Search Results (471)

This paper studies a backscatter-assisted pairwise cooperation scheme in a multi-user wireless powered communication network (WPCN), where pairs of wireless devices (WDs) first harvest wireless energy from an energy node (EN) and then transmit their information to an access point (AP). Under the proposed scheme, the two WDs in each pair first exchange their local messages and then cooperatively transmit to the AP in the uplink. To reduce the time and energy consumption of local information exchange, we exploit the short distance between paired users and realize message exchange through energy-conserving backscatter communication. Meanwhile, the proposed design effectively reuses the wireless power transfer (WPT) signal to enable simultaneous information exchange during the energy harvesting phase, thereby leaving more time and harvested energy for the subsequent cooperative uplink transmission. Based on this transmission protocol, we jointly optimize the time allocation, the user transmit power allocation, and the energy beamforming matrix at the EN to maximize the weighted sum rate. To tackle the resulting non-convex problem, we decompose it into two coupled subproblems and develop an alternating optimization algorithm to update the corresponding variables iteratively. Numerical results show that the proposed scheme achieves significant weighted sum rate improvement over representative benchmark methods. Full article

In this paper, we investigate secrecy energy efficiency (SEE) maximization in a rate-splitting multiple access (RSMA)-enabled UAV communication system, which consists of a communication UAV serving legitimate ground users (GUs) and a cooperative jamming UAV transmitting jamming signals to degrade the channel of the eavesdropper (Eve). Taking into account the propulsion energy consumption of fixed-wing UAVs, we formulate a non-convex SEE maximization problem by jointly optimizing communication scheduling, CUAV transmit power, and the trajectories of both UAVs. To tackle the non-convex problem, an iterative optimization algorithm combined with the Dinkelbach method and successive convex approximation (SCA) is developed to obtain a suboptimal solution. Simulation results demonstrate the convergence of the proposed algorithm and show the proposed joint optimization scheme significantly improves SEE compared with benchmark schemes. Full article

Automatic modulation recognition has been recognized as an effective technique for non-cooperative communication and intelligent transmission. In this paper, we propose a new lightweight method for automatic modulation recognition, aiming to extract crucial discriminative features of signals for higher recognition accuracy while reducing spatial costs. To enhance the dissimilarity between samples, this paper combines an improved attention block and convolutional operations with the recurrent neural network, focusing on key features during the training phase to efficiently differentiate signal sequences. By replacing standard convolutions with depthwise separable convolutions, the model’s computational complexity is reduced while enhancing its feature extraction capability. Furthermore, the method incorporates pruning to reduce ineffective features, decreasing the model size while maintaining performance. Experimental results on RadioML2016.10a demonstrate that the proposed method outperforms other comparative methods, exhibiting both higher recognition accuracy and smaller model size. To validate real-world applicability, the algorithm was implemented on a software-defined radio platform for signal transmission and reception under practical conditions, achieving an accuracy of 87.22% in the presence of environmental noise, thus confirming its effectiveness in real-world scenarios. Full article

Connected and autonomous vehicles (CAVs) increasingly rely on vehicle-to-everything (V2X) communication and distributed sensing infrastructures to support cooperative driving and intelligent transportation services. While these capabilities improve traffic efficiency and safety, they also expand the attack surface of vehicular networks and expose in-vehicle communication systems such as the Controller Area Network (CAN) bus to a wide range of cyber threats. Machine learning-based anomaly detection has emerged as a promising approach for identifying malicious CAN traffic patterns; however, conventional centralized learning requires large-scale data aggregation from vehicles, which raises privacy and scalability concerns. Federated learning (FL) enables collaborative model training across distributed vehicles without requiring the exchange of raw in-vehicle data, making it attractive for privacy-preserving vehicular security applications. Nevertheless, FL systems remain vulnerable to adversarial participants that manipulate local training data or model updates to poison the global model during aggregation. In this work, we present a systematic robustness evaluation of federated anomaly detection in connected vehicular networks under adversarial conditions. The study compares six aggregation strategies, including Federated Averaging (FedAvg), coordinate-wise Median, Trimmed Mean, Krum, Multi-Krum, and Geometric Median (GeoMed), within a non-IID federated CAN bus anomaly detection setting. The evaluation covers label-flipping attacks, gradient-scaling attacks, and a feature-triggered backdoor attack. In addition, the analysis examines malicious client participation, attack-strength variation, learning-rate sensitivity, Trimmed Mean beta sensitivity, multi-seed reliability, and server-side aggregation time. The results show that FedAvg is vulnerable under strong adversarial manipulation, while Trimmed Mean is sensitive to the selected trimming fraction. Median and GeoMed provide strong robustness against gradient-scaling attacks, whereas Multi-Krum achieves the strongest resistance to label-flipping and backdoor attacks. These findings demonstrate that no single aggregation strategy is optimal across all threat models. Instead, robust aggregation for federated CAV anomaly detection should be selected according to the expected attack type, reliability requirement, and computational overhead. Full article

Rapid localisation of trapped victims after urban disasters is essential but challenging because Bluetooth Low Energy (BLE) beacons are intermittent, radio propagation is obstructed by rubble, UAVs are energy-constrained, and real-world multi-UAV training is impractical in high-risk search-and-rescue (SAR) environments. This study formulates post-disaster victim localisation as a cooperative Dec-POMDP and adapts a model-aided federated multi-agent reinforcement learning framework based on FedQMIX. The proposed pipeline combines a lightweight LoS/NLoS surrogate channel model, PSO-based victim-position estimation, return-to-base and map-feasibility safety checks, an SAR-aligned shaped reward, and a leakage-free centralised training state based on estimated rather than ground-truth victim locations. Each UAV trains locally inside a learned digital-twin simulator and periodically shares only QMIX network parameters, avoiding the exchange of raw trajectories or RSSI logs. The framework is evaluated on two synthetic post-earthquake urban maps representing a compact return-to-base scenario and a larger reach-to-destination scenario. Across five independent seeds per method and map, Model-Aided FedQMIX achieves the highest and most stable victim-localisation performance, with the clearest advantage observed in the larger long-horizon scenario. Additional diagnostic tests examine reward-weight sensitivity, RF channel-shift robustness, BLE/smartphone hardware heterogeneity, non-IID client-data variation, and partial-client FedAvg under missing client updates. The results indicate that combining model-aided localisation cues, decentralised value factorisation, SAR-aligned objective design, and federated parameter sharing can improve the robustness of UAV-based victim-localisation policies. The framework also clarifies deployment considerations for federated SAR coordination, including communication payload, privacy boundaries, heterogeneous client experience, device variability, and intermittent connectivity. This study remains simulation-based, and future validation with real UAVs, BLE devices, and rubble-inspired testbeds is required before operational deployment. Full article

Neuronal excitability manifests itself mainly in the form of non-linear, self-regenerative waves of electricity moving along the surface of neuronal axons. These waves are commonly known as action potentials (APs). Theoretical and experimental investigations of the physical and functional characteristics of APs have broadly followed along the lines of the ionic hypothesis and the associated mathematical model introduced by Hodgkin and Huxley (HH). In the current form of this bioelectrical framework, adopted in mainstream physiology and other biological sciences, the axonal membrane is conceptualized as an electronic circuit where electric current is generated and propelled as a result of the time-dependent opening and closure of voltage-operated ion channel proteins, allowing passive flow of specific ions across and along the membrane, powered by their respective electrochemical gradients. Although representing mainstream research, the bioelectric perspective has been criticized for its narrow focus on the electrical characteristics of APs, whilst ignoring other physical manifestations of the nerve signal, particularly mechanical and thermal changes coinciding with AP propagation. As an alternative, a macroscopic thermodynamics-based acoustic theory has been outlined, in which all electric and non-electric manifestations of the nerve signal are considered as a result of a single density pulse in the axonal membrane carried by a reversible lipid membrane phase transition and momentum conservation. Representing a minority view, however, this unified, acoustic perspective on the physical nature of neuronal excitability is largely ignored by representatives of the bioelectric perspective. Here, we draw special attention to the philosophical dimension of the communication failure between the two communities of scientists. We argue that adherents of the bioelectric perspective favor a mechanist type of explanation, whilst supporters of the acoustic perspective are committed to so-called covering-law types of explanation. We conclude that it is this thus far unrecognized philosophical rift, rather than specific scientific differences in opinion, that blocks fruitful interdisciplinary cooperation necessary for building a comprehensive, fully integrated notion of the physical nature of neuronal excitability. Suggestions of how to bridge this conceptual gap are formulated. Full article

Conventional: Endodontic outcome criteria established by the American Association of Endodontists (AAE) and the European Society of Endodontology (ESE) rely heavily on radiographic, clinical, and functional parameters. These criteria may not be applicable to patients with special health care needs, who often present with limited cooperation, communication impairments, and altered pain perception. Objective: This study aims to propose an adapted classification system for evaluating non-surgical root canal treatment outcomes in this underserved population. Methods: Based primarily on the criteria established by the ESE and our clinical experience, a novel classification system was developed, delineating three outcome categories grounded in both clinical and radiographic parameters. This framework deliberately excludes the “functional” criterion and introduces a “not assessable” category. It was retrospectively applied to 217 non-surgical root canal treatments performed in 137 patients with special health care needs, each with a minimum one-year follow-up. Outcomes were categorized as “favorable,” “uncertain,” or “unfavorable.” Results: Using the proposed criteria, 87 treatments (40.0%) were classified as “favorable,” 88 (40.5%) as “uncertain,” and 42 (19.3%) as “unfavorable.” By contrast, the application of AAE/ESE standards resulted in a 71.9% “favorable” classification. Most “uncertain” outcomes occurred in patients with neurodevelopmental disorders, where clinical or radiographic evaluation was not feasible. Conclusions: We propose adapted clinical and radiographic criteria for assessing non-surgical root canal treatment outcomes in patients with special health care needs, though broader validation is required. The findings suggest that this procedure remains advisable in this population, with fewer than 20% showing an “unfavorable” long-term outcome. Full article

The utilization of drone swarms for cooperative missions is becoming increasingly prevalent. However, establishing high-concurrency and highly reliable communication links in complex environments remains a significant challenge. Existing methods based on traditional Medium Access Control (MAC) protocols struggle to cope with high-density collisions, while conventional deep reinforcement learning (DRL) approaches often encounter convergence difficulties in non-stationary interference environments, leading to notable limitations in anti-jamming robustness and algorithmic efficiency. To tackle this problem, this paper proposes a dynamic access algorithm based on Curriculum Learning-assisted Multi-Agent Proximal Policy Optimization (CL-MAPPO). Specifically, we adopt a Centralized Training with Decentralized Execution (CTDE) architecture to enable implicit spectrum cooperation within the swarm. Notably, we design a three-stage progressive curriculum learning mechanism—basic collision avoidance, load balancing, and dynamic anti-jamming—coupled with a phased reward reshaping strategy, guiding the agents to progressively master intelligent frequency-hopping decisions in complex environments. Experimental results demonstrate that in simulated scenarios involving dynamic sweep jamming and high-load multi-drone communication, the proposed method significantly outperforms baseline models such as Carrier Sense Multiple Access (CSMA), random frequency hopping, and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in terms of normalized throughput, channel collision rate, and convergence speed. This research provides theoretical support and an algorithmic foundation for achieving highly reliable access in large-scale swarm data links under harsh environmental conditions. Full article

Significant challenges will be encountered in next-generation cellular networks to achieve both high spectral efficiency (SE) and diverse quality of service (QoS) requirements simultaneously, particularly under stringent bandwidth and power budgets within highly dynamic and dense topologies. To address these challenges, we formulate an optimization problem in a multi-slice non-orthogonal multiple access (NOMA) system with underlay device-to-device (D2D) communications. This problem aims to maximize SE and satisfy user QoS demands by jointly optimizing power allocation and resource block (RB) assignment. To solve this non-convex and NP-hard problem, we propose a resource allocation mechanism based on joint optimization and cooperative multi-agent deep reinforcement learning (MADRL). Specifically, we construct an optimization framework based on successive convex approximation (SCA) and the Lagrange duality method to derive an analytical iterative solution for the optimal power allocation under a given RB assignment, thereby avoiding the inherent discretization error of the action space in pure learning methods. Furthermore, we propose a cooperative multi-agent algorithm based on dueling double deep Q-Network (CMAD3QN) to address the discrete RB assignment problem. Simulation results demonstrate that, compared with benchmark schemes, the proposed scheme exhibits faster convergence speed and significantly enhances system spectral efficiency while ensuring slice isolation and resource constraints. Full article

Background: To elucidate the evolution of metabolites and fungal communities during storage of fragrant japonica rice (Liaoxiangjing 1396), and to investigate the biosynthetic mechanisms of key compounds and their association with quality deterioration, this study examined rice samples stored under simulated conditions for 16 months. Method: Samples were collected at 4-month intervals (designated R20, R14, R13, R12, and R11). Metabolites were identified using GC-MS non-targeted metabolomics, while fungal community structure was analyzed through metagenomics. Core mechanisms were further elucidated via PLS-DA, KEGG pathway enrichment, and multiomics association analysis. Result: Results demonstrated that the fatty acid content of rice increased initially and then stabilized (from 12.24 mg/g in R20 to 17.63 mg/g in R12). A total of 263 metabolites were identified, with oxygenated organic compounds (38 species) and lipids/lepidid molecules (24 species) as the predominant categories. Twelve key differential metabolites were screened from the R20 and R12 groups, involving five major metabolic pathways, including amino acid metabolism and lipid metabolism. In the fungal community, Pseudomonas (60.2%) and Pantoea (38.19%) were dominant taxa, with a specific Pantoea species (Pantoea sp.) identified as a core potential biomarker. Multiomics association analysis revealed that Klebsiella dominated the ndhB energy metabolism pathway, while multiple bacteria cooperatively regulated the mcp chemotaxis pathway, interacting with monosaccharide and amino acid accumulation. Conclusions: This study reveals that the storage quality deterioration of fragrant japonica rice is driven by the “metabolite–microbe-pathway” chain regulation, and the dynamic changes in key metabolites and fungal communities can serve as quality early warning targets. Full article

Federations appear to play a relevant role in religious governance in Spain, acting as the collective representation of religious communities recognised by public authorities. Although they were formerly intended to interact with the national government through the signing of Cooperation Agreements and participation in the Advisory Committee on Religious Diversity, religious federations have increasingly developed a territorial projection towards Spain’s Autonomous Communities. This article explores how these organisations operate within Spain’s political and governance framework. To do so, it examines these territorial strategies by analysing a dataset of 129 federations across the 17 Autonomous Communities and the two Autonomous Cities (N = 19). Using descriptive statistics and Spearman correlation, the study maps the organisational patterns of these entities. The findings point to the predominance of nested federative organisations, alongside the presence of non-nested structures concentrated in territorially and institutionally dense regions. The coexistence of these two models cannot be accounted for solely by religious pluralism; institutional strategies also appear to play a part. While the dataset captures registered federations, informal coordination mechanisms remain beyond the scope of this analysis. Taken together, the article advances current debates on religious governance by offering the first systematic territorial mapping of federative organisational patterns in Spain. Full article

Uplink/downlink (UL/DL) decoupled access has emerged as a promising paradigm for heterogeneous cellular vehicle-to-everything (C-V2X) networks in beyond 5G (B5G) and 6G systems. In multi-operator scenarios, wireless service provider (WSP) selection becomes critical for vehicles to ensure communication quality while minimizing costs. This paper investigates the performance analysis and WSP selection problem in UL/DL decoupled access C-V2X networks. We derive tractable expressions for spectral efficiency of both UL and DL using stochastic geometry, considering three decoupled access cases where UL and DL independently associate with macro base stations (MBSs) or small base stations (SBSs). We formulate a hierarchical game framework combining evolutionary game for vehicle WSP selection and non-cooperative game for WSP bandwidth allocation. An evolutionary game algorithm is proposed to reach the equilibrium, and the uniqueness of Nash equilibrium in bandwidth allocation is proved. Extensive simulations validate the analytical results and demonstrate the convergence and stability of the proposed game framework. Full article

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

This paper investigates the application of cooperative relaying systems with non-orthogonal multiple access (NOMA) in low-altitude intelligent networking-enabled medical Internet of Things (IoT) and analyzes their transmission performance. First, to enhance the communication quality of remote base stations, we deploy a relaying unmanned aerial vehicle (UAV). A two-slot NOMA cooperative transmission mechanism is proposed accordingly. Next, for the NOMA-enhanced UAV-relayed smart healthcare system under Rician fading channels, an exact closed-form expression for the achievable rate is derived using the incomplete Gamma function. Then, to improve computational efficiency, a low-complexity approximation method based on Gauss–Chebyshev quadrature is designed, overcoming the high complexity of the exact expression. Finally, the simulation results validate a close match between the proposed approximation and the exact values (average approximation error below 6.17%), and demonstrate superior achievable rate performance compared to three state-of-the-art schemes. Full article

This paper proposes a new fuzzified multi-objective wireless communication optimization model that maximizes the quantity and placement of Reconfigurable Intelligent Surfaces (RISs). In order to meet realistic deployment constraints like non-overlapping and acceptable location, the model aims to decrease the number of deployed RISs while raising the achievable rate. The Modified Pied Kingfisher Optimization Algorithm (MPKOA) is suggested as a solution to this intricate optimization issue. MPKOA features many significant improvements over the traditional Pied Kingfisher Optimization Algorithm (PKOA), such as energy-based motion control, adaptive subgrouping, flock cooperation, and memory-driven re-perching. These techniques speed up convergence, improve solution precision, reduce computation time, and balance exploration and exploitation. MPKOA performs better than standard PKOA, Enhanced version of PKOA (EPKO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and other existing algorithms, according to extensive comparisons. MPKOA can achieve up to 20% higher optimization values and 30% faster convergence, according to simulation data. In addition, the proposed MPKOA reduces computational complexity and runtime by about 50% when compared to standard PKOA-based approaches since it only requires single fitness evaluation per iteration. This enables the deployment of fewer RISs while still achieving higher communication rates. In multiuser wireless systems, MPKOA offers a robust and effective approach to RIS placement optimization, which helps to boost capacity and provide more energy-efficient 6G communication networks. Full article