Search Results (2,011)

This paper presents an in-depth review of cybersecurity challenges and advanced solutions in modern power-generation systems, with particular emphasis on smart grids. It examines vulnerabilities in devices such as smart meters (SMs), Phasor Measurement Units (PMUs), and Remote Terminal Units (RTUs) to cyberattacks, including False Data Injection Attacks (FDIAs), Denial of Service (DoS), and Replay Attacks (RAs). The study evaluates cutting-edge detection and mitigation techniques, such as Cluster Partition, Fuzzy Broad Learning System (CP-BLS), multimodal deep learning, and autoencoder models, achieving detection accuracies of (up to 99.99%) for FDIA identification. It explores critical aspects of power generation, including resource assessment, environmental and climatic factors, policy and regulatory frameworks, grid and storage integration, and geopolitical and social dimensions. The paper also addresses the transmission and distribution (T&D) system, emphasizing the role of smart-grid technologies and advanced energy-routing strategies that leverage Artificial Neural Networks (ANNs), Generative Adversarial Networks (GANs), and game-theoretic approaches to optimize energy flows and enhance grid stability. Future research directions include high-resolution forecasting, adaptive optimization, and the integration of quantum–AI methods to improve scalability, reliability, and resilience. Full article

Long Short-Term Memory (LSTM) networks have revolutionized the field of deep learning, particularly in applications that require the modeling of sequential data. Originally designed to overcome the limitations of traditional recurrent neural networks (RNNs), LSTMs effectively capture long-range dependencies in sequences, making them suitable for a wide array of tasks. This survey aims to provide a comprehensive overview of LSTM architectures, detailing their unique components, such as cell states and gating mechanisms, which facilitate the retention and modulation of information over time. We delve into the various applications of LSTMs across multiple domains, including the following: natural language processing (NLP), where they are employed for language modeling, machine translation, and sentiment analysis; time series analysis, where they play a critical role in forecasting tasks; and speech recognition, significantly enhancing the accuracy of automated systems. By examining these applications, we illustrate the versatility and robustness of LSTMs in handling complex data types. Additionally, we explore several notable variants and improvements of the standard LSTM architecture, such as Bidirectional LSTMs, which enhance context understanding, and Stacked LSTMs, which increase model capacity. We also discuss the integration of Attention Mechanisms with LSTMs, which have further advanced their performance in various tasks. Despite their strengths, LSTMs face several challenges, including high Computational Complexity, extensive Data Requirements, and difficulties in training, which can hinder their practical implementation. This survey addresses these limitations and provides insights into ongoing research aimed at mitigating these issues. In conclusion, we highlight recent advances in LSTM research and propose potential future directions that could lead to enhanced performance and broader applicability of LSTM networks. This survey serves as a foundational resource for researchers and practitioners seeking to understand the current landscape of LSTM technology and its future trajectory. Full article

The integration of artificial intelligence (AI) and machine learning (ML) technologies in energy storage systems has emerged as a transformative approach in addressing the complex challenges of modern energy infrastructure. This comprehensive review examines current state of the art AI applications in energy storage, from battery management systems to grid-scale storage optimization. We analyze various AI techniques, including supervised learning, deep learning, reinforcement learning, and neural networks, and their applications in state estimation, predictive maintenance, energy forecasting, and system optimization. The review synthesizes findings from the recent literature demonstrating quantitative improvements achieved through AI integration: distributed reinforcement learning frameworks reducing grid disruptions by 40% and operational costs by 12.2%, LSTM models achieving state of charge estimations with a mean absolute error of 0.10, multi-objective optimization reducing power losses by up to 22.8% and voltage fluctuations by up to 71%, and real options analysis showing 45–81% cost reductions compared to conventional planning approaches. Despite remarkable progress, challenges remain in terms of data quality, model interpretability, and industrial implementation. This paper provides insights into emerging technologies and future research directions that will shape the evolution of intelligent energy storage systems. Full article

This paper proposes a real-time energy management strategy for low-voltage microgrids that combines short-horizon forecasting with a rule-based supervisory controller to coordinate battery energy storage usage and reactive power support provided by flexible alternating current transmission technologies. The central contribution is the forecast-informed, joint orchestration of active charging and reactive power dispatch to regulate voltage and preserve stability under large photovoltaic variability and uncertain electric vehicle demand. The work also introduces a resilience response index that quantifies performance under external disturbances, forecasting delays, and increasing levels of electric-vehicle integration. Validation is carried out through time-domain numerical simulations in MATLAB/Simulink using realistic solar irradiance and electric vehicle charging profiles. The results show that the coordinated strategy reduces voltage deviation events, maintains stable operation across a wide range of scenarios, and enables electric vehicle charging to be supplied predominantly by renewable generation. Sensitivity analysis further indicates that support from flexible alternating current devices becomes particularly decisive at high charging demand and in the presence of forecasting latency, underscoring the practical value of the proposed approach for distribution-level microgrids. Full article

Recent concerns regarding artificial intelligent (AI) technologies have spurred studies into improving wastewater treatment efficiency and identifying low-carbon processes. Treating one cubic meter of wastewater necessarily consumes a certain amount of chemicals and energy. Approximately 20% of the total chemical consumption is attributed to phosphorus and nitrogen removal, with the exact proportion varying based on treatment quality and facility size. To promote sustainability in wastewater treatment plants (WWTPs), there has been a shift from traditional control systems to AI-based strategies. Research in this area has demonstrated notable improvements in wastewater treatment efficiency. This review provides an extensive overview of the literature published over the past decades, aiming to advance the ongoing discourse on enhancing both the efficiency and sustainability of chemical dosing systems in WWTPs. It focuses on AI-based approaches utilizing algorithms such as neural networks and fuzzy logic. The review encompasses AI-based wastewater treatment processes: parameter analysis/forecasting, model development, and process optimization. Moreover, it summarizes six promising areas of AI-based chemical dosing, including acid–base regents, coagulants/flocculants, disinfectants/disinfection by-products (DBPs) management, external carbon sources, phosphorus removal regents, and adsorbents. Finally, the study concludes that significant challenges remain in deploying AI models beyond simulated environments to real-world applications. Full article

Smart grids (SGs) are becoming increasingly complex with the integration of communication, protection, and automation technologies. However, this digital transformation has introduced new vulnerabilities, especially false data injection attacks (FDIAs) and Denial of Service (DoS) attacks. FDIAs can subtly corrupt measurement data, misleading operators without triggering traditional bad data detection (BDD) methods in state estimation (SE), while DoS attacks disrupt the availability of sensor data, affecting grid observability. This paper presents a deep learning-based framework for detecting and localizing FDIAs, including under DoS conditions. A hybrid CNN, Transformer, and BiLSTM model captures spatial, global, and temporal correlations to forecast measurements and detect anomalies using a threshold-based approach. For further detection and localization, a Multi-layer Perceptron (MLP) model maps forecast errors to the compromised sensor locations, effectively complementing or replacing BDD methods. Unlike conventional SE, the approach is fully data-driven and does not require knowledge of grid topology. Experimental evaluation on IEEE 14–bus and 118–bus systems demonstrates strong performance for the FDIA condition, including precision of 0.9985, recall of 0.9980, and row-wise accuracy (RACC) of 0.9670 under simultaneous FDIA and DoS conditions. Furthermore, the proposed method outperforms existing machine learning models, showcasing its potential for real-time cybersecurity and situational awareness in modern SGs. Full article

This paper presents the design and implementation of an Intelligent Home Energy Management System in a smart home. The system is based on an economically decentralized hybrid concept that includes photovoltaic technology, a proton exchange membrane fuel cell, and a hydrogen refueling station, which together provide a reliable, secure, and clean power supply for smart homes. The proposed design enables power transfer between Vehicle-to-Home (V2H) and Home-to-Vehicle (H2V) systems, allowing electric vehicles to function as mobile energy storage devices at the grid level, facilitating a more adaptable and autonomous network. Our approach employs Double Deep Q-networks for adaptive control and forecasting. A Multi-Agent System coordinates actions between home appliances, energy storage systems, electric vehicles, and hydrogen power devices to ensure effective and cost-saving energy distribution for users of the smart grid. The design validation is carried out through MATLAB/Simulink-based simulations using meteorological data from Tunis. Ultimately, the V2H/H2V system enhances the utilization, reliability, and cost-effectiveness of residential energy systems compared with other management systems and conventional networks. Full article

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions. Full article

This review presents a critical analysis of Japan’s hydrogen strategy, focusing on the broader context of its decarbonization efforts. Japan aims to achieve carbon neutrality by 2050, with intermediate targets including 3 million tons of hydrogen use by 2030 and 20 million tons by 2050. Unlike countries with abundant domestic renewables, Japan’s approach emphasizes hydrogen imports and advanced storage technologies, driven by limited local renewable capacity. This review not only synthesizes policy and project-level developments but also critically evaluates Japan’s hydrogen roadmap by examining its alignment with global trends, technology maturity, and infrastructure scalability. The review integrates recent policy updates, infrastructure developments, and pilot project results, providing insights into value chain modeling, cost reduction strategies, and demand forecasting. Three policy conclusions emerge. First, Japan’s geography justifies an import-reliant pathway, but it heightens exposure to price, standards, and supply-chain risk; diversification across LH₂ and ammonia with robust certification and offtake mechanisms is essential. Second, near-term deployment is most credible in industrial feedstocks (steel, ammonia, methanol) and the maritime sector, while refueling rollout lags materially behind plan and should be recalibrated. Third, cost competitiveness hinges less on electrolyzer CAPEX than on electricity price, liquefaction, transport; policy should prioritize bankable offtake, grid-connected renewables and transmission, and targeted CAPEX support for import terminals, bunkering, and cracking. Japan’s experience offers a pathway in the global hydrogen transition, particularly for countries facing similar geographic and energy limitations. By analyzing both the progress and the limitations of Japan’s hydrogen roadmap, this study contributes to understanding diverse national strategies in the rapidly changing state of implementation of clean energy. Full article

This paper provides a comprehensive review of the development history, current status, and future trends of civil aviation meteorological services. With the rapid growth of global air traffic and the increasing complexity of operational environments, accurate and timely meteorological information has become indispensable for ensuring the efficiency and safety of civil aviation operations. Extreme weather events, in particular, have repeatedly demonstrated their potential to disrupt flight schedules, compromise passenger safety, and incur substantial economic losses, underscoring the critical need for robust meteorological service systems in the aviation sector. Against this backdrop, this paper first introduces the importance of civil aviation meteorological services in ensuring flight safety, improving flight regularity, and reducing operational costs. The development process is then detailed, from early infrastructure construction to the current modern and intelligent development, covering the evolution of observation equipment, forecasting technologies, and service models. When analyzing the current status, the paper discusses challenges such as the difficulty of accurate forecasting under complex weather conditions and multi-departmental collaboration issues, as well as improvement measures and achievements. Finally, it determines future trends, including the application of new technologies, such as artificial intelligence (AI) and big data, the expansion of service scope, and the strengthening of international cooperation, aiming to provide references for further improving the level of civil aviation meteorological services. Full article

Accurate and high-resolution spatio-temporal prediction of PM_2.5 concentrations remains a significant challenge for air pollution early warning and prevention. Advanced artificial intelligence (AI) technologies, however, offer promising solutions to this problem. A spatio-temporal prediction model is designed in this study, which is built upon a seq2seq architecture. This model employs an improved graph convolutional neural network to capture spatially dependent features, integrates time-series information through a gated recurrent unit, and incorporates an attention mechanism to achieve PM_2.5 concentration prediction. Benefiting from high-resolution satellite remote sensing data, the regional, multi-step and high-resolution prediction of PM_2.5 concentration in Beijing has been performed. To validate the model’s performance, ablation experiments are conducted, and the model is compared with other advanced prediction models. The experimental results show our proposed Spatio-Temporal Graph Convolutional Network with Attention Mechanism (STGCA) outperforms comparison models in multi-step forecasting, achieving root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of 4.21, 3.11 and 11.41% for the first step, respectively. For subsequent steps, the model also shows significant improvements. For subsequent steps, the model also shows significant improvements, with RMSE, MAE and MAPE values of 5.08, 3.69 and 13.34% for the second step and 6.54, 4.61 and 16.62% for the third step, respectively. Additionally, STGCA achieves the index of agreement (IA) values of 0.98, 0.97 and 0.95, as well as Theil’s inequality coefficient (TIC) values of 0.06, 0.08 and 0.10 proving its superiority. These results demonstrate that the proposed model offers an efficient technical approach for smart air pollution forecasting and warning in the future. Full article

This paper systematically examines the key developmental stages of China’s new energy vehicle (NEV) charging and battery swapping industry, analyzing technological breakthroughs, market expansion, and policy support in each phase. The study identifies three distinct stages: the initial exploration phase (before 2014), the comprehensive deployment phase (2014–2020), and the high-quality development phase (since 2021). The industry has established a diverse energy replenishment system centered on charging infrastructure, with battery swapping serving as a complementary approach. Policy implementation has yielded significant achievements, including rapid infrastructure expansion, continuous technological upgrades, innovative business models, and improved user experiences. However, persistent challenges remain, such as insufficient standardization, unprofitable business models, and coordination barriers between stakeholders. The paper forecasts future development trajectories, including the widespread adoption of high-power charging technology, intelligent charging system upgrades, integration of Solar Power, Energy Storage, and EV Charging, diversified operational ecosystems for charging/swapping facilities, deep integration of virtual power plants, and the construction of comprehensive energy stations. Policy recommendations emphasize strengthening standardization, optimizing regional coordination and subsidy mechanisms, enhancing participation in virtual power plant frameworks, promoting the interoperability of charging/swapping infrastructure, and advancing environmental sustainability through resource recycling. Full article

Gynecological cancer, especially breast, cervical, and ovarian cancer, are significant health issues affecting women worldwide. When screened they are mostly detected at later stages because of non-specific signs and symptoms as well as the unavailability of reliable screening methods. The improvement of early oncologic prediction methods is therefore needed to work out the survival rates, guide individualized treatment, and relieve healthcare pressures. Outcome forecasting and clinical detection are rapidly changing with the use of machine learning (ML), one of the promising technologies used to analyze complex biomedical data. Artificial intelligence (AI)-based ML models are capable of determining low-level trends and making accurate predictions of disease risk and outcomes, because they can combine different datasets (clinical records, genomics, proteomics, medical imaging) and learn to identify subtle patterns. Standard algorithms, including support vector machines, random forests, and deep learning (DL) models, such as convolutional neural networks, have demonstrated high potential in identifying the type of cancer, monitoring disease progression, and designing treatment patterns. This manuscript reviews the recent developments in the use of ML models to advance oncologic prediction tasks in gynecologic oncology. It reports on critical domains, like screening, risk classification, and survival modeling, as well as comments on difficulties, like data inconsistency, inability of interpretation of models, and issues of clinical interpretation. New developments, such as explainable AI, federated learning (FL), and multi-omics fusion, are discussed to develop these models and to make them applicable in practice because of their reliability. Conclusively, this article emphasizes the transformative role of ML in precision oncology to deliver improved, patient-centered outcomes to women who are victims of gynecological cancers. Full article

In the era of educational digital transformation, integrating information and communication technology (ICT) into school administration aligns with the goals of promoting personalized learning, equity, and teaching quality. This study examines how ICT reshapes management practices, addresses challenges, and achieves educational objectives. To explore ICT’s impact on school administration (2009–2024), we conducted a systematic scoping review of four databases (Web of Science, Scopus, ScienceDirect, and IEEE Xplore) following the PRISMA-ScR guidelines. Retrieved studies were screened, analyzed, and synthesized to identify key trends and challenges. The results show that ICT significantly improves administrative efficiency. Automated systems streamline routine tasks, allowing administrators to allocate more time to strategic planning. It enables data-driven decision-making. By analyzing large datasets, ICT helps identify trends in student performance and resource utilization, facilitating accurate forecasting and better resource allocation. Moreover, ICT strengthens stakeholder communication. Online platforms enable instant interaction among teachers, students, and parents, increasing the transparency and responsiveness of school administration. However, there are challenges. Data privacy concerns can erode trust, as student and staff data collection and use may lead to breaches. Infrastructure deficiencies, such as unreliable internet and outdated equipment, impede implementation. The digital divide exacerbates inequality, with under-resourced schools struggling to utilize ICT fully. ICT is vital in educational administration. Its integration requires a strategic approach. This study offers insights for optimizing educational management via ICT and highlights the need for equitable technological advancement to create an inclusive, high-quality educational system. Full article

This study examines the evolution of agricultural raw material exports in seven emerging economies of Central and Eastern Europe (Romania, Poland, Slovakia, Croatia, Bulgaria, the Czech Republic, and Hungary) from 1995 to 2023 and provides forecasts for 2024–2026 using ARIMA models. The results indicate a general downward trend in the share of agricultural raw material exports within total exports, reflecting ongoing economic modernization and a structural shift toward higher value-added products and industrial sectors. Romania, Poland, and Hungary remain as significant players in the cereals market, while Slovakia and the Czech Republic show the most pronounced transitions toward non-agricultural industries. Croatia, however, follows an atypical trajectory, maintaining a relatively high share of agricultural exports. Statistical tests (Dickey–Fuller) confirm the non-stationarity of the initial series, necessitating differencing for ARIMA modeling. Correlation analyses reveal a synchronized regional dynamic, with strong links among Poland, Slovakia, the Czech Republic, and Bulgaria. Forecasts suggest continued decline or stabilization at low levels for most countries: Romania (0.45% in 2026), Poland (0.93%), Slovakia (0.62%), Bulgaria (0.51%), the Czech Republic (0.95%), and Hungary (0.53%), while Croatia is an exception, with a projected moderate increase to 4.19% in 2026. Although the share of raw agricultural exports is decreasing, the findings confirm that agriculture remains a strategic sector for food security and regional trade. The study recommends investments in processing, technological modernization, and export market diversification to strengthen the competitiveness and resilience of the agricultural sector in the context of global economic transformations. Full article