Search Results (10,804)

Cancer remains one of the most common causes of death worldwide and imposes enormous social and economic burdens. Human tumor-associated NADH oxidase (ENOX2, also known as tNOX) is a cancer cell-specialized NADH oxidase that is expressed on the membranes of cancer cells. In this study, we investigated the potential role of ENOX2 in regulating stemness properties in oral cancer through a combination of in vitro, in vivo, and bioinformatics approaches. We found that ENOX2 physically interacted with the stem cell transcription factor, SOX2, in co-immunoprecipitation experiments. The expression and activity of ENOX2 were elevated in p53-functional SAS and p53-mutated HSC-3 oral cancer cell spheroids compared with their monolayer counterparts. Consistently, SIRT1, a downstream effector modulated by ENOX2 through NAD⁺ generation, was also upregulated in spheroid cultures. Functional studies further established that ENOX2 overexpression significantly enhanced spheroid formation, self-renewal properties, stem cell marker expression, and PKCδ expression, whereas ENOX2 knockdown produced the opposite effects. In xenograft models, ENOX2-overexpressing oral cancer cell spheroids exhibited enhanced tumorigenicity, while ENOX2-silenced spheroids formed significantly smaller tumors. Complementary analyses of public transcriptomic and proteomic datasets revealed elevated ENOX2 expression in human head and neck tumor tissues compared with adjacent normal tissues. Based on these findings and literature-supported correlations, we propose a putative ENOX2-SIRT1-SOX2 regulatory framework that may contribute to the acquisition and maintenance of stem-like properties of oral cancer cells. While the ENOX2–SOX2 interaction was experimentally validated, the roles of SIRT1 and other downstream components are inferred from bioinformatic analyses and prior studies; thus, this axis represents a hypothetical model that warrants further mechanistic investigation. Collectively, our results identify ENOX2 as a potential regulator of oral cancer stemness and provide a conceptual foundation for future studies aimed at elucidating its downstream pathways and clinical relevance in head and neck tumors. Full article

The trend of the world to electric mobility and the inclusion of renewable energy requires complex control and predictive models of Smart Electric Vehicle Charging Stations (SEVCSs). The paper describes an experimental artificial intelligence (AI) model that can be used to optimize EV charging in New York City based on ten years of historical load and weather information. Nonlinear environmental relationships with urban energy demand and the use of Neural Fitting and Regression Learner models in MATLAB were used to explore the nonlinear relationships between the environment and energy demand. The quality of the input data was maintained with a lot of preprocessing, such as outlier removal, smoothing, and time alignment. The performance measurements showed that there was a Mean Absolute Percentage Error (MAPE) of 4.9, and a coefficient of determination (R²) of 0.93, meaning that there was a high level of concordance between the predicted and measured load profiles. Such findings indicate that AI-based models can be used to replicate load dynamics during renewable energy variability. The research combines the findings of long-term and multi-source data with the short-term forecasting to address the research gaps of past studies that were limited to a few small datasets or single-variable-based time series, which will provide a replicable base to develop energy-efficient and intelligent EV charging networks in line with future grid decarbonization goals. The proposed neural network had an R² = 0.93 and RMSE = 36.4 MW. The Neural Fitting model led to less RMSE than linear regression and lower MAPE than the persistence method by a factor of about 15 and 22 percent, respectively. Full article

The energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation. Full article

Accurate short-term forecasting of photovoltaic (PV) output is critical to managing the variability of PV generation and ensuring reliable grid operation with high renewable integration. We propose an enhanced hybrid deep learning framework that combines Temporal Convolutional Networks (TCNs), Gated Recurrent Units (GRUs), and Random Forests (RFs) in an optimized weighted ensemble strategy. This approach leverages the complementary strengths of each component: TCNs capture long-range temporal dependencies via dilated causal convolutions; GRUs model sequential weather-driven dynamics; and RFs enhance robustness to outliers and nonlinear relationships. The model was evaluated on high-resolution operational data from the Yulara solar plant in Australia, forecasting horizons from 5 min to 1 h. Results show that the TCN-GRU-RF model consistently outperforms conventional benchmarks, achieving R² = 0.9807 (MAE = 0.0136; RMSE = 0.0300) at 5 min and R² = 0.9047 (RMSE = 0.0652) at 1 h horizons. Notably, the degradation in R² across forecasting horizons was limited to 7.7%, significantly lower than the typical 10–15% range observed in the literature, highlighting the model’s scalability and resilience. These validated results indicate that the proposed approach provides a robust, scalable forecasting solution that enhances grid reliability and supports the integration of distributed renewable energy sources. Full article

The massive influx of pelagic Sargassum in the Caribbean poses a serious environmental, social, and economic problem, as the stranded biomass is often treated as waste and deposited in landfills. This literature review synthesizes recent research highlighting its potential for valorization in various industries, turning this challenge into an opportunity. Sargassum has low levels of protein and lipids. Still, it is particularly rich in carbohydrates, such as alginates, fucoidans, mannitol, and cellulose, as well as secondary metabolites, including phenolic compounds, flavonoids, pigments, and phytosterols with antioxidant and bioactive properties. These biochemical characteristics allow for its application in renewable energy (bioethanol, biogas, biodiesel, and combustion), agriculture (fertilizers and biostimulants), construction (composite materials, cement additives, and insulation), bioremediation (adsorption of heavy metals and dyes), and in the health sector (antioxidants, anti-inflammatories, and pharmacological uses). A major limitation is its high bioaccumulation capacity for heavy metals, particularly arsenic, which increases environmental and health risks and limits its direct use in food and feed. Therefore, innovative pretreatment and bioprocessing are essential to mitigate these risks. The most promising approach for its utilization is a biorefinery model, which allows for the sequential extraction of multiple high-value compounds and energy products to maximize benefits, reduce costs, and sustainably transform Sargassum from a coastal pest into a valuable industrial resource. Full article

Water exchange capacity is critical for maintaining marine environmental quality and supporting the sustainable development of aquaculture. This study applies a high-resolution three-dimensional FVCOM hydrodynamic model coupled with the DYE-RELEASE module. The model was validated against tidal, current, and thermohaline observations. Water residence time (Tre) was used as the primary evaluation metric, supplemented by analyses of residual circulation, material diffusion, and regional variability, to systematically quantify the water exchange mechanisms and seasonal variations in the coastal waters of Changhai County under the combined influence of tides, wind forcing, and thermohaline conditions. Results show that overall residual currents in Changhai County are weak (average velocity: 0.032 m s⁻¹). However, local circulations and stagnation zones frequently develop near islands and channels, strongly influencing material diffusion. In summer, water exchange is primarily controlled by thermohaline effects, which strengthen density stratification, suppress vertical mixing, and modify circulation patterns, thereby reducing the efficiency of tide-driven exchange. Water exchange is weakest near Guanglu Island (46.6–48.6 d) and strongest near Haiyang Island (13–14 d). In winter, wind forcing dominates, enhancing vertical mixing and accelerating water renewal. Residence time in the Changshan Archipelago–Guanglu Island region decreases by 30–50% compared with summer. Overall, winter water renewal is 15–25% more efficient than in summer. This study demonstrates that water exchange in Changhai County is regulated by the combined effects of tides, wind forcing, and thermohaline dynamics. The identified spatial heterogeneity and seasonal characteristics provide a scientific basis for optimizing aquaculture planning and mitigating marine environmental risks. Full article

This study investigates the feasibility of absolute decoupling—where economies expand while CO₂ (Carbon Dioxide) emissions decline in absolute terms—by identifying its key macro–energy drivers across 79 countries (2000–2025). We construct a comprehensive panel of energy-system indicators and estimate the probability of decoupling using two complementary classifiers: a penalized logistic regression and a gradient-boosted decision tree model (GBM). The non-parametric GBM significantly outperforms the linear baseline (ROC–AUC ~0.80 vs. 0.67), revealing complex non-linearities in the transition process. Explainable AI analysis (SHAP) demonstrates that decoupling is not driven by GDP growth rates alone, but primarily by sharp reductions in energy intensity and the active displacement of fossil fuels. Crucially, our results indicate that increasing renewable capacity is insufficient for absolute decoupling if the fossil fuel share does not simultaneously decline. These findings challenge passive “green growth” narratives, suggesting that current policies are inadequate; achieving climate targets requires targeted mechanisms for active fossil fuel phase-out rather than merely relying on renewable additions or economic modernization. Full article

Understanding the spatiotemporal evolution of forest biomass energy potential is essential for supporting low-carbon land-use planning and regional energy transitions. China, characterized by pronounced spatial heterogeneity in forest resources and ecological conditions, provides an ideal case for examining how biophysical endowments and management factors shape biomass energy potential. This study constructs a province-level panel dataset for China covering the period from 1998 to 2018 and investigates long-term spatial patterns, regional disparities, and driving mechanisms using spatial visualization, Dagum Gini decomposition, and fixed-effects estimation. The results reveal a gradual spatial reorganization of forest biomass energy potential, with the national center of gravity shifting westward and northwestward, alongside a moderate dispersion of high-potential clusters from coastal areas toward the interior. Interregional transvariation is identified as the dominant source of regional inequality, indicating persistent structural differences among major regions. To explore future dynamics, a random forest model is employed to project provincial forest biomass energy potential from 2018 to 2028. The projections suggest moderate overall growth, smoother distributional structures, and a partial reduction in extreme provincial disparities. Central, southwestern, and northwestern provinces are expected to emerge as important contributors to future growth, reflecting ecological restoration efforts, expanding plantation forests, and improved forest management. The findings highlight a continued upward trend in national forest biomass energy potential, accompanied by a spatial shift toward inland regions and evolving regional disparities. This study provides empirical evidence to support region-specific development strategies, optimized spatial allocation of forest biomass resources, and integrated policies linking ecological sustainability with renewable energy development. Full article

As the proportion of renewable energy generation in the power grid continues to rise, the operational state of the power system changes frequently with fluctuations in renewable power output. However, the traditional fixed-weight multi-objective reactive power optimization method lacks the necessary flexibility and adaptability, as it is unable to dynamically adjust the priority levels of different objectives based on real-time operating conditions (such as load fluctuations and changes in network structure). As a result, its optimization decisions may deviate from the system’s most urgent economic or security needs. To address this issue, this paper proposes an adaptive multi-objective reactive power optimization control method. The proposed approach formulates the objective function as the weighted sum of system active power loss and voltage deviation at the grid connection point, with weight coefficients adaptively adjusted based on the voltage deviation at the grid connection point. First, the relationship between voltage fluctuations at the offshore wind farm grid connection point and active/reactive power output is analyzed, and a corresponding reactive power allocation model is established. Second, taking into account the input–output characteristics of wind turbine generators and static var compensators, a reactive power control model is constructed. Third, considering offshore operational constraints such as power and voltage limits, a weighted variation particle swarm optimization algorithm (WVPSO) is developed to solve for the reactive power control strategy. Finally, the proposed method is validated through tests using a practical offshore wind farm as a case study. The test results demonstrate that, compared with the traditional fixed-weight multi-objective reactive power optimization approach, the proposed method can rapidly adjust the priority of each optimization objective according to the real-time grid conditions, achieving effective coordinated optimization of both active power loss and voltage at the grid connection point, and the voltage deviation is kept within 5%, even with power system fluctuations. In addition, compared with the traditional PSO algorithm, for various test situations, WVPSO exhibits above 15% improvement in solution speed and enhanced solution accuracy. Full article

Accurate probabilistic forecasting of photovoltaic (PV) power generation is crucial for grid scheduling and renewable energy integration. However, existing approaches often produce prediction intervals with limited calibration accuracy, and the interdependence among meteorological variables is frequently overlooked. This study proposes a probabilistic forecasting framework based on a Multi-scale Temporal–Spatial Attention Quantile Regression Network (MTSA-QRN) and an adaptive calibration mechanism to enhance uncertainty quantification and ensure statistically reliable prediction intervals. The framework employs a dual-pathway architecture: a temporal pathway combining Temporal Convolutional Networks (TCN) and multi-head self-attention to capture hierarchical temporal dependencies, and a spatial pathway based on Graph Attention Networks (GAT) to model nonlinear meteorological correlations. A learnable gated fusion mechanism adaptively integrates temporal–spatial representations, and weather-adaptive modules enhance robustness under diverse atmospheric conditions. Multi-quantile prediction intervals are calibrated using conformalized quantile regression to ensure reliable uncertainty coverage. Experiments on a real-world PV dataset (15 min resolution) demonstrate that the proposed method offers more accurate and sharper uncertainty estimates than competitive benchmarks, supporting risk-aware operational decision-making in power systems. Quantitative evaluation on a real-world 40 MW photovoltaic plant demonstrates that the proposed MTSA-QRN achieves a CRPS of 0.0400 before calibration, representing an improvement of over 55% compared with representative deep learning baselines such as Quantile-GRU, Quantile-LSTM, and Quantile-Transformer. After adaptive calibration, the proposed method attains a reliable empirical coverage close to the nominal level (PICP₉₀ = 0.9053), indicating effective uncertainty calibration. Although the calibrated prediction intervals become wider, the model maintains a competitive CRPS value (0.0453), striking a favorable balance between reliability and probabilistic accuracy. These results demonstrate the effectiveness of the proposed framework for reliable probabilistic photovoltaic power forecasting. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

This paper addresses stability issues, including voltage fluctuation, a frequency offset, and broadband oscillation resulting from the high penetration of renewable energy in a photovoltaic high-permeability distribution network. This paper proposes an active support control strategy which is energy storage grid-connected based on a virtual synchronous generator (VSG). This strategy endows the energy storage system with virtual inertia and a damping capacity by simulating the rotor motion equation and excitation regulation characteristics of the synchronous generator, and effectively enhances the system’s ability to suppress power disturbances. The small-signal model of the VSG system is established, and the influence mechanism of the virtual inertia and damping coefficient on the system stability is revealed. A delay compensator in series with a current feedback path is proposed. Combined with the damping optimization of the LCL filter, the instability risk caused by high-frequency resonance and a control delay is significantly suppressed. The novelty lies in the specific configuration of the compensator within the grid–current feedback loop and its coordinated design with VSG parameters, which differs from traditional capacitive–current feedback compensation methods. The experimental results obtained from a semi-physical simulation platform demonstrate that the proposed control strategy can effectively suppress voltage fluctuations, suppress broadband oscillations, and improve the dynamic response performance and fault ride-through capability of the system under typical disturbance scenarios such as sudden illumination changes, load switching, and grid faults. It provides a feasible technical path for the stable operation of the distribution network with a high proportion of new energy access. Full article

With the increasing penetration of renewable energy, the efficient dispatch of integrated energy systems (IESs) is facing severe challenges. Addressing the uncertainty of renewable energy output and designing efficient market mechanisms are crucial for achieving economical and low-carbon operation of IES. To this end, this paper unveils a comprehensive modeling and optimization framework: Firstly, a Spatio-Temporal Diffusion Model (STDM) is proposed, which generates high-quality wind power forecasting data by accurately capturing its spatio-temporal correlations, thereby providing reliable input for IES dispatch. Subsequently, a stochastic optimal scheduling model for electricity–heat–carbon coupled IES is established, comprehensively considering carbon capture equipment and a carbon quota mechanism. Finally, a multi-IES Nash bargaining cooperative game model is developed, encompassing bilateral energy trading and bilateral carbon trading, to equitably distribute cooperative benefits. Simulation results demonstrate that the STDM model significantly outperforms baseline models in both forecasting accuracy and scenario quality, while the designed bilateral market mechanism enhances system economics by reducing the total operating cost by 19.63% and lowering the total carbon emissions by 4.09%. Full article

In third-generation centralized heating systems, qualitative regulation of the heat transfer medium parameters is mainly performed at heat sources, while quantitative regulation is implemented at central and individual heating points, with buildings remaining passive heat consumers. Unlike fourth-generation systems, such systems generally do not employ renewable energy sources, thermal energy storage, or low-temperature operating regimes. Third-generation centralized heating systems operate based on design high-temperature schedules and centralized control, without considering the actual thermal loads of consumers. Under conditions of physical deterioration of heating networks, hydraulic imbalance, and operational constraints, the actual parameters of the heat transfer medium supplied to buildings often deviate from design values, resulting in deviations of thermal conditions at the level of end consumers and disruptions of thermal comfort. This study proposes the concept of an intelligent active individual heating point (IAIHP), designed to provide adaptive qualitative–quantitative regulation of heat transfer medium parameters at the level of individual buildings. Unlike approaches focused on demand-side management, the use of thermal energy storage, or the integration of renewable energy sources, the proposed solution is based on the application of a local thermal energy source. The IAIHP compensates for deviations in heat transfer medium parameters and acts as a local thermal energy source within the building heat supply system (BHSS). Control of the IAIHP operation is performed by a developed automation system that provides combined qualitative and quantitative regulation of the heat transfer medium supplied to the BHSS. The study assesses the potential scale of IAIHP implementation in third-generation centralized heating systems, develops a methodology for selecting the capacity of a local heat source, and presents the operating algorithm of the automatic control system of the IAIHP. At present, the reconstruction of an individual heating point of a kindergarten connected via a dependent scheme is being carried out based on the developed project documentation. Modeling and calculations show that the application of the IAIHP makes it possible to ensure indoor thermal comfort by reducing the risk of temperature deviations, which are otherwise typically compensated for by electric heaters. The proposed concept provides a methodological basis for a gradual transition from third-generation to fourth-generation centralized heating systems, while equipping the IAIHP with an intelligent control system opens opportunities for improving the energy efficiency of urban heating networks. The proposed integrated solution and the developed automatic control algorithms exhibit scientific novelty and practical relevance for Russia and other countries operating third-generation centralized heating systems, including Northern and Eastern European states, where large-scale infrastructure modernization and the implementation of fourth-generation technologies are technically or economically constrained. Full article

In short-term power forecasting for wind farms, factors such as weather conditions and geographic location lead to certain correlations in the power output of different wind farms, resulting in complex coupling relationships between them. Traditional wind power forecasting methods often predict each wind farm independently, without considering these coupling relationships. To address this issue, this paper proposes a multi-task Transformer model based on multiple decoders, which accounts for the intrinsic connections between different wind farms, enabling joint power forecasting across multiple sites. The proposed model adopts a single encoder-multiple decoder structure, where a unified encoder processes all input data, and multiple decoders perform prediction tasks for each wind farm separately. Testing on actual wind farm data from the Inner Mongolia region of China shows that, compared to other forecasting models, the proposed model significantly improves the accuracy of power predictions for different wind farms. Full article