Search Results (37)

Yellow River irrigation districts must maintain food production under variable inflows, rigid diversion quotas, sedimentation, groundwater depletion, and soil salinization. This systematic review synthesized 79 journal articles from Web of Science and CNKI to clarify how resilience can be assessed and managed at the irrigation-district scale. The evidence indicates that resilience is a time-dependent combination of resistance, recovery, adaptability, and transformability within a coupled water source–canal–field–drainage–ecology–institution system. Although composite indices and hydrological–crop models have advanced, three gaps remain: operational thresholds rarely connect indicators to failure and recovery; farmer and institutional feedbacks are weakly represented; and assessments seldom translate into executable schedules. We, therefore, propose an irrigation-district-specific framework that couples water, sediment, salt, crops, ecology, and governance across basin–district–field scales without transferring risk between scales. Management priorities differ spatially: upstream districts require coordinated water–salt control; middle-reach well–canal systems require surface-water substitution and groundwater recovery; and downstream diversion districts require multi-source allocation and adaptive intake. A digital twin-based closed loop—continuous monitoring, forecasting, optimization, operational commands, and feedback correction—can translate diagnosis into canal rotation, recharge, drainage, and emergency actions. This review provides operational indicators and a decision-oriented research agenda for resilient irrigation modernization. Full article

To address the challenges of complex multi-objective trade-offs, tightly coupled physical constraints in reservoir dam safety dispatching, and fulfill the significant cognitive gaps in human–machine interaction, a framework with four deep cognitive layers and a physical computation layer is proposed which integrates large language models (LLMs) with multi-agent collaboration. The framework stratifies cognitive intelligence into interface translation, strategic cognition, tactical reasoning, and operational understanding layers; performs computation in the physical computation layer; and achieves deep coupling among agents in different layers through the Blackboard information sharing mechanism. The physical computation layer consists of the gate-opening discharge, water-level storage capacity, runoff and inflow, downstream risk calculation agents and a Pareto multi-objective optimizer to realize non-dominated sorting of multi-dimensional objectives encompassing dam safety, ecological loss, downstream risk, and operational complexity. Illustrative case analysis indicates that this framework can effectively parse user requirements expressed in natural language, generate dispatching schemes conforming to physical constraints, achieve error control and quantify the downstream risk. This research provides a scalable framework for the implementation of intelligent reservoir dispatching and can enhance the intelligence of digital twins of river basins. Full article

The creation of innovative learning environments in courses to provide sustained talent support has long remained a central research concern for high-quality social development. Neglected foundational skills in professional course clusters are often a hidden barrier to higher education for sustainable development (HESD). To close five persistent foundational skill gaps across improper citation (J1), ineffective figure use (J2), poor analysis (J3), irresponsible AI use (J4), and comprehensive application (J5) within the hydraulic engineering course cluster, a four-stage deliberate practice module (5Di-40Pr-5Tr-3Cm) has been embedded into a two-week hydraulic model experiment course, and its learning outcomes are systematically evaluated. A systematic analysis of its achievement levels across neglected foundational skill indicators of J1~J5 at each stage was conducted, stratified by the overall cohort and subgroups (P: objective demand, T: behavior type, G: optimization methods). The key findings include: ① deliberate practice demonstrates better teaching outcomes than lecture-based instruction, which can be evidenced in 2026, when J5’s achievement levels at the 3Cm stage yielded a moderate effect size relative to the 2025 lecture-based condition (d = 0.42); compared with the 2024 no-intervention baseline, the cumulative effect is a obvious increasing trend (d = 1.43); ② In far-transfer subgroup diagnosis, P2 (medium objective demand) shows a rank-order reversal, low at 40Pr but higher at 3Cm, and is identified as the “partial understanding” group and providing a diagnostic anchor for tiered intervention; ③ In near-transfer pathway diagnosis, J5’s low performance in 5Tr (65.35%, below overall mean of 83.09%; CV = 7%) stems from two distinct pathways: a “knowledge-deficit pathway” (max-decay subgroups) and a “processing-load pathway” (subgroups where J1, J2 do not exhibit max decay). In addition, stage-specific thresholds (40Pr: 90%, range 60~99%; 5Tr and 3Cm: 83% ± 3%, range of for 40Pr, mean = 90%, recommended range = 60~99%; for 5Tr, mean = 83% ± 3%, range = 65~96% and 75~90%) provide quantitative benchmarks for targeted intervention. These cumulative findings are intended to advance the evaluation paradigm of engineering practice courses from “total score attainment” toward “structural diagnosis” and align with the competency-oriented philosophy of higher education for sustainable development (HESD). Full article

The decentralized deployment of photovoltaic (PV) systems in urbanized polluted mountainous basins faces unique challenges due to complex topography, persistent cloud cover and winter smog conditions. This study quantifies the atmospheric impact of localized winter haze/smog and Saharan dust intrusions on PV performance in the intermontane basin of Ioannina, NW Greece. By integrating a Digital Twin (DT) methodology with real energy production data, two PV plants were evaluated, a ground-based and a rooftop installation, to isolate the energy deficits caused by aerosol attenuation. The DT model demonstrated high accuracy (R² = 0.847) against actual power generation data for Koutselio and R² = 0.865 for Mpafra PV plants, while MBE was near zero for both sites (−0.008 kWh and −0.139 kWh, respectively). Error analysis revealed that the highest modeling discrepancies occurred during scattered clouds and intense winter haze conditions, primarily due to low spatial resolution of CAMS that fails to adequately capture localized biomass burning (BB) events. Despite the reduction in direct sunlight during extreme winter BB events, results indicate that the overall energy loss is mild. This operational stability is primarily due to the ability of c-Si modules to effectively utilize near-infrared radiation, which penetrates the low-level haze layer, alongside the thermal efficiency gains provided by low early-morning temperatures. Crucially, the installation geometry may influence system vulnerability. Direct comparisons revealed a minor power deviation of −4.8% for the ground-based Koutselio plant, while for the Mpafra site, there was a +3.2% production surplus likely linked to the high sky-view factor the rooftop installation has, which manages to capture isotropic diffuse irradiance. However, the low CAMS resolution may misclassify the haze events within the basin, further contributing to these discrepancies. On the contrary, Saharan dust intrusions caused broadband light attenuation, dropping the power production significantly on both installations. Ultimately, this research provides critical insights into the resilience of solar systems under strong air pollution events within polluted valleys in Southern Balkans, highlighting the connection between panel design and atmospheric attenuation. Full article

This bibliometric study maps olive (Olea europaea L.) yield prediction research as a coherent scientific domain for the first time. A Scopus query (27 February 2026) yielded 84 peer-reviewed articles (2002–2025), from which co-authorship network analysis, Bradford’s and Lotka’s Laws, Latent Dirichlet Allocation topic modelling (LDA), and OLS regression on citation counts were applied. Publication output increased nearly fourfold across three periods: 1.7 articles yr⁻¹ (2002–2014), 4.4 yr⁻¹ (2015–2019), and 6.7 yr⁻¹ (2020–2025). The 84 articles involve 382 authors, 61 journals, and 1551 citations (H-index = 22). Network analysis reveals a concentrated Spanish–Italian co-authorship axis. OLS regression (adj. R² = 0.267) identifies article age and abstract length as the only significant citation predictors, consistent with cumulative exposure time and study scope as structural drivers. Term-frequency screening against 18 a priori concepts finds that transfer learning, federated learning, hyperspectral imaging, digital twins, and SHAP-based explainability are absent or marginal. The field is producing more papers than ever on a narrowing methodological base geographically concentrated in the Mediterranean basin. Priority gaps—explainable AI, multi-region datasets, sensor-fusion pipelines, and federated data infrastructure—align directly with European Farm to Fork and Horizon Europe objectives. Full article

This editorial synthesizes the key findings from 17 original research articles featured in the Special Issue on “Intelligent and Integrated Approaches for Efficient Oil and Gas Development.” The collection demonstrates a paradigm shift from purely data-driven methods toward physics-informed, interpretable, and operationally deployable intelligent systems across the upstream lifecycle. Advances span intelligent drilling with real-time model predictive control frameworks achieving sub-20 ms execution times and bottomhole pressure fluctuations below 0.30 MPa; AI-assisted reservoir characterization using multiscale convolutional neural networks, seismic waveform-constrained inversion, and geology-informed transformers that improve sandstone thickness prediction (R² = 0.895) and stratigraphic correlation (F1 = 0.886); production optimization through hybrid decomposition-ensemble models (R² = 0.954) and improved XGBoost (R² = 0.989); and enhanced oil recovery via self-assembled foam systems and polymer injector designs. Fundamental geochemical studies on the Qiongzhusi Formation shale and tight sandstone gas in the Ordos Basin provide critical geological constraints. The editorial identifies persistent challenges, including real-time performance versus physical fidelity, interpretability and uncertainty quantification, multi-scale integration, and generalizability across diverse geological settings. Future directions highlight reinforcement learning for autonomous operations, physics-informed digital twins, generative AI for subsurface scenario modelling, and integration with carbon capture, utilization, and storage. This Special Issue advances the convergence of petroleum engineering, artificial intelligence, and Earth sciences toward intelligent, secure, and sustainable hydrocarbon development. Full article

Addressing the challenges of vague ability assessment, delayed teaching adjustment, and fixed cognitive challenge levels in sustainable engineering practice courses, this study proposes a “goal elevation-matrix evaluation-dynamic regulation” tripartite coupled sustainable teaching model. The model employs a value-oriented assessment matrix as the core diagnostic tool, integrating a dual-threshold regulation mechanism and standard iteration strategy within a four-year “design-implementation-diagnosis-iteration” closed loop. Empirical evidence demonstrates that ① a three-tier diagnostic model (overall-module-indicator attainment levels) identifies structural problems of the teaching content and pinpoints the key bottleneck; ② replacing redundant high-scoring modules with basic skill modules eliminates extreme values, improves distribution gradients, and trends to class performance at 75~85%; ③ iterative standard calibration supports progressive student competence development along a “familiar problems → new challenges” pathway. This study provides an empirically validated methodological framework for systematically implementing “scientific rigor, practicality, and appropriate challenge” in engineering practice courses while fostering sustainable engineering literacy. Full article

Urbanization fundamentally fractures the natural water cycle, leading to a cascade of interconnected problems including increased flood risk, degraded water quality, stressed groundwater resources, and inefficient distribution networks. Traditional, fragmented management approaches that address these issues in isolation have proven inadequate. This research argues for a paradigm shift towards an Integrated Urban Water Management (IUWM) framework anchored in the concept of the “river-aquifer-pipe network continuum”, treating these components as a single, dynamic hydrological and infrastructural entity. Drawing upon a series of detailed case studies from Eastern Romania, this paper synthesizes the systemic impacts of development across the entire urban water system. Evidence from the Prut, Olt, and Bahlui river basins demonstrate how channelization exacerbates flood peaks and leads to severe biochemical degradation. Hydrogeological modeling of the Gherăești-Bacău wellfield reveals the vulnerabilities of over-extraction, while analysis of the Iași water network highlights the challenge of water losses in the aging infrastructure. In response, a modern, multi-tool approach is consolidated into a practical, three-stage framework for action: Diagnose, Prescribe, and Optimize. This framework advocates for (1) a comprehensive diagnosis using a suite of predictive numerical models (a “digital twin”); (2) the prescription of foundational, nature-based solutions, such as floodplain restoration, to heal core ecological functions; and (3) the continuous optimization of engineered infrastructure using smart, real-time control technologies. The synthesis concludes that an integrated, data-driven, and collaborative approach is the only sustainable path forward. Future research should focus on formally coupling these diagnostic models to create true Digital Twins of urban water systems—an essential step towards building resilient, water-secure cities for the 21st century. Full article

Climate change is reshaping territorial safety and water-resource management, calling for digital tools that integrate heterogeneous datasets, enable advanced analyses, and enhance decision-making transparency. This article documents the three-year digital transformation (2022–2025) of the Central Apennine River Basin District Authority (AUBAC), covering > 42,000 km² and serving 8.6 million residents in central Italy. Through an incremental methodology across three releases, AUBAC developed an integrated WebGIS consolidating 613 geospatial layers and near-real-time monitoring from 1844 IoT sensors, implementing a Level 1 (Diagnostic) Digital Twin. Measured results include 141,569 platform visits, an approximately 60% reduction in administrative burden, a 70–80% reduction in plan-processing times, over 5000 users participating in public consultations, and a 40–60% increase in perceived risk understanding. The article presents the research design, platform architecture, evaluation framework, challenges encountered, and recommendations for replicability. The platform supports climate adaptation, disaster-risk reduction, and integrated water-resource management, contributing to SDGs 6, 11, and 13. The experience demonstrates that territorial Digital Twins can deliver tangible operational gains within public administration while establishing a foundation for evolution toward predictive capabilities. Full article

Water scarcity in arid/semi-arid regions restricts agricultural sustainability systems and hinders the achievement of regional sustainable development goals, especially in northwest China’s extremely arid areas, where acute water supply–demand conflicts and inefficient traditional practices intensify competition for water between agricultural and ecological sectors. This study aims to verify the effectiveness of an intelligent automatic irrigation system in mitigating water scarcity pressures and enhancing agricultural sustainability in the Shule River Basin of northwestern China, a region where traditional irrigation methods not only yield suboptimal crop outputs but also undermine long-term water resource sustainability. A smart irrigation module, integrating “sensing–decision–execution” processes, was embedded within a digital twin platform to enable precise, resource-efficient water management that aligns with sustainable development principles. Sunflower (Helianthus annuus L.), the most popular cash crop in the area, was used as the test crop, with three soil moisture-based irrigation levels compared against traditional farmer practices. Key indicators including leaf area index (LAI), dry biomass, grain yield, and irrigation water use efficiency (IWUE) were systematically evaluated. The results showed that (1) LAI increased from the seedling to flowering stage, with smart irrigation treatments significantly outperforming farmer practices in both crop growth and water-saving effects, laying a foundation for sustainable yield improvement; (2) total dry biomass at maturity was positively correlated with irrigation amount but smart irrigation optimized the allocation of water resources to avoid waste, balancing productivity and sustainability; (3) grain yield peaked within 70–89% field capacity (fc), with further increases leading to diminishing returns and unnecessary water consumption that impairs sustainable water use; (4) IWUE followed a parabolic trend, reaching its maximum under the same optimal irrigation range, indicating that smart irrigation can maximize water productivity while preserving water resources for ecological and future agricultural needs. The digital twin-driven smart irrigation system enhances both crop yield and water productivity in arid regions, providing a scalable model for precision water management in water-stressed agricultural zones. The results provide a key empirical basis and technical approach for sustainably using irrigation water, optimizing water–energy–food–ecology synergy, and advancing sustainable agriculture in arid regions of Northwest China, which is crucial for achieving regional sustainable development objectives amid worsening water scarcity. Full article

Research shows that neurodegenerative processes do not develop from a single “broken” biochemistry process; rather, they develop when a complex multi-physics environment gradually loses its ability to stabilize the neuron via a collective action between the protein, ion, field and fluid dynamics of the neuron. The use of new technologies such as quantum-informed molecular simulation (QIMS), dielectric nanoscale mapping, fluid dynamics of the cell, and imaging of perivascular flow are allowing researchers to understand how the collective interactions among proteins, membranes and their electrical properties, along with fluid dynamics within the cell, form a highly interconnected dynamic system. These systems require fine control over the energetic, mechanical and electrical interactions that maintain their coherence. When there is even a small change in the protein conformations, the electric properties of the membrane, or the viscosity of the cell’s interior, it can cause changes in the high dimensional space in which the system operates to lose some of its stabilizing curvature and become prone to instability well before structural pathologies become apparent. AI has allowed researchers to create digital twin models using combined physical data from multiple scales and to predict the trajectory of the neural system toward instability by identifying signs of early deformation. Preliminary studies suggest that deviations in the ergodicity of metabolic–mechanical systems, contraction of dissipative bandwidth, and fragmentation of attractor basins could be indicators of vulnerability. This study will attempt to combine all of the current research into a cohesive view of the role of progressive loss of multi-physics coherence in neurodegenerative disease. Through integration of protein energetics, electrodynamic drift, and hydrodynamic irregularities, as well as predictive modeling utilizing AI, the authors will provide mechanistic insights and discuss potential approaches to early detection, targeted stabilization, and precision-guided interventions based on neurophysics. Full article

Accurate, high-frequency carbon emission forecasting is crucial for urban climate mitigation and achieving sustainable development goals. However, generalized models often result in lower prediction accuracy by overlooking the unique “sector specificity” of urban emission systems, namely, the different temporal patterns driven by distinct physical and economic factors across sectors. This study establishes a decision-support framework to select optimal forecasting models for distinct sectors. Using daily multi-sector carbon emission and meteorological data from Hangzhou, we evaluated 12 models across statistical, machine learning, and deep learning classes. Our three-stage design identified the best model for each sector, quantified the contribution of meteorological drivers, and assessed multi-step forecasting stability. The results indicated the lack of universality in generalized models, as no single model performed best across all sectors. A hybrid CNN-LSTM model outperformed other candidates for ground transport (R² = 0.635), while LSTM showed better performance for industry (R² = 0.866) and residential (R² = 0.978) sectors. Integrating meteorological factors only improved accuracy in weather-sensitive sectors (e.g., residential) and acted as noise in others (e.g., aviation). We conclude that a sector-specific strategy is more robust than a one-size-fits-all approach for carbon emission forecasting. By resolving the specific driving mechanisms of each sector this decision-support framework provides the granular data foundation necessary for precise urban energy dispatch and targeted emission reduction policies. Full article

Estimating design hydrographs in small and ungauged basins remains a significant challenge, primarily due to limited hydrometeorological data and the operational complexity of advanced modelling tools. This study presents an interactive digital twin platform to support hydrological modelling in such contexts. The aim of the proposed platform is to integrate three hydrological models—EBA4SUB (event-based rainfall–runoff model), COSMO4SUB (continuous rainfall–runoff model), and Virtual Rain (stochastic rainfall generator)—and automates key pre-processing tasks, including watershed delineation, Curve Number estimation, and rainfall input generation. Built on a three-tier architecture, the system comprises an interactive front end, a back-end database with spatial and meteorological data, and a suite of computational routines developed in Python and C#. The platform was deployed across the Lazio Region (Italy) for basins with contributing areas smaller than 400 km². Users can interactively select watersheds via a map-based interface, obtain preliminary hydrological characterizations, and export model-ready inputs and outputs. The proposed platform offers several advantages: it reduces model preparation time, facilitates access to advanced modelling tools, standardizes input data at the regional level, and ensures reproducible pre-processing workflows. By lowering the technical and time barriers of hydrological modelling, the digital twin provides an effective framework for bringing science-based tools closer to real-world practice. Full article

In response to the growing complexity of global exploration targets, traditional Euclidean geometric and linear statistical methods reveal inherent theoretical limitations in characterizing hydrocarbon reservoirs as complex geological bodies that exhibit simultaneous local disorder and global order. Fractal theory, with its core parameter systems such as fractal dimension and scaling exponents, provides an innovative mathematical–physics toolkit for quantifying spatial heterogeneity and resolving the multi-scale characteristics of reservoirs. This review systematically consolidates recent advancements in the application of fractal theory to oil and gas resource assessment, with the aim of elucidating its transition from a theoretical concept to a practical tool. We conclusively demonstrate that fractal theory has driven fundamental methodological progress across four critical dimensions: (1) In reservoir classification and evaluation, fractal dimension has emerged as a robust quantitative metric for heterogeneity and facies discrimination. (2) In pore structure characterization, the theory has successfully uncovered structural self-similarity across scales, from nanopores to macroscopic vugs, enabling precise modeling of complex pore networks. (3) In seepage behavior analysis, fractal-based models have significantly enhanced the predictive capacity for non-Darcy flow and preferential migration pathways. (4) In fracture network modeling, fractal geometry is proven pivotal for accurately characterizing the spatial distribution and connectivity of natural fractures. Despite significant progress, current research faces challenges, including insufficient correlation with dynamic geological processes and a scarcity of data for model validation. Future research should focus on the following directions: developing fractal parameter inversion methods integrated with artificial intelligence, constructing dynamic fractal–seepage coupling models based on digital twins, establishing a unified fractal theoretical framework from pore to basin scale, and expanding its application in low-carbon energy fields such as carbon dioxide sequestration and natural gas hydrate development. Through interdisciplinary integration and methodological innovation, fractal theory is expected to advance hydrocarbon resource assessment toward intelligent, precise, and systematic development, providing scientific support for the efficient exploitation of complex reservoirs and the transition to green, low-carbon energy. Full article

During the sudden closure of gates in long-distance gravity flow water supply projects, intense water hammer waves are generated. These waves can cause severe damage to the water supply tunnel structure, posing a significant threat to project safety. To develop an economical and effective hydraulic safety control strategy, this study uses the example of a specific gravity flow water supply project with long-distance and multi-fluctuation pressure tunnels in Zhejiang Province. A novel combined protection strategy was investigated, involving the conversion of construction branch tunnels into branch tunnel surge tanks combined with an overflow surge tank. Numerical simulations of gate closure-induced water hammer pressures were conducted using the method of characteristics. Additionally, the effectiveness of the overflow surge tank on controlling the surge water level in the branch tunnels was analyzed with respect to variations in its height, diameter, and impedance hole diameter. The results indicate that a 300 s linear gate closure without any protective measures induces severe water hammer pressure. Extending the closure time to 1200 s still results in pressures far exceeding the safety threshold. Converting construction branch tunnels into surge tanks effectively controlled the water hammer pressure; however, overflow issues emerged in some branch tunnels. The subsequent addition of an overflow surge tank at the end of the water supply system successfully eliminated the risk of overflow in the branch tunnels. Building upon this, multi-parameter optimization analysis was used to determine the optimal configuration for the overflow surge tank. This solution ensures hydraulic safety while maintaining cost-effectiveness. Both the maximum pressure and the minimum pressure along the water supply tunnel, as well as the surge water levels in all branch tunnels, meet the code requirements. Furthermore, the reduced size of the surge tank significantly lowered construction costs. The findings of this research provide theoretical foundations and technical support for similar long-distance gravity flow water supply projects. Full article