Search Results (9,821)

In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address this issue, this paper first introduces the Minkowski sum algorithm to map the feasible regions of dispersed individual units into a high-dimensional hypercube space, achieving efficient aggregation of large-scale schedulable capacity. Compared with conventional geometric or convex-hull aggregation methods, the proposed approach better captures spatio-temporal coupling characteristics and reduces computational complexity while preserving accuracy. Subsequently, aiming at the coordination challenge between day-ahead planning and real-time dispatch, a “hierarchical coordination and dynamic optimization” control framework is proposed. This three-layer architecture, comprising “day-ahead pre-dispatch, intraday rolling optimization, and terminal execution,” combined with PID feedback correction technology, stabilizes the output deviation within ±15%. This performance is significantly superior to the market assessment threshold. The research results provide theoretical support and practical reference for the engineering promotion of vehicle–grid interaction technology and the construction of new power systems. Full article

During the high-intensity mining of shallow-buried thick coal seams, the formation of a water-conducting fracture zone within the overburden is a primary cause of damage to the groundwater system. To address the challenge of balancing efficiency and cost in traditional water-retaining mining methods, this study proposes and validates a trapezoidal strip filling mining technology based on the “span reduction effect”. By developing a mechanical model of a four-sided simply supported thin plate representing the key layer, the fundamental mechanism of the filling body was elucidated. This mechanism involves the active adjustment of the support boundary, which effectively reduces the force span of the key layer. Furthermore, leveraging the fourth-power relationship (w ∝ a⁴) between deflection and span, the bending deformation of the overburden rock is exponentially mitigated. This study employs a four-tiered integrated verification system comprising theoretical modeling, physical simulation, numerical simulation, and engineering field testing: First, theoretical calculations indicate that reducing the effective span of the key layer by 40% can decrease its maximum deflection by 87%. Second, large-scale physical similarity simulations predict that implementing this filling method can significantly control the height of the water-conducting fracture zone, reducing it from 94 m under the collapse method to 58 m, which corresponds to a 45.5% reduction in surface settlement. Third, FLAC^3D numerical simulations further elucidated the mechanical mechanism by which the backfill system transforms stress distribution from “coal pillar-dominated bearing capacity” to “synergistic bearing capacity of backfill and coal pillars”. Shear failure in the critical layer was suppressed, and the development height of the plastic zone was restricted to approximately 54 m, showing high consistency with physical simulation results. Finally, actual measurements of water injection through the inverted hole underground provide direct evidence: The heights of the water-conducting fracture zones in the filling working face and the collapse working face are 59 m and 93 m, respectively, reflecting a reduction of 36.6%. Based on the consistency between measured and simulated results, the numerical model employed in this study has been effectively validated. Research indicates that employing trapezoidal strip filling technology based on principal stress dynamics regulation can effectively promote a shift in the failure mode of the overlying critical layer from “fracture–conduction” to “bending–subsidence”. This mechanism provides a clear mechanical explanation and predictable design basis for the green mining of shallow coal seams. Full article

This paper addresses the core conflict between long-range communication and ultra-low power requirements in sensing nodes for Wireless Sensor Networks (WSNs) by proposing a wake-up receiver (WuRx) design featuring nanowatt-level power consumption and high sensitivity. Conventional architectures are plagued by low energy efficiency, poor demodulation reliability, and insufficient clock synchronization accuracy, which hinders their practical application in real-world scenarios like WSNs. The proposed design employs an event-triggered mechanism, where a continuously operating, low-power WuRx monitors the channel and activates the main system only after validating a legitimate command, thereby significantly reducing standby power. At the system design level, a key innovation is direct conjugate matching between the antenna and a multi-stage rectifier, replacing the traditional 50 Ohm interface, which substantially improves energy transmission efficiency. Furthermore, a mean-detection demodulation circuit is introduced to dynamically generate an adaptive reference level, effectively overcoming the challenge of discriminating shallow modulation caused by signal saturation in the near-field region. At the baseband processing level, a configurable fault-tolerant correlator logic and a data-edge-triggered clock synchronization circuit are designed, combined with oversampling techniques to suppress clock drift and enhance the reliability of long data packet reception. Fabricated in a TSMC 0.18 µm CMOS process, the receiver features an ultra-low power consumption of 305 nW at 0.5 V and a high sensitivity of −47 dBm, enabling a communication range of up to 400 m in the 920–925 MHz band. Through synergistic innovation at both the circuit and system levels, this research provides a high-efficiency, high-reliability wake-up solution for long-range WSN nodes, effectively promoting the large-scale application of WSN technology in practical deployments. Full article

Accurate forecasting of heatwaves is critical for ensuring the safe operation of electricity grids. Focusing on the complex terrain of Sichuan, China, this study investigates the optimization of spectral nudging parameters within the Weather Research and Forecasting (WRF) model to improve predictions of heatwave events. To overcome the subjectivity inherent in the traditional selection of the spectral nudging cutoff wavenumber, we propose an objective method based on power-spectrum energy diagnostics of the background field. This method determines an optimal domain-specific cutoff wavenumber. A series of sensitivity experiments were designed for a significant heatwave event that affected the Sichuan electricity grid in August 2019. These experiments evaluated the impact of different spectral nudging configurations, which considered varying domain sizes and forecast lead times, on correcting large-scale circulation drift and enhancing near-surface air temperature forecasts. The results demonstrate the following: (1) For a smaller domain or a longer forecast lead time, spectral nudging effectively compensates for circulation drift induced by weakening lateral boundary constraints, significantly improving the forecast of heatwave intensity and spatial extent, representing a compensatory effect. (2) For a larger domain that already adequately resolves large-scale circulation evolution, spectral nudging can over-constrain the model’s internal dynamical processes, thereby degrading forecast performance, an outcome termed the over-constraint effect. (3) The proposed energy-threshold method provides an objective, physics-based strategy for identifying dominant large-scale waves and optimizing the spectral nudging cutoff wavenumber. This work offers practical insights for the operational application of spectral nudging over complex terrain to advance extreme temperature forecasting. Full article

Visual cues in urban street environments shape residents’ perceived safety, and these perceptions often differ across social groups. Using Shanghai as a case study, this research focuses on two vulnerable populations: older adults and migrants. In the context of rapid urban transformation and increasingly fine-grained governance, perceived safety not only reflects environmental experience but also relates to whether different social groups can receive equitable perceptual support and access to opportunities for public-space use. We trained a deep learning model and rated perceived safety using over 160,000 street-level images, integrated with demographic census data at the neighborhood level, to systematically examine inequalities in visual environment perception and underlying group-specific mechanisms. However, existing studies have largely relied on small-sample surveys or average-effect analyses, and systematic evidence remains limited that can simultaneously characterize city-scale inequalities in perceived safety, disparities in group exposure, and group-specific mechanisms, while translating findings into actionable guidance for targeted governance. Firstly, we quantified spatial inequality in perceived safety using the Gini coefficient and the Theil T index. Decomposition results indicate that the remaining disparity is primarily associated with between-group differences linked to social structure. Nonparametric tests and multiple linear regression further identified significant interactions between demographic characteristics (the share of older adults and the migrant proportion) and visual environmental features, confirming group-differentiated responses to comparable streetscape conditions. In addition, we developed a priority governance index that combines perceived safety scores with vulnerability indicators to spatially identify neighborhoods requiring targeted interventions. Results suggest relatively low overall spatial inequality in perceived safety at the city scale, while decomposition analyses reveal clear group-structured disparities between central and peripheral areas and between local residents and migrants. Migrants are more frequently concentrated in neighborhoods with lower perceived safety. Priority intervention areas are primarily older-adult communities in central districts and migrant settlements in peripheral areas, characterized by lower perceived safety and higher demographic vulnerability. These findings underscore the need to shift urban renewal from uniform improvements toward differentiated strategies that account for perceptual equity and social identity. Our main contribution is not the development of a new network architecture but the alignment of image-based perception estimates with demographic vulnerability at the neighborhood scale. By combining inequality decomposition with tests of interaction mechanisms, we provide governance-relevant evidence for identifying priority intervention areas and advancing fine-grained renewal decisions oriented toward visual justice. Full article

Forests play a critical role in Earth’s ecosystem, yet monitoring their long-term, large-scale spatiotemporal dynamics remains a significant challenge. This study addresses this gap by developing an integrated framework to map annual forest distribution in Hunan, China, from 1999 to 2023 at a high resolution of 30 m. Our methodology combines multi-temporal satellite imagery (Landsat 5/7/8/9) with key environmental variables, including digital elevation models, temperature, and precipitation data. To efficiently reconstruct historical maps, training samples were automatically derived from a reliable 2023 forest product using a transferable logic, drastically reducing manual annotation effort. Comprehensive evaluations demonstrate the robustness of our approach: (1) Qualitative analyses reveal superior spatial detail and temporal consistency compared to existing global forest maps. (2) Rigorous quantitative validation based on ∼9000 reference samples confirms high and stable accuracy (∼92.4%) and recall (∼91.9%) over the 24-year period. (3) Furthermore, comparisons with government forestry statistics show strong agreement, validating the practical utility of the data. This work provides a valuable, accurate long-term dataset that forms a scientific basis for critical downstream applications such as ecological conservation planning, carbon stock assessment, and climate change research, thereby highlighting the transformative potential of multi-source data fusion and automated methods in advancing geospatial monitoring. Full article

The frequency and intensity of tropical cyclones (TCs) in the Arabian Sea have increased in recent decades, heightening concerns regarding regional vulnerability and forecasting difficulties. This study examines the impact of the Madden–Julian Oscillation (MJO) on TCs activity—formation, frequency, and severity—over the Arabian Sea from 1982 to 2021. This study analyzes variations in convection, vertical wind shear (VWS), sea level pressure (SLP), and relative humidity (RH) across different MJO phases utilizing the best-track data from the India Meteorological Department (IMD), the Real-Time Multivariate MJO (RMM) index, and reanalysis datasets from the National Oceanic and Atmospheric Administration (NOAA) and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR). Results show that more than 80% of TCs form during the convectively active phases of the MJO (P1–P4). These phases have the most noticeable negative outgoing longwave radiation (OLR) anomalies, as well as higher mid-level moisture and low-pressure anomalies, which are good for cyclogenesis. On the other hand, suppressed phases (P6–P8) have positive outgoing longwave radiation, dry air in the middle troposphere, and high-pressure anomalies, which make it harder for TCs to form. While VWS is predominantly favorable during both active and inactive phases, thermodynamic and convective factors principally regulate the modulation of TC activity. The simultaneous presence of active MJO phases with positive Indian Ocean Dipole (pIOD) and neutral or El Niño conditions markedly increases TC frequency, highlighting a combined influence link between interannual–El Niño–Southern Oscillation (ENSO) and IOD– and intraseasonal (MJO) variability. Additionally, the association between MJO and the Indo-Pacific Warm Pool (IPWP) reveals that TC activity peaks during convectively active MJO phases under the second twenty years of this study, emphasizing the influence of large-scale oceanic warming on TC variability. These findings underscore the critical function of the MJO in regulating TC activity variability in the Arabian Sea and stress its significance for enhancing intraseasonal forecasting and disaster preparedness in the area. Full article

The increasing need for flexible and decentralized electricity systems in Colombia has renewed interest in biomass as a complementary renewable energy source beyond conventional large-scale applications. Rather than focusing on specific conversion technologies, this study develops an indicator-based framework aimed at qualifying the energetic suitability of diverse biomass resources for integration into electrical microgrids and distributed generation schemes. The research follows a documentary and comparative methodological design structured around sequential analytical stages, including the systematization of biomass resources, their physicochemical and energetic characterization based on reported data, conceptual analysis of the biomass-to-electricity pathway, and the formulation of quantitative energy indicators. These indicators are subsequently transformed into qualitative categories through a discretization procedure that enables relative comparison across resource types. Agricultural residues, livestock by-products, urban pruning waste, and residues from dedicated energy crops were considered within a unified analytical framework. The resulting indicator set captures resource availability, energy content, and conversion-relevant attributes, allowing biomass alternatives to be assessed in a consistent and comparable manner without relying on site-specific technological assumptions. By translating quantitative parameters into qualitative energy profiles, the proposed approach supports early-stage planning and decision-making for decentralized power systems. The framework provides a systematic basis for identifying biomass resources with favorable energetic characteristics and contributes to the broader discussion on sustainable and diversified electricity generation in Colombia. Full article

Pheromone-mediated chemical communication plays a central role in shaping the social organization and ecological success of S. invicta, a globally invasive eusocial insect characterized by a highly developed semiochemical signaling system. This review summarizes recent advances in the chemical ecology of S. invicta, with emphasis on the putative ecological roles of major pheromone classes, current understanding of the molecular and neurobiological basis of pheromone perception and signal processing, and the associations between chemical cues and colony-level social behavior dynamics. Furthermore, we evaluate progress in pheromone-based management approaches, including pheromone-enhanced baits and trail disruption techniques, highlighting both their potential to improve the specificity and efficacy of fire ant management and the current practical limitations for large-scale field applications. Finally, current significant knowledge gaps and challenges are discussed, particularly the partial characterization of pheromone identity, the ambiguous and biological significance of chemical cues, and challenges in applying laboratory research in pest management under field conditions. By linking chemical ecology, neurobiology, and invasion biology to pest management, this review outlines priority directions for future research and provides a theoretical foundation for developing more sustainable, targeted pest control approaches for fire ant management. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

The food industry generates large volumes of nutrient-rich by-products that remain underutilised despite their considerable biochemical potential. These materials originate predominantly from the fruit and vegetable, dairy, meat, and fish and seafood sectors and represent a substantial opportunity for sustainable valorisation. Fermentation has emerged as a powerful platform for converting such by-products into high-value ingredients, including bioactive compounds, functional metabolites, enzymes, antimicrobials, and nutritionally enriched fractions. This review synthesises recent advances in microbial fermentation strategies—spanning lactic acid bacteria, filamentous fungi, yeasts, and mixed microbial consortia—and highlights their capacity to enhance the bioavailability, stability, and functionality of recovered compounds across diverse substrate streams. Key technological enablers, including substrate pre-treatments, precision fermentation, omics-guided strain selection and improvement, and bioprocess optimisation, are examined within the broader framework of circular bioeconomy integration. Despite significant scientific progress, major challenges remain, particularly related to substrate heterogeneity, process scalability, regulatory alignment, safety assessment, and consumer acceptance. The review identifies critical research gaps and future directions, emphasising the need for standardised analytical frameworks, harmonised compositional databases, AI-driven fermentation control, integrated biorefinery concepts, and pilot-scale validation. Overall, the evidence indicates that integrated fermentation-based approaches—especially those combining complementary by-product streams, tailored microbial consortia, and system-level process integration—represent the most promising pathway toward the scalable, sustainable, and economically viable valorisation of food industry by-products. Full article

With the increase in mining intensity and scale, the damage to groundwater resources and surface ecology caused by coal mining has become the main problem facing coal development. Coal mining can cause a redistribution of stress field and stress concentration in local areas of overlying rock, resulting in varying degrees of movement and damage to the overlying rock. Quantitative analysis of the degree of migration and damage in different areas of overlying rock and zoning control is crucial for achieving loss reduction and green mining. In this paper, the overburden damage is divided into regions according to the different causes of formation, regional characteristics of severity, and other factors, and the specific calculation method is given. UDEC7.0 numerical simulation software is used to simulate the overlying rock damage, and the best mining parameters are provided through the area changes in different zones. The research conclusions are as follows: according to the different damage states of overburden rock, the damage of overburden rock can be divided into four parts: I, caving fracture zone, II, fracture development zone, III, sliding failure zone, and IV, slight failure zone. In the four zones, the damage in zones II and IV is relatively light. During the mining process, attention should be given to controlling the development of Zone I to prevent it from abnormally enlarging; for Zone II, hydraulic fracturing can be used when there is a thick, hard key layer that poses a water inrush risk; for Zone III, the focus should be on preventing surface step fractures caused by it. For example, when a thick, hard key layer is present in Zone II, hydraulic fracturing can be applied to avoid large area hanging roofs and severe rock pressure. When the mining height is low, it mainly affects the proportion of regions I and III. With the increase in mining height, the main affected region becomes the II region. The larger the mining height is, the larger the proportion of the II region. With the increase in propulsion speed, the impact range on the surface increases, but the area with severe damage is relatively reduced. With the increase in mining width, the proportion of relatively seriously damaged areas increased. On-site measurements have shown that when the speeds of 120,401 and 22,207 working faces are slow, the rock layer pressure shows a dense state, the overburden fracture is more fully developed, and the area proportion of I and II zones is increased, which reflects the phenomenon of dense surface fracture development on the surface. When the advancing speed is large, the area proportions of zones III and IV increase, and the damage scope decreases. The on-site testing verified the conclusions drawn from theoretical analysis and numerical simulation, which can guide other mines under similar conditions to achieve safe and green production. Full article

Although large-scale studies and potential pathways of genes on intramuscular fat (IMF) in livestock have been reported, research on circRNAs in yaks—a unique, low-IMF-content animal species that is native to the Qinghai–Tibetan Plateau—is still lacking. Based on previous high-throughput sequencing results on longissimus dorsi with different IMF content, a novel circRNA encoded by the VPS13C gene (designated as circVPS13C) was found to exhibit significant differential expression. Here, we systematically characterized the function and mechanism of circVPS13C on IMF deposition in yaks by adopting a series of experiments. Sequencing, RNase R processing, and nucleoplasmic separation experiments confirmed the circular structure feature of circVPS13C, and it was predominantly distributed in the cytoplasm. Furthermore, these experiments demonstrated that circVPS13C was mainly distributed in the cytoplasm. The circVPS13C/miR-5606-x/ECHDC3 axis was constructed through ceRNA network analysis and validated by dual-luciferase reporter and rescue experiments. Furthermore, the function of these three potential regulators during IMF deposition was investigated through CCK-8, BODIPY, Oil Red O staining, and qRT-PCR analyses, and results showed that both circVPS13C and miR-5606-x promoted the differentiation and inhibited the proliferation of yak intramuscular preadipocytes, while the function of ECHDC3 was the opposite. In conclusion, circVPS13C could act as a competitive endogenous RNA (ceRNA) sponge to sequester miR-5606-x, thereby relieving the inhibitory effect of miR-5606-x on ECHDC3. Full article

Sodium-ion batteries (SIBs) represent a topic of extreme interest in the research field, especially because the materials used are cheaper than those in lithium-ion batteries (LIBs). In SIBs, the choice of cathodes and electrolytes is very important because they will affect the energy density, cycling stability, and safety of the battery. This work focuses on the prospect of hybrid electrolyte cells that incorporate ionic liquids (ILs) into organic liquids in order to improve the safety and performance of SIBs. Organic solutes make ionic conductivity higher due to larger IL electrochemical windows, good thermal stability and low volatility. They have some issues like flammability, dissolution, and transport limitations, but these aspects could be solved by using hybrid electrolyte systems. In this study, we investigate the effect of using different salts and solvents on the characteristics of the SIBs. We analyze ionic conductivity, electrochemical stability, and the development of stable solid electrolyte interphase (SEI) in the SIBs by using hybrid electrolytes. Additionally, we demonstrate that the addition of ILs to organic electrolytes can improve their thermal stability, so as a result, the safety and lifecycle of the battery will be increased. In conclusion, this research shows how hybrid electrolytes could have great potential for SIB battery technology in high-performance and large-scale energy storage applications. Full article

Wind power plants (WPPs), as large-scale cyber–physical systems (CPSs), have become essential to renewable energy generation but are increasingly exposed to cyber threats. Attacks on supervisory control and data acquisition (SCADA) networks can cause cascading physical and economic impacts. The systematic synthesis of cyber risk analysis methods specific to WPPs and cyber–physical energy systems (CPESs) is a need of the hour to identify research gaps and guide the development of resilient protection frameworks. This study employs a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to review the state of the art in this area. Peer-reviewed studies published between January 2010 and January 2025 were taken from four major journals using a structured set of nine search queries. After removing duplicates, applying inclusion and exclusion criteria, and screening titles and abstracts, 62 studies were examined for analysis on the basis of a synthesis framework. The studies were classified along three methodological dimensions, qualitative vs. quantitative, model-based vs. data-driven, and informal vs. formal, giving us a unified taxonomy of cyber risk analysis approaches. Among the included studies, 45% appeared to be qualitative or semi-quantitative frameworks such as STRIDE, DREAD, or MITRE ATT&CK; 35% were classified as quantitative or model-based techniques such as Bayesian networks, Markov decision processes, and Petri nets; and 20% adopted data-driven or hybrid AI/ML methods. Only 28% implemented formal verification, and fewer than 10% explicitly linked cyber vulnerabilities to safety consequences. Key research gaps include limited integration of safety–security interdependencies, scarce operational datasets, and inadequate modelling of environmental factors in WPPs. This systematic review highlights a predominance of qualitative approaches and a shortage of data-driven and formally verified frameworks for WPP cybersecurity. Future research should prioritise hybrid methods that integrate formal modelling, synthetic data generation, and machine learning-based risk prioritisation to enhance resilience and operational safety of renewable-energy infrastructures. Full article