Search Results (1,932)

This discussion paper examines the evolution of freshwater distribution networks from smart to intelligent and ultimately meta-intelligent or wise systems, highlighting the transition from human-supervised operation to autonomous adaptive management. Smart systems integrate monitoring, automation and remote control through information technologies. Intelligent systems extend these capabilities by adding predictive analytics, demand forecasting and automated operational optimization. Wise systems further evolve through adaptive learning mechanisms that allow continuous self-improvement while minimizing dependence on operators. Evidence from Greek water utilities demonstrates practical applications and operational outcomes. The analysis discusses implementation challenges including investment costs, system complexity, data governance and resilience. Finally, the paper proposes design principles for scalable adaptive water networks applicable to utilities with different sizes, resources and levels of technological maturity. Full article

Diagnosing tilapia diseases in complex aquaculture environments is severely hindered by noisy backgrounds and limited high-quality pathological data. To overcome these bottlenecks, this study presents a two-stage diagnostic framework integrating an enhanced Segment Anything Model (SAM) with an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the first stage, SAM is augmented with a Convolutional Block Attention Module (CBAM) feature adapter and a Region Proposal Network (RPN)-based prompt encoder. This design enables the automated and precise extraction of irregular disease lesions by self-generating spatial prompts, thereby isolating water background noise. In the second stage, clinical color features extracted from the lesion masks are classified using ANFIS. To optimize performance on small-scale datasets, ANFIS parameters are trained via Particle Swarm Optimization (PSO) under a numerically stable One-vs-Rest (OvR) binary cross-entropy loss. Validated on the public dataset “Enhancing Disease Detection in Nile Tilapia”, our method delivers an average segmentation Dice coefficient of 86.2% and a classification accuracy of 93.5%. This hybrid approach demonstrates strong potential as a foundational baseline for the automated monitoring of aquaculture diseases. Full article

Plant-based food systems increasingly rely on heat-induced gelation of protein–starch mixtures, yet no focused synthesis has linked legume protein composition to mixed gel structure and function. This review critically analyses heat-induced gelation mechanisms in legume protein–starch systems, using the legumin-to-vicilin (L:V) ratio and starch origin as integrating design parameters. Legume storage proteins range from legumin-rich faba bean and Lupinus angustifolius, which form dense, disulfide-stabilised networks with high storage moduli, to vicilin-dominated mung bean, which produces weaker gels reliant on starch reinforcement. Pulse starches, characterised by high amylose content (24–45%), C-type crystallinity, and rapid amylose retrogradation upon cooling, act as a parallel gel-forming phase whose contribution scales inversely with protein network strength. Four protein–starch interaction modes, namely segregative phase separation, water competition, granule filler effects, and molecular complexation, jointly determine microstructure and rheological behaviour. A three-axis compositional framework defined by the L:V ratio, starch amylose content, and protein-to-starch ratio maps the gel design space. Variables favouring plant-based meat analogue performance, including high elastic modulus, yield stress, and hardness, are systematically opposed by dysphagia food requirements, including low yield stress, adequate lubrication, and soft fracture. This demonstrates that both application domains traverse the same compositional space in opposite directions. Critical research gaps include chickpea and lentil performance in meat analogue systems, mechanistic modelling of protein-matrix-mediated starch digestibility, and retrogradation kinetics during food storage. Full article

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity (ε* = ε′ – jε″) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ε′ and ε″ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ε″ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ε′ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios. Full article

The study examines the evolution, knowledge structure, and trends in snowmelt runoff prediction models. It identifies research hotspots, future directions, and offers a theoretical basis for accurate simulation and prediction. Utilizing CiteSpace software, 556 core Chinese and English publications from 2010 to 2025 were visually analyzed. Research on snowmelt runoff simulation shows: (1) Chinese publications are prominent in core journals like “Journal of Glaciology and Geocryology,” while English publications appear in high-impact journals like “Water Resources Research.” (2) Institutions like the University of Chinese Academy of Sciences, the Northwest Institute of Eco-Environment and Resources, and the University of California have formed a cross-regional research network. (3) International collaboration involves 42 countries, with a focus on China, the United States, and India. However, domestic institutional cooperation needs improvement. (4) Research trends in snowmelt runoff simulation have progressed from empirical statistics to remote sensing and model-driven physical mechanisms, and now to the integration of artificial intelligence with physical models. (5) The Chinese literature focuses on cold regions, while the English literature emphasizes intelligent modeling. This shift indicates a move towards “physical–intelligent” hybrid modeling. Future research should address challenges like model applicability in data-scarce areas, improving interpretability of complex models, quantifying uncertainties, and developing physically constrained deep learning models. Collaboration among institutions is crucial for enhancing water resource management and disaster warning systems in cold regions. Full article

The Gulf of Mexico, a geologically complex environment, supports mesophotic coral ecosystems, with reefs such as the Pinnacle Trend, Flower Garden Banks National Marine Sanctuary, the Florida Middle Ground reef system, and Pulley Ridge. Mountain Top Bank is a dome-shaped hardground feature located 60–150 m below the sea surface along the Mississippi–Alabama shelf. It appears to prolong the Pinnacle Trend towards the southeast, bridging the gap between mesophotic coral reefs east and west of the Mississippi Canyon. Shipborne high-resolution multibeam data (bathymetry, backscatter, and water-column) and an AUV photomosaic were collected over the site during several oceanographic expeditions. Data were analyzed and compiled into an ArcGIS geodatabase to produce the first benthic habitat map of Mountain Top Bank. The site is characterized by a network of outcrops and boulders interspersed within a predominately sandy environment. Different seabed features were correlated with the presence and abundance of a diverse array of biota across the phyla of Cnidaria, Porifera, Mollusca, Chordata, Echinodermata, and Rhodophyta. We found the benthic assemblage to be similar to those found at the Pinnacle Trend, supporting the hypothesis that Mountain Top Bank is part of the same reef system and acts as a topographic bridge between ecosystems on the east and west of the Mississippi Canyon. Full article

Aquaponic systems show complex multivariate dynamics in water quality parameters, with dissolved oxygen (DO) being a key indicator of biological stability. This study presents a dynamic multivariate predictive framework for short-term dissolved oxygen forecasting utilizing IoT data gathered from various heterogeneous aquaponic ponds. The issue is redefined as a regression task to forecast future DO values within a brief time-frame (~5 min), enabling early warning functionalities instead of utilizing a rule-based classification method. To ensure structural robustness across systems, we applied intra-pond percentile trimming and normalization procedures to mitigate the differences in scale between ponds. Using a Leave-One-Pond-Out (LOPO) validation scheme, we tested model performance and cross-system generalization. An MLP feedforward neural network with lagged temporal variables had an average RMSE of 0.83 on a normalized scale. Regime-based error analysis showed that the RMSE increased from 0.80 on stable conditions to 1.43 under high-volatile regimes. A comparative LSTM model did not produce substantial performance enhancements. Sensitivity analysis revealed lagged impacts of pH and turbidity on subsequent DO dynamics, indicating the need for operational measures such as aeration modification and suspended solids management. Full article

Accurately mapping surface freshwater bodies (e.g., ponds, reservoirs, and small lakes) is vital for managing insular ecosystems and communities. However, satellite-based extraction in coastal settings is challenged by seawater intrusion, complex topography, and cloud cover. Focusing on bedrock islands outside China’s Pearl River Estuary, this study developed a robust method to address these issues. We used both Gaofen-1 (GF-1) optical and Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) imagery, supported by field-collected water quality samples from surface freshwater body shorelines for model training and validation. The performance of two index-based methods (the Normalized Difference Water Index, NDWI, and the Normalized Difference Vegetation Index, NDVI), two machine learning algorithms (Random Forest, RF, and Support Vector Machine, SVM), and a U-Net convolutional neural network (U-Net) deep learning model was compared. The U-Net model achieved the highest accuracy, with Area Under the Curve (AUC) values of 0.881 (GF-1) and 0.840 (GF-3). It effectively discriminated freshwater from seawater and mitigated cloud interference, demonstrating superior precision and robustness over traditional methods. This work establishes a high-precision framework for monitoring island freshwater resources, supporting sustainable water management. The proposed framework provides a practical tool for tracking freshwater availability under climate variability and anthropogenic pressures, contributing to the monitoring of Sustainable Development Goal (SDG) indicator 6.3.2 on ambient water quality. Full article

The Guangdong Hong Kong Macao Greater Bay Area is a core economic region in China with a high incidence of typhoon storm surges. Its low-lying terrain and dense river networks make it vulnerable to severe disasters when typhoons overlap with astronomical tides. This study integrates typhoon, terrain, and tide level data from 2000 to 2024 to construct an ADCIRC (Advanced Circulation Model) v54.01 numerical model, identify risk factors and high-risk areas, and design and verify the effectiveness of coordinated prevention and control countermeasures. Results show that the model has reliable simulation accuracy with MAE < 0.2 m and RMSE < 0.3 m; typhoon intensity and terrain elevation are the dominant factors, with high-risk areas concentrated on the west bank of the Pearl River Estuary and Dongguan Water Town; the comprehensive “engineering + non-engineering” measures can reduce the inundation area by 60% and the inundation rate of high-risk areas from 85% to 22%, providing technical support for regional disaster prevention and control. The novelty of this study lies in the integrated approach of combining grey relational analysis and multiple linear regression to quantify the contribution of key influencing factors, coupled with scenario-based evaluation of coordinated engineering and non-engineering measures tailored to the complex terrain and river network characteristics of the GBA. Full article

Leakage detection in water distribution networks is a core component of smart water management. Addressing the limitations of traditional acoustic detection methods, which heavily rely on manual expertise, and the inadequate feature extraction and low recognition rates for minor leaks of existing deep learning models in complex noise environments, this study proposes a novel hybrid architecture CNN model named Incep-ResNet. The model innovatively integrates multi-scale feature extraction and deep residual learning, incorporating an SE attention mechanism to achieve adaptive recalibration of feature channels. Experimental results demonstrate that the model achieves a leakage identification accuracy of 96.6%, representing improvements of 6.7% and 7% compared to ResNet18 and GoogLeNet, respectively. It exhibits excellent noise resistance and feature extraction capabilities, providing a new technical solution for intelligent leakage detection. Full article

Accurate and efficient detection of cyanobacteria in microscopic images is important for automated water-quality monitoring, but remains challenging because of complex aquatic backgrounds, large scale variation, and uneven sample quality. This study proposes an adaptive multi-scale fusion enhanced RT-DETR framework for cyanobacteria detection. The baseline RT-DETR-R18 is improved by incorporating the SeFaster module for efficient feature extraction, the high-level screening-feature fusion pyramid network for semantic-guided multi-scale fusion, and the Wise-IoU loss for more stable localization learning under mixed-quality samples. Experiments on the reorganized EMDS-7 dataset show that the proposed method achieved 79.05% mAP@0.5, 66.03% mAP@0.5:0.95, 16.31 M parameters, 54.6 G FLOPs, and 70.85 FPS. The proposed model also obtained the highest mAP@0.5 across the seven cyanobacteria categories. Moreover, cross-dataset evaluations further suggest the stability and transferability of the model. These results indicate that the proposed framework demonstrates potential for effective cyanobacteria detection in microscopic images with a good balance between detection accuracy and computational efficiency. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Wave overtopping is one of the most complex coastal hazards to characterize in field conditions due to its high non-linearity and the interaction between unsteady hydrodynamics and wave–structure processes. To get insights into the underlying occurrence and persistence of overtopping, this study proposes an integration of numerical and data-driven models. Multi-month field observations made at a breakwater are used to investigate the hydro-meteorological parameters causing overtopping initiation and persistence. High-frequency video-derived overtopping detections are combined with coupled ADCIRC–UnSWAN (ADvanced CIRCulation–Unstructured Simulating WAves Nearshore) hindcasts to construct near-structure hydro-meteorological conditions. The results reveal a clear dynamical asymmetry showing that overtopping initiation corresponds to exceedance of crest elevation at individual wave-scale associated with elevated wave height, water level, wave steepness, and wind characteristics, whereas overtopping persistence depends on short-term temporal effects associated with wave energy, direction, and sustained water levels. Gradient-boosted decision trees, temporal convolutional networks, and Transformer models are employed, demonstrating that persistence cannot be inferred from instantaneous sea-states alone, indicating a separation of timescales between triggering and sustained overtopping dynamics. These findings provide field-scale evidence of distinct hydrodynamic regimes governing overtopping processes, highlighting the importance of temporal characteristics for understanding overtopping dynamics and developing predictive coastal hazard frameworks. Full article

Heterogeneous hydrogels capable of complex, programmable deformation are highly desirable for soft actuators, yet general strategies that simultaneously impart structural anisotropy, rapid responsiveness, and mechanical robustness remain limited. Here, a gradient anisotropic natural rubber-poly(N-isopropylacrylamide) (NR-PNIPAM) composite hydrogel is developed through a simple one-pot polymerization strategy by coupling pH-regulated colloidal stability with gravity-directed redistribution of natural rubber latex particles. Under an optimized pH window, NR nanoparticles gradually migrate during gelation and are fixed as a continuous gradient within the PNIPAM network, generating built-in structural asymmetry for nonuniform deformation. Meanwhile, NR nanoparticles act as soft reinforcing domains to improve mechanical strength, while water-soluble graphene nanosheets provide efficient photothermal conversion for remotely-controlled near-infrared (NIR)-responsive actuation. Benefiting from this synergistic design, the hydrogel exhibits programmable bending and localized folding with high actuation rates of 129° s⁻¹ and 46° s⁻¹, respectively, along with a tensile strength of 0.32 MPa and an active lifting capability exceeding 70 times its own weight. The material further enables biomimetic gripping and lifting under NIR stimulation. This work establishes a general route to robust gradient hydrogels by integrating colloidal regulation, structural anisotropy, and photothermal actuation, offering a versatile platform for high-performance soft intelligent systems. Full article

Soil erosion (SE) is a constant, complex land degradation process, a common natural disaster that occurs all over the world and severely impacts soil fertility, food security, and environmental balance. Soil erosion depends on many factors, including soil properties, slope, vegetation, rainfall amount and intensity, and anthropogenic activities. There are two main natural erosive forces by which soil is eroded and transported—water and wind. Water erosion refers to the detachment, transportation, and deposition of soil particles (solid runoff) into river networks. These particles, varying in size and composition, are the main products of soil erosion and most strongly affect river ecosystems. Solid runoff, or sediment-laden runoff, affects water quality, destroying habitats, carrying pollutants, reducing reservoir storage, and causing flooding. Erosion control activities also influence river ecosystems in different ways. Hydrotechnical facilities, a major erosion control practice, can alter the composition of aquatic biota by disrupting longitudinal connectivity and isolating populations. Reforestation and afforestation are other erosion control practices that have a strong impact on ecosystems. Stormwater retention systems in urban and forest areas are also important measures addressed in this review. This review examines complex environmental interactions and the roles of erosion and erosion control activities in river ecosystems. During the research, several key points were established: erosion and erosion control activities significantly affect river ecosystems. There is a lack of quantitative analysis of erosion intensity and its influence on ecosystems. This is probably due to the exceptional complexity and diversity of river ecosystems, but such a study would provide important information about complex relationships in nature. Full article