Search Results (1,689)

The rapid proliferation of distributed energy resources (DERs), including photovoltaics, wind power, battery energy storage, and electric vehicles, has transformed traditional power systems into highly decentralized and data-rich environments. Virtual power plants (VPPs) have emerged as a key mechanism for aggregating these heterogeneous assets and enabling coordinated control, market participation, and grid-support functions. Recent advances in artificial intelligence (AI) have further elevated the scalability, autonomy, and responsiveness of VPP operations. This paper presents a comprehensive review of AI for VPPs, organized around a taxonomy of machine learning, deep learning, reinforcement learning, and hybrid approaches, and examines how these methods map to core VPP functions such as forecasting, scheduling, market bidding, aggregation, and ancillary services. In parallel, we analyze enabling architectural frameworks—including centralized cloud, distributed edge, hybrid cloud–edge collaboration, and emerging 5G/LEO satellite communication infrastructures—that support real-time data exchange and scalable deployment of intelligent control. By integrating methodological, functional, and architectural perspectives, this review highlights the evolution of VPPs from rule-based coordination to intelligent, autonomous energy ecosystems. Key research challenges are identified in data quality, model interpretability, multi-agent scalability, cyber-physical resilience, and the integration of AI with digital twins and edge-native computation. These findings outline promising directions for next-generation intelligent VPPs capable of delivering secure, flexible, and self-optimizing DER aggregation at scale. Full article

Software-Defined Networking (SDN) has emerged as a fundamental architecture for future Internet systems by enabling centralized control, programmability, and fine-grained traffic management. However, the logical centralization of the SDN control plane also introduces critical vulnerabilities, particularly to Distributed Denial-of-Service (DDoS) attacks that can severely disrupt network availability and performance. To address these challenges, machine-learning (ML) techniques have been increasingly adopted to enable intelligent, adaptive, and data-driven DDoS detection mechanisms within SDN environments. This study presents a PRISMA-guided systematic literature review of recent ML-based approaches for DDoS detection in SDN-based networks. A comprehensive search of IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar identified 38 primary studies published between 2021 and 2025. The selected studies were systematically analyzed to examine learning paradigms, experimental environments, evaluation metrics, datasets, and emerging architectural trends. The synthesis reveals that while single machine-learning classifiers remain dominant in the literature, hybrid and ensemble-based approaches are increasingly adopted to improve detection robustness under dynamic and high-volume traffic conditions. Experimental evaluations are predominantly conducted using SDN emulation platforms such as Mininet integrated with controllers, including Ryu and OpenDaylight, with performance commonly measured using accuracy, precision, recall, and F1 score, alongside emerging system-level metrics such as detection latency and controller resource utilization. Public datasets, including CICIDS2017, CICDDoS2019, and InSDN, are widely used, although a significant portion of studies rely on custom SDN-generated datasets to capture control-plane-specific behaviors. Despite notable advances in detection accuracy, several challenges persist, including limited generalization to low-rate and unknown attacks, dependency on synthetic traffic, and insufficient validation under real-time operational conditions. Based on the synthesized findings, this review highlights key research directions toward intelligent, scalable, and resilient DDoS defense mechanisms for future Internet architectures, emphasizing adaptive learning, lightweight deployment, and integration with programmable networking infrastructures. Full article

The cement industry is central to sustainable manufacturing due to its high energy demand and associated CO₂ emissions. In cement production, a substantial share of electrical energy is consumed in the clinker grinding circuit, where Blaine fineness (specific surface area, cm²/g), a key quality output, affects both cement performance and specific energy consumption. However, laboratory Blaine measurements are typically available with a 30–60 min delay, which limits timely process interventions and may promote conservative operating practices (e.g., precautionary over-grinding) to secure quality. This study develops machine-learning models to predict the finished-product Blaine fineness (Blaine-F) from routinely recorded industrial quality-control inputs, including XRF-based oxide composition, derived chemical moduli (lime saturation factor, LSF; silica modulus, SM; alumina modulus, AM), laser-diffraction particle-size distribution descriptors (Q10/Q50/Q90 corresponding to D10/D50/D90 percentile diameters; and R3 residual fractions at selected cut sizes), and intermediate in-process fineness (Blaine-P). The models were trained on over 200 finished-product samples obtained from the quality-control laboratory information management system (LIMS) of Seza Cement Factory (SYCS Group, Turkey). Ridge regression, Random Forest, XGBoost, LightGBM, and CatBoost were tuned using RandomizedSearchCV with five-fold cross-validation and evaluated on a held-out test set using MAE, RMSE, and R². The results show that the linear baseline provides limited explanatory power (Ridge: R² ≈ 0.50), consistent with the strongly non-linear behavior of the grinding–separation system, whereas tree-based ensemble methods achieve higher predictive accuracy. XGBoost yields the best overall performance (R² = 0.754; RMSE = 76.9 cm²/g), while Random Forest attains R² = 0.744 with the lowest MAE (61.7 cm²/g). Explainability analyses indicate that Blaine-F is primarily influenced by the fine-tail PSD descriptor Q10 (D10 particle size) and the intermediate fineness Blaine-P, whereas chemistry-related variables (e.g., LSF and SiO₂, and particularly SM) provide secondary yet meaningful contributions. These findings support the use of the proposed model as a virtual sensor to reduce decision latency associated with delayed laboratory Blaine measurements and to enable tighter fineness targeting. Potential energy and CO₂ implications should be quantified using site-specific, plant-calibrated relationships between kWh/t and Blaine fineness, rather than inferred as measured outcomes within the present study. Full article

Methane emissions from abandoned coal mining operations represent a persistent environmental and safety challenge in post-mining regions undergoing urban redevelopment. As urban infrastructure expands over former underground workings, the uncontrolled migration of mine gas can compromise public safety, exacerbate greenhouse gas emissions, and undermine sustainable development goals. This study investigates the origin of methane emissions detected in an urban area of the municipality of Lupeni, Romania, following the commissioning of a new natural gas distribution pipeline installed within a historically mined perimeter. The emissions had not been previously reported and were unexpectedly discovered during technical inspections conducted after the gas network installation, highlighting the absence of historical data on gas presence in this area. This is the first documented case of an accidental discovery of methane emissions in an urban perimeter overlying historical coal mine workings in Romania, granting this study a pioneering status, both scientifically and in terms of urban risk management. The findings emphasize that administrative mine closure does not equate to risk closure, as latent methane emissions may reactivate during urban transformations (e.g., excavations, utility upgrades, drainage changes). To ensure a scientifically sound and sustainable risk assessment, an integrated diagnostic framework was applied, combining chronological field monitoring with chromatographic gas composition analysis. This methodology enabled precise attribution of the methane source to abandoned underground mine workings, excluding the public gas network as a contributor. Based on this diagnosis, a controlled drainage and methane recovery system was implemented, resulting in the elimination of detectable concentrations at all monitoring points by February 2025. The captured methane was redirected for local energy use, transforming an environmental liability into a usable resource. This intervention supports circular economy principles and aligns with EU climate and energy transition goals. The proposed methodological framework provides a replicable model for identifying and managing residual mine gas in post-industrial urban environments. Although emission fluxes were not quantified, concentration-based screening enabled risk diagnosis, prioritization, and targeted intervention. These findings are relevant to EU Regulation (2024/1785) on methane emission reduction, emphasizing the need to include post-mining methane (AMM) in urban planning and environmental monitoring strategies. Limitations of the study include the absence of baseline data and the inability to calculate total methane flux. However, the results support immediate and practical risk mitigation and highlight the need for future work focused on long-term monitoring and emission quantification. This case provides critical insights for other post-mining cities in Central and Eastern Europe facing similar challenges at the intersection of legacy coal infrastructure and modern urban development. This study is designed as a concentration-based diagnostic and risk-oriented case study and does not aim to quantify methane emission fluxes. Full article

Dental implantation affects masticatory bite and muscle forces. The temporomandibular joint (TMJ) bears a substantial amount of these masticatory forces. Thus, the objective of the current study was to investigate whether dental implantation alters the human mandibular condyle. Among 556 images, 54 and 22 CBCT scans were successfully identified from 27 patients (10 males and 17 females; 54.93 ± 19.46 years) in the control group and 11 patients (3 males and 8 females; 51.32 ± 13.13 years) in the implant group, respectively. In the control group, CBCT images were obtained longitudinally at the time of implantation and after the post-implantation healing period, both prior to crown placement. In the implant group, CBCT images were obtained at the time of crown placement on a single-tooth implant and after the functional loading period following crown placement. Left and right mandibular condyles were digitally isolated from the images. The bone mineral density (BMD) parameters and morphological changes were assessed using frequency plots of BMD and TMJ osteoarthritis (OA) counts, respectively. In the control group, BMD values were not significantly different between the first and second scans. In contrast, the implant group showed a significant decrease in BMD values, along with a marginal increase in TMJ OA counts after the functional loading period. The TMJ OA counts were highest in the anterior regions, followed by the middle and posterior regions. Most regions showed significantly reduced BMD values, except the antero-lateral and antero-central regions. The current findings give an insight that dental implantation may alter the morphology and BMD of human mandibular condyles. The TMJ OA counts increased, while BMD decreased during the functional loading period of more than 3 months following implantation. Masticatory loading associated with the dental implant likely increases the load on the TMJ, which could stimulate new bone formation to balance the load distribution on the mandibular condyle. Full article

Environmental DNA (eDNA) metabarcoding has become a powerful, non-invasive method for detecting aquatic organisms. However, optimal sampling strategies for benthic taxa in lentic ecosystems remain unclear. This study evaluated the effectiveness of eDNA metabarcoding in detecting freshwater Unionidae mussels in lake water columns and examined their spatial and seasonal distribution patterns. We validated a mini-barcode primer targeting the mitochondrial 16S rDNA of unionid mussels through controlled laboratory experiments and field tests, confirming reliable amplification and accurate taxonomic assignment of freshwater bivalve DNA. Field surveys were conducted in four lakes within the Nakdong River basin, where eDNA samples were collected from littoral zones and from surface, mid-, and bottom layers of central lake areas during autumn and winter. Metabarcoding analysis identified 79 amplicon sequence variants (ASVs) representing four unionid taxa, with Cristaria plicata and Sinanodonta lauta comprising the majority of reads and ASVs. Overall, the number of Unionidae eDNA reads showed no significant seasonal differences, but there was notable spatial variation among sampling locations. Read numbers were significantly lower in littoral zones compared to central lake areas, with no significant differences detected among depth layers within the central zones. Species-specific analyses revealed contrasting spatial patterns: C. plicata had higher read abundance in mid- and bottom layers, while S. lauta was more frequently detected in surface and littoral samples. These findings suggest that the distribution of freshwater mussel eDNA in lakes is primarily influenced by spatial factors related to habitat preference and hydrodynamic mixing, rather than by seasonal variation during stable periods. This study offers practical insights for designing effective eDNA sampling strategies for benthic invertebrates in lentic ecosystems. Full article

The transition toward active distribution networks requires advanced control solutions capable of handling the rapid dynamics of distributed energy resources. This paper proposes a low-cost, intelligent IoT architecture designed for the real-time optimization and analysis of energy systems within low-voltage networks. Unlike centralized monitoring approaches constrained by communication latency, the proposed solution leverages Intelligent Edge Processing (IEP) implemented on ESP32 embedded nodes to optimize data flow and decision-making. This architecture executes stability assessments directly at the network edge, calculating critical analysis indicators such as the Voltage Deviation Index (VDI) and Rate of Change of Frequency (RoCoF). The system was validated on the CIGRE European LV benchmark under severe stress scenarios, including rapid solar transients and voltage sags. The results demonstrate that the proposed architecture effectively coordinates storage interventions, ensuring voltage recovery within 300 ms and maintaining power quality within EN 50160 limits even during severe voltage sags. The study validates the feasibility of using industrial IoT edge computing as a resilient, non-wire alternative for modernizing complex energy systems. Full article

Biological invasion is a major driver of biodiversity loss and ecosystem disruption, with non-native aquatic species threatening ecological integrity and economic stability. The Qinling Mountains, located in central China, serve as a crucial barrier between temperate and subtropical climate zones, and separate the Yellow and Yangtze River basins. This study investigates the role of these geographical barriers in regulating the distribution and invasion risk of non-native aquatic species. We identified 27 non-native species in Shaanxi Province based on occurrence records compiled from field survey conducted between 2012 and 2024 (and from 2019 to 2024 in the Yellow River mainstream of the Shanxi–Shaanxi Gorge), including 13 high-risk species, such as Trachemys scripta elegans, Procambarus clarkii, Sander lucioperca, and Hypomesus olidus. Using the Aquatic Species Invasiveness Screening Kit and species distribution models, we identified the Hanjiang River in the Yangtze basin and Weihe River estuary in the Yellow River basin as high-risk areas for these species. Mean annual temperature was the primary environmental factor influencing species distribution, with species adapted to cooler conditions predominantly found north of the Qinling Mountains, while those preferring warmer climates are more common in the south. Our findings highlight the Qinling Mountains as both a physical and climatic barrier, limiting cross-basin dispersal and creating distinct invasion patterns. However, human activities such as inter-basin water-transfer projects, damming, and aquaculture practices have gradually weakened the barrier’s effectiveness, facilitating the spread of invasive species. We recommend prioritizing monitoring efforts in cross-basin water-transfer regions, focusing on high-risk species adapted to both cooler and warmer climates, and incorporating environmental DNA (eDNA)-based monitoring in recipient areas of inter-basin water-transfer projects for early detection and control to minimize ecosystem damage. Full article

This study examines the political dilemma in tourism governance in Mandalika, Indonesia, focusing on three key components: accountability, transparency, and resource allocation. This research aims to reframe the role of political activity to align with the principles of community benefit and justice. Data collection was conducted through a survey from August to September 2025, with 465 questionnaires distributed. A total of 444 questionnaires (95.48%) were deemed valid, while 21 (4.52%) did not meet the criteria and were excluded from the analysis. Data were analyzed through Microsoft Excel and SmartPLS version 4.1.1. The results show that a serious political problem leads to less freedom in how things are managed, making it harder to trust the systems for accountability, transparency, and resources in tourism governance. This condition is closely related to the dominance of the central authority, which holds significant control over the tourism industry and positions it as a national strategic sector. Consequently, the limited policy flexibility and strict restrictions of the tourism management framework leave local authorities and communities with limited maneuvering options. Statistical testing supports significant relationships, both direct and indirect. This study recommends more genuine and balanced integration between national and local authorities to create mutually beneficial opportunities, strengthen sustainability, and enhance international competitiveness through multi-stakeholder engagement in more inclusive governance. This research employs a quantitative, exploratory approach to elucidate the political dynamics and constraints that limit the involvement of local tourism authorities and communities in tourism management in Mandalika, Indonesia. Full article

The evolution of Hybrid Power Supply Systems (HPSSs) has extended from ground-based microgrids to the safety-critical domain of More Electric Aircraft (MEA). This paper presents a comprehensive review of dynamic power allocation strategies, bridging the gap between mature ground-based control theories and the stringent operational requirements of aerospace systems. Strategies are systematically classified into centralized, decentralized, and distributed architectures based on control structures. Evaluations indicate that centralized strategies, while effective in microgrids, achieve global optimality but face reliability constraints in airborne environments. In contrast, decentralized strategies based on virtual impedance ensure the high reliability and “plug-and-play” modularity essential for avionics yet often yield suboptimal coordination. Consequently, distributed cooperative control is identified as the most promising paradigm to bridge this gap, synthesizing optimization with fault tolerance. Finally, critical challenges in adapting these technologies to aviation—spanning algorithmic determinism and airworthiness certification—are discussed, and future trends in hybrid intelligence and digital twin-based verification are outlined for next-generation airborne energy systems. Full article

A monitor and control framework for quantum-key-distribution (QKD) networks equipped with switching capabilities was developed. On the one hand, this framework provides real-time visibility into operational metrics. Specifically, it extracts essential data, such as the switching capabilities of QKD modules, the number of keys stored in buffer queues of the QKD links, and the respective key generation and consumption rates along these links. On the other hand, this framework allows software-defined networking (SDN) applications to operate on the collected information and address the cryptographic needs of the network. The SDN applications dynamically adapt the configuration of the switched network to align with its changing demands, e.g., prioritizing key availability on critical paths, responding to link failures, or reallocating generation capacity to prevent bottlenecks. This contribution demonstrates that the combination of switched QKD, centralized control, and global optimization strategies enables efficient, policy-driven operation of QKD networks. The cryptographic resources are allocated to maximize performance and resilience while remaining aligned with the specific policies set by network administrators. Full article

The dominance of centralized artificial intelligence architectures raises significant concerns regarding privacy, data ownership, and control. These limitations have motivated the development of decentralized learning paradigms that aim to remove reliance on a central authority during model training. While federated learning represents an intermediate step by allowing distributed training without raw data exchange, it still depends on a centralized server which could lead to single-point vulnerabilities. Beyond this, a fully decentralized learning in general faces challenges in security vulnerabilities, absence of governance, and lack of incentive alignment. Recent advances in blockchain technology offer a promising foundation for addressing these issues. This paper provides a systematic analysis of blockchain’s mechanism-level roles in security, consensus, smart contract, and incentives to support decentralized learning. By reviewing state-of-the-art approaches, this paper suggests that appropriately designed blockchain architectures have the potential to enable practical, secure, and incentive-compatible decentralized learning as technological capabilities continue to evolve. Full article

Background: Fibromyalgia (FM) is a chronic pain condition primarily linked to central sensitization, although peripheral tissue-related factors have also been suggested. Ultrasound strain elastography (SEL) provides a semi-quantitative, operator-dependent estimate of tissue deformation under standardized compression, yet evidence comparing SEL findings and pressure pain sensitivity between FM and healthy controls at standardized tender-point sites remains limited. Objective: To compare pressure pain threshold (PPT) and SEL-derived tissue deformation between women with FM and healthy controls across standardized FM tender-point sites. Methods: In this cross-sectional study, 84 women (42 with FM; 42 healthy controls) were recruited from a private rehabilitation center in Málaga (Spain). PPT and SEL were assessed bilaterally at 13 standardized tender-point sites. Between-group differences were examined using Student’s t-test or the Mann–Whitney U test according to distribution. Results: Women with FM exhibited lower PPT across all assessed sites (p < 0.01) and lower SEL-derived deformation scores at most sites, whereas no between-group SEL differences were observed at the dominant and non-dominant forearm, non-dominant lower cervical region, dominant paraspinal region, and bilateral lateral pectoral region. Conclusions: Compared with controls, women with FM showed reduced pressure pain thresholds and site-dependent differences in SEL-derived tissue deformation at standardized tender-point sites. Given the cross-sectional and exploratory design, SEL findings should be interpreted cautiously and considered non-diagnostic; heterogeneity across anatomical sites should be considered in future confirmatory and longitudinal studies. Full article

High penetration of distributed photovoltaic (PV) generation has transformed active distribution networks into inverter-dominated systems, where maintaining voltage stability, minimizing power losses, and maximizing renewable utilization under uncertainty remain significant challenges. Conventional centralized optimal power flow (OPF) and ADMM-based distributed optimization methods suffer from scalability limitations, high computational latency, and reliance on accurate system models, while single-agent reinforcement learning approaches such as PPO struggle with non-stationarity and lack of coordination in multi-inverter settings. To address these limitations, this paper proposes a coordinated control framework based on Multi-Agent Proximal Policy Optimization (MAPPO) for photovoltaic inverter clusters. By adopting centralized training with decentralized execution, the proposed approach enables effective coordination among heterogeneous inverter agents while preserving real-time autonomy. The framework explicitly incorporates network-level objectives, inverter operational constraints, and stochastic irradiance and load uncertainties, allowing agents to learn adaptive and robust control strategies. Simulation studies on a modified IEEE 33-bus active distribution network demonstrate that the proposed MAPPO-based method reduces voltage deviations by more than 40%, decreases network losses by approximately 25%, and lowers photovoltaic curtailment ratios by nearly 50% compared with centralized optimization approaches. In addition, MAPPO achieves significantly faster and more stable convergence than independent PPO under highly variable operating conditions.b These results indicate that MAPPO provides a scalable and resilient alternative to conventional optimization and single-agent learning methods, offering a practical pathway to enhance hosting capacity, operational robustness, and renewable integration in future active distribution networks. Full article

In recent years, the surge in network traffic has led to a substantial increase in energy consumption, making the construction of green and energy-efficient networks a critical challenge in the field of communications. Software-Defined Networking (SDN), with its centralized control characteristic, provides a new paradigm for the collaborative scheduling of actuators. However, traditional distributed network architectures lack global regulation capabilities, resulting in low resource utilization. Moreover, existing SDN traffic management methods mostly adopt fixed-weight reward functions, which are difficult to adapt to the dynamic fluctuation of network traffic and device heterogeneity, failing to meet the real-time and stability requirements of actuators in control scenarios. To address these issues, this study proposes a Dynamic Weight Generation Deep Q-Network (DWG-DQN) framework. By integrating a Long Short-Term Memory (LSTM) network with the SDN actuator scheduling mechanism, the system dynamically generates adaptive weight vectors, enabling real-time collaborative optimization of energy consumption, load balancing, and bandwidth utilization. Experimental results demonstrate that in fat-tree topology experiments, the proposed method achieves a 12.23% increase in average reward, a 33.93% reduction in energy consumption, a 31.12% improvement in load balancing, and a 24.03% enhancement in bandwidth utilization. Compared with fixed-weight method, it consistently outperforms in key performance indicators. The dynamic weight generation mechanism effectively solves the multi-objective optimization problem of actuators in dynamic network environments, offering a viable solution for the intelligent scheduling of actuators in SDN-based green traffic management. Full article