Search Results (448)

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

This paper proposes a novel Twin Delayed Deep Deterministic Policy Gradient (TDDPG)-based joint optimization algorithm for hybrid reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems in Internet of Vehicles (IoV) scenarios. The proposed system model achieves deep integration of sensing and communication by superimposing the communication and sensing signals within the same waveform. To decouple the complex joint design problem, a dual-DDPG architecture is introduced, in which one agent optimizes the transmit beamforming vector and the other adjusts the RIS phase shift matrix. Both agents share a unified reward function that comprehensively considers multi-user interference (MUI), total transmit power, RIS noise power, and sensing accuracy via the CRLB constraint. Simulation results demonstrate that the proposed TDDPG algorithm significantly outperforms conventional DDPG in terms of sum rate and interference suppression. Moreover, the adoption of a hybrid RIS enables an effective trade-off between communication performance and system energy efficiency, highlighting its practical deployment potential in dynamic IoV environments. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

As inevitable outcomes of the digital economy’s low-carbon development, green data centers play a crucial role in environmental impact and underlying mechanisms. This study focuses on green data center establishment as a representative practice, utilizing Chinese A-share listed companies and urban data from 2009 to 2023 to construct a multi-period difference-in-differences model. From a supply chain perspective, we investigate the impact of green data centers on corporate carbon emissions and their mechanisms. The results demonstrate that regional establishment of green data centers significantly promotes corporate carbon emission reduction, with conclusions remaining robust after a series of comprehensive robustness and endogeneity tests. This process primarily operates through two channels: green total factor energy efficiency and green attention. Green data center establishment significantly enhances green total factor energy efficiency and corporate green attention. The more developed the regional digital infrastructure and the higher the computing power development levels, the stronger the incentive effect on corporate carbon reduction. Heterogeneity analysis reveals that green data centers have more significant promoting effects on carbon emission reduction in state-owned enterprises and high-tech enterprises. This research contributes to a deeper understanding of the effects, mechanisms, and regional variations related to green data centers in facilitating corporate carbon emission reduction. Full article

Data sharing within and across large, complex systems is one of the most topical challenges in the current IT landscape, and the energy domain is no exception. As the sector becomes more and more digitized, decentralized, and complex, new Big Data and AI tools are constantly emerging to empower stakeholders to exploit opportunities and tackle challenges. They enable advancements such as the efficient operation and maintenance of assets, forecasting of demand and production, and improved decision-making. However, in turn, innovative systems are necessary for using and operating such tools, as they often require large amounts of disparate data and intelligent preprocessing. The integration of and communication between numerous up-and-coming technologies is necessary to ensure the maximum exploitation of renewable energy. Building on existing developments and initiatives, this paper introduces a multi-layer Reference Architecture for the reliable, secure, and trusted exchange of data and facilitation of services within the energy domain. Full article

In recent years, global energy transition has pushed distributed generation (DG) to the forefront in relation to new energy development. Most existing studies focus on DG or energy storage planning but lack co-optimization and power tracking analysis. To address this problem, a multi-objective genetic algorithm-based collaborative planning method for photovoltaic (PV) and energy storage is proposed. On this basis, power flow tracking technology is further introduced to conduct a detailed analysis of distributed energy power allocation, providing support for system operation optimization and responsibility sharing. To verify the validity of the model, a 14-node distribution network is used as an example. Voltage stability, PV consumption rate, and economy are taken as objective functions. By solving the three scenarios, it is determined that the introduction of energy storage increases the PV consumption rate from 85.6% to 96.3%; the average network loss for the whole day increases from 1.81 MW to 2.40 MW. Utilizing power tracking techniques, various causes were analyzed; it was found that the placement of energy storage leads to a multidirectional and repetitive flow of power. Full article

Obesity-driven inflammation disrupts gut barrier integrity and promotes inflammatory bowel disease (IBD). Emerging evidence highlights gut hormones—including glucagon-like peptide-1 (GLP-1), glucagon-like peptide-2 (GLP-2), glucose-dependent insulinotropic polypeptide (GIP), peptide YY (PYY), cholecystokinin (CCK), and apolipoprotein A4 (APOA4)—as key regulators of metabolism and mucosal immunity. This review outlines known mechanisms and explores therapeutic prospects in IBD. GLP-1 improves glycemic control, induces weight loss, and preserves intestinal barrier function, while GLP-2 enhances epithelial repair and reduces pro-inflammatory cytokine expression in animal models of colitis. GIP facilitates lipid clearance, enhances insulin sensitivity, and limits systemic inflammation. PYY and CCK slow gastric emptying, suppress appetite, and attenuate colonic inflammation via neural pathways. APOA4 regulates lipid transport, increases energy expenditure, and exerts antioxidant and anti-inflammatory effects that alleviate experimental colitis. Synergistic interactions—such as GLP-1/PYY co-administration, PYY-stimulated APOA4 production, and APOA4-enhanced CCK activity—suggest that multi-hormone combinations may offer amplified therapeutic benefits. While preclinical data are promising, clinical evidence supporting gut hormone therapies in IBD remains limited. Dual GIP/GLP-1 receptor agonists improve metabolic and inflammatory parameters, but in clinical use, they are associated with gastrointestinal side effects that warrant further investigation. Future research should evaluate combination therapies in preclinical IBD models, elucidate shared neural and receptor-mediated pathways, and define optimal strategies for applying gut hormone synergy in human IBD. These efforts may uncover safer, metabolically tailored treatments for IBD, particularly in patients with coexisting obesity or metabolic dysfunction. Full article

As renewable energy technologies advance, identifying efficient photocatalytic materials for water splitting to produce hydrogen has become an important research focus in materials science. This study presents a multi-task regression model (MTRM) designed to predict the conduction band minimum (CBM), valence band maximum (VBM), and solar-to-hydrogen efficiency (STH) of inorganic materials. Utilizing crystallographic and band gap data from over 15,000 materials in the SNUMAT database, machine-learning methods are applied to predict CBM and VBM, which are subsequently used as additional features to estimate STH. A deep neural network framework with a multi-branch, multi-task regression structure is employed to address the issue of error propagation in traditional cascading models by enabling feature sharing and joint optimization of the tasks. The calculated results show that, while traditional tree-based models perform well in single-task predictions, MTRM achieves superior performance in the multi-task setting, particularly for STH prediction, with an MSE of 0.0001 and an R² of 0.8265, significantly outperforming cascading approaches. This research provides a new approach to predicting photocatalytic material performance and demonstrates the potential of multi-task learning in materials science. Full article

The building sector holds a significant position in the global energy consumption share, and its environmental impact continues to intensify, making the construction industry a key player in sustainable development. The application of life cycle assessment on buildings (LCA-B) is widely employed to evaluate building energy and environment performance, and thus is of great significance for ensuring the sustainability of the project. This work aims to provide a systematic overview of LCA-B development based on reviewed literature. A three-stage mixed research method is adopted in this study: Firstly, an overall analysis framework is constructed, and 327 papers related to building life cycle assessment published between 2009 and 2025 are screened out by using the bibliometric method; Then, through scientometrics analysis, the journal regions, sources, scholars, and keyword evolution are revealed and analyzed using VOSviewer tool, and the hotspots in the field of LCA-B (e.g., integration of building information modeling (BIM) in LCA-B, multi-dimensional framework of environment–society–culture) are preliminarily explored based on the selected highly cited papers. The research finds that: (1) the performance of low energy buildings is better than that of net zero energy buildings from the perspective of LCA; (2) software compatibility and data exchange are the main obstacles in the integration of BIM-LCA; (3) a multi-dimensional LCA framework covering the social or cultural aspects is expected for a comprehensive assessment of building performance. This study provides a systematic analysis and elaboration of review articles related to LCA-B and thereby provides researchers with in-depth insight into this field. Full article

In this work, we examine the correlation between financial inclusion and the Environmental, Social, and Governance (ESG) factors of sustainable development with the assistance of an exhaustive panel dataset of 103 emerging and developing economies spanning 2011 to 2022. The “Account Age” variable, standing for financial inclusion, is the share of adults owning accounts with formal financial institutions or with the providers of mobile money services, inclusive of both conventional and digital entry points. Methodologically, the article follows an econometric approach with panel data regressions, supplemented by Two-Stage Least Squares (2SLS) with instrumental variables in order to control endogeneity biases. ESG-specific instruments like climate resilience indicators and digital penetration measures are utilized for the purpose of robustness. As a companion approach, the paper follows machine learning techniques, applying a set of algorithms either for regression or for clustering for the purpose of detecting non-linearities and discerning ESG-inclusion typologies for the sample of countries. Results reflect that financial inclusion is, in the Environmental pillar, significantly associated with contemporary sustainability activity such as consumption of green energy, extent of protected area, and value added by agriculture, while reliance on traditional agriculture, measured by land use and value added by agriculture, decreases inclusion. For the Social pillar, expenditure on education, internet, sanitation, and gender equity are prominent inclusion facilitators, while engagement with the informal labor market exhibits a suppressing function. For the Governance pillar, anti-corruption activity and patent filing activity are inclusive, while diminishing regulatory quality, possibly by way of digital governance gaps, has a negative correlation. Policy implications are substantial: the research suggests that development dividends from a multi-dimensional approach can be had through enhancing financial inclusion. Policies that intersect financial access with upgrading the environment, social expenditure, and institutional reconstitution can simultaneously support sustainability targets. These are the most applicable lessons for the policy-makers and development professionals concerned with the attainment of the SDGs, specifically over the regions of the Global South, where the trinity of climate resilience, social fairness, and institutional renovation most significantly manifests. Full article

Domain Generation Algorithms (DGAs) remain a persistent technique used by modern malware to establish stealthy command-and-control (C&C) channels, thereby evading traditional blacklist-based defenses. Detecting such evolving threats is especially challenging in decentralized environments where raw traffic data cannot be aggregated due to privacy or policy constraints. To address this, we present FedSAGE, a security-aware federated intrusion detection framework that combines Variational Autoencoder (VAE)-based latent representation learning with unsupervised clustering and resource-efficient client selection. Each client encodes its local domain traffic into a semantic latent space using a shared, pre-trained VAE trained solely on benign domains. These embeddings are clustered via affinity propagation to group clients with similar data distributions and identify outliers indicative of novel threats without requiring any labeled DGA samples. Within each cluster, FedSAGE selects only the fastest clients for training, balancing computational constraints with threat visibility. Experimental results from the multi-zones DGA dataset show that FedSAGE improves detection accuracy by up to 11.6% and reduces energy consumption by up to 93.8% compared to standard FedAvg under non-IID conditions. Notably, the latent clustering perfectly recovers ground-truth DGA family zones, enabling effective anomaly detection in a fully unsupervised manner while remaining privacy-preserving. These foundations demonstrate that FedSAGE is a practical and lightweight approach for decentralized detection of evasive malware, offering a viable solution for secure and adaptive defense in resource-constrained edge environments. Full article

Mitochondrial dysfunction is a key driver of neurological disorders due to the brain’s high energy demands and reliance on mitochondrial homeostasis. Despite advances in genetic characterization, the heterogeneity of mitochondrial diseases complicates diagnosis and treatment. Mitochondrial dysfunction spans a broad clinical spectrum, from early-onset encephalopathies to adult neurodegeneration, with phenotypic and genetic variability necessitating integrated models of mitochondrial neuropathology. Mutations in nuclear or mitochondrial DNA disrupt energy production, induce oxidative stress, impair mitophagy and biogenesis, and lead to neuronal degeneration and apoptosis. This narrative review provides a structured synthesis of current knowledge by classifying mitochondrial-related neurological disorders according to disrupted biochemical pathways, in order to clarify links between genetic mutations, metabolic impairments, and clinical phenotypes. More specifically, a pathway-oriented framework was adopted that organizes disorders based on the primary mitochondrial processes affected: oxidative phosphorylation (OXPHOS), pyruvate metabolism, fatty acid β-oxidation, amino acid metabolism, phospholipid remodeling, multi-system interactions, and neurodegeneration with brain iron accumulation. Genetic, clinical and molecular data were analyzed to elucidate shared and distinct pathophysiological features. A comprehensive table synthesizes genetic causes, inheritance patterns, and neurological manifestations across disorders. This approach offers a conceptual framework that connects molecular findings to clinical practice, supporting more precise diagnostic strategies and the development of targeted therapies. Advances in whole-exome sequencing, pharmacogenomic profiling, mitochondrial gene editing, metabolic reprogramming, and replacement therapy—promise individualized therapeutic approaches, although hurdles including heteroplasmy, tissue specificity, and delivery challenges must be overcome. Ongoing molecular research is essential for translating these advances into improved patient care and quality of life. Full article

This study investigates an aerodynamic optimization framework inspired by marine biological morphology, utilizing the sailfish profile as a basis for airfoil configuration. Through Latin hypercube experimental design combined with optimization algorithms, four key geometric variables governing the airfoil’s hydrodynamic characteristics were systematically analyzed. Parametric studies revealed that pivotal factors including installation angle significantly influenced the fluid dynamic performance metrics of lift generation and pressure drag. Response surface methodology was employed to establish predictive models for these critical performance indicators, effectively reducing computational resource consumption and experimental validation costs. The refined bio-inspired configuration demonstrated multi-objective performance improvements compared to the baseline configuration, validating the computational framework’s effectiveness for hydrodynamic profile optimization studies. Furthermore, a coaxial dual-rotor vertical axis turbine configuration was developed, integrating centrifugal and axial-flow energy conversion mechanisms through a shared drivetrain system. The centrifugal rotor component harnessed tidal current kinetic energy while the axial-flow rotor module captured wave-induced potential energy. Transient numerical simulations employing dynamic mesh techniques and user-defined functions within the Fluent environment were conducted to analyze rotor interactions. Results indicated the centrifugal subsystem demonstrated peak hydrodynamic efficiency at a 25° installation angle, whereas the axial-flow module achieves optimal performance at 35° blade orientation. Parametric optimization revealed maximum energy extraction efficiency for the centrifugal rotor occurs at λ = 1.25 tip-speed ratio under Re = 1.3 × 10⁵ flow conditions, while the axial-flow counterpart attained optimal performance at λ = 1.5 with Re = 5.5 × 10⁴. This synergistic configuration demonstrated complementary operational characteristics under marine energy conversion scenarios. Full article

This paper addresses the challenges of broadband impedance identification in wind farms connected to the power grid, where broadband oscillations can compromise grid stability. Traditional impedance modeling approaches, including white-box and black/grey-box methods, face limitations in real-world applications, particularly when dealing with commercial new energy units with unknown control structures. To overcome these challenges, a novel real-time impedance identification method is proposed for PMSGs(Permanent Magnet Synchronous Generators). The method, called ASSA (Attention-based Shared and Specific Architecture), utilizes a multi-task neural network model combined with an attention mechanism to improve the accuracy of impedance fitting across different frequency bands. A broadband impedance dataset is constructed offline under various operating conditions, incorporating uncertainties like wind speed. The proposed approach offers an efficient solution for impedance identification, enhancing the stability and reliability of grid-connected renewable energy systems. Full article