Search Results (966)

This study aims to investigate the mechanical behavior of cement mortar and concrete through a hybrid approach that integrates artificial intelligence (AI) techniques with finite element modeling (FEM). Support Vector Machine (SVM) models with Radial Basis Function (RBF) and polynomial kernels, along with Multilayer Perceptron (MLP) neural networks, were employed to predict the compressive strength (Fc) and flexural strength (Fs) of cement mortar incorporating nano-silica (NS) and micro-silica (MS). The dataset comprises 89 samples characterized by six input parameters: water-to-cement ratio (W/C), sand-to-cement ratio (S/C), nano-silica-to-cement ratio (NS/C), micro-silica-to-cement ratio (MS/C), and curing age. Simultaneously, the axial compressive behavior of C20-grade concrete was numerically simulated using the Concrete Damage Plasticity (CDP) model in ABAQUS, with stress–strain responses benchmarked against the analytical models proposed by Mander, Hognestad, and Kent–Park. Due to the inherent limitations of the finite element software, it was not possible to define material models incorporating NS and MS; therefore, the simulations were conducted using the mechanical properties of conventional concrete. The SVM-RBF model demonstrated the highest predictive accuracy with RMSE values of 0.163 (R² = 0.993) for Fs and 0.422 (R² = 0.999) for Fc, while the Mander model showed the best agreement with experimental results among the FEM approaches. The study demonstrates that both the SVM-RBF and CDP-based modeling approaches serve as robust and complementary tools for accurately predicting the mechanical performance of cementitious composites. Furthermore, this research addresses the limitations of conventional FEM in capturing the effects of NS and MS, as well as the existing gap in integrated AI-FEM frameworks for blended cement mortars. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

Global climate change has intensified the frequency, intensity, and spatial heterogeneity of drought events, posing severe threats to the stability of terrestrial ecosystems. Plant drought resilience, which encompasses a plant’s capacity for drought resistance, post-stress recovery, and long-term adaptation and transformation to sustain ecosystem functionality, has emerged as a central focus in botanical and ecological research. This review synthesizes the conceptual evolution of plant drought resilience, from early emphasis on resistance and recovery to the current multi-dimensional framework integrating adaptation and transformation, and synthesizes advances in understanding multi-scale drought resilience in terrestrial plants—spanning molecular, physiological, individual, community, and ecosystem levels. Key mechanisms include molecular/physiological adaptations (osmotic adjustment, antioxidant defense, hydraulic regulation, carbon–water reallocation via gene networks and aquaporins), morpho-anatomical traits (root architectural plasticity, leaf structural modifications, and hydraulic vulnerability segmentation), community/ecosystem drivers (biodiversity effects, microbial symbioses, and soil–plant–feedback dynamics). We critically evaluate quantitative metrics and expose critical gaps, including neglect of stress legacy effects, oversimplified spatiotemporal heterogeneity, and limited integration of concurrent stressors. Future research should prioritize multi-scale and multi-dimensional integrated analysis, long-term multi-scenario simulations with field validation, and harnessing plant–microbe interactions to enhance drought resilience, providing a theoretical basis for ecosystem sustainability and agricultural production under climate change. Full article

Monitoring the development of greenhouse crops is essential for optimising yield and ensuring the efficient use of resources. A system for monitoring hemp (Cannabis sativa L.) cultivation under greenhouse conditions using computer vision has been developed. This system is based on open-source automation software installed on a single-board computer. It integrates various temperature and humidity sensors and surveillance cameras, automating image capture. Hemp seeds of the Tiborszallasi variety were sown. After germination, plants were transplanted into pots. Five specimens were selected for growth monitoring by image analysis. A surveillance camera was placed in front of each plant. Different approaches were applied to analyse growth during the early stages: two traditional computer vision techniques and a deep learning algorithm. An average growth rate of 2.9 cm/day was determined, corresponding to 1.43 mm/°C day. A mean MAE value of 1.36 cm was obtained, and the results of the three approaches were very similar. After the first growth stage, the plants were subjected to water stress. An algorithm successfully identified healthy and stressed plants and also detected different stress levels, with an accuracy of 97%. These results demonstrate the system’s potential to provide objective and quantitative information on plant growth and physiological status. Full article

Lichens, complex symbiotic associations between fungi and photosynthetic partners, are widespread in terrestrial ecosystems but remain poorly studied in tropical dry forests (TDFs). This review synthesizes current knowledge on the diversity, ecological roles, adaptive traits, and ethnobotanical uses of lichens in TDFs, with a focus on the Neotropics. As most lichens discussed here are crustose species that inhabit tree bark, this paper also provides a thoughtful review of the origin, distribution, and highly heterogeneous floristic composition of TDFs, which directly shape lichen habitats. It discusses how lichens have evolved to cope with seasonal water stress, emphasizing desiccation tolerance as a key feature of the symbiosis. This review also explores lichen community composition, interactions with host trees, microclimatic conditions, herbivory, and soil crust formation. Despite evidence of high species richness, functional diversity, and ecological importance, lichens in TDFs are largely overlooked in conservation strategies. Moreover, several regions remain vastly understudied, and many species likely remain undescribed. Ethnolichenological practices, though scarce, underscore the cultural and medicinal value of these organisms. Given the high rates of habitat loss and endemism in TDFs, there is a pressing need to expand research on lichen diversity and to investigate the evolutionary origins of their survival strategies. The conservation of these lichens is inseparable from the conservation of TDFs themselves. Understanding how lichens adapt to the harsh and variable conditions of TDFs is essential for integrating them into biodiversity conservation and ecosystem restoration frameworks. Full article

Groundwater depletion poses a critical challenge to global water security, threatening ecosystems, agriculture, and sustainable development. The Mississippi Delta, a region heavily reliant on groundwater for agriculture, has experienced significant groundwater level declines due to intensive irrigation. Traditional in situ monitoring methods, while valuable, lack the spatial coverage necessary to capture regional groundwater dynamics comprehensively. This study addresses these limitations by leveraging downscaled Gravity Recovery and Climate Experiment (GRACE) data to estimate groundwater levels using random forest modeling (RFM). We applied a machine-learning approach, utilizing the “Forest-based and Boosted Classification and Regression” tool in ArcGIS Pro, (ESRI, Redlands, CA) to predict groundwater levels for April and October over a 10-year period. The model was trained and validated with well-water level records from over 400 monitoring wells, incorporating input variables such as NDVI, temperature, precipitation, and NLDAS data. Cross-validation results demonstrate the model’s high accuracy, with R² values confirming its robustness and reliability. The outputs reveal significant groundwater depletion in the central Mississippi Delta, with the lowest water level observed in the eastern Sunflower and western Leflore Counties. Notably, April 2014 recorded a minimum water level of 18.6 m, while October 2018 showed the lowest post-irrigation water level at 54.9 m. By integrating satellite data with machine learning, this research provides a framework for addressing regional water management challenges and advancing sustainable practices in water-stressed agricultural regions. Full article

The rock strata traversed by frozen shafts in coal mines located in western regions are predominantly composed of weakly cemented, water-rich sandstones of the Cretaceous system. Investigating the rheological damage behavior of saturated sandstone under frozen conditions is essential for evaluating the safety and stability of these frozen shafts. To explore the damage evolution and creep characteristics of Cretaceous sandstone under the coupled influence of low temperature and in situ stress, a series of triaxial creep tests were conducted at a constant temperature of −10 °C, under varying confining pressures (0, 2, 4, and 6 MPa). Simultaneously, acoustic emission (AE) energy monitoring was employed to characterize the damage behavior of saturated frozen sandstone under stepwise loading conditions. Based on the experimental findings, a fractional-order creep constitutive model incorporating damage evolution was developed to capture the time-dependent deformation behavior. The sensitivity of model parameters to temperature and confining pressure was also analyzed. The main findings are as follows: (1) Creep deformation progressively increases with higher confining pressure, and nonlinear accelerated creep is observed during the final loading stage. (2) A fractional-order nonlinear creep model accounting for the coupled effects of low temperature, stress, and damage was successfully established based on the test data. (3) Model parameters were identified using the least squares fitting method across different temperature and pressure conditions. The predicted curves closely match the experimental results, validating the accuracy and applicability of the proposed model. These findings provide a theoretical foundation for understanding deformation mechanisms and ensuring the structural integrity of frozen shafts in Cretaceous sandstone formations of western coal mines. Full article

Tunnel systems are often confronted with issues such as cracks, water seepage, and exposed tendons, all of which compromise their structural integrity. This study utilizes an advanced robotic system equipped with a 3D laser scanner to capture data on visible lining defects. By analyzing the distribution of defects across various tunnel segments, we explore the mechanisms underlying structural cracks. Finite element software is employed to assess stress, deformation, and crack progression within the tunnel linings. The result found that the diversion tunnel’s segments exhibit notable variations: 66.0% of the defects are concentrated in the upper flat section, while 34.0% are found in the inclined shaft segment. Cracks, primarily located in the vault area, characterize these defects. Under water pressure, stress deformation in the intact lining follows a linear escalation pattern. Specifically, after the formation of cracks measuring 0.1 m, 0.2 m, and 0.3 m, circumferential stresses increase by approximately 4.50%, 9.10%, and 15.10%, respectively. Numerical simulations reveal significant stress concentration near the cave entrance at the upper flat break. Crack propagation at the arch crown is found to pose a greater risk than at the sides of the arch waist. These findings offer valuable scientific insights and practical implications for improving safety and enabling intelligent monitoring of power station tunnels. Full article

Urine pH serves as an indicator of systemic acid–base balance and helps detect early-stage urinary and renal disorders. However, conventional monitoring methods rely on instruments or manual procedures, limiting their use among vulnerable groups such as infants and bedridden elderly individuals. In this study, a pH-responsive smart hydrogel was developed and integrated into diapers to enable real-time, equipment-free, and visually interpretable urine pH monitoring. An optimised degumming process enabled one-step preparation of a silk fibroin–sericin aqueous solution. We employed a visible light-induced photo-crosslinking strategy to fabricate a dual-network hydrogel with enhanced strength and stability. Increasing sericin content accelerated gelation (≤15 min) and improved performance, achieving a maximum stress of 54 kPa, strain of 168%, and water absorption of 566%. We incorporated natural anthocyanins and fine-tuned them to produce four distinct colour changes in response to urine pH, with significantly improved colour differentiation (ΔE). Upon contact with urine, the hydrogel displays green within the normal pH range, indicating a healthy state. At the same time, a reddish-purple or blue colour serves as a visual warning of abnormal acidity or alkalinity. This intelligent hydrogel system combines rapid gelation, excellent mechanical properties, and a sensitive visual response, offering a promising platform for body fluid monitoring. Full article

Cementitious materials are susceptible to water ingress due to their hydrophilicity and porous microstructure, which can cause premature destruction and compromise long-term durability. Integral hydrophobic modification using alkyl silanes is an effective strategy for enhancing water resistance, while the influence of different silanes on early-age properties (within the first 7 d) of various binder systems remains unclear. This study investigates the rheology, flowability, setting behavior, reaction kinetics, compressive strength, and hydrophobicity of ordinary Portland cement (OPC) and alkali-activated fly ash–slag (AAFS) pastes incorporating alkyl silanes of varying alkyl chain lengths, i.e., methyl-(C1TMS), butyl-(C4TMS), octyl-(C8TMS), and dodecyl-trimethoxysilane (C12TMS). In OPC, C1TMS reduced yield stress and plastic viscosity by 33.6% and 21.0%, respectively, and improved flowability by 27.6%, whereas C4TMS, C8TMS, and C12TMS showed the opposite effects. In contrast, the effect of alkyl silanes on rheology and flowability of AAFS was less pronounced. Silanes delayed setting of OPC and AAFS by 5.6–164.4%, with shorter alkyl chains causing greater retardation. C1TMS and C4TMS inhibited early-age heat release and decreased the 1-day compressive strength by 14.8–35.7% in OPC and 82.0–84.5% in AAFS, whereas longer-chain silanes had comparatively minor effects. The hydrophobic performance in both binder systems was strongly correlated with alkyl chain length. C8TMS exhibited the best hydrophobicity in OPC, achieving a water contact angle of 145° and a 75.7% reduction in water sorptivity, while C4TMS demonstrated the highest hydrophobicity in AAFS. This study provides fundamental guidance for the rational selection of alkyl silanes in OPC and AAFS systems, offering insights into the design of multifunctional water-resistant cementitious composites for marine structures, building facades, and other applications with waterproofing requirements. Full article

Latin American countries continue to face critical challenges in ensuring safe and continuous access to drinking water, particularly in rural and peri-urban areas. This article presents a territorial and institutional diagnosis of drinking water access in the Piura region (Peru). It is a coastal region with approximately 2 million inhabitants, characterized by environmental stress, governance fragmentation, and social inequality. The study adopts a structural documentary approach based on academic literature and validated institutional data to analyze spatial disparities in water coverage, continuity, and quality. It identifies structural and institutional barriers—such as overlapping mandates, limited local capacity, and the absence of monitoring systems—to universal access. The findings also highlight the limitations of isolated innovation efforts, such as pilot projects led by universities and private companies, which often lack mechanisms for institutional integration and policy scaling. The analysis is framed within international water governance frameworks, including the OECD Principles and the Integrated Water Resources Management paradigm, and aligns with Sustainable Development Goal 6. The study offers a multi-scalar perspective grounded in local realities and identifies governance research gaps in rural Peru. Results underscore the need for territorialized planning, strengthened coordination, and inclusive governance to achieve sustainable and equitable water access in fragile contexts. Full article

Compound dry and hot events or extremes (CDHEs) have emerged as major climatic threats to agricultural production and food security in the middle reaches of the Yangtze River Basin (MRYRB), a critical grain-producing region in China. However, agricultural risks associated with CDHEs, incorporating both natural and socio-economic factors, remain poorly understood in this area. Using a Hazard-Exposure-Vulnerability (HEV) framework integrated with a weighting quantification method and supported by remote sensing technology and integrated geographic data, we systematically assessed the spatiotemporal dynamics of agricultural CDHE risks and corresponding crop responses in the MRYRB from 2000 to 2019. Results indicated an increasing trend in agricultural risks across the region, particularly in the Poyang Lake Plain (by 21.9%) and Jianghan Plain (by 9.9%), whereas a decreasing trend was observed in the Dongting Lake Plain (by 15.2%). Spatial autocorrelation analysis further demonstrated a significant negative relationship between gross primary production (GPP) and high agricultural risks of CDHEs, with a spatial concordance rate of 52.6%. These findings underscore the importance of incorporating CDHE risk assessments into agricultural management. To mitigate future risks, we suggest targeted adaptation strategies, including strengthening water resource management and developing multi-source irrigation systems in the Poyang Lake Plain, Dongting Lake, and the Jianghan Plain, improving hydraulic infrastructure and water source conservation capacity in northern and southwestern Hunan Province, and prioritizing regional risk-based adaptive planning to reduce agricultural losses. Our findings rectify the longstanding assumption that hydrological abundance inherently confers robust resistance to compound drought and heatwave stresses in lacustrine plains. Full article

Fuel cells have become a fundamental technology in the development of clean energy systems, playing a vital role in the global shift toward a low-carbon future. With the growing need for sustainable hydrogen production, perfluoro sulfonic acid (PFSA) ionomer membranes play a critical role in optimizing green hydrogen technologies and fuel cells. This study aims to investigate the effects of different environmental and solvent treatments on the chemical and physical properties of Nafion N−115 membranes to evaluate their suitability for both hydrogen production in proton exchange membrane (PEM) electrolyzers and hydrogen utilization in fuel cells, supporting integrated applications in the local and global green hydrogen economy. To achieve this, Nafion N−115 membranes were partially dissolved in various solvent mixtures, including ethanol/isopropanol (EI), isopropanol/water (IW), dimethylformamide/N-methyl-2-pyrrolidone (DN), and ethanol/methanol/isopropanol (EMI), evaluated under water immersion and thermal stress, and characterized for chemical stability, mechanical strength, water uptake, and proton conductivity using advanced electrochemical and spectroscopic techniques. The results demonstrated that the EMI-treated membrane showed the highest proton conductivity and maintained its structural integrity, making it the most promising for hydrogen electrolysis applications. Conversely, the DN-treated membrane exhibited reduced stability and lower conductivity due to solvent-induced degradation. This study highlights the potential of EMI as an optimal solvent mixture for enhancing PFSA membranes performance in green hydrogen production, contributing to the advancement of sustainable energy solutions. Full article

Addressing the need to optimize human settlement quality in arid and semi-arid regions under rapid urbanization, this study innovatively constructs an evaluation framework integrating greenness, thermal conditions, impervious surfaces, water bodies, and air transparency. Focusing on 12 prefecture-level cities in Inner Mongolia, Northern China, it systematically reveals the spatial differentiation characteristics and driving mechanisms of human settlement quality. Findings indicate the following: (1) Regional human settlement quality exhibits a spindle-shaped structure dominated by the medium grade (Excellent: 18.13%, High: 23.34%, Medium: 46.48%, Low: 12.04%), with Ulanqab City having the highest proportion of Excellent areas (25.26%) and Ordos City the lowest proportion of Low-grade areas (6.20%), reflecting a critical transition period for regional quality enhancement. (2) Spatial patterns show pronounced east-west gradients and functional differentiation: western arid zones display significant blue-green space advantages but face high-temperature stress and rigid water constraints, eastern humid zones benefit from superior ecological foundations with weaker heat island effects, the core Hetao Plain experiences strong heat island effects due to high impervious surface density, while industrial cities confront prominent air pollution pressures. Consequently, implementing differentiated strategies—strengthening ecological protection/restoration in High/Low-grade zones and optimizing regulation to drive upgrades in Medium-grade zones—is essential for achieving three sustainable pathways: compact development, blue-green space optimization, and industrial upgrading, providing vital decision-making support for enhancing human settlement quality and promoting sustainable development in ecologically fragile cities across northern China. Full article

Mesocotyl elongation is the key determinant of deep-sowing tolerance in maize. Sowing at an appropriate depth allows the seedling to exploit water and nutrients stored in deeper soil layers, thereby enhancing its ability to withstand drought and other abiotic stresses. Mesocotyl elongation is regulated by the phytohormones brassinosteroid (BR), auxin (IAA), gibberellin (GA), and reactive oxygen species (ROS). However, whether and how BR coordinates IAA, GA, and ROS to control mesocotyl elongation in maize remains unclear. Here, we demonstrated that BRs orchestrate ROS, IAA, and GA signaling to remodel cell-wall metabolism in mesocotyl cells, promote cell elongation, and, consequently, strengthen deep-sowing tolerance. BR promoted mesocotyl elongation through multiple routes: (1) decreasing the contents of cell-wall components (hemicellulose, cellulose, and pectin); (2) activating cell-wall-loosening enzymes (cellulase, pectinase, and acidic xylanase); and (3) disturbing ROS homeostasis by elevating superoxide dismutase (SOD) activity. Combined treatments of BR with either IAA or GA further enhanced mesocotyl elongation in a concentration-dependent manner. In deep-sowing trials (15 cm), application of BR alone or in combination with IAA or GA markedly increased mesocotyl length and emergence rate, thereby improving deep-sowing tolerance. Our work indicated that BR integrated ROS, IAA, and GA signals to restructure the cell wall and derived mesocotyl cell elongation, providing both theoretical insights and practical strategies for breeding maize varieties with enhanced deep-sowing tolerance. Full article