Search Results (6,405)

Soil moisture critically governs terrestrial energy and water cycles. Precise monitoring of soil water content is essential for precision agriculture, drought early warning, and water resource management. Ground-based observations offer limited spatial coverage, and satellite remote sensing generally lacks high spatial resolution. Unmanned aerial vehicle (UAV) remote sensing, which provides centimeter-level spatial detail, can effectively address this gap and has therefore attracted considerable attention in soil moisture inversion research. Using CiteSpace, we performed a bibliometric analysis of 97 Chinese papers from the China National Knowledge Infrastructure (CNKI) and 321 English papers from the Web of Science Core Collection (2014–2025). The field has expanded rapidly since 2018, with China occupying a leading role. Domestically, Northwest A&F University represents a major research cluster, while the Chinese Academy of Sciences leads internationally. Key research topics include UAVs, soil moisture, machine learning, hyperspectral sensing, canopy temperature, and precision agriculture. Research themes have progressed from reliance on vegetation indices and temperature data toward the integration of hyperspectral and thermal infrared measurements, and toward the use of machine learning approaches to improve inversion models and achieve more accurate estimations. This study delineates the classification and developmental context of a knowledge system for soil moisture inversion using UAV remote sensing. Current work concentrates on integrating multi-sensor data with machine learning, while future efforts will emphasize coupling physical mechanisms with deep learning. These findings offer researchers a clear view of the field’s frontiers and a basis for planning future studies. Full article

Underwater images often suffer from mixed degradations, including motion blur, which reduce structural clarity and adversely affect downstream vision tasks. To address this problem, we propose a physically guided Transformer framework for underwater motion deblurring. The proposed method combines two-stage cepstrum-based blur estimation with a point spread function (PSF)-guided self-attention mechanism. Specifically, blur parameters are first robustly estimated through cepstrum analysis, ellipse fitting, and negative-peak refinement, and the resulting PSF is then embedded into the Transformer attention module to guide feature aggregation. On the real underwater benchmark datasets UIEB Challenge-60 and EUVP330, the proposed method achieves UIQM/UCIQE scores of 4.09/0.56 and 3.40/0.58, respectively, significantly outperforming UFPNet and Phaseformer, thereby demonstrating superior perceptual restoration in terms of sharpness, contrast, and color consistency. On the synthetic test set, the proposed method attains 24.23 dB PSNR and 0.918 SSIM, outperforming both recent deep models and classical non-blind deconvolution methods, which confirms its strong restoration fidelity and structural consistency. In the controlled water-tank experiments, the proposed method consistently achieves the best performance under different camera motion speeds, demonstrating excellent robustness and practical applicability. Overall, the proposed framework provides an effective and physically interpretable solution for underwater motion deblurring. Full article

Offshore wind energy is an important source of clean energy. Single-post platforms, due to their simple structure and strong stability, can adapt to deep water environments through buoyancy and ballast systems, have small motion responses, and have low construction and maintenance costs. They are suitable for offshore wind energy development in deep-sea areas and help expand the application of offshore wind power. This paper conducts a coupled response analysis of offshore wind turbine foundations and mooring systems, as well as an optimization study on the form and number of mooring lines. Under the premise of considering the safety and economy of floating wind turbines, the mooring lines have been optimally arranged. The study calculates frequency-domain responses, time-domain responses, and mooring line forces under the constraints of the original three-line mooring system. Based on this benchmark, the study further optimizes the mooring forms and numbers for the same platform, analyzing four, six, and eight single mooring lines, as well as three groups of single-line, double-line, and triple-line mooring configurations. Finally, using AQWA software (2024 R1), the responses and mooring line forces of different mooring configurations were calculated, and the preferred mooring arrangement for this stepped single-post platform was determined to be a three-group, three-line system (a total of nine mooring lines). The mooring line tension decreased substantially from the original 3.2 × 10⁶ N to 1.8 × 10⁶ N, while the dynamic response was reduced to one-sixth of its original level. Meanwhile, this study provides strong support for the utilization of offshore wind energy and the construction of offshore wind turbine platforms and mooring systems. Full article

The Yanshangou landslide, located in the Baihetan Reservoir area, poses severe potential threats to the normal operation of the reservoir due to its distinct deformation characteristics and high sensitivity to reservoir water level fluctuations. This study systematically investigates the geological background, deformation characteristics, stability evolution, and landslide-induced surge hazards of the Yanshangou landslide in the Baihetan Reservoir area. This work only considers the influence of reservoir water level fluctuations, which is the dominant factor controlling the current progressive deformation of the landslide. Field surveys and GNSS/deep displacement monitoring results revealed that the Yanshangou landslide exhibits obvious staged deformation characteristics, and the landslide deformation rate was closely coupled with the dynamic changes in reservoir water level. A slope stability evaluation method integrating the Morgenstern–Price limit equilibrium method and Richard’s equation was established, and the results indicated that the Yanshangou landslide has low saturated permeability. Therefore, its factor of safety (FOS) presents a clear four-stage variation trend in response to reservoir water level fluctuations. A Smoothed Particle Hydrodynamics (SPH)-based numerical model was further developed to simulate the landslide-induced surges under two typical reservoir water level scenarios (815 m and 765 m). The simulation results demonstrated that a high reservoir water level led to more intense surges with greater height and higher velocity, while a low reservoir water level resulted in surges with a wider propagation range along the reservoir bank. The research findings of this study provide a comprehensive theoretical basis and detailed data support for the prevention and mitigation of geological hazards in the Baihetan Reservoir area, and also offer a reference for the hazard management of similar reservoir landslides worldwide. Full article

Accurate retrieval of visibility grades is critical for transportation safety. Due to the highly complex meteorological backgrounds, traditional global deep learning models frequently struggle with limited physical traceability and feature heterogeneity. To address these challenges by enhance physical traceability and reduces heterogeneity, this study proposes a scenario-adaptive visibility retrieval framework based on multi-source synergy, namely TabPFN-ExtraTrees (TabPFN-ET), targeting major transportation routes in Anhui Province, China. Fusing Fengyun-4 (FY-4A/4B) satellite multispectral observations with ground meteorological data, this framework utilizes the divide-and-conquer routing mechanism of ExtraTrees to decouple the complex, heterogeneous feature space into highly homogeneous sub-scenarios. Subsequently, the TabPFN model conducts high-precision inference within each specific subspace. Evaluations on a class-balanced benchmark demonstrate that TabPFN-ET achieves an Overall Accuracy of 0.681, outperforming baseline models such as SAINT across various metrics. Furthermore, this paper conducts a physically consistent analysis of the framework. Feature importance and node profiling corroborate its physical consistency: the FY-4 upper-level water vapor channel (Channel 09) and near-surface humidity act as the macroscopic atmospheric stability and microscopic thermodynamic constraints, respectively, driving the model’s scene decoupling and inference. Cross-regional tests in Jiangsu provide preliminary indications of context-specific transferability. Full article

Arid agricultural systems face increasing sustainability challenges due to water scarcity, climate variability, and structural resource constraints. Although Artificial Intelligence (AI) is widely promoted as a key enabler of sustainable agriculture, empirical evidence on its long-term effects on agriculture-related Sustainable Development Goals (SDGs), particularly in arid regions, remains limited. This study investigates the role of AI in supporting sustainable agricultural development in Saudi Arabia’s Ha’il region. Using annual data from 1995 to 2025, AI adoption—proxied by SDG9 indicators that reflect AI-enabling digital infrastructure and innovation readiness rather than observed on-farm AI deployment—is examined in relation to a composite Sustainable Agricultural Development Goals index (SADGH), which integrates SDG2 (food security), SDG6 (water management), SDG8 (economic performance), SDG12 (responsible production), SDG13 (climate action), and SDG15 (land sustainability). Econometric analysis based on a Vector Error Correction Model (VECM) reveals a stable long-run relationship between AI adoption and agricultural sustainability, with approximately 32% of short-term disequilibrium corrected annually. In the short run, AI adoption is positively associated with food security, economic performance, and land sustainability, while water- and climate-related indicators adjust more gradually. Dynamic analyses suggest that AI-related shocks may generate cumulative effects over time. In addition, deep learning models using Long Short–Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are applied within an exploratory framework to capture potential nonlinear dynamics and generate indicative forecasts. The GRU model shows lower prediction errors; however, results should be interpreted with caution, given the limited sample size. Overall, the findings suggest that AI may contribute to sustainable agricultural development in arid regions, while highlighting the need for further research based on larger datasets. Full article

This study examined how the extraction medium used to obtain Quercus robur extracts influenced the properties of electrospun poly(vinyl alcohol) (PVA) mats intended for potential active packaging applications. Extracts prepared with 50% ethanol and with a choline chloride:lactic acid:water system were incorporated into PVA spinning solutions, and their effects on solution properties, fiber morphology, thermal behavior, crosslinking response, and polyphenol release were evaluated. The type of extraction medium affected both the electrospinning process and the structure of the resulting materials. Ethanol-derived extracts reduced solution viscosity and promoted the formation of thinner fibers, whereas systems containing the choline chloride:lactic acid:water-derived extract showed higher conductivity and lower electrospinning stability. Crosslinking with tannic acid in water led to the collapse of the fibrous structure, while ethanolic tannic acid treatment preserved the nanofibrous morphology more effectively. FTIR analysis indicated differences in intermolecular interactions within the polymer matrix, consistent with the observed changes in structural stability and release behavior. Thermal analysis showed that ethanol-derived extracts lowered the thermal stability of the PVA matrix, whereas the choline chloride:lactic acid:water-derived system altered the degradation pathway and increased the amount of solid residue formed during heating. Release studies demonstrated a rapid burst release for ethanol-based mats and a more sustained release profile for mats containing the choline chloride:lactic acid:water-derived extract. Selected extract-containing and ethanol–tannic acid-crosslinked mats also showed antibacterial activity against Staphylococcus aureus. The results showed that the extraction medium significantly affected polymer–extract interactions and the functional properties of electrospun PVA mats. At the same time, the conclusions refer specifically to the tested solvent systems, and broader generalization to other natural deep eutectic solvent-type formulations requires further comparative studies. Full article

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention. Full article

The widely applied empirical Darcy’s law in geotechnical engineering faces significant challenges in describing low-velocity flow processes in low-permeability porous media such as tight sandstones containing irreducible water. A deep understanding of low-velocity non-Darcy two-phase flow behavior in low-permeability porous media is essential for evaluating the development of ultra-low-permeability reservoirs. In this study, seven low-permeability three-dimensional digital cores with distinct pore structures were constructed based on realistic ultra-low-permeability sandstones. Using the lattice Boltzmann method, pore-scale investigations of water displacing oil were conducted. Low-velocity two-phase flow behavior under varying wettability conditions, pore structures, and fluid viscosities was simulated. The underlying mechanisms of low-velocity non-Darcy flow in ultra-low-permeability sandstones were examined, leading to a modified low-velocity non-Darcy flow equation. This improved model was subsequently applied to numerical simulations of ultra-low-permeability reservoirs. The results demonstrate that non-Darcy effects manifest primarily as nonlinearities in seepage curves, representing a marked departure from conventional Darcy’s law. Low-velocity non-Darcy (LVND) flow is predominantly constrained by the influence of complex pore-throat structures and capillary forces on fluid distribution. The dynamic equilibrium among capillary forces arising from residual water saturation, viscous forces, and pressure gradients constitutes the fundamental mechanism governing the onset of LVND flow. Enhanced nonlinear behavior is observed with increasing viscosity of the invading phase and elevated capillary forces. Substantial discrepancies in reservoir production dynamics are identified between LVND and classical Darcian regimes. Through pore-scale numerical simulations, this study systematically elucidates LVND behavior during bi-phasic flow in low-permeability porous media, while identifying critical controlling factors. These findings provide scientific rationale and technical support for addressing geological engineering challenges in tight sandstone formations. Full article

Rock–fill composite slopes formed during the transition from underground to open-pit mining in metal mines are highly susceptible to interface hydraulic weakening and sudden sliding under intense rainfall, mainly due to the permeability contrast between the two media. Taking the Shizhuyuan Mine as a case study, a coupled hydro-mechanical numerical model was developed in ABAQUS 2025 to investigate slope stability under different rainfall patterns and interface strength degradation scenarios. The spatiotemporal evolution of seepage and deformation fields was examined in detail, with particular attention given to the variation of the safety factor, the distribution of pore water pressure along the interface, and the characteristics of interface slip. The results show that: (1) the deterioration of the hydraulic condition within the slope is governed by the water-blocking effect of the interface and the infiltration threshold of the surface layer. Under the same total rainfall, prolonged low-intensity rainfall is more likely than short-duration intense rainfall to produce sustained deep infiltration, and the factor of safety decreases from the initial 1.369 to 1.173 (0.005 m/h, 288 h) and 1.255 (0.02 m/h, 72 h), respectively, indicating that the former exerts a more pronounced weakening effect on slope stability. (2) Slope instability exhibits a clear interface-controlled pattern. Regardless of the degree of parameter degradation, the base of the plastic zone consistently develops along the rock–fill interface, accompanied by extensive plastic deformation within the overlying fill material. (3) Failure initiates at the slope toe where the mechanical equilibrium along the rock–fill interface is first disturbed. Under the combined influence of topographic conditions and the water-blocking effect of the interface, rainfall infiltration tends to converge toward the slope toe and form a local high-pore-pressure zone, resulting in a marked reduction in the effective normal stress at the interface. Once the local shear stress exceeds the shear strength, yielding is triggered first at the slope–toe interface, which then induces plastic deformation in the overlying fill material and ultimately leads to overall slope instability. Full article

A large number of low-resistivity and low-permeability reservoirs have been discovered in the deep Paleogene strata of the Wenchang Sag. These reservoirs are characterized by complex porosity–permeability relationships and difficulties in fluid property identification, which restrict the progress of exploration and development operations. However, existing reservoir studies mostly focus on either low-permeability or low-resistivity reservoirs, with relatively few investigations targeting this specific type. Using petrological analysis and physical property testing as the main methods, combined with sedimentary and diagenetic studies, this paper examines the characteristics and genesis of low-resistivity and low-permeability reservoirs in the Paleogene of the Wenchang Sag. The results show that the Paleogene reservoirs are dominated by lithic quartz sandstones, with secondary pores as the main reservoir space, consisting of medium–small pores and fine throats. Samples of the same grain size exhibit a favorable porosity–permeability correlation. Based on capillary pressure curve morphology, the reservoirs can be classified into three types: high mercury intrusion saturation with low displacement pressure, medium mercury intrusion saturation with medium displacement pressure, and medium mercury intrusion saturation with medium–high displacement pressure. The low porosity and permeability are mainly attributed to the fact that the reservoir rocks are primarily deposited in near-source braided fluvial delta underwater distributary channels, resulting in low compositional and textural maturity of sandstones. Strong compaction resistance leads to a significant reduction in primary pores during burial, and intergranular cement filling further deteriorates physical properties. On the other hand, rapid lithological changes and complex pore structures give rise to abundant isolated pores and poor connectivity, leading to high irreducible water saturation. Coupled with high formation water salinity, these factors collectively give rise to low-resistivity reservoirs in the study area. This study clarifies the formation mechanism of low-permeability and low-resistivity reservoirs in the Paleogene of the Wenchang Sag, providing guidance for reservoir evaluation in subsequent oil and gas exploration and serving as a reference for analogous areas. Full article

This study addresses the optimal scheduling of a proton exchange membrane fuel cell (PEMFC)-based building combined cooling, heating, and power (CCHP) system, aiming to improve operational efficiency and flexibility under coupled electricity–thermal–cooling demands and load uncertainty. A physics-informed scheduling environment was developed using component models and multi-energy balance constraints, including a PEMFC with waste-heat recovery, a lithium bromide absorption chiller, a reversible heat pump with condenser heat recovery to thermal storage, a battery energy storage system, and a hot-water thermal storage tank. The dispatch problem was formulated as a Markov decision process and solved using deep reinforcement learning with TD3; performance was evaluated on typical summer and winter days, and robustness was tested by generating 100 scenarios with 30% demand perturbations. The results show that TD3 learns coordinated multi-energy dispatch patterns consistent with seasonal operation and reduces hydrogen consumption relative to a rule-based strategy under uncertainty while requiring millisecond-level inference time. Dynamic programming achieved slightly lower hydrogen consumption but incurred orders-of-magnitude higher computation time. Overall, TD3 provides a practical trade-off between near-optimal performance, robustness, and real-time applicability for PEMFC-based building CCHP scheduling. Full article

Gas content is a key parameter for coalbed methane (CBM) resource evaluation and production potential assessment. The accurate prediction of gas content in deep coal reservoirs is more challenging than in shallow reservoirs because both adsorbed gas and free gas must be considered. In this study, continuous logging data from the deep No. 8 coal seam in the Daning–Jixian block were integrated with measured gas content-related parameters to construct a quantitative logging interpretation framework for deep CBM reservoirs. First, the relationships between logging parameters and Langmuir volume, Langmuir pressure, porosity, and water saturation were analyzed. Then, multiple linear regression models were established to predict these key intermediate parameters, which were subsequently used to calculate adsorbed gas, free gas, and total gas content. The model was further applied to the DJ 52 well area for spatial prediction. The results show that the total gas content ranges from 24.49 to 32.90 cm³/g. The high-gas-content area is mainly located in the north-central part of the study area, whereas the southern part shows relatively lower gas content, partly due to the influence of coal seam thickness and reservoir property heterogeneity. The proposed method provides an interpretable and practical approach for deep CBM gas content evaluation using logging data. Full article

A ternary gel composed of polyvinyl alcohol (PVA), polyethyleneimine (PEI), and polyaluminum chloride (PAC) was prepared to address the limited controllability of gelation and the insufficient high-temperature resistance to re-ignition observed in existing mine fire prevention and extinguishing gels. Based on an orthogonal experimental design, the optimal formulation was identified as 14% PVA, 7% PEI, and 5.5% PAC (by mass), achieving a gelation time of 8.2 min. Microscopic characterization revealed that the gel forms a dense, interconnected three-dimensional network structure capable of effectively encapsulating the coal particles. Fourier transform infrared spectroscopy (FTIR) analysis showed that gel treatment resulted in a 29.8% reduction in the peak area of free hydroxyl groups. Thermogravimetric–differential scanning calorimetry (TG-DSC) analysis indicated that the gel increased the ignition temperature by 33.27 °C and shifted the maximum exothermic peak temperature by 13.28 °C. Fire suppression experiments demonstrate that the gel could continuously lower the temperature of high-temperature coal without re-ignition, demonstrating significantly superior performance compared to traditional sodium silicate gel. This gel achieves highly efficient fire prevention and suppression through the cooperative effects of water retention, oxygen barriers, and chemical passivation, providing a new material for the prevention and control of spontaneous coal combustion in deep mines. Full article

Under the combined pressures of climate change and irrigated cropland expansion, groundwater tables are declining rapidly across arid regions, thereby intensifying water limitation in riparian ecosystems. However, the mechanisms by which dominant riparian tree species coordinate multiple functional traits to maintain carbon–water balance remains poorly understood. This study investigated coordinated ecophysiological trait shifts of Populus euphratica Oliv. along a groundwater-depth gradient (2.19, 4.88, and 7.45 m) in the middle reaches of the Tarim River (China), hereafter referred to as shallow, middle, and deep groundwater depths, respectively. We quantified photosynthetic, hydraulic, stomatal, leaf anatomical and nutrient traits, and estimated long-term intrinsic water-use efficiency (WUE_i) from foliar δ¹³C. As the groundwater table declined, (1) photosynthetic capacity and photochemical performance decreased, whereas WUE_i increased markedly from 38.5 ± 2.9 to 54.2 ± 1.0 μmol mmol⁻¹, accompanied by the lowest transpiration rate at the deep groundwater depth (4.6 ± 0.5 mmol m⁻² s⁻¹); (2) stomatal and anatomical adjustments consistent with water-loss reduction were observed, including a significant decline in stomatal density from 93.5 ± 14.5 to 79.3 ± 17.4 pores mm⁻², and reduced stomatal size and stomatal area fraction (−20.3% and −32.7%, respectively); (3) the percentage loss of hydraulic conductivity increased, whereas sapwood-specific hydraulic conductivity declined, accompanied by greater sapwood investment relative to leaf area, with Huber value rising from 0.06 ± 0.02 to 0.11 ± 0.04 mm² cm⁻² at deep water depth; and (4) chlorophyll concentrations and leaf water content declined, whereas structural investment increased, as reflected by higher specific leaf mass and leaf dry matter content, and leaf nutrients were enriched, with total nitrogen and total phosphorus increasing by 67.1% and 42.0%, respectively. Trait-WUE_i relationships further indicated that WUE_i covaried most strongly with leaf anatomical and nutrient traits. These results demonstrate that increasing groundwater depth was associated with coordinated shifts in carbon assimilation, water-use regulation, hydraulic function, and nutrient allocation in P. euphratica. Such trait coordination may help explain how this species persists under chronic water limitation in arid riparian forests. Full article