Search Results (2,278)

The spatiotemporal evolution of ecosystem services and the elucidation of their driving mechanisms constitute a central scientific issue in territorial spatial optimization and regional sustainable development. Taking Gansu Province, a core area of the ecological security barrier in northwestern China, as the study area, this study integrates land-use, natural geographic, and socioeconomic data from 2000 to 2020. Using a land-use transfer matrix, the InVEST model, the Geographical Detector, and the PLUS model, we constructed a comprehensive analytical framework that combines historical evolution analysis, spatial differentiation identification, and multi-scenario simulation and prediction. The framework was used to systematically reveal the spatiotemporal dynamics of four core ecosystem services, namely carbon storage (CS), water yield (WY), habitat quality (HQ), and soil retention service (SDR), and to analyze their natural and socioeconomic driving mechanisms, while also simulating land-use change and ecosystem-service responses under the natural development, ecological protection, and urban expansion scenarios in 2030. The results show that, from 2000 to 2020, land use in Gansu Province was dominated by grassland (average proportion: 33.34%) and unused land (average proportion: 41.35%). Urban land expanded from 660.52 km² to 2227.36 km², with its share increasing from 0.15% to 0.50%, mainly through the conversion of cropland and grassland. Ecosystem services exhibited marked spatial differentiation: CS increased from east to west; WY showed an increasing pattern from northwest to southeast; HQ was lower in the central and southeastern regions and higher in the western and southern regions; and SDR was dominated by low-value areas in the northwest (average proportion: 84.81%). Driving-mechanism analysis indicated that slope was the core natural factor affecting CS, HQ, and SDR (q = 0.18–0.45), while mean annual precipitation dominated the variation in WY (q = 0.31–0.35). The influence of socioeconomic factors such as GDP increased gradually over time, showing an evolutionary trend from natural dominance to coordinated natural–socioeconomic regulation. Multi-scenario simulation further showed that, under the ecological protection scenario, grassland area increased significantly (+0.60%), the proportions of medium-value CS zones and high-value WY zones increased, and ecosystem services were optimized overall; under the urban expansion scenario, cropland and urban land expanded (+0.87% and +0.23%, respectively), imposing potential pressure on part of the ecosystem-service functions. These findings provide a scientific basis for optimizing territorial spatial planning, strengthening the ecological security barrier, and promoting regional sustainable development in Gansu Province. The methodological framework also offers a broadly applicable reference for ecologically sensitive arid and semi-arid regions in northwestern China. Full article

In semi-arid regions, sustainable groundwater management for irrigation is critical for agricultural productivity and food security. This study presents an integrated methodological framework combining hydrochemical characterization, machine learning (ML) modeling, and explainable artificial intelligence (XAI) to predict the Irrigation Water Quality Index (IWQI) in the Ain Oussera plain, Djelfa Province, Algeria. A total of 191 groundwater samples were collected from November 2023 to September 2024 and analyzed for major ions and physicochemical parameters. Multiple irrigation suitability indices were calculated, including Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard (MH), Permeability Index (PI), Residual Sodium Carbonate (RSC), Soluble Sodium Percentage (SSP), and Kelly’s Ratio (KR). Five ML models were developed and evaluated for IWQI prediction: Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Regression. Results showed that 55% of groundwater samples exhibited low to no restrictions for irrigation use, while 19% required high to severe restrictions. The XGBoost model demonstrated superior performance, with the highest R² (0.95) and the lowest RMSE (3.22) among all tested algorithms. SHAP (SHapley Additive exPlanations) analysis provided a transparent interpretation of model predictions, identifying electrical conductivity and Sodium Adsorption Ratio as the most influential parameters affecting IWQI, while chloride, sodium, total hardness, and magnesium had minimal impact. Spatial mapping using Inverse Distance Weighting (IDW) interpolation in ArcGIS 10.8 revealed considerable spatial variability in water quality throughout s the plain. This research addresses a critical gap in North African groundwater management by integrating ML predictive capabilities with XAI transparency, providing water resource managers and agricultural stakeholders with interpretable, data-driven tools for sustainable irrigation planning in water-stressed semi-arid environments. Full article

The anchovy (Engraulis japonicus) is a highly abundant but underutilized fish resource in China, primarily due to its extreme post-harvest perishability. This study expanded the utilization of anchovy by developing a blended surimi from anchovy and golden threadfin bream, an I-optimal mixing design experiment was performed to optimize the formulation, and the effects of soy protein isolate (SPI), egg white powder (EWP), and yeast protein (YP) on the gel properties were investigated. The results of sensory evaluation and model prediction indicated that SPI had the most pronounced positive effect on the sensory characteristics of the gels, especially improving the elasticity, followed by EWP. Furthermore, the SPI-rich sample exhibited superior gel strength and chewiness, which was attributed to the increased β-sheet structure and the highest content of disulfide bonds in the protein network. And the water hold capacity of SPI-rich sample increased by 6.0%. The YP-rich group showed the strongest hydrophobic interactions and exhibited a significant enhancement in water hold capacity of 7.7%, which also provided a notable improvement in gel strength. The results showed that EWP contributed to the smoothness of the surimi, but it had no significant impact on water distribution, water-holding capacity, or the content of disulfide bonds within the gel network. Moreover, the EWP-rich group exhibited reduced the gel strength, hardness, and chewiness of the gel, resulting in the lowest overall sensory score of the surimi. Therefore, the optimal composite ratio was determined to be SPI:EWP:YP = 5.45%:2.55%:2.00%. These findings provided a precise blending strategy for developing high-quality surimi products from anchovy, offering a viable technical pathway for the value-added utilization of this resource. Full article

This study applied time-series modeling using autoregressive integrated moving average (ARIMA) to compare the growth performance of tilapia broodfish in pond and recirculating aquaculture systems (RAS) from June 2023 to May 2024. Descriptive statistics showed a higher mean percentage weight gain under RAS (26.69%) than pond culture (23.75%), although monthly variability in the RAS dataset was influenced by an outlier, which may be attributed to influential exogenous factors rather than water-quality parameters. Normality, stationarity, and autocorrelation diagnostics confirmed that both datasets were appropriate for ARIMA modeling without differencing. Multiple ARIMA models were evaluated based on RMSE, MAPE, MAE, AIC, BIC, and residual behavior; ARIMA (1,0,1) emerged as the best fit for both systems. Forecasting up to May 2028 revealed stable long-term growth patterns, with RAS consistently showing slightly higher forecasted growth compared to pond culture, although the difference remained small in absolute terms. Predictions remained within model-generated 95% confidence intervals; however, these results indicate internal model consistency rather than independent validation of predictive accuracy. The findings highlight that RAS offers more consistent and slightly superior growth performance, supporting its potential for optimized broodfish production. Recommendations emphasize adopting RAS for enhanced growth predictability and improved management in tilapia aquaculture. Full article

The starch and moisture content of saffron corms are critical indicators of their flowering potential and yield. This study investigated the use of rapid, minimally invasive VNIR reflectance spectroscopy measurement to assess these parameters. The measurements were used to develop predictive models through four machine learning algorithms (PLSR, RF, SVR, and GPR). Spectral data were obtained from 130 fresh corm samples. Wavelength analysis identified key starch-sensitive intervals (~930–1000 nm and ~1150–1220 nm) and a broad moisture-sensitive region (~900–1350 nm). Among the evaluated models, the combination of the multiplicative scatter correction pre-processing method and Gaussian process regression (MSC-GPR) demonstrated the optimal predictive performance for water content (R² = 0.92, RMSE = 0.71%, RPD = 4.56, RPIQ = 5.37), and the combination of the MSC method and partial least squares regression (PLSR-MSC) demonstrated moderate performance for starch content (R² = 0.73, RMSE = 28.7 mg g⁻¹, RPD = 2.14, RPIQ = 2.81, dry weight). These results demonstrate the viability of VNIR spectroscopy as a minimally invasive tool for the pre-planting assessment of saffron corm quality under laboratory conditions. The method provides a laboratory-based framework for corm screening and selection, with potential for future adaptation to field settings using portable spectrometers following expanded calibrations and advanced modeling techniques. Full article

Inland waters are critical nodes in the global nitrogen cycle, where microbial processes govern transformations that impact water quality and ecosystem functioning. Inland waters are critical nodes in the global nitrogen cycle, where microbial processes govern transformations that impact water quality and ecosystem functioning. To systematically map the knowledge structure and to identify evolving trends in this field, a bibliometric analysis was conducted using CiteSpace on 2459 publications from the Web of Science Core Collection (1990–2024). The results reveal a significant increase in publications after 2010, peaking at 228 in 2024, with China (1541 articles) and the Chinese Academy of Sciences (776 articles) being the leading country and institution, respectively. Keyword co-occurrence and cluster analyses identify a core conceptual framework centered on microbial communities, nitrogen transformation processes (e.g., denitrification, anammox), and aquatic habitats (e.g., lakes, rivers). Based on keyword emergence and temporal trends, the analysis suggests an evolution in research focus across four dimensions: research subjects (from microbial biomass to keystone taxa), core questions (from process rates to predictive manipulation), methodological tools (from culturing to multi-omics), and mechanistic understanding (from linear pathways to complex networks). These observed patterns indicate a progressive refinement of the field. The findings provide a structured overview of the literature and may inform future research directions, but should be interpreted as bibliometric trends rather than definitive conclusions about the state of the science. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

Water quality prediction in non-stationary environmental systems requires not only high predictive accuracy but also structural robustness under physical, ecological, and operational constraints. This study reframes multi-model fusion as a constraint-governed inference architecture and synthesizes advances in rule-based adjudication, reliability-aware aggregation, post-fusion projection, dual-track adaptation, and hierarchical backoff control. By establishing a taxonomy of boundary constraints—specifically mass conservation, reaction kinetics, hydraulic transport, and ecological tipping points—an admissible prediction manifold identifies key structural limitations in existing paradigms, particularly their vulnerability to physical inconsistency and diminished reliability during non-stationary distribution shifts. A unified end-to-end robust framework is proposed in which candidate predictions are separated from admissibility validation, uncertainty is directly coupled to aggregation logic, and degradation pathways are explicitly defined under distribution shift. Furthermore, a multidimensional robustness evaluation matrix is introduced, incorporating structural consistency, ecological compliance, calibration quality, and adaptive stability alongside conventional accuracy metrics. The study advances water quality forecasting from model-centric optimization toward architecture-level governance, demonstrating that constraint-aware designs improve structural consistency, robustness under distribution shifts, and early warning reliability, providing a systematic reference for developing resilient, transparent, and operationally deployable environmental prediction systems. Full article

Moisture content is a key quality attribute in dried areca nuts, affecting subsequent processing performance and storage stability, yet routine measurement by oven-drying is time-consuming and destructive. This study developed a rapid and non-destructive method for determining moisture content in dried areca nuts by integrating near-infrared spectroscopy with chemometric and machine learning-assisted methodologies. Various spectral preprocessing methods, feature wavelength selection algorithms, and modeling approaches were compared. The results indicated that Multiplicative Scatter Correction (MSC) most effectively eliminated physical scattering interference. The Partial Least Squares Regression (PLSR) model established using full-wavelength spectra demonstrated optimal predictive performance. It achieved a coefficient of determination for the prediction set (Rp²), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) of 0.9639, 0.1960, and 10.3461, respectively, indicating excellent predictive accuracy and robustness. Feature wavelength selection did not enhance model performance in this study, which can be attributed to the broad absorption bands of water in the near-infrared spectrum and its complex interactions with the sample matrix where the full spectrum data retains essential information more comprehensively. This research provides a reliable and practical technical means for moisture management in areca nuts, offering important support for quality assurance and standardized production practices within the areca industry. Full article

Safe and sustainable water sources are a serious global concern because of growing population, urbanization, industrialization, and climate change. The conventional water surveillance systems that rely on periodic sampling and laboratory analysis fail to provide time-sensitive and high-resolution data utilized for proactive water management. Artificial Intelligence of Things (AIoT) offers a viable solution, as they can provide tools of constant active monitoring and predictive analytics. The integration of IoT sensor networks with machine learning (ML) methods enables real-time data-driven water resource monitoring and intelligent decision-making, enhances water quality assessment, supports early detection of anomalies, improves predictive capabilities for floods and droughts, and facilitates efficient irrigation and reservoir management, ultimately leading to sustainable and resilient water management systems. The paper presents an extensive overview of AIoT solutions for water quality monitoring and water resource management, including IoT sensor networks for real-time data acquisition, machine learning methods for prediction, classification, anomaly detection, and edge computing platforms for data processing and decision support. This study also highlights existing possibilities, obstacles, and research gaps identified through a review of the recent literature. Key challenges reported across multiple studies include limited data availability, sensor calibration bias, integration of heterogeneous data, and insufficient model interpretability. Advanced paradigms such as digital twin systems, TinyML, federated learning, and explainable AI (XAI) are examined as enabling technologies to enhance system efficiency, flexibility, and transparency. Future research directions are outlined to develop scalable, interpretable, and real-time water management solutions. Full article

Ongoing urbanization continuously reshapes water quality, habitat structure, and biological communities in river ecosystems; however, its impacts on host-associated microbial communities remain poorly documented. The fish gut microbiota, a critical interface between the aquatic environment and host physiology, is widely recognized as an integrative indicator of both environmental change and host ecological traits. This study established a continuous urbanization gradient along Shanghai’s Suzhou River, spanning from suburban areas through the outer and inner ring roads to the city center. Five common wild fish species (Coilia nasus, Hemiculter bleekeri, Culter alburnus, Acheilognathus macropterus, and Pseudorasbora parva) were collected, and their gut microbiota were characterized via high-throughput 16S rRNA gene sequencing. Significant variation in OTU richness, alpha diversity, and community structure was observed across urbanization gradients and among fish species. Principal coordinate analysis revealed that samples from suburban areas were structurally distinct from those collected in other zones, whereas inner-ring and urban-core areas exhibited substantial compositional overlap. Taxonomic analysis revealed that Firmicutes and Pseudomonadota dominated all samples; however, their relative abundances and genus-level composition varied considerably among fish species and across the urbanization gradient. PICRUSt-based functional prediction indicated that metabolic pathways predominated, particularly those involved in global and overview maps, carbohydrate metabolism, amino acid metabolism, energy metabolism, and metabolism of cofactors and vitamins. Collectively, these findings demonstrate that fish gut microbial communities exhibit spatial structuring along the urbanization gradient, with species-specific responses linked to ecological traits. This study provides valuable data on host-associated microbial communities in urban rivers and offers a reference for incorporating microbial indicators into urban water ecological assessments. Full article

There is increasing interest in delivering greater resilience to climate change through integrated catchment management that includes Nature-based Solutions (NbS) such as riparian buffer strips, tree-planting and wetlands. Governmental organisations also seek to use water quality modelling to understand the mass of different pollutants avoided per feature for appraisal of nutrient-neutrality purposes, but the assessment of efficacy is not yet fully developed, nor is it clear what it implies at the catchment-scale. We introduce three open, freely distributable models to help understanding efficacy and risk-reduction of buffer-strips at the plot (JUMP), waterbody (Fieldmouse), and national (HYPE) scales to help understand risk-reduction and help objectively quantify improvements in catchment resilience. These approaches have been developed across a range of projects but are also being investigated in more detail as part of the modelling element to the NERC Freshwater Quality programme QUANTUM project. Here we report how the particle tracking model predicts the need for very slow velocities, high loss rates or other processes to achieve buffer strip efficacies in common use—slowing the flow alone is unlikely to achieve these results. Upscaling these results to the catchment scale on the Yeo highlights another significant concept, that of the need to define a catchment scale efficacy for a particular Nature-based Solution, given the practicalities of implementation. We demonstrate how HYPE can be used to target and model mitigations and permits both upscaling nationally and through-time source apportionment to help identify when design efficacies may not be achieved in practice. Full article

Water kefir grains are complex probiotic granules that can efficiently ferment fruit and vegetable juices and significantly improve product flavor. However, the mechanisms of flavor formation remain unclear, which limits the process optimization of this technology. This study investigated the mechanisms involved in flavor formation during the fermentation of strawberry juice with water kefir grains. The results showed that as fermentation progressed, the total acidity increased, whereas the pH value and soluble solids content decreased. Additionally, the contents of citric acid and malic acid gradually decreased with fermentation, while the contents of lactic, acetic, and succinic acid increased, and three soluble sugars showed reduced levels. A total of 218 volatile compounds were identified. Eight dominant bacterial genera and one dominant yeast species were detected. Significant correlations between some key microorganisms and flavor compounds were observed. Specifically, Lactiplantibacillus was positively correlated with hexyl acetate. Meanwhile, Gluconobacter and Acetobacter were positively correlated with methyl (Z,Z)-9,12-octadecadienoate, isoamyl acetate, etc. In contrast, LAB such as Lacticaseibacillus and Schleiferilactobacillus showed the opposite correlations with these key flavor compounds. Saccharomyces showed a positive correlation with ethyl palmitate, ethyl propionate, phenylsuccinic acid, and 1-pentanol. The main flavor compound metabolic pathways were predicted and they were significantly related with yeasts, acetic acid bacteria, and lactic acid bacteria. Overall, this study offers a theoretical basis for the directional regulation and optimization of the flavor quality of strawberry juice fermented with water kefir. Full article

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

Mold infection by Aspergillus and Penicillium spp. in Sithong sweet tamarind (Tamarindus indica L.) during commercial postharvest storage poses quality and food safety risks. However, the current visual detection method, which involves randomly cracking open the pods, is both destructive and laborious. The integration of near-infrared spectroscopy (NIRS) with artificial neural networks (ANN) enables rapid and non-destructive detection while capturing non-linear biochemical–spectral relationships, offering advantages over conventional destructive and linear analytical methods. It was tested as a mold classifier in sweet tamarind pods preserved in commercial ambient conditions (25 °C, 60% relative humidity) for five weeks. Six hundred pods were examined weekly using interactance spectroscopy (800–2500 nm) with six measurement points per pod and four spectral preprocessing methods. The ANN outperformed partial least squares discriminant analysis (PLS-DA) across all storage weeks, peaking at Week 2 with standard normal variate (SNV) preprocessing (prediction accuracy: 85.00%; sensitivity: 0.84; specificity: 0.86; F1-score: 0.85). Advanced tissue degeneration caused spectral heterogeneity, which decreased performance at Week 4 (prediction accuracy: 71.82–76.36%). Principal component loadings identified mold-induced water redistribution and carbohydrate depletion wavelengths at 938, 975–980, and 1035 nm. Week-adaptive calibration is essential for implementation because of the large difference between week-specific model accuracy (up to 85%) and overall storage model accuracy (63.53%). These findings provide a mechanistic underpinning for smaller wavelength-selective sensors and temporally adaptive mold screening systems in commercial tamarind storage. Full article