Search Results (5,319)

Medium-voltage energy storage converter equipment is an important component of the new generation of ship power and power systems. Virtual space vector pulse width modulation, as a modulation optimization method to improve the neutral-point voltage imbalance in medium- and high-voltage multilevel energy storage converters, has become a research hotspot for T-type three-level energy storage inverter modulation methods due to its significant balancing effect and simple implementation. However, the current research method of constructing virtual vectors through redundant small vectors has limitations in regulating the neutral-point potential under full (especially high) modulation ratios. This paper proposes a modulation method that uses hybrid variable virtual small vectors and virtual medium vectors through optimization selection and reconstruction of basic vectors. This method ensures that the neutral-point charge change of the vector is zero and the common-mode voltage is minimized within the switching period under the full modulation ratio, achieving the purpose of controlling the neutral-point voltage balance and suppressing the common-mode voltage. Finally, simulation and experimental results show that the proposed method has good neutral-point voltage regulation and common-mode voltage suppression capabilities within the full modulation ratio range, and the system also has strong robustness and adaptability under different load conditions. Full article

Efficient urban land use is a cornerstone of sustainable city development, yet the drivers of such efficiency are increasingly complex in an era of spatial transformation. As industrial specialization and collaboration deepen, cities are becoming interconnected through complex networks. These “invisible threads” are redefining the dynamics of land use and spatial efficiency. This study examines the influence of intercity industrial networks on urban land use efficiency by constructing urban networks from multi-regional input–output data and evaluating city performance using a super-SBM model. We employed Tobit regression and mediation analysis to identify the mechanisms. Results indicate that both the quantity and quality of urban network connections significantly enhance land use efficiency, with notable differences across city types. The positive effect of industrial network centrality is most pronounced in large cities. In growing cities, both the number and quality of industrial linkages promote efficiency, whereas in shrinking cities, connection quality is more critical than quantity. Mechanism analysis reveals that industrial networks improve land use efficiency primarily by expanding intermediate goods markets and fostering technological innovation. Full article

With the accelerating global energy transition, wind power has become a core pillar of renewable energy systems. However, its inherent intermittency and volatility pose significant challenges to the safe, stable, and economical operation of power grids—making ultra-short-term wind power prediction a critical technical link in optimizing grid scheduling and promoting large-scale wind power integration. Current forecasting techniques are plagued by problems like the inadequate representation of features, the poor separation of features, and the challenging clarity of deep learning models. This study introduces a method for the prediction of wind energy using spatiotemporal contrastive learning, employing seasonal trend decomposition to encapsulate the diverse characteristics of time series. A contrastive learning framework and a feature disentanglement loss function are employed to effectively decouple spatiotemporal features. Data on geographical positions are integrated to simulate spatial correlations, and a convolutional network of spatiotemporal graphs, integrated with a multi-head attention system, is crafted to improve the clarity. The proposed method is validated using operational data from two actual wind farms in Northwestern China. The research indicates that, compared with typical baselines (e.g., STGCN), this method reduces the RMSE by up to 38.47% and the MAE by up to 44.71% for ultra-short-term wind power prediction, markedly enhancing the prediction precision and offering a more efficient way to forecast wind power. Full article

To enhance both the environmental performance and mechanical properties of phosphate solid waste backfill materials, this study examines the effects of corn straw fibre (CS), rice straw fibre (RS), and jute fibre (JF), each at five lengths (3–15 mm) and five dosages (0.1–0.5 wt%), on the rheological behaviour, mechanical strength, and microstructural characteristics of the backfill slurry. The experimental results showed that the incorporation of natural fibres markedly improved both the compressive and tensile strengths of backfill materials. For example, incorporating CS at a length of 12 mm and a dosage of 0.2 wt% increased the compressive and tensile strengths by 144.4% and 18.8%, respectively. Likewise, RS at 3 mm and 0.2 wt% increased the strengths by 68.3% and 11.9%, while JF at 12 mm and 0.5 wt% enhanced them by 108.2% and 14.9%, respectively. Ion leaching experiments and XPS analyses confirmed that the incorporation of natural fibres effectively adsorbed and immobilized phosphorus and fluorine in phosphogypsum. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses revealed that the improved mechanical strength was primarily attributed to fibre-bridging effects and enhanced fibre–matrix bonding. Furthermore, nuclear magnetic resonance (NMR) analysis demonstrated that incorporating natural fibres reduced the porosity of backfill materials (from 12.9% to 8.14%) while increasing their density. This study provides an experimental foundation for optimizing backfill materials and recommends a 12 mm CS fibre length at a dosage of 0.2 wt% to improve the stability and safety of mine fill structures. Full article

An internal geomembrane (GMB)–clay seepage control system is an important form of seepage control structure for rockfill dams. In order to investigate the stress and deformation characteristics of GMB in GMB–clay core-wall rockfill dams (GMCWRD) under different construction and operation conditions, the stress and deformation fields of GMCWRDs were calculated by numerical simulation under a variety of working conditions. The stress and deformation characteristics of the dam and GMB during the impoundment period were investigated, and the influences of the spreading thickness of the clay core-wall and the location of the GMB defects and hydraulic head on the stress and deformation of the GMB were analyzed. The results show that the maximum tensile strain of the GMB upstream of the clay core-wall during the impoundment period occurs at the anchorage of the GMB and the concrete cut-off, with a maximum tensile strain of 2.70%. With the increase in the spreading thickness of the clay core-wall, the maximum tensile stress and strain of the GMB fluctuated. Under the dam construction and foundation conditions in this paper, when the spreading thickness of the clay core-wall was 2 m, the tensile stress and strain of GMB were at the lowest level. As the defect location of the GMB decreases, the phreatic line of the dam gradually increases, and the seepage discharge of the dam and the tensile strain of the GMB gradually increase, with the maximum tensile strain of 3.98%. The maximum deformation of the GMB in each case is much smaller than the maximum elastic deformation range of the selected PVC GMB, and the conclusion of the study provides a certain scientific basis for the design and construction of the seepage control of the core rockfill dam. Full article

Optimizing aeration, fertilization, and irrigation is vital for improving greenhouse tomato production while mitigating soil greenhouse gas (GHG) emissions. This study investigated the combined effects of three aeration levels (A1: single Venturi, A2: double Venturi, CK: no aeration), two fertilization rates (F1: 180 kg/ha, F2: 240 kg/ha), and two irrigation levels (I1: 0.8 E_pan, I2: 1.0 E_pan) on tomato yield, CO₂, N₂O, and CH₄ emissions, net GHG emissions, net global warming potential (NGWP), and GHG intensity (GHGI) across Spring–Summer and Autumn–Winter seasons. Results showed that aeration and fertilization significantly increased CO₂ and N₂O emissions but reduced CH₄ emissions. Warmer conditions in Spring–Summer elevated all GHG emissions and yield compared to Autumn–Winter seasons. Tomato yield, net GHG emissions, NGWP, and GHGI were 12.05%, 24.3%, 14.46%, and 2.37% higher, respectively, in Spring–Summer. Combining the Maximal Information Coefficient and TOPSIS models, the optimal practice was A1-F1-I1 in Spring–Summer and A2-F1-I1 in Autumn–Winter seasons. These results provide a theoretical basis for selecting climate-smart management strategies that enhance yield and environmental sustainability in greenhouse tomato systems. Full article

Reservoir sedimentation, a global challenge causing an annual loss of 0.8–1% of reservoir storage capacity, disrupts fluvial sediment continuity and impacts ecology and societal needs. This study focuses on the Pulangi IV reservoir in the Philippines, a shallow and wide reservoir facing significant sedimentation issues. The research aims to investigate drawdown flushing and dredging of a flushing channel for future sustainable drawdown sluicing. A test flushing event was conducted and monitoring data, including discharge, suspended sediment concentration, bathymetry, and grain size distribution, were collected. Laboratory analyses, such as critical shear stress tests, were performed for model calibration. A 3D reservoir model and a 1D sediment transport model were applied incorporating cohesive sediment behavior. Scenarios were simulated to assess drawdown flushing, dredging and downstream impacts. Results highlight the importance of drawdown level, with cohesive sediment properties playing a critical role. Sedimentation downstream of the dam, resulting from dumped or flushed sediments, was low. However, downstream ecological and morphodynamic monitoring was found to be essential for all modeled strategies. This study demonstrates potential for establishing a flushing channel enabling future sustainable drawdown sluicing during floods by conducting repeated drawdown flushing in combination with dredging in the upper reservoir. Full article

Sensors are widely used in inland waterway buoys to monitor their position, but the collected data are often affected by noise, outliers, and irregular sampling intervals. To address these challenges, a standardized data processing framework is proposed. Outliers are identified using a hybrid approach combining interquartile range filtering and Isolation Forest algorithm. Interpolation methods are adaptively selected based on time intervals. For short-term gaps, cubic spline interpolation is applied, otherwise, a method that combines dominant periodicity estimation with physical constraints based on power spectral density (PSD) is proposed. An adaptive unscented Kalman filter (AUKF), integrated with the Singer motion model, are applied for denoising, dynamically adjusting to local noise statistics and capturing acceleration dynamics. Afterwards, a set of time-frequency features are extracted, including centrality, directional dispersion, and wavelet transform-based features. Taking the lower Yangtze River as a case study, representative buoys are selected based on dynamic time warping similarity. The features analysis result show that the movement of buoys is closely related to the dynamics dominated by the semi-diurnal tide, and is also affected by runoff and accidents. The method improves the quality and interpretability of buoy motion data, facilitating more robust monitoring and hydrodynamic analysis. Full article

Water supply systems (WSSs) are energy-intensive infrastructure that present significant opportunities for decarbonization through the integration of renewable energy (RE). This study evaluated the RE generation potential within the WSSs of Incheon Metropolitan City (IMC), Republic of Korea, using a site-specific, data-driven approach. Three RE technologies were considered: solar photovoltaic (PV) systems installed in water-treatment plants (WTPs), micro-hydropower (MHP) utilizing the residual head at the inlet chamber of a WTP, and in-pipe MHP recovery using the discharge from water supply tanks in water distribution networks. Actual facility data, hydraulic simulations, and spatial analyses were used to estimate an annual RE generation potential of 32,811 MWh in the WSSs of IMC, including 18,830 MWh from solar PV in WTPs, 4938 MWh from MHP in WTPs, and 9043 MWh from in-pipe MHP. This corresponds to an energy self-sufficiency rate of approximately 22.3%, relative to the IMC WSS total annual electricity consumption of 147,293 MWh in 2022. The results demonstrated that decentralized RE deployment within existing WSSs can significantly reduce grid dependency and carbon emissions. This study provides a rare empirical benchmark for RE integration in large-scale WSSs and offers practical insights for municipalities seeking energy-resilient and climate-aligned infrastructure transitions. Full article

Soft robots demonstrate significant advantages in applications within complex environments due to their unique material properties and structural designs. However, they also face challenges in fault diagnosis, such as nonlinearity, time variability, and the difficulty of precise modeling. To address these issues, this paper proposes a fault diagnosis method based on multimodal spatiotemporal features and ensemble learning. First, a sliding-window Kalman filter is utilized to eliminate noise interference from multi-source signals, constructing separate temporal and spatial representation spaces. Subsequently, an adaptive weight strategy for feature fusion is applied to train a heterogeneous decision tree model, followed by a dynamic weighted voting mechanism based on confidence levels to obtain diagnostic results. This method optimizes the feature extraction and fusion process in stages, combined with a dynamic ensemble strategy. Experimental results indicate a significant improvement in diagnostic accuracy and model robustness, achieving precise identification of faults in soft robots. Full article

Although the sediment–water interface of deep and large reservoirs is recognized as a dominant source of internal phosphorus (P) loading, the quantitative hierarchy of environmental drivers and their interaction thresholds remains poorly resolved. Here, we integrate 512 studies to provide the first process-based synthesis that partitions P release fluxes among temperature, pH, dissolved oxygen, salinity, sediment properties, and microbial activity across canyon, valley, and plain-type reservoirs. By deriving standardized effect sizes from 61 data-rich papers, we show that (i) a 1 °C rise in bottom-water temperature increases soluble reactive P (SRP) flux by 12.4% (95% CI: 10.8–14.0%), with sensitivity 28% lower in Alpine oligotrophic systems and 20% higher in warm monomictic basins; (ii) a single-unit pH shift—whether acid or alkaline—stimulates P release through distinct desorption pathways,; and (iii) each 1 mg L⁻¹ drop in dissolved oxygen amplifies release by 31% (25–37%). Critically, we demonstrate that these drivers rarely act independently: multi-factor laboratory and in situ analyses reveal that simultaneous hypoxia and warming can triple the release rate predicted from single-factor models. We further identify that >75% of measurements originate from dam-proximal zones, creating spatial blind spots that currently limit global P-load forecasts to ±50% uncertainty. To close this gap, we advocate coupled metagenomic–geochemical observatories that link gene expression (phoD, ppk, pqqC) to real-time SRP fluxes. The review advances beyond the existing literature by (1) establishing the first quantitative, globally transferable framework for temperature-, DO-, and pH-based management levers; (2) exposing the overlooked role of regional climate in modulating temperature sensitivity; and (3) providing a research agenda that reduces forecasting uncertainty to <20% within two years. Full article

Compound dry and hot events or extremes (CDHEs) have emerged as major climatic threats to agricultural production and food security in the middle reaches of the Yangtze River Basin (MRYRB), a critical grain-producing region in China. However, agricultural risks associated with CDHEs, incorporating both natural and socio-economic factors, remain poorly understood in this area. Using a Hazard-Exposure-Vulnerability (HEV) framework integrated with a weighting quantification method and supported by remote sensing technology and integrated geographic data, we systematically assessed the spatiotemporal dynamics of agricultural CDHE risks and corresponding crop responses in the MRYRB from 2000 to 2019. Results indicated an increasing trend in agricultural risks across the region, particularly in the Poyang Lake Plain (by 21.9%) and Jianghan Plain (by 9.9%), whereas a decreasing trend was observed in the Dongting Lake Plain (by 15.2%). Spatial autocorrelation analysis further demonstrated a significant negative relationship between gross primary production (GPP) and high agricultural risks of CDHEs, with a spatial concordance rate of 52.6%. These findings underscore the importance of incorporating CDHE risk assessments into agricultural management. To mitigate future risks, we suggest targeted adaptation strategies, including strengthening water resource management and developing multi-source irrigation systems in the Poyang Lake Plain, Dongting Lake, and the Jianghan Plain, improving hydraulic infrastructure and water source conservation capacity in northern and southwestern Hunan Province, and prioritizing regional risk-based adaptive planning to reduce agricultural losses. Our findings rectify the longstanding assumption that hydrological abundance inherently confers robust resistance to compound drought and heatwave stresses in lacustrine plains. Full article

Macroinvertebrates are a crucial part of aquatic ecosystems and significantly contribute to the maintenance of their health and stability. Our aims were to explore spatio-temporal patterns in macroinvertebrate communities and evaluate the ecological health of various parts of the Poyang Lake Basin during the early stage of a fishing ban. We collected samples using a Peterson grab sampler and conducted ecological evaluations using the B-IBI index. A total of 107 species of macroinvertebrates were identified, and most species were arthropods. The density and biomass of macroinvertebrates significantly differed among seasons and water bodies. No significant differences in diversity among seasons were observed; however, diversity significantly varied among water bodies. Environmental parameters such as water depth, pH, turbidity, total nitrogen, total phosphorus, and chlorophyll a played a crucial role in shaping the community structure of macroinvertebrates. Most of the sampling sites were classified as healthy or sub-healthy, indicating that the fishing ban policy has started to have a positive effect. The effects of this ban are achieved through a cascading sequence of processes, including the elimination of fishing disturbance, the restoration of habitat structure, and the reallocation of trophic energy, in addition to increases in microhabitat diversity associated with habitat heterogeneity. Together, these processes drive the multidimensional recovery of macroinvertebrate communities, manifested as increased species richness, higher density and biomass, and elevated B-IBI scores. Full article

Droughts present persistent and severe challenges to the security of regional water supplies, particularly in arid and semiarid regions such as northern China. Traditional reservoir operation models that prioritize water supply reliability or economic efficiency often fail to adequately address the risks posed by extreme drought events. In this study, we develop a novel risk-informed multiobjective reservoir operation model that incorporates three key performance indicators: reliability, resilience, and vulnerability (RRV). This model aims to improve drought response and enhance the overall stability of the water supply system. It is applied to a multisource water supply system composed of the Nierji Reservoir and various water-user sectors. Unlike traditional models, this approach explicitly balances the trade-offs among supply reliability, recovery capability, and water shortage during drought periods. Comparative analyses with conventional strategies (CSs) under both a six-year consecutive dry period and a representative single dry year demonstrate the superior performance of the RRV-based model in drought management. Specifically, the model reduces the average supply disruption duration from 8–10 to 4–6 ten-day intervals, increases water supply reliability to 90%, decreases the maximum single-event shortage depth to 22 × 10⁶ m³, and lowers the average water shortage to 221 × 10⁶ m³. Agricultural water shortages are reduced, although slight increases occur in other sectors. The results highlight resilience as the most influential objective in the model, and its inclusion or exclusion can be adjusted based on different drought response priorities. This study presents a novel and adaptive framework for reservoir operation under drought conditions, offering practical implications for improving the resilience and efficiency of regional water resource systems in the context of climate change. Full article

The stability of rock slope–anchoring systems is one of the core concerns in protecting the ecological environment and ensuring the safe operation of hydropower, transportation, and construction projects. The stability evaluation index system is a critical factor influencing the accuracy of such assessments. This study establishes a stability evaluation index system for rock slope–anchoring systems by incorporating multi-factor influence mechanisms. The approach involves indicator screening, development of a hierarchical analytical structure, definition of classification criteria, and comparative analysis. The results indicate the following: (1) The proposed index system fully considers the deformation and failure modes of rock slopes, the factors influencing stability, and the safety-related parameters of anchoring structures. (2) It comprehensively captures the multi-factor influence patterns affecting the stability of the rock slope–anchoring system. (3) Compared with traditional empirical and equal-interval grading methods, the grading standards defined by this system are more accurate, better reflect the intrinsic data characteristics, and yield higher classification precision. Full article