Search Results (1,860)

In recent years, hybrid models that integrate Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) have achieved significant improvements in hyperspectral image classification (HSIC). Nevertheless, their complex architectures often lead to computational redundancy and inefficient feature fusion, particularly struggling to balance global modeling and local detail extraction in high-dimensional spectral data. To solve these issues, this paper proposes a Spectral-Cube Gated Harmony Network (SCGHN) that achieves efficient spectral–spatial joint feature modeling through a dynamic gating mechanism and hierarchical feature decoupling strategy. There are three primary innovative contributions of this paper as follows: Firstly, we design a Spectral Cooperative Parallel Convolution (SCPC) module that combines dynamic gating in the spectral dimension and spatial deformable convolution. This module adopts a dual-path parallel architecture that adaptively enhances key bands and captures local textures, thereby significantly improving feature discriminability at mixed ground object boundaries. Secondly, we propose a Dual-Gated Fusion (DGF) module that achieves cross-scale contextual complementarity through group convolution and lightweight attention, thereby enhancing hierarchical semantic representations with significantly lower computational complexity. Finally, by means of the coordinated design of 3D convolution and lightweight classification decision blocks, we construct an end-to-end lightweight framework that effectively alleviates the structural redundancy issues of traditional hybrid models. Extensive experiments on three standard hyperspectral datasets reveal that our SCGHN requires fewer parameters and exhibits lower computational complexity as compared with some existing HSIC methods. Full article

To address the key issues in cooperative collision avoidance of multiple aircraft, such as unknown dynamics, external disturbances, and limited communication resources, this paper proposes a reference correction method based on the Artificial Potential Field-Control Barrier Function (APF-CBF) and combines it with a dynamic event-triggered mechanism to achieve efficient cooperative control. This paper adopts a Fuzzy Wavelet Neural Network (FWNN) to design a finite-time disturbance observer. By leveraging the advantages of FWNN, which integrates fuzzy logic reasoning and the time-frequency locality of wavelet basis functions, this observer can synchronously estimate system states and unknown disturbances, to ensure the finite-time uniformly ultimate boundedness of errors and break through the limitation of insufficient robustness in traditional observers. Meanwhile, the APF is embedded in the CBF framework. On the one hand, APF is utilized to intuitively describe spatial interaction relationships, thereby reducing reliance on prior knowledge of obstacles; on the other hand, CBF is used to strictly construct safety constraints to overcome the local minimum problem existing in APF. Additionally, the reference correction mechanism is combined to optimize trajectory tracking performance. In addition, this paper introduces a dynamic event-triggered mechanism, which adjusts the triggering threshold by real-time adaptation to error trends and mission phases, realizing “communication on demand”. This mechanism can reduce communication resource consumption by 49.8% to 69.8% while avoiding Zeno behavior. Theoretical analysis and simulation experiments show that the proposed method can ensure the uniformly ultimate boundedness of system states and effectively achieve safe collision avoidance and efficient formation tracking of multiple aircraft. Full article

Closing resistors in ultra-high-voltage (UHV) gas-insulated circuit breakers (GCBs) are critical components designed to suppress inrush currents and transient overvoltages during switching operations. However, in practical service, these resistors are subjected to repeated mechanical impacts and transient electrical stresses, leading to degradation of their electrical contact interfaces, fluctuating resistance values, and potential failure of the entire breaker assembly. Existing studies mostly simplify the closing resistor as a constant resistance element, neglecting the coupled electro-thermal–mechanical effects that occur during transient events. In this work, a comprehensive modeling framework is developed to investigate the dynamic electrical contact characteristics of a 750 kV GCB closing resistor under transient closing impacts. First, an electromagnetic transient model is built to calculate the combined inrush and power-frequency currents flowing through the resistor during its pre-insertion period. A full-scale mechanical test platform is then used to capture acceleration signals representing the mechanical shock imparted to the resistor stack. These measured signals are fed into a finite element model incorporating the Cooper–Mikic–Yovanovich (CMY) electrical contact correlation to simulate stress evolution, current density distribution, and temperature rise at the resistor interface. The simulation reveals pronounced skin effect and current crowding at resistor edges, leading to localized heating, while transient mechanical impacts cause contact pressure to fluctuate dynamically—resulting in a temporary decrease and subsequent recovery of contact resistance. These findings provide insight into the real-time behavior of closing resistors under operational conditions and offer a theoretical basis for design optimization and lifetime assessment of UHV GCBs. Full article

The Philippines, a country with abundant natural resources, has set a high 35% renewable energy (RE) share target by 2030. However, progress is slow with the implementation of its key policy mechanisms. Through the years, the Department of Energy has slowly increased the goals from 30% to 35% by 2030 and even up to 50% by 2050%. The key legal framework for the Philippine Renewable Energy sector is the Renewable Energy Act of 2008, which outlines key policy mechanisms: Renewable Portfolio Standards (RPSs), Net Metering, the Green Energy Auction Program (GEAP), and the Green Energy Option Program (GEOP). This paper analyzes the implementation and enforcement of the key policy mechanisms along with factors affecting their intended rollout. Along with the policy mechanism issues, this paper highlights key institutional and structural issues for the stakeholders of the RE sector. The main issues can be attributed to the incoherence of government agencies such as the Department of Energy (DOE), the Energy Regulatory Commission (ERC), and the National Grid Corporation of the Philippines (NGCP). Other issues include insufficient transmission infrastructure, resistance from Distribution Utilities (DUs) and Electric Cooperatives (ECs), and weak Local Government Unit (LGU) participation. The paper provides recommendations on the key issues of policy mechanisms and structural and institutional bottlenecks. The main recommendations that will help achieve the intended purpose of the drivers of RE are to strengthen the National Renewable Energy Board (NREB) and other agency capabilities, provide financial incentives to utilities, streamline permitting and other processes, and prioritize grid development for areas with RE development. For the targets of the DOE to be achieved, the main drivers for the RE sector must be revisited and fixed at their core. Achieving the RE targets of the DOE will need strong leadership and sustained focus on renewable energy development led by the government. Full article

Ten years after the Paris Agreement the escalating global geopolitical turmoil and waning interest in climate change’s effects, posit cities again as critical arenas for addressing the global energy transition. Drawing on the concept of the city as a living entity, the role of energy at the urban scale is considered not only as a technical infrastructure but as a complex system embedded in the spatial, political, and social fabric. The energy transition is situated within the broader context of urban governance and spatial planning, arguing that energy should be considered a foundational urban good essential to everyday life and ensuring equitable development. The study adopts a conceptual and literature-based approach, synthesizing insights from urban studies, energy geography, and climate governance literature. Special attention is given to the Italian context, where a lack of coordination across European, national, and regional political levels hinders energy transition efforts. Key references include theoretical frameworks on urban metabolism, socio-technical systems, and planning innovation, focusing on the intersection of infrastructure, policy, and local agency. The findings highlight the need to reframe energy planning as an integral part of urban and territorial governance. While grounded in Italy, the study’s insights reveal how governance fragmentation and multi-level coordination barriers resonate with European urban energy challenges, offering transferable lessons for territories with complex political and spatial systems. This would help integrate energy concerns into urban design, reduce consumption through spatial organization, and foster civic and institutional cooperation for rapid, often unplanned local energy actions to respond more swiftly to crises than traditional planning mechanisms. As a result, embedding energy within urban policy and spatial design fosters co-evolution between energy production, behavioral change, and infrastructural transformation. Recognizing this is vital for global urban policy and planning to drive resilient, equitable transitions in a rapidly changing energy landscape. Full article

Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on. Full article

In off-road scenes, segmentation targets exhibit significant scale progression due to perspective depth effects from oblique viewing angles, meaning that the size of the same target undergoes continuous, boundary-less progressive changes along a specific direction. This asymmetric variation disrupts the geometric symmetry of targets, causing traditional segmentation networks to face three key challenges: (1) inefficientcapture of continuous-scale features, where pyramid structures and multi-scale kernels struggle to balance computational efficiency with sufficient coverage of progressive scales; (2) degraded intra-class feature consistency, where local scale differences within targets induce semantic ambiguity; and (3) loss of high-frequency boundary information, where feature sampling operations exacerbate the blurring of progressive boundaries. To address these issues, this paper proposes the ProCo-NET framework for systematic optimization. Firstly, a Progressive Strip Convolution Group (PSCG) is designed to construct multi-level receptive field expansion through orthogonally oriented strip convolution cascading (employing symmetric processing in horizontal/vertical directions) integrated with self-attention mechanisms, enhancing perception capability for asymmetric continuous-scale variations. Secondly, an Offset-Frequency Cooperative Module (OFCM) is developed wherein a learnable offset generator dynamically adjusts sampling point distributions to enhance intra-class consistency, while a dual-channel frequency domain filter performs adaptive high-pass filtering to sharpen target boundaries. These components synergistically solve feature consistency degradation and boundary ambiguity under asymmetric changes. Experiments show that this framework significantly improves the segmentation accuracy and boundary clarity of multi-scale targets in off-road scene segmentation tasks: it achieves 71.22% MIoU on the standard RUGD dataset (0.84% higher than the existing optimal method) and 83.05% MIoU on the Freiburg_Forest dataset. Among them, the segmentation accuracy of key obstacle categories is significantly improved to 52.04% (2.7% higher than the sub-optimal model). This framework effectively compensates for the impact of asymmetric deformation through a symmetric computing mechanism. Full article

Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults and cyber–physical threats, especially false data injection attacks. We propose a consensus-based estimation algorithm that integrates a nearly constant velocity model with saturation-based filtering to suppress impulsive measurement variations and promote robust, distributed state estimation. To counteract adversarial conditions, we incorporate a dynamic false data injection detection and isolation mechanism that uses innovation thresholds to identify and disregard suspicious measurements before they can degrade the global estimate. The effectiveness of the proposed algorithms is demonstrated through a series of simulation-based case studies under both benign and adversarial conditions. The results show that increased network connectivity and higher consensus iteration rates improve estimation accuracy and convergence speed, while properly tuned saturation filters achieve a practical balance between fault suppression and accurate estimation. Furthermore, under localized, coordinated, and transient false data injection attacks, the detection mechanism successfully identifies compromised agents and prevents their data from corrupting the distributed global estimate. Overall, this study illustrates that the proposed algorithm provides a simplified fault-tolerant solution that significantly enhances the accuracy and resilience of distributed target tracking without imposing excessive communication or computational burdens. Full article

Under the background of global green sustainable development and the urgent need to understand complex regional innovation systems, it is crucial to scientifically assess China’s Tourism Green Innovation Efficiency (TGIE) as a dynamic networked system and reveal its system-level evolution driving mechanism. This article presents the construction of the TGIE evaluation indicator system, measures the inter-provincial TGIE in China in 2011–2023 based on the three-stage super-efficiency SBM-DEA model, analyzes the spatial correlation network characteristics of TGIE by using the motif analysis method and the social network analysis method, and explores the evolutionary driving mechanism by using the time-exponential random graph model (TERGM). The study shows the following: (1) The TGIE of China exhibits a regional distribution pattern characterized by “high in the east and low in the west.” The efficiency of the eastern coastal region is significantly higher than that of the central and western regions, and the overall efficiency shows a fluctuating upward trend. (2) The local structure of China’s TGIE network is dominated by the chain structure, and the partially closed structure is gradually enhanced. It indicates that the bridge role of intermediary nodes in the cross-regional flow of innovation resources is becoming more and more significant. (3) The overall network evolves from a single center to a polycentric collaboration model. High-efficiency regions attract low-efficiency regions to collaborate through high connectivity, and intermediary nodes play a key role in connecting high- and low-efficiency regions. (4) The evolution of China’s TGIE network is driven by both exogenous and endogenous dynamics, showing significant path dependence and path creation characteristics. This study enhances the theoretical framework of complex systems in tourism innovation and offers theoretical support and policy insights for optimizing the network structure of China’s TGIE as a complex adaptive system and maximizing regional cooperation networks. Full article

This paper examines the evolving role of social economy organisations (SEOs) in advancing sustainability and contributing to the green transition. While traditionally focused on social inclusion and local development, SEOs are increasingly integrating environmental objectives into their operations, particularly through circular economy (CE) practices, decarbonisation strategies, and short food supply chains (SFSCs). Based on qualitative research and the analysis of 16 good practices from five European countries, the study demonstrates how SEOs create blended social and environmental value by combining economic, social, and ecological goals. The findings show that SEOs foster environmental sustainability by reducing resource consumption and carbon emissions, creating green jobs, strengthening local cooperation, and raising environmental awareness within communities. Importantly, SEOs emerge not only as service providers but also as innovators and agents of change in local ecosystems. The paper concludes with policy recommendations to enhance the role of SEOs in the green transition and identifies directions for future research, particularly regarding the measurement of their long-term environmental impact and the conditions enabling effective collaboration with public and private sector actors. Full article

To improve the traffic efficiency of connected and automated vehicles (CAVs) in on-ramp merging areas, this study proposes a novel region-level multi-agent reinforcement learning framework, Regionally Coordinated Nash-Advantage Decomposition Deep Q-Network with Conflict-Aware Q Fusion (RC-NashAD-DQN). Unlike existing vehicle-level control methods, which suffer from high computational overhead and poor scalability, our approach abstracts on-ramp and main road areas as region-level control agents, achieving coordinated yet independent decision-making while maintaining control precision and merging efficiency comparable to fine-grained vehicle-level approaches. Each agent adopts a value–advantage decomposition architecture to enhance policy stability and distinguish action values, while sharing state–action information to improve inter-agent awareness. A Nash equilibrium solver is applied to derive joint strategies, and a conflict-aware Q-fusion mechanism is introduced as a regularization term rather than a direct action-selection tool, enabling the system to resolve local conflicts—particularly at region boundaries—without compromising global coordination. This design reduces training complexity, accelerates convergence, and improves robustness against communication imperfections. The framework is evaluated using the SUMO simulator at the Taishan Road interchange on the S1 Yongtaiwen Expressway under heterogeneous traffic conditions involving both passenger cars and container trucks, and is compared with baseline models including C-DRL-VSL and MADDPG. Extensive simulations demonstrate that RC-NashAD-DQN significantly improves average traffic speed by 17.07% and reduces average delay by 12.68 s, outperforming all baselines in efficiency metrics while maintaining robust convergence performance. These improvements enhance cooperation and merging efficiency among vehicles, contributing to sustainable urban mobility and the advancement of intelligent transportation systems. Full article

In the Eastern Cape Province of South Africa, rural women agripreneurs encounter ongoing structural challenges in accessing formal finance, securing land rights, and gaining leadership roles, despite their vital contribution to agriculture and food security. This research combines a thematic review of secondary sources from 2018 to 2024 with an embedded case study based on primary qualitative data with women involved in the Citrus Growers Association–Grower Development Company (CGA–GDC) public–private partnership. This dual approach connects local, real-world entrepreneurial experiences with global financial inclusion initiatives, especially the G20 Women’s Empowerment Principles and the G20 Development Agenda. The findings highlight a consistent gap between policy and practice: while frameworks at both national and international levels advocate for women’s financial inclusion, actual implementation in rural agribusiness often neglects gender differences. Women’s engagement is limited by insecure land rights, restricted access to formal credit, male-controlled cooperative management, and insufficient gender-specific data monitoring. Drawing comparative insights from Kenya, India, and West Africa, the study proposes seven interconnected policy suggestions, such as establishing gender-disaggregated data systems, expanding women-led cooperatives, reforming land tenure laws, including entrepreneurial financial literacy in capacity-building programmes, and utilising gender-sensitive digital finance solutions. By connecting grassroots empirical evidence with global policy discussions, this study aims to contribute to academic debates and practical efforts to develop gender-responsive financial ecosystems, thereby boosting women’s economic independence, entrepreneurial activity, and rural progress in South Africa and similar contexts in the Global South. Full article

The use of commercial off-the-shelf smart devices in digital signage for sentient spaces is emerging as a promising solution within smart city environments. In such scenarios, these devices are often required to execute resource-intensive applications despite limited local computational capacity. Although cloud and fog infrastructures have been proposed to offload demanding workloads, they are not always suitable due to privacy and security concerns. As a result, executing sentient space applications directly on smart devices may exceed their processing capabilities. To address this limitation, state-of-the-art solutions have introduced load balancing techniques for smart devices. However, these approaches typically rely on centralized coordination or require extensive system profiling, making them unsuitable for sentient spaces, where device availability is intermittent and cooperative behavior must remain lightweight, adaptive, and decentralized. This paper proposes a distributed load balancing strategy tailored for sentient spaces that operate without reliance on cloud or fog infrastructures. The approach is based on reactive cooperation among neighboring devices and employs a local feasibility-check mechanism to determine when to offload computation and which neighboring devices are available to process it. The proposed solution is evaluated in a laboratory setting that emulates a real-world sentient space scenario within a commercial mall. Experimental results show the effectiveness of the proposed approach in maintaining real-time performance and mitigating local computational overload without relying on centralized infrastructure. Even under dynamic operating conditions, the system achieves a load balancing execution time of 5 ms on an ARM Cortex-A53 processor integrated in an AMD Zynq UltraScale+ platform. Full article

This article discusses unattributable small-scale marine oil spills, particularly focusing on their environmental and socio-economic impacts in Musandam, Oman. There is a research gap in the literature on unattributable small-scale marine oil spills that reflects the lack of attention paid to these minor yet frequent spills, whose perpetrators invariably escape detection and accountability. The research method combines a literature review with extensive fieldwork, including community mapping, key informant interviews, and focus group discussions, to understand the extent, causes, and challenges of untraceable spills. The findings reveal significant ecological damage, economic losses for local fishers and tourism, and systemic issues of untraceability, limited enforcement, and inadequate compensation mechanisms. The article recommends establishing a regional compensation scheme, deploying advanced detection technologies, improving spill reporting, and fostering regional cooperation to enhance spill traceability, upgrade remediation techniques, and obtain redress for affected communities. These recommendations aim to inform policy actions that mitigate environmental risks and uphold environmental justice in the Arabian Gulf region. Full article

This article presents the results of a participatory urban diagnosis conducted in District 2 of La Paz, Bolivia, as part of an educational cooperation project aimed at exploring the spatial and symbolic dimensions of urban insecurity. Drawing on feminist and intersectional frameworks, this research combined participatory action methods, digital surveys, and collective mapping to identify patterns of fear and exclusion in public space. The analysis revealed significant disparities in how insecurity is perceived and experienced by different social groups—especially women, Indigenous peoples, and LGTBQ+ individuals—highlighting the importance of spatial configuration, social presence, and care infrastructure in shaping everyday urban life. The project also served as a pedagogical innovation, integrating architecture students into a process of civic engagement and co-production of knowledge. The resulting ‘Map of Gender Insecurity’ contributed to local planning efforts through the “Seguras, No Valientes” initiative. While the limited representation of some groups restricts statistical generalization, the approach offers a replicable model for linking research, education, and public action in pursuit of more inclusive and safer cities. Full article