Search Results (1,401)

The transition towards smart megacities has deeply integrated Artificial Intelligence (AI) with power–transport networks. While AI empowers complex operations like multi-network coordinated dispatch and emergency rescue, current algorithm-centric perspectives largely ignore its massive physical energy costs. Accordingly, this Perspective examines the dual role of AI, considering it not only as an intelligent decision-support tool but also as a potential source of additional stress on physical infrastructure. First, through a structured synthesis of the representative literature, we deconstruct the functional dependencies between algorithms and physical infrastructures, identifying how AI reshapes the operational paradigms of power, ground transport, and aerial networks under routine and emergency scenarios. We then introduce the concept of the “Computation–Energy Paradox.” Integrating conceptual analysis with a quantitative case study of a typical community, we illustrate a plausible failure mechanism: during extreme disasters, intensified AI invocation for emergency management generates surging computational loads, which paradoxically exacerbate power shortages and reduce the operating margin of already weakened systems. In addition, we analyze core engineering bottlenecks, including spatiotemporal computation–energy mismatches and physical constraints in extreme edge environments. To address these challenges, we outline a prospective roadmap encompassing lightweight emergency AI and computation–power-coordinated offloading mechanisms. Finally, the sustainable development of such systems suggests a paradigm shift: AI must evolve from a purely virtual algorithm into a physical component of an integrated compute–power–transport system. Full article

With the rapid evolution of next-generation mobile communications, semantic communication has emerged as an intelligent communication paradigm capable of surpassing the Shannon limit. A fundamental prerequisite for this paradigm is the synchronization of background knowledge between the transmitter and receiver, making the semantic knowledge base (SKB) a critical cornerstone. However, effectively selecting appropriate content from massive cloud-based knowledge repositories for edge deployment remains a significant challenge. This paper conducts systematic research to address the key issues in the flow deployment of SKBs at the edge, including insufficient adaptation to personalized preferences, inadequate timeliness management, and the complexity of multi-objective optimization. First, a comprehensive system model is constructed, integrating user preferences, knowledge relevance, transceiver matching degree, and the Age of Information (AOI). Second, the Generative Adversarial Network (GAN)-assisted Preference-based Reinforcement Learning (GaPbRL) algorithm is proposed. The experimental results demonstrate that this method outperforms traditional schemes in terms of knowledge-base hit rate, transceiver matching degree, and algorithm convergence speed, while significantly reducing the overhead of manual fine-tuning. This study provides a robust framework for the personalized and efficient cloud–edge collaborative deployment of SKBs. Full article

The Internet of Things (IoT) market is currently undergoing a period of unprecedented, rapid evolution, leading to the enabling of novel and diverse applications spanning both the civilian and industrial sectors. A significant proportion of these emerging use cases, particularly those in domains such as maritime communications and forestry management, require service continuity and connectivity within geographically remote regions, where conventional terrestrial infrastructure is often absent or economically unfeasible. To bridge this coverage gap and achieve truly ubiquitous connectivity, the recent 3GPP initiative to extend 5G services into Non-Terrestrial Segments (NTNs) holds substantial promise. This expansion is crucial for ensuring that massive Machine-Type Communication (mMTC) services can be reliably provisioned globally. This paper aims to detail the progress in standardization and academic activities towards the design and deployment of NTN-based Narrowband IoT (NB-IoT) systems, which are the leading NTN mMTC enabler in the 3GPP portfolio. We will specify the challenges faced by these systems and outline the solutions proposed thus far. We conclude the paper with a discussion on already operational systems and lessons learned from their deployment and operation. Full article

The identity security of massive metering terminals in low-voltage power distribution systems is a foundational requirement for smart grids. These devices, deployed in physically exposed user-side environments, face threats of identity impersonation, data tampering, and future quantum attacks. Conventional public-key certificates and pre-shared key schemes lack quantum resistance and scalability, respectively, while existing post-quantum solutions do not address the combination of hierarchical key management and batch authentication that power metering at scale demands. This paper proposes a lattice-based hierarchical identity authentication method that maps the three-tier power system architecture onto a three-level key derivation chain of hierarchical identity-based signatures, compresses multiple terminal lattice signatures into a compact aggregate verifiable in a single equation, and embeds key agreement into the authentication flow so that terminals and the substation derive independent session keys without extra round trips. A prototype was implemented and tested across multiple security levels. The signature communication volume remained quasi-constant as the terminal count increased, reducing the signature transmission delay on narrowband PLC links. All terminals successfully established independent session keys with exact agreement between both sides. Security reduces to the average-case hardness of the small integer solution problem on lattices, satisfying mutual authentication, conditional anonymity, and unlinkability. The proposed scheme trades higher signing latency for security based on quantum-hard lattice assumptions, group batch authentication, and protocol integration, offering a viable pathway for identity security upgrades in power metering systems during the quantum migration window. Full article

This paper proposes a priority-aware EP-ALOHA framework with predictive radio resource allocation for heterogeneous machine-to-machine (M2M) devices in 5G massive machine-type communication (mMTC) networks. The proposed framework extends conventional EP-ALOHA by introducing M2M priority classes, priority-dependent delay constraints, and Exploration Phase resource block (RB) allocation. The RB-allocation problem is formulated as an integer-constrained optimization problem, where the objective is to improve effective radio channel utilization while satisfying delay constraints for different priority classes. A Genetic Algorithm-based optimization procedure is used to generate optimization-derived RB-allocation targets under different traffic and system parameter settings. These targets are then used to train and evaluate predictive RB-allocation models, including Random Forest, Neural Network, Gradient Boosting, and Linear Regression. The simulation results show that the proposed priority-aware EP-ALOHA method achieves a higher successful access probability than the considered baseline schemes within the feasible operating region. For predictive RB allocation, the Neural Network achieved the best test-set performance, with MSE = 25.5002, RMSE = 5.0498 RBs, MAE = 3.2629 RBs, and R² = 0.9810. A separate computational evaluation showed that Random Forest inference reduced the mean allocation-decision time from 213.54 ms for GA-based optimization to 15.20 ms, corresponding to a 14.05-fold speed-up on the evaluated platform. In addition, M2M device activity probability forecasting is evaluated using Bayesian estimation, LSTM, moving average, and exponential smoothing. LSTM achieves the lowest forecasting error, while exponential smoothing provides a close and computationally simpler alternative. The results indicate that the proposed framework can support proactive and priority-aware resource management for heterogeneous M2M traffic, while the learning-based components are used as approximation and forecasting tools rather than as universally superior solutions. Full article

Tourism plays an important role in the global economy, contributing massively to the gross domestic product (GDP) and creating numerous jobs. The introduction of emerging technologies can accelerate the sector’s growth through personalization, improved user experience and better operational efficiency. The present work investigates the impact of visible light communications (VLC) in the tourism and hospitality industry based on the analysis of the recent literature published in the last decade, with the scope of improving tourist experience, operational efficiency and sustainability. Additionally, this work aims to critically evaluate the advantages and disadvantages of implementing VLC technology in the tourism and hospitality industry. For these purposes, this study presents a narrative review of the recent academic literature. The findings indicate that VLC technology can be used in a wide range of tourism-related applications, including contactless hotel services, indoor positioning and navigation, secure communications, accessibility solutions for visually impaired individuals and energy-efficient lighting infrastructure. In addition, this review demonstrates VLC’s potential to support the development of smart and sustainable tourism destinations through the integration of user-centered communication and illumination infrastructure. Finally, this work also identifies several challenges that affect large-scale deployment, including implementation costs, line-of-sight dependency and ongoing standardization issues. Full article

The losses caused by frequent natural disasters are increasing day by day, and the short-video platform has become the core digital space for the public to pay attention to disasters and express their demands. In the face of massive multimodal data, how to automatically extract features and quantify their impact on public behavior is a key technical challenge facing information systems and computational social sciences. To address this issue, this study proposes an automated multimodal data mining and modeling framework that integrates YOLOv11-based computer vision with BERT-based natural language processing for disaster short-video analysis. Real-world short-video and interaction data were automatically collected using web crawling. YOLOv11 was employed to identify and quantify two types of visual information—relief information and suffering information—while BERT was used to extract a text-based rescue satisfaction index. Then, the partial least squares structural equation model was used to explore the driving mechanism of information characteristics on public engagement. It was found that the content of relief information in videos has a significant positive impact on satisfaction but a significant negative impact on engagement. The content of suffering information has a significant negative impact on satisfaction but a significant positive impact on engagement. In addition, satisfaction has a significant negative impact on engagement and plays an intermediary role between the two types of information content and engagement. By integrating YOLOv11 and BERT into a unified short-video analytics framework, this study extends automated multimodal disaster information analysis and provides practical support for optimizing emergency communication and disaster information-release strategies. Full article

This study explored the utilization of coal gangue with high iron content to synthesize poly-ferric-alum-sulfate (PAFS) and coal gangue leaching residue-PAFS (CG@PAFS) coagulants, which were subsequently applied to treat landfill leachate and its concentrate. The results demonstrated that the PAFS coagulant exhibited notable removal efficiency for key pollutants, including chemical oxygen demand (COD) and total phosphorus (TP), with removal rates of 23.8% and 77.5%, respectively. Its performance was comparable to polyferric sulfate (PFS) and superior to aluminum polysulfate (PAS). Mechanistic investigations revealed that Fe³⁺ in the PAFS coagulant reacted chemically with phosphate ions, forming insoluble precipitates and facilitating phosphate removal through flocculation. Furthermore, PAFS also exhibited moderate removal efficiency for COD and ammonia nitrogen (NH₃-N) via flocculation processes. From a sustainability perspective, this study advances a circular economy model by simultaneously addressing two pressing environmental challenges: the valorization of coal gangue (a massive industrial solid waste) and the treatment of landfill leachate (a hazardous wastewater). Importantly, the post-treatment sludge, rich in available phosphorus and silicon, demonstrates promising potential for beneficial reuse as a soil conditioner, thereby closing the material loop and reducing reliance on virgin chemical resources. This integrated waste-to-resource approach aligns with multiple Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 12 (Responsible Consumption and Production). Overall, this study provides valuable insights into the resource-efficient utilization of coal gangue and presents an effective, low-carbon, and sustainable approach for landfill leachate management. Full article

The extensive deployment of the Power Internet of Things (PIoT) relies on dual-mode communication (HPLC + HRF) for robust data acquisition. However, under massive bursty traffic, conventional static MAC superframe scheduling struggles to reconcile high throughput with stringent reliability constraints. To mitigate this, we propose a dynamic adaptive scheduling scheme. Initially, a joint PHY-MAC layer dual-mode system architecture is proposed. At the MAC layer, a dual-link parallel multiplexing contention access mechanism is applied; at the physical layer, a capacity bottleneck determination model is established, incorporating log-normal–Bernoulli–Gaussian mixed noise and multipath fading. Subsequently, an extended two-dimensional Markov chain analytically derives key performance indicators, including equivalent collision probability, joint outage probability, access delay, and network throughput. Building upon this, a Q-learning-based algorithm is proposed. By constructing an asymmetric penalty–reward function, the central coordinator (CCO) autonomously optimizes the Contention Access Period (CAP) to Contention-Free Period (CFP) ratio under dynamic node scales. Simulations demonstrate this methodology effectively averts channel congestion during extreme concurrent traffic surges. Ultimately, it strictly preserves service reliability while substantially augmenting the concurrent carrying capacity and resource utilization of the dual-mode network. Full article

Humanity faces not isolated problems but a PolyCrisis, which is a set of 26 tightly interwoven existential crises spanning ecological collapse, planetary overheating, chronic disease epidemics, institutional fragility and social breakdown. Each crisis amplifies the others through cascading feedback loops, and 16 of these crises possess independently the capacity to cause human extinction or civilizational collapse. We are not entering an emergency, but we are already in a state of emergency. Multiple planetary boundaries have been transgressed, and climate tipping points are being crossed now. Extinction rates match historical great mass extinction events, while our food systems, primarily responsible for over half these crises, simultaneously drive hunger, obesity and chronic diseases. We argue that this PolyCrisis is not the result of isolated failures, but is best understood as the predictable, systemic outcome of Planet A, the prevailing Operating System of our mainstream civilization, characterized by economics of unbounded extraction and hoarding, violence-based and profit-based food systems, short-term thinking, and unlimited growth imperatives on a finite planet. Planet B is our proposed PolySolution framework, a complete alternative Operating System grounded in empirical reality and proven solutions. It integrates animal-free food systems releasing up to 5 billion hectares for rewilding, regenerative economics measuring non-violence and biocapacity, circular economy minimizing waste, technological restraint with democratic governance, seven-generation thinking, and PolyCommunity coordination, collaboration and co-creation of the PolySolution. It calls for the immediate emergency implementation of two planetary-scale MegaSolutions: (a) “Together Around Food”, implementing universal, free access to gourmet whole-food, plant-based vegan meals worldwide, eliminating hunger and accelerating food and health systems transformation, and (b) Cool, halting planetary overheating through agricultural emissions elimination, massive rewilding for carbon sequestration, and comprehensive stabilization of the life-support systems of our planet. Full article

To address the risks of centralized single-point failures and data privacy leaks associated with massive data storage in Intelligent Transportation Systems (ITS) and Vehicle-to-Everything (V2X) environments, this paper proposes a distributed secure storage architecture based on blockchain. However, traditional Practical Byzantine Fault-Tolerant (PBFT) algorithms suffer from scalability bottlenecks in large-scale dynamic networks, such as high communication overhead and low consensus efficiency. Therefore, this paper designs a hierarchical and reputation-aware improved consensus mechanism (H-PBFT). First, this mechanism introduces a geographical location grouping strategy, dividing all network nodes into several local consensus groups and leveraging edge computing characteristics to achieve rapid consensus within each group. Second, a multi-dimensional reputation evaluation model (comprehensively considering historical behavior, performance, and availability) is constructed to dynamically elect representative nodes from each group to participate in global consensus, thereby significantly reducing the communication complexity from O(N2). Simulation results show that compared with standard PBFT, Q-PBFT, and APBFT, H-PBFT exhibits significant advantages in consensus latency, throughput, and view switching recovery time, and maintains high system robustness even in complex network environments with malicious nodes. Full article

Frozen mysid shrimp is the core feed for commercial seahorse aquaculture, yet quality deterioration caused by long-term cold storage severely constrains the survival and reproductive performance of seahorses. By integrating targeted metabolomics, physicochemical spoilage indicators, and 16S rRNA high-throughput sequencing, this study multidimensionally analyzed the stage-specific mechanisms of nutritional degradation and microecological deterioration of mysid feed at −20 °C under fresh (0 months), short-term frozen (2 months), and long-term frozen (10 months) conditions. The results demonstrate that short-term storage (2 months) effectively suppresses microbial-induced spoilage of mysids; however, freeze–thaw stress triggers cellular lysis, leading to a significant depletion of key water-soluble feeding-attractant amino acids, notably glycine and aspartate. In contrast, long-term frozen storage (10 months) induces severe lipid peroxidation and a massive accumulation of total volatile basic nitrogen (TVB-N). Microbiome analysis confirmed that the fundamental trigger for feed deterioration during the late storage stage is the explosive community succession of psychrotrophic specific spoilage organisms (SSOs), with Shewanella, Photobacterium, and Pseudoalteromonas emerging as the absolute dominant taxa. The highly active extracellular lipases and proteases secreted by these microbial communities extensively degrade the structural lipids of the feed, ultimately resulting in a paradoxical rebound in free fatty acid content during long-term storage. In summary, our research comprehensively details how freezing compromises diet quality through specific biological and chemical pathways. This establishes essential scientific groundwork aimed at refining commercial preservation techniques, executing precise dietary enrichments, and ultimately securing long-term viability across the captive Hippocampus breeding sector. Full article

The integration of Unmanned Aerial Vehicle (UAV) swarms with Cell-Free massive Multiple Input Multiple Output (CF-mMIMO) networks offers promising prospects for large-scale crop monitoring in precision agriculture. CF-mMIMO provides macro-diversity and uniform channel quality across large agricultural fields. However, practical deployment demands jointly optimizing 3D trajectories and transmit power to maximize energy efficiency and field coverage simultaneously. This is challenging due to the limited battery capacity, mandatory return-to-depot constraints, and collision avoidance requirements. In this paper, we introduce a joint sensing–communication utility function that captures the trade-off between energy efficiency and field coverage completeness. To provide a scalable and distributed solution for rotary-wing UAV swarms, we develop a multi-agent deep reinforcement learning (MADRL) methodology based on the Multi-Agent Proximal Policy Optimization (MAPPO) approach. We adopt the Centralized Training with Decentralized Execution (CTDE) strategy, in which a CF-mMIMO central processing unit (CPU) serves as a global critic during training. At execution time, each UAV independently runs a lightweight local policy that adapts its trajectory and transmit power in real time based on battery state and air-to-ground channel variations. Simulation results reveal that the proposed MAPPO-CTDE approach outperforms existing benchmarks. Unlike prior methods that require instantaneous global CSI or neglect the sensing–communication coupling, the proposed approach simultaneously achieves high field coverage completeness, robust communication energy efficiency, and a high depot-return rate under hard battery constraints without any inter-UAV communication overhead at execution time. Full article

Accurate network traffic prediction is important for the autonomy, resilience and resource orchestration of 6G and AI-native communication infrastructures, while also supporting green networking and digital twin network applications. However, existing data-driven prediction models face several limitations: over-reliance on massive labeled data, physically implausible predictions, black-box non-interpretability and over-parameterization that impairs edge deployment. To address these issues, this paper proposes a Physics-Informed Network Traffic Prediction (PINTP) framework for graph topology network traffic prediction, which formalizes network traffic evolution as Graph-based Advection–Diffusion–Reaction (ADR) equations and embeds physical regularization into the neural architecture. The framework adopts a hybrid differentiation paradigm unifying automatic differentiation for temporal dynamics and spectral graph theory-derived operators for discrete spatial topologies, and designs a physics-constrained composite loss function with data-driven collocation to balance data fidelity and physical consistency. Experiments are conducted in two complementary settings: a 100-node synthetic random-graph benchmark that evaluates the full graph-topological formulation, and a topology-unavailable real-world telemetry proxy based on Alibaba Cluster Trace v2018 for evaluating sparse-label physics-informed temporal regularization. Comparative analysis with mainstream baselines, including Multilayer Perceptron (MLP), Spatio-Temporal Graph Convolutional Network (STGCN), Graph WaveNet, Transformer, Temporal Convolutional Network (TCN), and XGBoost, shows that the proposed PINTP/PINN implementation achieves a test $R^2$ of 0.898 and MSE of 0.000723 on the 100-node synthetic graph benchmark, close to the strongest Transformer result ($R^2=0.900$, MSE = 0.000710), while using substantially fewer trainable parameters. PINTP/PINN also outperforms Graph WaveNet, STGCN and TCN in this setting, indicating that physics-informed regularization can remain competitive as graph size increases. On the Alibaba proxy task, PINTP/PINN achieves the strongest result among the evaluated models with a test $R^2$ of 0.963. In an independent Alibaba ablation protocol, physical regularization (e.g., $\lambda =10.0$) reduces the mean squared error by 89.15% compared with pure data-driven models and helps mitigate overfitting. This work presents a systematic PINTP framework for graph topology network traffic prediction, achieving competitive prediction accuracy with high parameter efficiency and a degree of physical interpretability. It helps address several limitations of traditional data-driven models, indicates potential for future deployment-oriented studies on real-time network management and resource-constrained edge analytics, and provides an interpretable modeling route for physics-informed network analytics in next-generation communication systems. Full article

Hybrid renewable energy systems (HRESs) represent a promising strategy for reducing carbon emissions across multiple sectors by integrating complementary resources such as solar, wind, hydropower, and energy storage technologies. Identifying the most suitable location for a pilot installation requires a comprehensive evaluation that balances technical performance, environmental benefits, social considerations, and economic feasibility. This study employs an enhanced multi-criteria decision analysis (MCDA) framework, supported by machine learning (ML) techniques, to assess four pilot sites developed within the HY4RES project: a rural community, an aquaculture facility, a port installation, and an agriculture network. A comprehensive set of key performance indicators (KPIs) was established to capture technical, environmental, social, and economic dimensions. These include the degree of hybridization, carbon intensity, community benefit scores, net present value, levelized cost of energy, and payback period. After collecting and normalizing the site-specific data, ML EL-SVM, decision tree, and logistic regression models as computational surrogates designed to bypass the multi-step, matrix inversion mathematical requirements of the AHP when screening massive numbers of future scenario outputs supporting consistency checks and sensitivity exploration were used, along with criterion adjustments, to refine the relative importance of each KPI. The Analytical Hierarchy Process (AHP) was employed to assess potential factors and rank the sites, with the rural site achieving the highest overall score in the system, driven by its complex four-source hybrid configuration and strong community-level benefits. The agriculture scheme ranked second, demonstrating significant potential for carbon emission reductions. The port pilot placed third, distinguished by high technical innovation but more limited social impact. The aquaculture site ranked fourth, primarily due to environmental scores, despite its economic self-sufficiency. Full article