Search Results (19,051)

The increasing demand for decarbonized urban environments has intensified interest in integrating renewable energy systems within cities. This review investigates the potential of urban wind energy as a promising technology in the development of Positive Energy Districts, supporting the transition toward climate-neutral urban areas. A systematic analysis of recent literature is presented, covering methodologies for urban wind resource assessment, including Geographic Information Systems (GIS)-based mapping, wind tunnel experiments, and Computational Fluid Dynamics simulations. The study also reviews available small-scale wind technologies, with emphasis on building-integrated wind turbines, and evaluates their contribution to local energy self-sufficiency. The integration of urban wind systems with energy storage, Power-to-Heat solutions, and smart district networks is discussed within the PED framework. Despite technical, economic, and social challenges, such as low wind speeds, turbulence, and public acceptance, urban wind energy offers temporal complementarity to solar power and can enhance district-level energy resilience. The review identifies key technological and methodological gaps and proposes strategic directions for optimizing urban wind deployment in future sustainable city planning. Full article

This study investigates the influence of pressure groups on the structure of the Greek economy, emphasizing their function as intermediaries between civil society and policymaking institutions. The significance of this research lies in revealing how organized interests—operating in an environment of informal and weakly regulated lobbying—shape sectoral dynamics and policy outcomes. The central hypothesis is that sectors represented by strong and well-organized pressure groups, such as manufacturing, tourism, and public administration, exhibit higher and more stable shares of Gross Value Added (GVA) due to their lobbying capacity and institutional access. To test this hypothesis, the paper integrates qualitative institutional analysis with a quantitative econometric model based on sectoral data from 2013 to 2023. The descriptive results indicate patterns consistent with the hypothesis that organized pressure groups are associated with sectoral resilience and performance. Nevertheless, the findings also suggest that excessive or unregulated influence may distort economic allocation and weaken transparency. The study concludes that establishing clearer oversight and accountability mechanisms is essential to ensure that the role of pressure groups supports democratic integrity and balanced economic development. Full article

Aiming at the problems that traditional heuristic algorithms struggle to capture the complex correlations between damaged equipment and dynamically adjust maintenance task requirements in different task scenarios, the Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) optimization scheduling method for damaged equipment maintenance tasks is proposed, the purpose is to enhance the efficiency of optimization scheduling in dynamic scenarios. By constructing an attribute graph of damaged equipment and maintenance units, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) are utilized to mine the correlations between nodes. A hierarchical reward function is designed in conjunction with DRL to dynamically adjust the multi-objective weights of maximizing importance, minimizing maintenance time. Hard and soft constraints such as maintenance skill matching, spare parts inventory, and threat thresholds are incorporated into the multi-objective optimization model to achieve real-time scheduling of maintenance tasks in an uncertain task environment. Case studies show that this method can effectively balance multi-objective conflicts through dynamic weight adjustment and online re-optimization mechanisms, making it suitable for multi-constraint task scenarios, compared with the Discrete Particle Swarm Optimization (DPSO) algorithm. GNN-DRL reduces the number of convergence iterations by 40%, improves the learning efficiency by 40%, and enhances the quality of the optimal solution by 11%, effectively improving the efficiency of maintenance task scheduling for damaged equipment. Full article

With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent architectures have limitations when addressing new threats, such as insufficient real-time response capabilities. To address these issues, this paper presnts an LLM-layered collaborative security architecture (LLM-LCSA) for multimachine collaborative security. This architecture optimizes the spatiotemporal fusion efficiency of multisource asynchronous data through cloud–edge–end collaborative deployment, combining an end lightweight LLM, an edge medium LLM, and a cloud-based foundation LLM. Additionally, a Mixture of Experts (MoEs) intelligent algorithm that dynamically activates the most relevant expert models by leveraging a threat–expert association matrix is introduced, thereby increasing the accuracy of complex threat identification and dynamic adaptability. Moreover, a resource-aware multi-objective optimization model is constructed to generate optimal decisions under resource constraints. Simulation results indicate that compared with traditional methods, LLM-LCSA achieves an average 7.92% improvement in the threat detection accuracy, reduces the system’s total response time by 44.52%, and enables resource scheduling during off-peak periods. This architecture provides an efficient, intelligent, and scalable solution for secure collaboration among UAV swarms. Future research should further explore its application potential in 6G network integration and large-scale swarm environments. Full article

Comprehensive highway scene understanding and robust traffic risk inference are vital for advancing Intelligent Transportation Systems (ITS) and autonomous driving. Traditional approaches often struggle with scalability and generalization, particularly under the complex and dynamic conditions of real-world environments. To address these challenges, we introduce a novel structured prompting and multi-agent collaborative knowledge distillation framework that enables automatic generation of high-quality traffic scene annotations and contextual risk assessments. Our framework orchestrates two large vision–language models (VLMs): GPT-4o and o3-mini, using a structured Chain-of-Thought (CoT) strategy to produce rich, multiperspective outputs. These outputs serve as knowledge-enriched pseudo-annotations for supervised fine-tuning of a much smaller student VLM. The resulting compact 3B-scale model, named VISTA (Vision for Intelligent Scene and Traffic Analysis), is capable of understanding low-resolution traffic videos and generating semantically faithful, risk-aware captions. Despite its significantly reduced parameter count, VISTA achieves strong performance across established captioning metrics (BLEU-4, METEOR, ROUGE-L, and CIDEr) when benchmarked against its teacher models. This demonstrates that effective knowledge distillation and structured role-aware supervision can empower lightweight VLMs to capture complex reasoning capabilities. The compact architecture of VISTA facilitates efficient deployment on edge devices, enabling real-time risk monitoring without requiring extensive infrastructure upgrades. Full article

Coastal and island tourism represents a key and environmentally sensitive component of the global tourism system, integrating ecological, cultural, and economic dimensions within marine and insular environments. This study presents a comprehensive bibliometric analysis of 1226 Scopus-indexed journal articles in accordance with the PRISMA protocol. By combining performance analysis and science mapping, it examines publication dynamics, thematic structures, intellectual foundations, and global collaboration patterns. The results show steady growth that accelerates after 2010, reflecting the development of descriptive case-based studies to multidisciplinary research. The research landscape reveals four major thematic clusters focusing on tourism development and management, governance and sustainability, climate change adaptation, and technological innovation. The intellectual structure is characterized by seminal works and conceptual foundations that have shaped the development of the field. However, global productivity and collaboration show significant geographic imbalances. This study provides a consolidated understanding of how coastal and island tourism scholarship has evolved and highlights the need for greater theoretical integration, inclusivity, and cooperation to promote sustainable and resilient tourism futures. Full article

The conventional OLSR protocol faces substantial challenges in highly dynamic and interference-intensive UAV environments, including high mobility, frequent topology changes, and insufficient adaptability to electromagnetic interference. This paper proposes a cross-layer improved OLSR protocol, OLSR-LCN, that integrates three evaluation metrics—link lifetime (LL), channel interference index (CII), and node load (NL)—to enhance communication stability and network performance. The proposed protocol extends the OLSR control message structure and employs enhanced MPR selection and routing path computation algorithms. LL prediction enables proactive selection of stable communication paths, while the CII helps avoid heavily interfered nodes during MPR selection. Additionally, the NL metric facilitates load balancing and prevents premature node failure due to resource exhaustion. Simulation results demonstrate that across different UAV flight speeds and network scales, OLSR-LCN protocol consistently outperforms both the OLSR and the position-based OLSR in terms of end-to-end delay, packet loss rate, and network efficiency. The cross-layer optimization approach effectively addresses frequent link disruptions, interference, and load imbalance in dynamic environments, providing a robust solution for reliable communication in complex FANETs. Full article

Urban traffic congestion leads to daily delays, driven by outdated, rigid control systems. As vehicle numbers grow, fixed-phase signals struggle to adapt to real-time conditions. This work presents a decentralized Multi-Agent Reinforcement Learning (MARL) system to manage a traffic cell composed of five intersections, introducing the novel Strategic Anti-Blocking Phase Adjustment (SAPA) module, developed to enable dynamic phase time adjustments. The goal is to optimize arterial traffic flow by adapting strategies to different traffic generation patterns, simulating priority movements along circular or radial arterials, such as inbound or outbound city flows. The system aims to manage diverse scenarios within a cell, with the long-term goal of scaling to city-wide networks. A Visible Light Communication (VLC) infrastructure is integrated to support real-time data exchange between vehicles and infrastructure, capturing vehicle position, speed, and pedestrian presence at intersections. The system is evaluated through multiple performance metrics, showing promising results: reduced vehicle queues and waiting times, increased average speeds, and improved pedestrian safety and overall flow management. These outcomes demonstrate the system’s potential to deliver adaptive, intelligent traffic control for complex urban environments. Full article

This paper presents a robust control strategy for three-phase inverters that combines Sliding Mode Control with a Washout Filter (SMC-w) to achieve low harmonic distortion and high dynamic stability. The proposed approach addresses the critical challenge of maintaining the stability of a high-quality output signal while ensuring robustness against disturbances and adaptability under variable, unbalanced, and nonlinear loads. The proposed hybrid controller integrates the fast response and disturbance rejection capability of SMC with the filtering properties of the washout stage, effectively mitigating low-frequency chattering and steady-state offsets. A detailed stability analysis is provided to ensure the closed-loop convergence of the SMC–w. Simulation results obtained in MATLAB–Simulink demonstrate significant improvements in transient response, total harmonic distortion, and robustness under unbalanced and nonlinear load conditions compared to conventional control methods. The inverter demonstrated rapid tracking of the reference signals with a minimal error margin of 3%, effective frequency regulation with a low steady-state error, and resilience to input disturbances and load variations. For instance, under a load variation from 20 Ω to 5 Ω, the system maintained the output voltage accuracy within a 3% error threshold. In addition, the input perturbations and frequency shifts in the reference signals were effectively rejected, confirming the robustness of the control strategy. Furthermore, the integration of the SMC proved to be highly effective in reducing harmonic distortion and delivering a stable and high-quality sinusoidal output. The integration of the washout filter minimized the chattering phenomenon typically associated with the SMC, further enhancing the smooth response and reliability of the system. This study highlights the potential of SMC–w to optimize power quality and operational stability. This study offers significant insights into the development of advanced inverter systems that can operate in dynamic and challenging environments. Full article

In recent years, unmanned aerial vehicle (UAV) technology has been increasingly widely used in natural disaster rescue. To enable fast and accurate localization of rescue targets in disaster environments, this paper proposes a multi-level coordinate system transformation method for quadrotor UAVs based on monocular camera position compensation. First, the preprocessed image object is transformed from pixel coordinates to camera coordinates. Second, to address the issue that coupling errors between the camera and UAV coordinate systems degrade the accuracy of coordinate conversion and target positioning, a Static–Dynamic Compensation Model (SDCM) for UAV camera position error is established. This model leverages a UAV attitude-based compensation mechanism to enable accurate conversion of camera coordinates to UAV coordinates and north-east-down (NED) coordinates. Finally, according to the Earth model, a multi-level continuous conversion chain from the target coordinates to the Earth-centered–Earth-fixed (ECEF) coordinates and the world-geodetic-system 1984 (WGS84) coordinates is constructed. Extensive experimental results show that the accuracy of the overall positioning method is improved by approximately 23.8% after completing our camera position compensation, which effectively enhances the positioning performance under the basic method of coordinate transformation, and provides technical support for the rapid rescue in the post-disaster phase. Full article

Peer-to-peer (P2P) streaming networks are widely used for large-scale multimedia delivery, but they continue to face challenges related to reliability, security, and scalability. To address these issues, we propose a hybrid framework that integrates distributed software-defined networking (DSDN) with blockchain to provide a more reliable and secure P2P streaming environment. Based on this proposal, we simulated three scenarios using NS-3: P2P with blockchain only, P2P with DSDN only, and P2P with the combined DSDN–blockchain model. Network performance was evaluated through three key metrics: throughput, latency, and energy consumption. Furthermore, the hybrid model’s security was validated under simulated attack scenarios by integrating Intrusion Detection System and Access Control List (ACL) modules within the DSDN controller and an encryption module for data-in-transit. The experiments were conducted under varying numbers of nodes to assess scalability and consistency. Across all network sizes, the hybrid model consistently outperformed the single-technology scenarios. At 50 nodes, for example, the hybrid approach achieved 8–9 percent higher throughput, 5–6 percent lower latency, and 7–21 percent better energy efficiency compared to blockchain or DSDN alone. Overall, the findings demonstrate that combining DSDN and blockchain yields a P2P streaming network with enhanced performance, making the integration highly beneficial for future multimedia streaming applications. Full article

With the rapid expansion of the civil aviation industry, the surge in flight numbers has led to increasingly pronounced issues of air route congestion and flight conflicts. 4D trajectory prediction, by dynamically adjusting aircraft paths in real time, can prevent air route collisions, alleviate air traffic pressure, and ensure flight safety. Therefore, this paper proposes a combined model—GAT-BiGRU-TPA—based on the Spatio-Temporal Graph Neural Network (STGNN) framework to achieve refined 4D trajectory prediction. This model integrates Graph Attention Networks (GAT) to extract multidimensional spatial features, Bidirectional Gated Recurrent Units (BiGRU) to capture temporal dependencies, and incorporates a Temporal Pattern Attention (TPA) mechanism to emphasize learning critical temporal patterns. This enables the extraction of key information and the deep fusion of spatio-temporal features. Experiments were conducted using real trajectory data, employing a grid search to optimize the observation window size and label length. Results demonstrate that under optimal model parameters (observation window: 30, labels: 4), the proposed model achieves a 45.72% reduction in mean Root Mean Square Error (RMSE) and a 43.40% decrease in Mean Absolute Error (MAE) across longitude, latitude, and altitude compared to the optimal baseline BiLSTM model. Prediction accuracy significantly outperforms multiple mainstream benchmark models. In summary, the proposed GAT-BiGRU-TPA model demonstrates superior accuracy in 4D trajectory prediction, providing an effective approach for refined trajectory management in complex airspace environments. Full article

Sampling-based algorithms are pivotal for high-dimensional UAV path planning, especially in 3D urban environments. The Rapidly-Exploring Random Tree (RRT) suffers from inadequate sampling methods and a single, fixed sampling policy, which lead to elongated paths and higher computational cost. To address this, we propose a Dynamic Adaptive DACS-RRT* algorithm that builds a dynamic, bidirectional sampling space and fuses low-discrepancy Halton sampling with bridge (narrow-passage) sampling, fundamentally tailoring the sampling process to urban settings. We further construct an adaptive, coordinated sampling strategy that dynamically adjusts between straight-to-goal and frustum-cone expansions by computing their probabilities, thereby overcoming the limitations of a single strategy and strengthening directional guidance. After generating a path, we perform multi-objective smoothing to make UAV trajectories better suited to urban environments. Through simulations in three distinct urban scenarios—and in comparison with five baseline algorithms—DACS-RRT* shows improvements in path length, convergence time, node count, iteration count, obstacle clearance, and turning angle, further validating its practicality in urban settings. Full article

The paper presents an automatic frequency calibration (AFC) technique for a charge pump-based phase-locked loop (CPPLL) with 5–6 μsec correction time. The architecture detects frequency offset in real time while keeping the loop active and performs a variable gain calibration that increases the correction gain at large frequency offsets to accelerate lock acquisition and gradually reduce the gain near locking frequency to suppress residual oscillation and overshoot. The implemented synthesizer rapidly re-acquires the lock within several adjacent coarse-tuning codes after frequency drift and maintains continuous operation without interruption. It demonstrates that the designed AFC achieves seamless frequency recovery in dynamically varying environments. Fabricated in a 65 nm CMOS process, the prototype fractional-N synthesizer occupies an active area of 0.603 mm² and operates over a 5.3–6.2 GHz tuning range. At 5.8 GHz, the design achieves a phase noise of −107 dBc/Hz at 1 MHz offset and consumes 21.5 mW from a 1.2 V supply. Full article

Traditional control of robotic systems relies on the availability of an exact model, which assumes complete knowledge of the robot’s parameters and all dynamic effects. However, this idealized scenario rarely holds in practice, as real-world interactions introduce unpredictable environmental influences, friction, and edge effects. This paper presents a novel data-driven approach to modeling and estimating robot dynamics by leveraging data collected during the robot’s movements. The proposed method operates without prior knowledge of the system parameters, thereby addressing the limitations of conventional model-based control strategies in complex and uncertain environments. Our unified data-driven framework integrates classical control theory with modern machine learning techniques, including system identification, physics-informed neural networks (PINNs), and deep learning. We demonstrate its efficacy in the case of a two-link robotic manipulator that achieves superior trajectory tracking and robustness to unmodeled dynamics. The technique is modular and can be extended to manipulators with more joints. Full article