Search Results (6,627)

Rising ocean temperatures due to climate change, combined with the intensification of anthropogenic activity, may lead to changes in the physiology and distribution of native species. Compounding climate stress, microplastic particles (MPs) enter the oceans through wastewater and the breakdown of macroplastics. Depending on their composition, they can be harmful and act as a vehicle for toxic substances, although their effects on native Antarctic and subantarctic species are unknown. Notothenioid fish are members of this group and are found inside and outside Antarctica, such as the Harpagifer, which has adapted to the cold and is particularly sensitive to thermal increases. Here, we aimed to evaluate the innate immune response in the head kidney, spleen, and foregut of two notothenoid fish, Harpagifer antarcticus and Harpagifer bispinis, exposed to elevated temperatures and PVC (polyvinyl chloride) microplastics. Adults from both species were collected on King George Island (Antarctica) and Punta Arenas (Chile), respectively. Specimens were assigned to a control group or exposed to a temperature increase (TI) or PVC microplastics (MPs), separately or in combination (MPs + TI). MP exposures were oral (gavage) for 24 h or aqueous (in a bath) for 24 and 48 h. Using real-time qPCR, we evaluated the relative gene expression of markers involved in the innate immune response, including tlr2 (toll-like receptor 2), tlr4 (toll-like receptor 4), myd88 (myeloid differentiation factor 88), nfkb (nuclear factor kb), il6 (interleukin 6), and il8 (irterleukin 8). We found differences between treatments when H. antarcticus and H. bispinis were exposed independently to MPs or thermal increase (TI) in the experiment with a cannula, showing an up-regulation in transcripts. In contrast, a down-regulation was observed when exposed in combination to MP + TI, which looked to be tissue-dependent. However, transcripts related to innate immunity in the bath experiment increased when exposure to both stressors was combined, mostly at 48 h. These results highlight the importance of evaluating the effects of multiple stressors, both independently and in combination, and whether these species will have the capacity to adapt or survive under these conditions, especially in waters where temperature is increasing and pollution is also rising, primarily from MP-PVC, a plastic widely used in various industries and among the population. Full article

In the field of water process engineering, accurately predicting the total nitrogen (TN) concentration distribution in the Semi-Enclosed Bay area is of great importance for water quality assessment, pollution control, and scientific management. Due to the coupling of multiple influencing factors, the pollution process is complex, and traditional monitoring methods struggle to achieve large-scale, long-term real-time observation. Although numerical simulations can reproduce TN transport processes, they are computationally expensive and have low prediction efficiency. To address this, this study develops a deep learning hybrid model that integrates a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) network, referred to as the TCN-LSTM-Hybrid Model, to predict the spatiotemporal distribution of TN concentration fields in Shenzhen Bay. Comparative experiments show that this model outperforms traditional models such as TCN, LSTM, GRU, and MLP in terms of prediction accuracy and spatial generalization, offering higher computational efficiency and breaking through the limitations of “point-based prediction” by achieving “field-based prediction,” thereby providing a new path for pollutant simulation in complex ocean environments, supporting more informed decision making in ocean and coastal management. Full article

In plain river networks, a sluggish flow due to the flat terrain and hydraulic structures significantly reduces water’s capacity for self-purification, leading to persistent water pollution that threatens aquatic ecosystems and human health. Despite being critical, effective water quality prediction proves challenging in such regions, with current models lacking either physical interpretability or temporal accuracy. To address this gap, both a process-based model (MIKE 21) and a deep learning model (CNN-LSTM-Attention) were developed in this study to predict key water quality indicators—dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP)—in a typical river network area in Jiaxing, China. This site was selected for its representative complexity and acute pollution challenges. The MIKE 21 model demonstrated strong performance, with R² values above 0.88 for all indicators, offering high spatial resolution and mechanistic insight. The CNN-LSTM-Attention model excelled in capturing temporal dynamics, achieving an R² of 0.9934 for DO. The results indicate the complementary nature of these two approaches: while MIKE 21 supports scenario-based planning, the deep learning model enables highly accurate real-time forecasting. The findings are transferable to similar river network systems, providing a robust reference for selecting modeling frameworks in the design of water pollution control strategies. Full article

Among the main man-made water pollutants that pose a danger to the environment are oil products, heavy metals, and radionuclides, as well as micro- and nanoplastics. To purify such waters, it is necessary to use advanced methods, with sorption being one of them. The aim of this work is to develop a nano-functionalized composite, comprising magnetically responsive, thermally expanded graphite (TEG) and the natural clay bentonite, and to assess its ability to purify man-made contaminated waters. Throughout the course of the research, the methods of scanning electron microscopy, optical microscopy, dynamic light scattering, radiometry, and atomic absorption spectrophotometry were used. The use of the TEG–bentonite composite for the purification of the model water, simulating radioactively contaminated nuclear power plant (NPP) effluent, reduced the content of organic substances by 10–15 times, and the degree of extraction of cesium, strontium, cobalt, and manganese was between 81.4% and 98.8%. The use of the TEG–bentonite composite for the purification of real radioactively contaminated water obtained from the object “Shelter” (“Ukryttya” in Ukrainian), in the Chernobyl Exclusion Zone, Ukraine, with high activity, containing organic substances, including micro- and nanoplastics, reduced the radioactivity by three orders of magnitude. The use of cesium-selective sorbents for additional purification of the filtrate allowed for further decontamination of radioactively contaminated water with an efficiency of 99.99%. Full article

Background and Objectives: To evaluate the effect of a ureteral access sheath (UAS) on the maximal intra-pelvic pressure (IPP max) during retrograde lithotripsy of hard and soft stones in a porcine model. Materials and Methods: A 22 Fr percutaneous tract was established in the upper calyces of the kidneys in three female pigs. A custom-made Foley catheter with a urodynamic catheter was inserted into the pelvicalyceal system and connected to a urodynamic device for real-time IPP measurement. A Pusen Uscope 7.5 Fr single-use ureteroscope (Zhuhai Pusen Medical Technology, Jinhua, China) with manual pump irrigation was used. BegoStone™ powder (Bego, Lincoln, RI, USA) was prepared in two powder-to-water ratios (15:3 and 15:6) to create hard and soft stones, respectively. Stones were positioned in the pelvicalyceal system through the percutaneous tract, and retrograde intrarenal lithotripsy was performed in three settings: without UAS and with a 9.5/11 Fr UAS, with lasing in the center of the pelvis, and during lithotripsy of soft and hard stones. Results: With manual pump irrigation and without a UAS, the IPP max reached 55 cmH₂O during lasing in the pelvis center. During lithotripsy of soft and hard stones, the IPP max increased to 62 and 65 cmH₂O, respectively. Using a UAS, the IPP max was significantly lower: 18 cmH₂O in the center of the pelvis, and 25 and 29 cmH₂O during lithotripsy of soft and hard stones, respectively. Conclusions: Manual pump irrigation without a UAS can elevate IPP max to potentially unsafe levels during retrograde correct flexible lithotripsy, even when using a 7.5 Fr flexible scope. The addition of a UAS helps maintain the IPP max within safer limits. Full article

Digital twins (DTs) offer significant promise for condition-based maintenance of floating offshore wind turbines (FOWTs); however, existing solutions typically compromise either on physical rigor or real-time computational performance. This paper presents a real-time DT framework that resolves this trade-off by embedding a hydro-elastic reduced-order model (ROM) that accurately captures structural dynamics and fluid–structure interaction. Integrated in a cloud-ready Internet of Things architecture, the ROM reconstructs full-field displacements, von Mises stresses, and fatigue metrics with near real-time responsiveness. Validation on the 5 MW OC4-DeepCWind semi-submersible platform shows that the ROM reproduces finite-element (FEM) displacements and stresses with relative errors below 1%. A three-hour load case is solved in 0.69 min for displacements and 3.81 min for stresses on a consumer-grade NVIDIA RTX 4070 Ti GPU—over two orders of magnitude faster than the full FEM model—while one million fatigue stress histories (1000 hotspots × 1000 operating scenarios) are processed in 37 min. This efficiency enables continuous structural monitoring, rapid *what-if* assessments and timely decision-making for targeted inspections and adaptive control. By effectively combining physics-based reduced-order modeling with high-throughput computation, the proposed framework overcomes key barriers to DT deployment: computational overhead, physical fidelity and scalability. Although demonstrated on a steel platform, the approach is readily extensible to composite structures and multi-turbine arrays, providing a robust foundation for cost-effective and reliable deep-water wind-energy operations. Full article

This study investigates the capacity of green and brown algae to sustainably remove oxyanions from contaminated waters, highlighting their cost-effectiveness. Often considered biomass waste and contributors to organic contamination, these algae can be used as effective biosorbents, aligning with circular economy principles and sustainable waste management. Various pre-treatments were tested to enhance adsorption capacity, with mixed results regarding their effectiveness. The focus then shifted to the use of Cladophora sericea algae for the uptake and removal of selenium species, specifically selenite (Se(IV)) and selenate (Se(VI)). The effects of different operational parameters on oxyanion uptake by algae were studied in batch mode. The assessments were conducted on a single-component and a multi-component synthetic matrix. The results indicate that pH significantly impacts biosorption, with equilibrium achieved in 90 min. Both pseudo-first-order and pseudo-second-order models provided a good fit to the experimental data. The algae’s retention capacity for selenium remained largely unaffected by the presence of other anions, a key advantage for application in complex real effluent matrices. Kinetic studies performed under different values of initial pollutant concentration and biosorbent mass indicate a biosorbed amount at an equilibrium of 570 µg g⁻¹. Full article

Waste stabilization ponds (WSPs) in humid, subtropical climates rely on stable temperatures and mechanical aeration to promote microbial activity. These critical infrastructures can lack operational resources to ensure efficient treatment, which can impact downstream communities. This study aims to use remote water quality sensor data to establish trends in a yearly dataset and correlate various water quality parameters for simplistic identification of pond health. A facultative WSP was monitored in two stages: the primary settling over a period of 14 months to evaluate partially treated water, and the secondary treatment pond for a period of 11 months to monitor final stage water quality parameters. A statistical analysis was performed on the measured parameters (dissolved oxygen, temperature, conductivity, pH, turbidity, nitrate, and ammonium) to establish a comprehensive yearly, seasonal, and monthly dataset to show fluctuations in water parameter correlations. Standard relationships in dissolved oxygen, conductivity, pH, and temperature were traced during the seasonal fluctuations, which provided insight into nitrogen processing by microbial communities. During this study, the summer period showed the most variability, specifically a deviation in the dissolved oxygen and temperature relationship from a yearly moderate negative correlation (−0.593) to a moderate positive correlation (0.459), indicating a direct relationship. The secondary treatment pond data showed more nitrogen species correlation, which can indicate final cycling during seasonal transitions. Understanding pond dynamics can lead to impactful, proactive operational decisions to address pond imbalance or chemical dosing for final treatment. By establishing parameter correlations, facilities with WSPs can strategically integrate sensor networks for real-time pond health and treatment efficiency monitoring during seasonal fluctuations. Full article

Swimming pools, symbols of luxury in tourism-driven Greece, raise concerns about water consumption in water-scarce regions. This study assesses their hydrological impact in two regions of Southern Greece, West Mani (Peloponnese) and Naxos Island (Cyclades), within the water–energy–food nexus framework, evaluating the resulting trade-offs. Using satellite imagery, we identified 354 pools in West Mani (11,738 m²) and 556 in Naxos (26,825 m²). Two operational scenarios were evaluated: complete seasonal emptying and refilling (Scenario 1) and one-third annual water renewal (Scenario 2). Annual water use ranged from 39,000 to 51,000 m³ in West Mani and 98,000 to 124,000 m³ in Naxos—equivalent to the needs of 625–2769 and 1549–6790 people in West Mani and Naxos, respectively. In Naxos, this volume could alternatively irrigate 27–40 hectares of potatoes, producing food for 700–1500 people. Energy requirements, particularly where desalination is used, further increase the burden, with Naxos pools requiring 384–846 MWh annually. Although swimming pools are highly visible water consumers, their overall contribution to water scarcity is modest compared to household and agricultural uses. Their visibility, however, amplifies public concern. Rainwater harvesting, requiring collection areas 10–24 times larger than pool surface areas, especially in residential and hotel settings, could make pools largely self-sufficient. Integrating such measures into water management and tourism policy can help balance luxury amenities with resource conservation in water-scarce Mediterranean regions. Full article

Transportation networks are critical lifelines in national infrastructure but are increasingly exposed to risks arising from climate variability, cyber threats, aging assets, and limited resources. This paper presents a scoping review of 58 peer-reviewed studies published between 2015 and 2025 that examine the role of Artificial Intelligence (AI) in strengthening infrastructure resilience, with transportation systems adopted as the strategic case. The review classifies applications along five dimensions: technological approach, infrastructure sector, transportation linkage, resilience/security aspect, and key research gaps. Findings show that AI, machine learning (ML), and the Internet of Things (IoT) dominate current applications, particularly in predictive maintenance, intelligent monitoring, early-warning systems, and optimization. These applications extend beyond transport to energy, water, and agri-food systems that indirectly sustain transport resilience. Persistent challenges include affordability, data scarcity, infrastructural limitations, and limited real-world validation, especially in Sub-Saharan African contexts. The paper synthesizes cross-sector pathways through which AI enhances transport resilience and outlines practical implications for policymakers and practitioners. A targeted research agenda is also proposed to address methodological gaps, enhance deployment in resource-constrained settings, and promote hybrid and explainable AI for trust and scalability. Full article

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

Currently, maritime navigation safety risks—particularly those related to ship navigation—are primarily assessed through traditional rule-based methods and expert experience. However, such approaches often suffer from limited accuracy and lack real-time responsiveness. As maritime environments and operational conditions become increasingly complex, traditional techniques struggle to cope with the diversity and uncertainty of navigation scenarios. Therefore, there is an urgent need for a more intelligent and precise risk prediction method. This study proposes a ship risk prediction framework that integrates a deep learning model based on Long Short-Term Memory (LSTM) networks with Bayesian risk evaluation. The model first leverages deep neural networks to process time-series trajectory data, enabling accurate prediction of a vessel’s future positions and navigational status. Then, Bayesian inference is applied to quantitatively assess potential risks of collision and grounding by incorporating vessel motion data, environmental conditions, surrounding obstacles, and water depth information. The proposed framework combines the advantages of deep learning and Bayesian reasoning to improve the accuracy and timeliness of risk prediction. By providing real-time warnings and decision-making support, this model offers a novel solution for maritime safety management. Accurate risk forecasts enable ship crews to take precautionary measures in advance, effectively reducing the occurrence of maritime accidents. Full article

We report a novel tablet-based reverse titration system for rapid, point-of-use measurement of water hardness, overcoming key limitations of conventional EDTA titration. Reagents are encapsulated in pullulan matrix giving two separate tablets. The first tablet contains the Eriochrome black T (EBT) and N-cyclohexyl-3-aminopropanesulfonic acid (CAPS) buffer, while the second encapsulates ethylenediaminetetraacetic acid (EDTA) disodium salt dihydrate. The system employs a trimodal detection strategy: qualitative screening via immediate color change with the EBT tablet, semi-quantitative estimation through combined tablet dissolution and adjusting the sample volume to a reference level, and quantitative determination using reverse titration, where water is gradually added until the red wine endpoint appears. This approach enhances interference tolerance from competing metal ions and improves accuracy over traditional methods. Testing with real water samples showed excellent agreement with standard titration. The tablets remain stable for over seven months, and the system eliminates the need for skilled personnel, laboratory equipment, or bulky instrumentation. This low-cost, user-friendly, and interference-tolerant platform enables rapid and accurate water hardness assessment at the point of use. Full article

The impact freezing of supercooled water droplets poses a significant threat to the safety of aircraft and power transmission equipment. In recent years, extensive research has been conducted using numerical methods to investigate this phenomenon. However, existing models often incorrectly predict premature freezing near the droplet–air contact line during the early stage of impact, thereby unreasonably suppressing the spreading process in these regions. To address this limitation, this study proposes a velocity-gate-based activation control strategy for the Darcy momentum source, enabling its dynamic adjustment during simulation. The methodology integrates the Volume of Fluid (VOF) model, the solidification model, and the dynamic contact angle (DCA) model with the proposed dynamic Darcy source, while accounting for the influence of supercooling on physical properties. The numerical simulations are performed using COMSOL Multiphysics 6.3 and validated against experimental spreading factor data. The results demonstrate that the proposed methodology effectively eliminates nonphysical freezing during the initial spreading stage, and the predicted spreading factors agree well with experiments, with a maximum relative deviation of up to 11.7% across all simulated cases. The proposed approach improves consistency with real-world behavior and enhances the reliability of existing numerical tools for aircraft icing prediction and anti-icing design. Full article

Stricter requirements on nutrient removal in wastewater treatment are being imposed by rapid urbanization and tightening water-quality standards. Despite their excellent solid–liquid separation and effective biological treatment, MBRs in conventional operation remain hindered by membrane fouling, limited robustness to influent variability, and elevated energy consumption. In recent years, precise process control and resource-oriented operation have been enabled by the integration of artificial intelligence (AI) with MBRs. Advances in four areas are synthesized in this review: optimization of MBR control architectures, intelligent adaptation to multi-source wastewater, regulation of membrane operating parameters, and enhancement of nitrogen and phosphorus removal. According to reported studies, increases in total nitrogen and total phosphorus removal have been achieved by AI-driven strategies while energy use and operating costs have been reduced; under heterogeneous influent and dynamic operating conditions, stronger generalization and more effective real-time regulation have been demonstrated relative to traditional approaches. For large-scale deployment, key challenges are identified as improvements in model interpretability and applicability, the overcoming of data silos, and the realization of multi-objective collaborative optimization. Addressing these challenges is regarded as central to the realization of robust, scalable, and low-carbon intelligent wastewater treatment. Full article