Search Results (9,516)

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. 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

This paper presents a study of the tonality of sound emitted by a wind farm into the surrounding environment. The wind turbines installed at the site have a rated power of 3.0 MW. The aim of the study was to analyse the tonality of sounds in the environment at the nearest residential area. The issue of tonal noise near the wind farm was identified during routine periodic noise monitoring. An experienced survey team identified the phenomenon and carried out preliminary field analyses. Detailed studies were then carried out to identify the environmental hazard and failure-free operation of the turbines. The recorded acoustic events are described in detail and an in-depth analysis is carried out. An action plan has been implemented in consultation with the wind farm operator to reduce tonal sound emissions to the surrounding environment. As a result of these interventions, tonal noise from the wind turbines was successfully reduced. It was determined that the detection of the potential tonality of the sounds emitted by wind turbines should take place during the analysis (active listening) of the .wav file, synchronised with Fast Fourier Transform (FFT) analysis. Conducting tonality assessments solely during field measurements may lead to incorrect identification of tonal sources. Full article

Against the backdrop of China’s economic transformation and the transition towards sustainable industrial systems, optimizing ownership structures to enhance the resilience and bargaining power of manufacturing suppliers has become crucial for building sustainable supply chains. This study empirically examines the impact of ownership concentration on supplier bargaining power using data from manufacturing companies listed on the Shanghai and Shenzhen A-share markets from 2008 to 2022, integrating insights from principal-agent theory and industrial dynamics within a sustainability-oriented framework. The findings reveal: (1) Ownership concentration significantly strengthens the bargaining power of supplier enterprises, contributing to more stable and equitable supply chain relationships. (2) R&D investment plays a partial mediating role between ownership concentration and supplier bargaining power, suggesting that innovation efforts—often aligned with green and sustainable technologies—can reshape dependency dynamics. (3) Industry competitiveness negatively moderates the relationship between ownership concentration and supplier bargaining power, indicating that intense competition may undermine the governance advantages of concentrated ownership in sustainable value creation. (4) Heterogeneity analysis shows that the positive effect of ownership concentration is more pronounced in central and western regions, state-owned enterprises, and large firms, highlighting contextual factors in achieving sustainable supply chain governance. Full article

Urban energy systems in Andean cities face growing pressure to accommodate rising electricity demand while progressing toward decarbonization and grid modernization. Residential rooftop photovoltaic (PV) generation offers a promising pathway to enhance transformer utilization, reduce emissions, and improve distribution network performance. However, most GIS-based rooftop solar assessments remain disconnected from operational constraints of urban electrical networks, limiting their applicability for distribution planning. This study examines the technical and environmental feasibility of integrating residential PV distributed generation into the urban distribution network of an Andean city by coupling high-resolution geospatial solar potential analysis with monthly aggregated electricity consumption (MEC) and transformer loadability (LD) information. A GIS-driven framework identifies suitable rooftops based on solar irradiation, orientation, slope, shading, and three-dimensional urban geometry, while MEC data are used to perform energy-balance and planning-level transformer LD assessments. Results indicate that approximately 1.16 MW of rooftop PV capacity could be integrated, increasing average transformer LD from 21.5% to 45.8% and yielding an annual PV generation of about 1.9 GWh. This contribution corresponds to an estimated avoidance of 1143 metric tons of CO${}_2$ per year. At the same time, localized reverse power flow causes some transformers to reach or exceed nominal capacity, highlighting the need to explicitly consider network constraints when translating rooftop solar potential into deployable capacity. By explicitly linking rooftop solar resource availability with aggregated electricity consumption and transformer LD, the proposed framework provides a scalable and practical planning tool for distributed PV deployment in complex mountainous urban environments. Full article

Traditional fusion methods for integrating multi-source gravity data rely on predefined mathematical models that inadequately capture complex nonlinear relationships, particularly at wavelengths shorter than 10 km. We developed a convolutional neural network incorporating differential marine geodetic data (DMGD-CNN) to enhance marine gravity anomaly recovery from HY-2A satellite altimetry. The DMGD-CNN framework encodes spatial gradient information by computing differences between target points and their surrounding neighborhoods, enabling the model to explicitly capture local gravity field variations. This approach transforms absolute parameter values into spatial gradient representations, functioning as a spatial high-pass filter that enhances local gradient information critical for short-wavelength gravity signal recovery while reducing the influence of long-wavelength components. Through systematic ablation studies with eight parameter configurations, we demonstrate that incorporating first- and second-order seabed topography derivatives significantly enhances model performance, reducing the root mean square error (RMSE) from 2.26 mGal to 0.93 mGal, with further reduction to 0.85 mGal achieved by the differential learning strategy. Comprehensive benchmarking against international gravity models (SIO V32.1, DTU17, and SDUST2022) demonstrates that DMGD-CNN achieves 2–10% accuracy improvement over direct CNN predictions in complex topographic regions. Power spectral density analysis reveals enhanced predictive capabilities at wavelengths below 10 km for the direct CNN approach, with DMGD-CNN achieving further precision enhancement at wavelengths below 5 km. Cross-validation with independent shipborne surveys confirms the method’s robustness, showing 47–63% RMSE reduction in shallow water regions (<2000 m depth) compared to HY-2A altimeter-derived results. These findings demonstrate that deep learning with differential marine geodetic features substantially improves marine gravity field modeling accuracy, particularly for capturing fine-scale gravitational features in challenging environments. Full article

This article introduces a new flexible distribution, called the alpha power Topp–Leone Dagum (APTLDa) distribution, which extends the classical Dagum model by combining the Topp–Leone generator with the alpha power transformation (APT). The proposed distribution is capable of modeling data with symmetrical and asymmetrical shapes for the probability density and hazard rate functions. This makes it suitable for lifetime and reliability data analysis. Several important statistical properties of the new distribution are derived, including the quantile function, moments, entropy measures, order statistics, and reliability-related functions. Parameter estimation is carried out using the maximum likelihood method, and the performance of the estimators is examined through an extensive simulation study under different sample sizes and parameter settings. The simulation results demonstrate the consistency and good finite-sample behavior of the estimators. The practical usefulness of the proposed distribution is illustrated through applications to two real datasets, where its performance is compared with several competing models. The results show that the APTLDa distribution provides a flexible and effective alternative for modeling lifetime data. Full article

The oily wastewater produced by transformer oil leakage contains pollutants such as mineral oil, metal particles, aged oil and additives, which can disrupt the dissolved oxygen balance in water bodies, pollute soil and endanger human health through the food chain, causing serious environmental pollution. Effective oil–water separation technology is the key to ecological protection and resource recovery. This paper reviews the principles, influencing factors and research progress of traditional (gravity sedimentation, air flotation, adsorption, demulsification) and new (nanocomposite adsorption, metal–organic skeleton materials, superhydrophobic/superlipophilic modified films) transformer oil–water separation technologies. Traditional technologies are mostly applicable to large-particle-free oil and are difficult to adapt to complex matrix wastewater. However, the new technology has significant advantages in separation efficiency (up to over 99.5%), selectivity and cycling stability (with a performance retention rate of over 85% after 20–60 cycles), breaking through the bottlenecks of traditional methods. In the future, it is necessary to develop low-cost and efficient separation technologies, promote the research and development of intelligent responsive materials, upgrade low-carbon preparation processes and their engineering applications, support environmental protection treatment in the power industry and encourage the coupling of material innovation and processes. Full article

The rapid evolution of CRISPR/Cas technology has transformed genome editing across biological systems in which zebrafish have emerged as a powerful vertebrate model for functional genomics and disease research. Due to its transparency, genetic similarity to humans, and suitability for large-scale screening, zebrafish is an appropriate system for translating molecular discoveries into biomedical and environmental applications. Thereby, this review highlights the recent progress in zebrafish gene editing, targeting innovations in ribonucleoprotein delivery, PAM-flexible Cas variants, and precision editors. These approaches have greatly improved editing accuracy, reduced mosaicism, and enabled efficient F0 phenotyping. In the near future, automated microinjections, optimized guide RNA design, and multi-omics validation pipelines are expected to enhance reproducibility and scalability. Although recent innovations such as ribonucleoprotein delivery, PAM-flexible Cas variants, and precision editors have expanded the zebrafish genome-editing toolkit, their benefits are often incremental and context-dependent. Mosaicism, allele complexity, and variable germline transmission remain common, particularly in F0 embryos. Precision editors enable defined nucleotide changes but typically exhibit modest efficiencies and locus-specific constraints in zebrafish. Consequently, rigorous validation, standardized workflows, and careful interpretation of F0 phenotypes remain essential. This review critically examines both the capabilities and limitations of current zebrafish gene-editing technologies, emphasizing experimental trade-offs, reproducibility challenges, and realistic use cases. Full article

Microorganisms have emerged as prolific and versatile producers of steroidal natural products, displaying a remarkable capacity for structural diversification that extends far beyond classical sterol frameworks. This review critically examines steroidal metabolites isolated from microbial sources, with a particular emphasis on marine-derived and endophytic fungi belonging to the genera Aspergillus and Penicillium, alongside selected bacterial and lesser-studied fungal taxa. Comparative analysis reveals that these organisms repeatedly generate distinctive steroid scaffolds, including highly oxygenated ergostanes, secosteroids, rearranged polycyclic systems, and hybrid architectures arising from oxidative cleavage, cyclization, and Diels–Alder-type transformations. While many reported compounds exhibit cytotoxic, anti-inflammatory, antimicrobial, or enzyme-inhibitory activities, the biological relevance of these metabolites varies considerably, highlighting the need to distinguish broadly recurring bioactivities from isolated or strain-specific observations. By integrating structural classification with biosynthetic considerations and bioactivity trends, this review identifies key steroidal frameworks that recur across taxa and appear particularly promising for further pharmacological investigation. In addition, current gaps in mechanistic understanding and compound prioritization are discussed. Finally, emerging strategies such as genome mining, biosynthetic gene cluster analysis, co-culture approaches, and synthetic biology are highlighted as powerful tools to unlock the largely untapped potential of microbial genomes for the discovery of novel steroidal scaffolds. Together, this synthesis underscores the importance of microorganisms as a dynamic and expandable source of structurally unique and biologically relevant steroids, and provides a framework to guide future discovery-driven and mechanism-oriented research in the field. Full article

In the context of the era where the maritime power strategy converges with the wave of the digital economy, the digital economy provides a critical transformational opportunity for marine fisheries to break through the traditional extensive model and achieve high-quality development. Based on panel data from 41 coastal cities in China from 2003 to 2022, this study empirically examines the enabling effect of the digital economy on marine fisheries from the perspective of total factor productivity. The findings are as follows: First, the development of the digital economy promotes the improvement of total factor productivity in marine fisheries, but this is primarily achieved through “innovation-driven” expansion of the production frontier, while its potential in “efficiency catch-up” has not yet been fully realized. Second, the enabling effect exhibits distinct spatial heterogeneity, with its positive impact concentrated in cities in the South China Sea region, where industrial foundations and policy environments are more aligned. Third, the influence of the digital economy demonstrates nonlinear threshold characteristics; when technology promotion and industrial collaboration surpass specific thresholds, the enabling effect significantly strengthens, but as innovation capability improves, its marginal contribution shows a diminishing trend. Accordingly, it is recommended to deepen the application of digital technologies in core processes, transitioning from “isolated applications” to “systematic integration.” Simultaneously, tailored regional development strategies should be formulated to align with the resource endowments and development stages of each maritime region. On this basis, efforts should be made to improve technology promotion and industrial support systems, construct a collaborative and efficient digital fishery ecosystem, and facilitate the sustainable transition of marine fisheries from factor-driven to innovation-driven growth. Full article

Artificial intelligence (AI) is transforming numerous sectors, and its integration into the legal domain holds significant potential for automating labor-intensive tasks, enhancing judicial decision-making, and improving overall system efficiency. This study introduces an AI-powered model, named the Legal Judgment Prediction Ensemble (LJPE), which is designed to predict legal case outcomes by leveraging historical judicial data. By using natural language processing (NLP) techniques, feature engineering, and a complex two-level stacking ensemble, the LJPE model has better predictive accuracy at 94.68% compared to modern legal language and conventional machine learning models. Moreover, the findings underline the predictive strength of textual features obtained from case facts, vote margins, and legal-specific features. This study offers a solid technical solution for predicting legal judgments for the responsible use of the model, helping to create a more efficient, transparent, and fair legal system. Full article

Background: Aging in Western societies poses a growing challenge, placing increasing pressure on healthcare costs. Early identification of infections in elderly nursing home residents is crucial to reduce complications, mortality, and the burden on emergency departments. Methods: We performed a comparative analysis of machine learning models using XGBoost classifiers for infection detection, addressing incomplete daily physiological measurements (Heart Rate, Oxygen Saturation, Body Temperature, and Electrodermal Activity) through strict imputation protocols. We evaluated three model variants—Basic (clinical only), Air Pollution-added, and Social Media-integrated—while incorporating a novel Basal Module to personalize physiological baselines for each resident. Results: Results from the binary model indicate that physiological data provides a necessary baseline for immediate screening. Notably, social media integration emerged as a powerful forecasting tool, extending the predictive horizon to a 6-day lead time with an F1-score of 0.97. Complementarily, air pollution data ensured robust immediate detection (“nowcasting”). In the multiclass scenario, external data resolved the “semantic gap” of vital signs, improving sensitivity for specific infections (e.g., acute respiratory and urinary tract infections) to over 90%. Conclusions: These findings highlight that the strategic integration of environmental and digital signals transforms the system from a reactive monitor into a proactive early warning tool for long-term care facilities. Full article

Modern power systems have become increasingly complex, making the detailed modeling and analysis of large-scale networks computationally demanding and often impractical. Therefore, network reduction techniques are essential for representing a smaller area of interest while preserving the electrical behavior of the complete system. For electromagnetic transient (EMT) studies, such as load rejection analysis, reduced networks are commonly derived using classical methods like Kron reduction under maximum power transfer conditions. However, this approach can lead to discrepancies in load flow and short-circuit levels between the reduced and complete systems. In addition, Kron reduction may introduce negative resistances in the reduced-order model, compromising system stability by producing non-passive equivalents and potentially causing unrealistic or numerically unstable EMT simulations. To address these limitations, this paper proposes an optimization-based approach, termed PSO-OPF-Kron, which integrates Optimal Power Flow (OPF) with the Particle Swarm Optimization (PSO) algorithm to refine the equivalent network parameters. The method optimally determines power injections, bus voltages, transformer tap settings, and impedances to align the reduced model with the full system’s operating point and short-circuit levels. Validation on the IEEE 39-bus system demonstrates that the proposed method significantly improves accuracy and numerical stability, ensuring reliable EMT simulations for load rejection studies. Full article

The paper provides a comprehensive overview of stray losses in conductive structural parts of power transformers, addressing the effects of stray magnetic fields on simple conductive plates, the distribution of additional losses across structural components and measures for their reduction. It examines the (im)possibility of directly measuring stray losses and presents methods for their indirect measurement, highlighting the generation of fault gases due to thermal faults and the importance of understanding multiphysical (electromagnetic–thermal) coupling in calculating stray losses. A problem rarely mentioned in the literature but confirmed here by measurements, is the excessive heating of the connecting elements of the clamping system caused by circulating currents. Full article