Search Results (1,370)

Arsenic (As) contamination has significantly increased in recent decades due to anthropogenic activities. This is a serious challenge for human health, environmental quality, and crop productivity. Biochar (BC) is an important practice used globally to remediate polluted soils. Likewise, melatonin (MT) has also shown tremendous results in mitigating metal toxicity and improving crop productivity. Nevertheless, the mechanism of combined BC and MT in alleviating As toxicity in rice (Oryza sativa L.) remains unexplored. In this study, we investigated how As affected rice and how the combined BC and MT facilitated As tolerance. The study comprised a control, As stress (100 mg kg⁻¹), As stress (100 mg kg⁻¹) + BC (2%), As stress (100 mg kg⁻¹) + MT (100 µM) and As stress (100 mg kg⁻¹) + BC (2%) + MT (100 µM). Arsenic significantly decreased rice growth and yield by increasing electrolyte leakage (EL), malondialdehyde (MDA), and hydrogen peroxide (H₂O₂). Co-applying BC and MT substantially enhanced rice growth and yield by increasing chlorophyll synthesis (48.12–92.42%) leaf water contents (40%), antioxidant activities (ascorbate peroxide: 56.43%, catalase: 55.14%, peroxidase: 57.77% and superoxide dismutase: 57.52%), proline synthesis (41.35%), MT synthesis (91.53%), and phytochelatins synthesis (125%) nutrient accumulation in rice seedlings and soil nutrient availability. The increased rice yield with BC + MT was also linked with reduced H₂O₂ production, As accumulation, soil As availability, and an increase in OsAPx6, OsCAT, OsPOD, OsSOD OsASMT1, and OsASMT2 and a decrease in expression of OsABCC1. Biochar + MT enhanced residual OM- and Fe, ((Fe₂As) and Mn (Mn₃(AsO₄)₂) bound forms of As leading to a substantial increase in rice growth and yield. Thus, the combination of BC and MT is an eco-friendly approach to mitigate As toxicity and improve rice productivity. Full article

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

A shallow quaternary sedimentary aquifer within the river alluvial deposits of eastern Uzbekistan is increasingly vulnerable to the impacts of climate change and anthropogenic activities. Despite its essential role in supplying water for domestic, agricultural, and industrial purposes, the aquifer system remains poorly understood. This study employed a three-dimensional MODFLOW-based groundwater flow model to assess climate change impacts on water budget components under the SSP5-8.5 scenario for 2020–2099. Model calibration yielded RMSE values between 0.25 and 0.51 m, indicating satisfactory performance. Simulations revealed that lateral inflows from upstream and side-valley alluvial deposits contribute over 84% of total inflow, while direct recharge from precipitation (averaging 120 mm/year, 24.7% of annual rainfall) and riverbed leakage together account for only 11.4%. Recharge occurs predominantly from November to April, with no recharge from June to August. Under future scenarios, winter recharge may increase by up to 22.7%, while summer recharge could decline by up to 100%. Groundwater storage is projected to decrease by 7.3% to 58.3% compared to 2010–2020, indicating the aquifer’s vulnerability to prolonged dry periods. These findings emphasize the urgent need for adaptive water management strategies and long-term monitoring to ensure sustainable groundwater use under changing climate conditions. Full article

Sequential drought and heat stress pose a growing threat to forest ecosystems in the context of climate change, yet systematic evaluation methods for woody plants remain limited. This study aimed to develop a comprehensive screening platform for identifying woody plant species tolerant to sequential drought and heat stress among 27 native species growing in Korea. A sequential stress protocol was applied: drought stress for 2 weeks, followed by high-temperature exposure at 45 °C. Physiological indicators, including relative water content (RWC) and electrolyte leakage index (ELI), were used for preliminary screening, supported by phenotypic observations, Evans blue staining for cell death, and DAB staining to assess oxidative stress and recovery ability. The results revealed clear differences among species. Chamaecyparis obtusa, Quercus glauca, and Q. myrsinaefolia exhibited strong tolerance, maintaining high RWC and low ELI values, while Albizia julibrissin was highly susceptible, showing severe membrane damage and low survival. DAB staining successfully distinguished tolerance levels based on oxidative recovery. Additional species such as Camellia sinensis, Q. acuta, Q. phillyraeoides, Q. salicina, and Ternstroemia japonica showed varied responses: Q. phillyraeoides demonstrated high tolerance, T. japonica showed moderate tolerance, and Q. salicina was relatively sensitive. The integrated screening system effectively differentiated tolerant species through multiscale analysis—physiological, cellular, and morphological—demonstrating its robustness and applicability. This study provides a practical and reproducible framework for evaluating sequential drought and heat stress in trees and offers valuable resources for urban forestry, reforestation, and climate-resilient species selection. Full article

Alkaline electrolytic water hydrogen generation, a key driver in the growth of hydrogen energy, heavily relies on high-efficiency and high-purity ion exchange membranes. In this study, three-dimensional (3D) wrinkled reduced graphene oxide (WG) nanosheets obtained through a simple thermal reduction process and two-dimensional (2D) graphene oxide act as building blocks, with ethylenediamine as a crosslinking stabilizer, to construct a unique 3D/2D 2 nm-tunneling structure between the GO and WG sheets through via an amide connection at a WG/GO ratio of 1:1. Here, the wrinkled graphene (WG) undergoes a transition from two-dimensional (2D) graphene oxide (GO) into three-dimensional (3D) through the adjustment of surface energy. By increasing the interlayer spacing and the number of ion fluid channels within the membranes, the E-W/G membrane has achieved the rapid passage of hydroxide ions (OH⁻) and simultaneous isolation of produced gas molecules. Moreover, the dense 2 nm nano-tunneling structure in the electrolytic water process enables the E-W/G membrane to attain current densities >99.9% and an extremely low gas crossover rate of hydrogen and oxygen. This result suggests that the as-prepared membrane effectively restricts the unwanted crossover of gases between the anode and cathode compartments, leading to improved efficiency and reduced gas leakage during electrolysis. By enhancing the purity of the hydrogen production industry and facilitating the energy transition, our strategy holds great potential for realizing the widespread utilization of hydrogen energy. Full article

In this study, we report the development of a novel magnetized coal fly ash-supported nano-silver composite (AgNPs/MCFA) for dual-functional applications in wastewater treatment: the efficient degradation of methyl orange (MO) dye and broad-spectrum antibacterial activity. The composite was synthesized via a facile impregnation–reduction–sintering route, utilizing sodium citrate as both a reducing and stabilizing agent. The AgNPs/MCFA composite was systematically characterized through multiple analytical techniques, including Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and vibrating sample magnetometry (VSM). The results confirmed the uniform dispersion of AgNPs (average size: 13.97 nm) on the MCFA matrix, where the formation of chemical bonds (Ag-O-Si) contributed to the enhanced stability of the material. Under optimized conditions (0.5 g·L⁻¹ AgNO₃, 250 °C sintering temperature, and 2 h sintering time), AgNPs/MCFA exhibited an exceptional catalytic performance, achieving 99.89% MO degradation within 15 min (pseudo-first-order rate constant k_a = 0.3133 min⁻¹) in the presence of NaBH₄. The composite also demonstrated potent antibacterial efficacy against Escherichia coli (MIC = 0.5 mg·mL⁻¹) and Staphylococcus aureus (MIC = 2 mg·mL⁻¹), attributed to membrane disruption, intracellular content leakage, and reactive oxygen species generation. Remarkably, AgNPs/MCFA retained >90% catalytic and antibacterial efficiency after five reuse cycles, enabled by its magnetic recoverability. By repurposing industrial waste (coal fly ash) as a low-cost carrier, this work provides a sustainable strategy to mitigate nanoparticle aggregation and environmental risks while enhancing multifunctional performance in water remediation. Full article

This study focuses on the optimization of valve assemblies in downhole rod pumping units (DRPUs), which remain the predominant artificial lift technology in oil production worldwide. The research addresses the critical issue of premature failures in DRPUs caused by leakage in valve pairs, i.e., a problem that accounts for approximately 15% of all failures, as identified in a statistical analysis of the 2022 operational data from the Uzen oilfield in Kazakhstan. The leakage is primarily attributed to the accumulation of mechanical impurities and paraffin deposits between the valve ball and seat, leading to concentrated surface wear and compromised sealing. To mitigate this issue, a novel valve assembly design was developed featuring a flow turbulizer positioned beneath the valve seat. The turbulizer generates controlled vortex motion in the fluid flow, which increases the rotational frequency of the valve ball during operation. This motion promotes more uniform wear across the contact surfaces and reduces the risk of localized degradation. The turbulizers were manufactured using additive FDM technology, and several design variants were tested in a full-scale laboratory setup simulating downhole conditions. Experimental results revealed that the most effective configuration was a spiral plate turbulizer with a 7.5 mm width, installed without axis deviation from the vertical, which achieved the highest ball rotation frequency and enhanced lapping effect between the ball and the seat. Subsequent field trials using valves with duralumin-based turbulizers demonstrated increased operational lifespans compared to standard valves, confirming the viability of the proposed solution. However, cases of abrasive wear were observed under conditions of high mechanical impurity concentration, indicating the need for more durable materials. To address this, the study recommends transitioning to 316 L stainless steel for turbulizer fabrication due to its superior tensile strength, corrosion resistance, and wear resistance. Implementing this design improvement can significantly reduce maintenance intervals, improve pump reliability, and lower operating costs in mature oilfields with high water cut and solid content. The findings of this research contribute to the broader efforts in petroleum engineering to enhance the longevity and performance of artificial lift systems through targeted mechanical design improvements and material innovation. Full article

The practice in agriculture of spreading polyethylene (PE) film over the soil surface as mulch is a common, global practice that aids in conserving water, increasing crop yields, suppressing weed growth, and decreasing growing time. However, these films are typically only used for a single growing season, and thus, their use and non-biodegradability come with some serious environmental consequences due to their persistence in the soil and potential for microplastic pollution, particularly when retrieval and disposal options are poor. On the microscale, particles < 5 mm from degraded films have been observed to disrupt soil structure, impede water and nutrient cycling, and affect soil organisms and plant health. On the macroscale, there are obvious and serious environmental consequences associated with the burning of plastic film and its leakage from poorly managed landfills. To maintain the crop productivity afforded by mulching with PE film while avoiding the environmental downsides, the development and use of biodegradable polymer technologies is being explored. Here, the efficacy of a newly developed, water-dispersible, sprayable, and biodegradable polyester–urethane–urea (PEUU)-based polymer was compared with two commercial PE mulches, non-degradable polyethylene (NPE) and OPE (ox-degradable polyethylene), in a greenhouse tomato growth trial. Water savings and the effects on plant growth and soil characteristics were studied. It was found that PEUU provided similar water savings to the commercial PE-based mulches, up to 30–35%, while showing no deleterious effects on plant growth. The results should be taken as preliminary indications that the sprayable, biodegradable PEUU shows promise as a replacement for PE mulch, with further studies under outside field conditions warranted to assess its cost effectiveness in improving crop yields and, importantly, its longer-term impacts on soil and terrestrial fauna. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

Leakages in water distribution networks pose a formidable challenge, often leading to substantial water wastage and escalating operational costs. Traditional methods for leak detection often fall short, particularly when dealing with complex or subtle data patterns. To address this, a comprehensive comparison of fourteen machine learning algorithms was conducted, with evaluation based on key performance metrics such as multi-class classification metrics, micro and macro averages, accuracy, precision, recall, and F1-score. The data, collected from an experimental site under leak, major leak, and no-leak scenarios, was used to perform multi-class classification. The results highlight the superiority of models such as Random Forest, K-Nearest Neighbours, and Decision Tree in detecting leaks with high accuracy and robustness. Multiple models effectively captured the nuances in the data and accurately predicted the presence of a leak, burst, or no leak, thus automating leak detection and contributing to water conservation efforts. This research demonstrates the practical benefits of applying machine learning models in water distribution systems, offering scalable solutions for real-time leak detection. Furthermore, it emphasises the role of machine learning in modernising infrastructure management, reducing water losses, and promoting the sustainability of water resources, while laying the groundwork for future advancements in predictive maintenance and resilience of water infrastructure. Full article

The encapsulation of hydrophobic substances remains a significant challenge due to limitations such as low loading efficiency, leakage, and poor distribution within microcapsules. This study introduces a novel strategy utilizing colloidosomes assembled from polyelectrolyte microcapsules (PMCs). PMCs were fabricated via layer-by-layer (LbL) assembly on manganese carbonate (MnCO₃) or calcium carbonate (CaCO₃) cores, followed by core dissolution. A solvent gradient replacement method was employed to substitute the internal aqueous phase of PMCs with kerosene, enabling the formation of colloidosomes through self-assembly upon resuspension in water. Comparative analysis revealed that MnCO₃-based PMCs with smaller diameters (2.5–3 µm vs. 4.5–5.5 µm for CaCO₃) exhibited 3.5-fold greater stability, attributed to enhanced inter-capsule interactions via electrostatic and hydrophobic forces. Confocal microscopy confirmed the structural integrity of colloidosomes, featuring a liquid kerosene core encapsulated within a PMC shell. Temporal stability studies indicated structural degradation within 30 min, though 5% of colloidosomes retained integrity post-water evaporation. PMC-based colloidosomes exhibit significant application potential due to their integration of colloidosome functionality with PMC-derived structural features—semi-permeability, tunable shell thickness/composition, and stimuli-responsive behavior—enabling their adaptability to diverse technological and biomedical contexts. This innovation holds promise for applications in drug delivery, agrochemicals, and environmental technologies, where controlled release and stability are critical. The findings highlight the role of core material selection and solvent engineering in optimizing colloidosome performance, paving the way for advanced encapsulation systems. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

Traditional manual inspection methods for tunnel lining leakage are subjective and inefficient, while existing models lack sufficient recognition accuracy in complex scenarios. An intelligent leakage identification model adaptable to complex backgrounds is therefore needed. To address these issues, a Vision Transformer (ViT) was integrated into the UNet architecture, forming an SE-TransUNet model by incorporating SE-Block modules at skip connections between the encoder-decoder and the ViT output. Using a hybrid leakage dataset partitioned by k-fold cross-validation, the roles of SE-Block and ViT modules were examined through ablation experiments, and the model’s attention mechanism for leakage features was analyzed via Score-CAM heatmaps. Results indicate: (1) SE-TransUNet achieved mean values of 0.8318 (IoU), 0.8304 (Dice), 0.9394 (Recall), 0.8480 (Precision), 0.9733 (AUC), 0.8562 (MCC), 0.9218 (F1-score), and 6.53 (FPS) on the hybrid dataset, demonstrating robust generalization in scenarios with dent shadows, stain interference, and faint leakage traces. (2) Ablation experiments confirmed both modules’ necessity: The baseline model’s IoU exceeded the variant without the SE module by 4.50% and the variant without both the SE and ViT modules by 7.04%. (3) Score-CAM heatmaps showed the SE module broadened the model’s attention coverage of leakage areas, enhanced feature continuity, and improved anti-interference capability in complex environments. This research may provide a reference for related fields. Full article

As the urban underground natural gas pipeline network expands, the explosion risk arising from methane accumulation in drainage ditches due to pipeline leakage has increased severely. A two-dimensional numerical model—9.7 m in length (including a 1-m obstacle section), 0.1 m in diameter, and with a water volume fraction of 0.2—was developed to address the flexible boundary characteristics of urban underground ditches. The investigation examined the influence of methane concentration on explosion propagation characteristics. Results indicated that, at a methane concentration of 11%, the peak pressure attained 157.9 kPa, and the peak temperature exceeded 3100 K—all of which were significantly higher than the corresponding values at 10%, 13%, and 16% concentrations. Explosion-induced water motion exerted a cooling effect that inhibited heat and pressure transfer, while obstacles imposed partial restrictions on flame propagation. Temporal profiles of temperature and pressure exhibited three distinct stages: “initial stability–rapid rise–attenuation”. Notably, at a methane concentration of 16%, the water column formed by fluid vibration demonstrated a pronounced cooling effect, causing faster decreases in measured temperatures and pressures compared to other concentrations. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article