Search Results (738)

Vertical containment barriers—critical for intercepting contaminant transport in subsurface environments—demand materials that balance low permeability with adequate strength, particularly in stress-sensitive mountainous terrain. Plastic concrete, as a key barrier material, provides essential properties, including exceptional stress relaxation, to suppress fracture development under compressive loads, coupled with effective seepage control. This study examines its strength performance through experiments on varied mixing techniques (dry, wet, and 24 h hydration), unconfined compression under uncontaminated conditions (water–binder ratios: 1.3–2.1, bentonite content: 20–30%, ages: 14–90 days), barium ion immersion (1–5 g/L, pH 7–11), and dry–wet cycling (10 cycles). Key findings demonstrate that (1) the strength of samples prepared by dry mixing and wet mixing is lower than that of samples mixed for 24 h, and all specimens met the target design strength following 28 days of curing; (2) under pollution-free conditions, strength decreases with higher water–binder ratios and bentonite content, showing a linear relationship. Strength increases exponentially with age; (3) in the presence of Ba²⁺, strength gradually decreases as Ba²⁺ concentration and pH increase, particularly notably at 3 g/L Ba²⁺ and pH 11. Strength increases with age, following a power relationship; (4) under dry–wet cycles, ion concentration has minimal impact on sample quality and surface state but significantly affects strength, with higher ion concentrations leading to greater strength loss and susceptibility to cycles; (5) during solution immersion, higher ion concentrations and pHs result in greater strength loss and worse erosion resistance. Full article

The excessive emission of cadmium (Cd²⁺) poses a serious threat to the aquatic environment due to its high toxicity and bioaccumulation potential. This study constructed three types of vertical-subsurface-flow constructed wetlands configured with iron–carbon–zeolite composite substrates, including an iron–carbon–zeolite constructed wetland (TF-CW), a zeolite–iron–carbon constructed wetland (FT-CW), and an iron–carbon–zeolite mixed constructed wetland (H-CW), to investigate the purification performance and mechanisms of constructed wetlands for cadmium-containing wastewater (0~6 mg/L). The results demonstrated that iron–carbon–zeolite composite substrates significantly enhanced Cd²⁺ removal efficiency (>99%) through synergistic redox-adsorption mechanisms, where the iron–carbon substrate layer dominated Fe-Cd co-precipitation, while the zeolite layer achieved short-term cadmium retention through ion-exchange adsorption. FT-CW exhibited superior NH₄⁺-N removal efficiency (77.66%~92.23%) compared with TF-CW (71.45%~88.05%), while iron–carbon micro-electrolysis effectively inhibited NO₃⁻-N accumulation (<0.1 mg/L). Under cadmium stress, Typha primarily accumulated cadmium through its root systems (>85%) and alleviated oxidative damage by dynamically regulating antioxidative enzyme activity, with the superoxide dismutase (SOD) peak occurring at 3 mg/L Cd²⁺ treatment. Microbial community analysis revealed that iron–carbon substrates promoted the relative abundance of Bacteroidota and Patescibacteria as well as the enrichment of Saccharimonadales, Thauera, and Rhodocyclaceae (genera), enhancing system stability. This study confirms that iron–carbon–zeolite CWs provide an efficient and sustainable technological pathway for heavy metal-contaminated water remediation through multidimensional mechanisms of “chemical immobilization–plant enrichment–microbial metabolism”. Full article

This study investigates the internal structure and lithologic variability of slope deposits in a small catchment in the Polish Outer Carpathians using pedological methods supported by geochemical analyses and Electrical Resistivity Tomography (ERT). It addresses the occurrence of lithologic discontinuities in the soils of flysch-dominated mountain areas. Diagnostic criteria from the WRB system—based on particle-size distribution and the content and lithology of coarse fragments—were applied to identify lithologic discontinuities, complemented by computation of sand and silt separates on a clay-free basis. Geochemical analyses and ERT were then used to assess their likely origin. Three major vertical sections were distinguished, separated by discontinuities: an uppermost unit consisting of aeolian material mixed with solifluctional deposits; a middle unit dominated by solifluctional materials; and a lowermost unit composed of colluvial deposits. The study confirms the utility of ERT in detecting subsurface differentiation of stratified slope sediments and provides a model for interpreting pedosedimentary sequences in Carpathian low-mountain environments. Full article

Underground mining environments are highly hazardous, often prone to gas explosions, cave-ins, and fires that may trap miners during emergencies. The accurate, real-time localization of miners is vital for effective self-escape and rescue operations. Although the Mine Improvement and New Emergency Response (MINER) Act of 2006 mandates communication and tracking systems, most current solutions rely on low-power devices and line-of-sight methods that are ineffective in GPS-denied, dynamic subsurface conditions. Delay-Tolerant Networking (DTN) has emerged as a promising alternative by supporting message relay through intermittent links. In this work, we propose a deep learning framework that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict miner locations using simulated DTN-based movement data. The model was trained on a simulated dataset of 1,048,575 miner movement entries, predicting miner locations across 26 pillar classes. It achieved an 89% accuracy, an 89% recall, and an 83% F1-score, demonstrating strong performance for real-time underground miner localization. These results demonstrate the model’s potential for the real-time localization of trapped miners in GPS-denied environments, supporting enhanced self-escape and rescue operations. Future work will focus on validating the model with real-world data and deploying it for operational use in mines. Full article

Studying the effects of environmental factors on microbial community assemblies is crucial for understanding microbial biodiversity and ecosystem processes. Although numerous studies have explored the spatial patterns of microbial communities in surface soils, bacterial community distributions in subsurface layers remain poorly understood. We investigated multiple community metrics of soil bacteria in arid and semi-arid grasslands in China, and the V4 region of 16S rDNA was analyzed using soil property measurements, fluorescent PCR, and high-throughput sequencing techniques. Specifically, copiotrophic taxa dominate the topsoil, whereas oligotrophic taxa are prevalent in nutrient-limited subsoil. Bacterial diversity decreases from the topsoil to subsoil, and bacterial distribution and ecological community composition exhibit a strong dependence on environmental factors. Moreover, microbial interaction networks demonstrated a progressive simplification with increasing soil depth: topsoil communities displayed higher modularity and a greater prevalence of positive interactions, whereas subsoil networks were significantly less complex. Null model analyses evidenced assembly mechanisms: deterministic processes (particularly homogeneous selection) dominated the bacterial community assembly, but their influence weakened with depth, whereas stochastic processes (e.g., dispersal limitation) increased progressively from the topsoil to subsoil. The PLS-PM analysis demonstrated that the relative influence of abiotic factors (e.g., climatic conditions and nutrient availability), biotic factors (interspecific interactions), along with drift and dispersal limitations on fungal community assembly exhibited depth-dependent patterns. This study provides novel insights into the vertical stratification of bacterial community in arid and semi-arid grasslands, and advances our understanding of pedogenic process under climate change and microbial adaptive strategies in heterogeneous soil environments. Full article

Recent advances in vision–language models (VLMs) have transformed the field of robotics. Researchers are combining the reasoning capabilities of large language models (LLMs) with the visual information processing capabilities of VLMs in various domains. However, most efforts have focused on terrestrial robots and are limited in their applicability to volatile environments such as ocean surfaces and underwater environments, where real-time judgment is required. We propose a system integrating the cognition, decision making, path planning, and control of autonomous marine surface vehicles in the ROS2–Gazebo simulation environment using a multimodal vision–LLM system with zero-shot prompting for real-time adaptability. In 30 experiments, adding the path plan mode feature increased the success rate from 23% to 73%. The average distance increased from 39 m to 45 m, and the time required to complete the task increased from 483 s to 672 s. These results demonstrate the trade-off between improved reliability and reduced efficiency. Experiments were conducted to verify the effectiveness of the proposed system and evaluate its performance with and without adding a path-planning step. The final algorithm with the path-planning sub-process yields a higher success rate, and better average path length and time. We achieve real-time environmental adaptability and performance improvement through prompt engineering and the addition of a path-planning sub-process in a limited structure, where the LLM state is initialized with every application programming interface call (zero-shot prompting). Additionally, the developed system is independent of the vision–LLM archetype, making it scalable and adaptable to future models. Full article

Unmanned aerial vehicles (UAVs) are an ideal platform for the remote sensing of riverbeds in small, tree-lined streams, allowing unobstructed viewing of the channel at high spatial resolution. However, effective UAV surveying of these riverbeds is hindered by a range of phenomena associated with the complex light environments of rivers, and small tree-lined streams in particular, including reflections of the overlying cloud layer from the water surface, sunglint on the water surface, and shadows from topography and riparian vegetation. We used UAV imagery acquired from small, tree-lined streams under different light conditions to identify the prevalence of the main phenomena—reflections of clouds, sunglint, and shadows—that hinder the ability to discern the riverbed. We characterized how large a constraint these phenomena are on the optimal imaging window. We then examined the degree to which sub-optimal light conditions may restrict this window, both within the year and within the day, across Europe. Our investigations suggest that different regions across Europe will have different priorities with regard to imaging, with surveys in northern rivers emphasizing avoiding low irradiant intensity in winter and those in southern rivers emphasizing avoiding sunglint around midday. We use our findings to suggest a protocol for improved riverbed imaging that is specific to the light environment of the stream under investigation. Full article

Nitrogen (N) fertilizer management in winter wheat production faces challenges from volatilization losses and sub-optimal application strategies. This is particularly problematic in the Southern Great Plains, where environmental conditions during top-dressing periods favor N losses. This study evaluated the effects of a fertilizer placement method, enhanced-efficiency fertilizers, and application timing on grain yield and protein concentration (GPC) across six site-years in Oklahoma (2016–2018). Treatments included broadcast applications of untreated urea and SuperU^® (urease/nitrification inhibitor-treated urea). These were compared with subsurface placement using single-disc and double-disc drilling systems, applied at 67 kg N ha⁻¹ during January, February, or March. Subsurface placement increased the grain yield by 324–391 kg ha⁻¹ compared to broadcast applications at sites with favorable soil conditions. However, responses varied significantly across environments. Enhanced-efficiency fertilizers showed limited advantages over untreated urea. Benefits were most pronounced during February applications under conditions favoring volatilization losses. Application timing effects were more consistent for GPC than for the yield. Later applications (February–March) increased GPC by 0.8–1.2% compared to January applications. Treatment efficacy was strongly influenced by soil pH, equipment performance, and post-application environmental conditions. This indicates that N management benefits are highly site-specific. These findings demonstrate that subsurface placement can improve nitrogen use efficiency (NUE) under appropriate conditions. However, success depends on matching application strategies to local soil and environmental factors rather than adopting universal recommendations. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Optical materials are widely used in large optical systems such as lithography machines and astronomical telescopes. However, optical materials inevitably produce subsurface damage (SSD) during lapping and polishing processes, degrading the laser damage threshold and impacting the service life of the optical system. The large surface roughness of the lapped optical materials further increases the difficulty of the nondestructive detection of SSD. Quantum dots (QDs) show great development potential in the nondestructive detection of SSD in lapped materials. However, existing QD-based SSD detection methods ignore the polarization sensitivity of QDs to excitation light, which affects the detection accuracy of SSD. To address this problem, this paper explores the fluorescence polarization properties of QDs in the SSD of optical materials. First, the detection principle of SSD based on the fluorescence polarization of QDs is investigated. Subsequently, a fluorescence polarization detection system is developed to analyze the fluorescence polarization properties of QDs in SSD. Finally, the SSD is detected based on the studied polarization properties. The results show that the proposed method effectively improves the detection rate of SSD by 10.8% and thus provides guidance for evaluating the quality of optical material and optimizing optical material processing technologies. The research paradigm is equally applicable to biomedicine, energy, optoelectronics, and the environment, where QDs have a wide range of applications. Full article

Many low-temperature applications, such as rocket engines and liquefied natural gas (LNG) transport pumps, necessitate ultra-low-temperature operational environments. In these conditions, the properties of lubricating oils and greases are significantly influenced by temperature, leading to the widespread adoption of solid lubrication. Currently, there is no international research regarding the influence of bearing coatings on the subsurface stress distribution in raceways. The Lundberg–Palmgren (L-P) theory states that subsurface stress variations govern bearing lifespan. Therefore, this paper utilizes existing formulas and Python programming to calculate the subsurface stress field of the inner raceway in a MoS₂ solid-lubricated angular contact ball bearing. Furthermore, it analyzes the impacts of factors such as coating material properties, slide-to-roll ratio, traction coefficient, and load on its subsurface stress field. The results reveal that for solid-lubricated ball bearings, as the load increases, the maximum subsurface stress shifts closer to the center of the contact area, and the maximum subsurface shear stress becomes more concentrated. As the traction coefficient increases, the stress on the XZ-plane side increases and its position moves closer to the surface, while the opposite trend is observed on the other side. Additionally, the maximum value of the subsurface von Mises stress is approximately 0.64P₀, and the maximum value of the orthogonal shear stress component τ_yz in the subsurface is approximately 0.25P₀. Full article

Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, humus, P₂O₅, K₂O, nitrogen), and vegetation/surface indices (NDVI, SAVI, LCI, BSI) derived from Sentinel-2 imagery. Using kriging, fuzzy k-means clustering, percentile-based classification, and Weighted Overlay Analysis (WOA), MZs were generated for a five-year period (2018–2022), with 2–8 zone classes. Stability and agreement were assessed using the Cohen Kappa, Jaccard, and Dice coefficients on systematic grid samples. Results showed that EM38-MK2 and humus-weighted BSP data produced the most consistent zones (Kappa > 0.90). Sentinel-2 indices demonstrated strong alignment with subsurface data (r > 0.85), offering a low-cost alternative in data-scarce settings. Optimal zoning was achieved with 3–4 classes, balancing spatial coherence and interpretability. These findings underscore the importance of multi-source data integration for robust and scalable MZ delineation and offer actionable guidelines for both data-rich and resource-limited farming systems. This approach promotes sustainable agriculture by improving input efficiency and allowing for targeted, site-specific field management. Full article

Civil engineering structures with damage, defects, or subsurface utilities create a high-contrast exploration environment. These anomalies of interest exhibit different electromagnetic properties from the surrounding medium, and ground-penetrating radar (GPR) has the potential to accurately locate and map their three-dimensional (3D) distributions. However, full-waveform inversion (FWI) for GPR data struggles to simultaneously reconstruct high-resolution 3D images of both permittivity and conductivity models. Considering the magnitude and sensitivity disparities of the model parameters in the inversion of GPR data, this study proposes a 3D dual-parameter FWI algorithm for GPR with a composite model constraint strategy. It balances the gradient updates of permittivity and conductivity models through performing total variation (TV) regularization and minimum support gradient (MSG) regularization on different parameters in the inversion process. Numerical experiments show that TV regularization can optimize permittivity reconstruction, while MSG regularization is more suitable for conductivity inversion. The TV+MSG composite model constraint strategy improves the accuracy and stability of dual-parameter inversion, providing a robust solution for the 3D imaging of subsurface anomalies with high-contrast features. These outcomes offer researchers theoretical insights and a valuable reference when investigating scenarios with high-contrast environments. Full article

Non-orthogonal multiple access (NOMA) technology can effectively improve spectrum efficiency, unmanned aerial vehicle (UAV) communication has the advantage of flexible deployment, and reconfigurable intelligent surface (RIS) can intelligently control the wireless transmission environment. Traditional communication systems have problems such as limited coverage and low spectrum efficiency in complex scenarios. However, a key challenge in deploying RIS-assisted NOMA-UAV communication systems lies in how to jointly optimize the UAV flight trajectory, power allocation strategy, and RIS phase offset to achieve the maximum average achievable rate for users. The non-convex nature of the optimization complicates the problem, making it challenging to find an efficient solution. Based on this, this paper presents a RIS-assisted NOMA-UAV communication system consisting of one UAV, one RIS, and two ground users. To achieve the maximum average rate for users, the UAV flight trajectory, power allocation strategy, and RIS phase offset are jointly optimized. For the non-convex problem, we decompose it into three sub-problems based on its inherent structural characteristics and use an alternating iterative approach to gradually converge to a feasible solution. The simulation results demonstrate that the proposed scheme offers significant advantages in the application scenario. Compared to other benchmark schemes, it delivers superior performance improvements to the communication system and offers higher practical value. Full article

To explore the feasibility of integrated deficit water-biogas slurry irrigation under indirect subsurface drip irrigation, three deficit irrigation levels (60%FC, 70%FC, and 80%FC; FC represents field capacity) were established during the three growth stages of tomatoes. The results indicated that biogas slurry irrigation treatments increased the soil organic matter content in the root zone and water use efficiency (WUE) and reduced soil pH. As the degree of deficit increased, the plant height and stem diameter of tomatoes decreased significantly (p < 0.05), particularly during the seedling and flowering-fruiting stages. A mild deficit during the seedling stage was beneficial for subsequent plant growth, yielding maximum leaf area (6871.42 cm² plant⁻¹). Moderate deficit treatment at the seedling stage maximized yield, which was 19.79% higher than the control treatment in 2020 and 19.22% higher in 2021. The WUE of severe deficit treatment at the maturity stage increased by 26.6% (2020) and 31.04% (2021) compared to the control treatment. Comprehensive evaluation using TOPSIS combined with the weighted method revealed that severe deficit treatment at the maturity stage provided the best comprehensive benefits for tomatoes. In summary, deficit irrigation at different growth stages positively influenced tomato growth, quality, and soil environment in response to water-biogas slurry irrigation. Full article