advanced multi-physics models. The paper concludes with targeted research directions to improve model calibration, validation, and performance-based design of concrete systems for high-temperature and repository applications. Full article

Chronic liver injury is accompanied by coordinated disturbances in lipid trafficking and inflammatory–fibrogenic signaling. Transforming growth factor beta 1 (TGF-β1) signaling has been implicated in hepatic fibrogenesis and tumor-associated remodeling and may co-vary with disturbances in lipid trafficking. Lisosan G (LG), a fermented wheat-derived nutraceutical, has reported antioxidant and anti-inflammatory activity and may influence these interconnected pathways. This study evaluated whether dietary LG alters the lipid composition of plasma lipoprotein fractions and hepatic TGF-β1 levels across distinct liver contexts. Seventy-two female Wistar rats were randomized into nine groups (n = 8/group) defined by liver condition, consisting of healthy control (Control), non-neoplastic liver (PH), and neoplastic liver injury (HCC; PH followed by diethylnitrosamine, DEN), and diet (standard diet, SD + 2.5% LG, or SD + 5% LG). Plasma lipoproteins (VLDL, LDL, HDL₁, HDL₂) were isolated by stepwise KBr density-gradient ultracentrifugation, and cholesterol (TC), phospholipids (PL), and triacylglycerols (TG) were quantified in each fraction. Hepatic TGF-β1 was measured by ELISA and normalized to total protein. LG effects depended strongly on baseline liver status, with significant Condition × Diet interactions for most lipid endpoints and for hepatic TGF-β1. In healthy rats, LG produced fraction-selective remodeling rather than uniform lipid lowering, including increased VLDL-TG at both doses and non-linear changes in cholesterol distribution across LDL and HDL subfractions. After PH, LG broadened lipid remodeling, including reduced VLDL-PL, increased VLDL-TG (both doses), and an increase in LDL-TC at 5% LG, accompanied by marked changes in HDL₁/HDL₂ cholesterol partitioning. In HCC, LG induced pronounced, often dose-dependent increases in LDL-associated lipids (LDL-PL, LDL-TG, LDL-TC) and increased HDL₁-TC while decreasing HDL₂-TC. Hepatic TGF-β1 was elevated in PH and further increased in HCC versus controls; LG reduced hepatic TGF-β1 in a condition-dependent manner, with the strongest reduction at 5% LG in HCC. Dietary Lisosan G remodels circulating lipoprotein lipid composition in a liver-status-dependent manner and is associated with reduced hepatic TGF-β1 abundance in injured liver, most prominently in neoplastic injury. These findings are consistent with the notion that nutraceutical interventions may show stronger phenotypic effects under perturbed metabolic–fibrogenic states than under stable physiology, while highlighting the need for mechanistic work to distinguish altered lipoprotein secretion from changes in peripheral clearance and to assess pathway-level TGF-β signaling. Full article

The Jurassic lacustrine oil shale in southwest China has become a primary production layer due to its high yield and substantial reserves. However, influenced by the lacustrine environment, the vertical profile of the lacustrine shale reservoir shows alternating deposits of shale and carbonate rock. This complex lithological combination results in significant heterogeneity in reservoir types, reservoir distribution, and internal structure. Currently, research on micro-pore structure and hydrocarbon storage mechanisms in lacustrine shales is insufficient, necessitating the elucidation of their micro-characteristics to support future exploration and development. This research focuses on the Da’anzhai Member of Jurassic Ziliujing Formation. Various techniques—including organic geochemical analysis, X-ray diffraction, physical property testing, gradient centrifugation, and gradient drying NMR monitoring—were employed to investigate the micro-pore structure and fluid storage mechanisms of the lacustrine shale reservoir. The following insights were gained from this research. The organic matter pores (OMP) and inorganic pores (IP) developed within the Da’anzhai lacustrine shale reservoir together create the storage space for shale oil, while micro-fractures further enhance the reservoir’s storage capacity and flow performance. Lacustrine shale oil exists in three storage states: mobile oil, bound oil, and adsorbed oil. Mobile oil is primarily located within the micro-fractures and large pores (greater than 350 nm) of the shale reservoir and is the main target for industrial extraction. Bound oil is mainly found in the meso-pores, micropores, and narrow pore structures between rock grains (30 nm to 350 nm), and, theoretically, could potentially be developed through engineering methods such as hydraulic fracturing. Adsorbed oil, due to its close binding with organic matter and clay mineral surfaces, is difficult to release effectively using conventional techniques. The OM abundance, the mineral composition of lacustrine shale, and the pore structure all influence the storage states of shale oil. While a high TOC value increases the amount of mobile oil, the strong adsorption properties of kerogen and organic matter lead to the accumulation of adsorbed oil, which inhibits oil flow. Clay minerals further restrict oil flow by enhancing adsorption, while brittle minerals facilitate the movement of mobile oil by expanding pore space. Based on fractal geometry theory and multi-scale testing results, the large pores in the Da’anzhai lacustrine shale have a high fractal dimension and exhibit complex shapes. However, as pore complexity increases, the amount of adsorbed oil rises significantly, which in turn reduces the proportion of movable oil. Full article

This study evaluates the performance of artificial intelligence (AI) technologies for wetland classification in the province of Alberta, Canada, using integrated remote sensing inputs, including airborne light detection and ranging (LiDAR), orthophotography, and multi-sensor satellite imagery (Sentinel-1, Sentinel-2, PlanetScope). Our primary objective was to assess whether AI-driven modelling approaches, specifically machine learning (ML) and deep learning (DL), can meet Alberta’s provincial wetland mapping standards. We hypothesized that integrating high-resolution LiDAR with multi-seasonal optical and radar data composites into advanced AI algorithms would achieve the required classification accuracy, detail, and minimum mapping unit targets. We tested several methodologies in four ecologically distinct pilot areas representing Alberta’s Boreal, Grassland, and Parkland Natural Regions. AI models included ensemble ML using Extreme Gradient Boosting (XGBoost) and Random Forest, and a DL U-Net convolutional neural network (CNN). AI models were trained on expert-labelled photoplots and validated using in situ field surveys. Our findings demonstrate that both ML and DL models met and, in several cases, exceeded the provincial mapping standards with validation overall accuracies surpassing >70% (form), >80% (class), and >90% (wetland–upland). U-Net CNN models generally produced the highest overall accuracies and most precise wetland extent delineation, but XGBoost offered finer detail and granularity for detailed mapping of rare wetland forms. Integrating LiDAR data and derivatives further enhanced model performance, improving accuracy by as much as 13%. Based on these outcomes, we provide a set of recommendations for scaling up these approaches, focusing on model selection, LiDAR imagery integration, and the continued value of field surveys to support the operational scaling of AI-driven classification approaches for wetland inventory updates across Alberta’s diverse landscapes. However, key challenges remain in scaling up this approach due to the cost of acquiring high-resolution LiDAR and satellite imagery. Full article

Human interferences have set off a multitude of morphological responses of the lower Rhine in Germany and the Netherlands. We share insights from thirty years of studies on these responses in the Niederrhein below Xanten and the branches in the delta. Elementary analyses of the 1D Saint-Venant–Exner equations explain the downstream flattening and upstream steepening of the longitudinal bed profile due to retrogressive erosion in response to river training, bend cut-offs and sediment mining. Three reasons make a 2D approach necessary for modelling the seemingly 1D problem of large-scale morphological response: (i) transverse variations in bed sediment composition, (ii) sediment division at river bifurcations, and (iii) the possibility that non-erodible layers in bends cause either erosion or sedimentation of the longitudinal bed profile. The Pannerdense Kop and IJsselkop bifurcations are in a state of quasi-equilibrium, essentially unstable but developing slowly. Considerable spatiotemporal variations in the sediment composition of the riverbed surface pose a challenge to stabilizing the longitudinal bed profile by matching gradients in flow velocity to gradients in bed sediment composition. As these variations form a major knowledge gap, we recommend research on the state and dynamics of sediment size and layer structure in the upper metres of the riverbed. Full article

Ceramic Matrix Composites (CMCs) have emerged as a structural material alternative to nickel superalloys for high-pressure turbines (HPT) components operating at high temperature, like shrouds. Despite the outstanding thermal stability of the CMCs, limited cooling is still necessary due to the extreme thermal operating conditions necessary to maximize engine performance and minimize fuel consumption. The design of CMC components, indeed, must consider a maximum service temperature that should not be exceeded to avoid damage and accelerated oxidation. The cooling, on the other hand, may induce the formation of thermal gradients and thermal stresses. In this work, different design options for the cooling system are investigated to minimize the thermal stresses of an HPT shroud-like geometry subjected to maximum temperature constraints on the material. Cooling is obtained via colder air jet streams (air taken from the compressor), whose impact position (the surface where the cold air impacts the component) has a different effect on the temperature field and on the induced stress field. Besides stress evaluation with different cooling systems, an ONERA damage model is investigated at a key location to potentially take into account stress components acting simultaneously and potential stiffness degradation of the CMC. Finally, the design evaluation of potential discrete crack propagation is discussed. A standard cohesive elements approach has been compared with a brittle element death approach. The results showed that the cohesive element approach resulted in shorter crack propagation, underestimating the actual crack behavior due to the embedded stiffness degradation method, while the element death returned encouraging results as a quicker, less complex, but still accurate design evaluation. Full article

The regeneration of bone and the repair of large segmental bone defects represent critical challenges in regenerative medicine. Natural bone tissue is an anisotropic material characterized by an intricate gradient distribution in structure, mechanical properties, and biochemical composition; this multi-dimensional heterogeneity is crucial for maintaining its physiological functions and guiding regeneration. Although tissue engineering scaffolds have demonstrated significant potential in the treatment of bone defects, homogeneous or single-gradient scaffolds often struggle to precisely recapitulate the high degree of heterogeneity and anisotropy of natural bone from the macroscopic to the microscopic level, thereby limiting their capability in repairing complex bone defects. In recent years, biomimetic gradient scaffolds—particularly those employing multi-gradient synergistic designs that integrate physical structure, biochemical composition, and mechanical properties—have emerged as a research frontier in this field due to their ability to accurately mimic the natural bone microenvironment and regulate cellular behavior. This research aims to systematically review the latest research progress in gradient scaffolds for bone tissue engineering. First, gradient characteristics of biomimetic gradient bone scaffolds are summarized; second, the design strategies for gradient scaffolds are discussed in depth, with a focus on the applications and advantages of advanced fabrication techniques, such as additive manufacturing, in constructing multi-dimensional gradient structures; finally, based on current research findings, the emerging development trends and future research directions of biomimetic gradient bone scaffolds are outlined to provide a reference for innovative breakthroughs in the field of bone tissue engineering. Full article

Background/Objectives: Multilayer zirconia restorations can feature a shade gradient or a strength gradient, with layers differing in color or phase composition within the same material. The aim of this in vitro study was to evaluate the color stability in all layers of multilayer zirconia after exposure to staining solutions and artificial aging. Methods: Square-shaped specimens (N = 120) of color A2 were fabricated from 4Y-PSZ and 3Y/4Y-PSZ multilayer zirconia—Katana STML, DD Cube One ML, and Katana YML—and their baseline color values (T0) were measured with a clinical spectrophotometer (VITA Easyshade V). The specimens were randomly divided into four groups (n = 10/gp) and immersed in physiologic solution, 0.2% chlorhexidine gluconate (CHX) mouth rinse, and staining coffee solution. Then, they were measured continuously for 7 (T1), 14 (T2), and 21 days (T3). The last group of specimens underwent accelerated aging in a steam autoclave at 134 °C and 2 bar pressure and measured after 1 (T1), 3 (T2), and 5 h (T3). After the immersion process and artificial aging, discoloration values (ΔE) were calculated using the formula ΔE = [(ΔL*)² + (Δa*)² + (Δb*)²]^1/2 and analyzed with the SPSS v 23.0 software with a p value < 0.05. Results: All specimens showed significant color differences in the T3 measurements after exposure to coffee and CHX, with the highest ΔE values in the enamel layers. Katana YML showed the most significant differences in ΔE in the cervical layers after exposure to artificial aging. Conclusions: Multilayer zirconia exhibited dependent optical changes, with the enamel layers being the most affected after exposure to staining solutions. Gradient pigmentation and differences in phase composition caused differences in color to the multilayer zirconia layers after exposure to staining solutions and artificial aging. Full article

We present WM-Classroom v1.0, a pedagogical multi-species agent-based model (ABM) designed for educational purposes in predator–prey–harvest systems. The model embeds a predator, two prey breeds, and human harvesters on a homogeneous 50 × 50 grid with weekly time steps, implementing random movement, abstract energetics, prey consumption, reproduction, legal harvest with species-specific cut-offs and seasons, optional predator control, and a poaching switch. After basic technical checks (energetic calibration, prey composition, herbivore viability), we explore the consistency of the model under illustrative scenarios including no hunting, single-prey harvest, hunter-density and season-length gradients, predator removal, and poaching. In the no-hunting baseline (n = 100), mean end-of-run abundances were 22 deer, 159 boar, and 45 wolves, with limited extinction events. Deer-only harvest often drove deer to very low end-of-run counts (mean 1–16) with extinctions in 2–7/10 replicates across cut-offs, whereas boar-only harvest showed higher persistence (mean 11–74) and boar extinctions occurred only at the lowest cut-off (3/10). Increasing hunter numbers or season length depressed prey and could indirectly reduce wolves via prey depletion. Legal predator control reduced predators as designed, while poaching had little effect under the implemented rules. Because interaction and prey-choice rules are simplified for transparency, outcomes should be interpreted as conditional on model assumptions. WM-Classroom v1.0 provides a didactic sandbox for courses, professional training, and outreach, with extensions (habitat heterogeneity, age/sex structure, probabilistic diet/kill success, and calibration/validation) outlined for future versions. Full article