Search Results (21,892)

The transition toward active and converter-dominated distribution networks is increasing the need for Digital Real-Time Simulation (DRTS) platforms capable of supporting the validation, operation, and planning of modern power systems with high penetrations of Distributed Energy Resources (DERs). This review aims to critically examine the technological and methodological barriers that continue to limit the evolution of DRTS from a validation tool toward an operational cyber–physical infrastructure for future intelligent power systems. Particular attention is devoted to computational scalability, communication latency, Hardware-in-the-Loop (HIL), model conversion, proprietary “black-box” devices, digital twins, and the computational strategies required to preserve deterministic real-time execution. The reviewed literature indicates that maintaining real-time determinism while preserving high-fidelity electromagnetic transient (EMT) models remains one of the principal technological challenges for future DRTS platforms. The analysis further shows that many of the current limitations associated with digital twins, grid-forming technologies, and large-scale industrial deployment originate not from isolated technological deficiencies, but from the interaction among computational, communication, interoperability, synchronization, and model-management constraints. The evidence indicates that overcoming these limitations requires coordinated advances in computational architectures, communication infrastructures, model automation, interoperability, and cyber–physical integration rather than isolated hardware improvements. Overall, this review argues that the future impact of DRTS will depend not only on improvements in simulation performance, but also on its evolution into an interoperable and experimentally oriented cyber–physical infrastructure capable of supporting phenomenological analysis and the next generation of intelligent, resilient, and autonomous power systems. Full article

Background: The gut microbiota–brain axis is a highly integrated bidirectional communication network operating through neural, neuroendocrine, immune, and metabolic pathways that maintain central nervous system homeostasis and has emerged as a promising complementary therapeutic target for neurological and neuropsychiatric disorders. Objective: The objective of this study is to systematically evaluate the effects of prebiotics, probiotics, and psychobiotics on gut–brain communication and neurological and behavioral outcomes, including neuroinflammatory markers, HPA-axis parameters, and microbial metabolites. Methods: A systematic review was conducted in accordance with the PRISMA 2020 guidelines. Literature searches of PubMed, Scopus, and Google Scholar from 2020 up to March 2026 identified 81 eligible studies (48 preclinical studies, 27 randomized controlled trials, and 6 quasi-experimental clinical studies) from 7358 records. Primary outcomes were systematically categorized into four domains: (1) cognitive performance and social/adaptive behavior; (2) neuroinflammatory markers (TNF-α, IL-6, IL-1β) and barrier integrity; (3) HPA-axis parameters (cortisol/corticosterone); and (4) neuroactive microbial metabolites (short-chain fatty acids and tryptophan derivatives). Methodological quality and risk of bias were assessed independently by two reviewers using the SYRCLE tool for preclinical studies and the Joanna Briggs Institute (JBI) tools for randomized and quasi-experimental clinical trials, with summary visualizations generated using the robvis web application (version 0.3.0). Results: Neurodegenerative diseases (n = 31; 38.3%) and mood disorders (n = 29; 35.8%) were the most frequently investigated conditions. Probiotics predominated (n = 61), followed by prebiotics (n = 12) and synbiotics (n = 8). Descriptively, 92% of included studies reported improvements in at least one evaluated outcome. However, this unweighted observation reflects effect direction rather than clinical magnitude, encompassing primary and secondary endpoints across highly heterogeneous sample sizes, study designs, and risk-of-bias profiles. Conclusions: Microbiota-targeted interventions show promise as complementary strategies for neurological disorders; however, substantial methodological heterogeneity, unstandardized dosing, and the absence of quantitative meta-analysis preclude definitive clinical recommendations. Successful translation will require harmonized protocols, strain-specific functional characterization, and precision microbiota-based trials. Full article

As an emerging communication paradigm, semantic communications (SC) focuses on the semantic content of information transmission, aiming to achieve more efficient and accurate information interaction. However, SC requires an in-depth analysis of information semantics and accurate adaptation to application scenarios. With the substantial growth in information volume, richness, and application diversity, the resources, constraints, and requirements associated with SC systems have also increased. Optimal resource allocation (ORA) of SC can effectively deal with these practical problems, a key technology for improving communication efficiency. In complex network environments and special scenarios, communication resources are limited. Increasing transmission efficiency, ensuring accuracy and quality of information, and reducing energy consumption can be achieved by rationally allocating bandwidth, power, and related resources. Since SC technology is still in its early stages of development, there is a lack of a comprehensive review of ORA for SC in the existing literature. This paper provides a comprehensive review of ORA for SC. First, the basic concepts, characteristics and development motivations for SC and ORA are reviewed. Then, a comprehensive analysis of the key technologies for ORA in SC is presented, covering end-to-end and semantic network multi-link ORA. These technologies include technology for predicting resource demand based on semantic understanding, resource optimization technology for semantic information (SI) processing and transmission, and technology for dynamic resource adjustment. Then, a conceptual ORA framework is synthesized from the reviewed technologies to unify key design principles and provide a foundation for future research. In addition, this paper provides research prospects in future trends of ORA for SC, comprising emerging artificial intelligence (AI) and machine learning, laying the foundation for next-generation intelligent communication networks. Finally, this paper points out the main application direction of ORA in SC, which reflects the practical significance of this study. Full article

Background: Internet of Things (IoT) deployments face complex network dependencies and persistent data limitations that constrain machine learning (ML) intrusion detection systems (IDS). Objective: To synthesise peer-reviewed machine learning IDS research for IoT, focusing on dependency-driven failure propagation and chronic data scarcity, positioned against 2024–2025 EU regulatory requirements (NIS2, the Cyber Resilience Act). Eligibility criteria: Peer-reviewed empirical studies proposing or evaluating a machine learning or deep learning IoT intrusion detection method published in English from January 2018 (limited pre-2018 exceptions for seminal works). Information sources: IEEE Xplore, SpringerLink, Elsevier ScienceDirect, Scopus, Web of Science, and Google Scholar, searched on 12 February 2025. Risk of bias: Each candidate was scored against four criteria (objectives clarity, methodological soundness, reproducibility, IoT-security relevance); studies scoring at least 3 out of 4 were retained. Screening and scoring were performed by one reviewer, with a second reviewer independently checking 20 percent of records. Synthesis methods: Narrative thematic synthesis; heterogeneous metrics and incompatible datasets across studies precluded quantitative meta-analysis. Included studies: Of 427 records identified, 52 studies initially met inclusion criteria; a post hoc independently validated reconstruction of individual QA1–QA4 scores subsequently found that six did not meet the threshold or topical eligibility criteria, yielding a final 46-study corpus. Main findings: Generative adversarial networks (GANs) dominate dataset augmentation work, graph-based communication analysis addresses dependency modelling, and methods based on transformers or federated learning emerge from 2023 onward. Certainty of evidence: No formal grading (GRADE) applies to this narrative synthesis. Confidence in the corpus composition is high, following independent QA1–QA4 validation, while confidence in the thematic findings is moderate given single-reviewer screening and judgment-based classification. Conclusions: We identify three recurring gaps: real-time detection under resource constraints, dependency-aware detection, and regulatory compliance. Closing these gaps requires detection methods that treat IoT security as a networked and regulated system rather than an isolated device classification problem. Registration: Open Science Framework, 10.17605/OSF.IO/NMAK4 (registered retrospectively). No external funding supported this review. Full article

In the complex problem of air-to-ground mission planning, multi-UAV systems face significant challenges such as system complexity and heterogeneity, insufficient target observability, and difficulties in collaborating information sharing. To address these issues, this paper proposes a novel learnable sparse attention graph architecture (SAGA). This architecture deeply integrates graph reasoning and policy optimization within the MAPPO framework and includes three innovative mechanisms: (i) a GATv2-based graph neural network encoder that performs multi-round distributed consensus on the communication graph among UAVs via a multi-head attention mechanism, enabling selective aggregation of tactical information; (ii) an edge predictor that learns to prune low-value communication links, generating a sparse and mission-adaptive communication topology; and (iii) an L1 sparsity penalty term that further enhances communication efficiency. In a self-developed simulation environment for heterogeneous multi-UAV mission planning, comprehensive comparative experiments were conducted against the following baseline reinforcement learning algorithms: MADDPG, MATD3, QMIX, MAPPO, TarMAC, DGN, and G2ANet. The experimental results show that SAGA achieves reward values of 390 and 1100 in small-scale and large-scale scenarios, and outperforms the best-performing baseline algorithm by more than 20% across all operational performance metrics. Generalization experiments validate the model’s robust transfer capability under unknown defense deployment modes. Ablation experiments further confirmed the individual contributions of the three components. This study provides an innovative and effective method for mission planning of heterogeneous multi-UAV systems in partially observable adversarial environments. Full article

Recent advances in cutaneous neuroimmunology have substantially expanded our understanding of inflammatory skin diseases, revealing an intricate bidirectional network linking peripheral sensory neurons, resident immune cells, and structural skin components. Among the mediators orchestrating this communication, substance P (SP) and mast cells have emerged as pivotal regulators connecting neuronal activation with immune responses, vascular dysfunction, chronic inflammation, and persistent pruritus. Beyond the canonical neurokinin-1 receptor (NK1R), the identification of the Mas-related G protein-coupled receptor X2 (MRGPRX2) has fundamentally reshaped mast-cell biology by establishing an IgE-independent pathway of neuropeptide-induced activation. This narrative review examines the molecular mechanisms underlying the mast cell–SP neuroimmune axis and discusses its contribution to the pathogenesis of allergic contact dermatitis and atopic dermatitis. Unlike the traditional approach, which primarily considers atopic dermatitis as the reference model for cutaneous neuroimmune interactions, allergic contact dermatitis may provide a useful model for examining how neuroimmune amplification integrates with delayed T-cell-mediated inflammation. These concepts are subsequently applied to atopic dermatitis, where they operate within the broader context of type 2 inflammation, epidermal barrier dysfunction, and chronic pruritus. Rather than replacing classical immunopathogenic models, this emerging neuroimmune perspective complements them by identifying bidirectional communication between sensory neurons and mast cells as a dynamic amplifier of adaptive immune responses. This integrated perspective not only provides a unifying interpretation of inflammatory dermatitis but also offers a conceptual basis for exploring similar neuroimmune mechanisms across other immune-mediated skin disorders and for generating future mechanism-based therapeutic hypotheses. Full article

Aero-engine remaining useful life (RUL) prediction supports condition-based maintenance, yet most data-driven models assume fixed sensor availability. Power-supply, acquisition, or communication failures can invalidate this assumption. We propose the Remaining Useful Life Dual-Attention Robust Network (RUL-DARNet), which combines a convolutional neural network–long short-term memory (CNN–LSTM) backbone with training-stage whole-channel Sensor Dropout (SD), Mask-Aware (MA) feature attention, and temporal attention. SD exposes the model to reduced sensor sets, whereas MA excludes unavailable channels from feature-attention normalization using an explicit availability mask. Ten seeds and ten paired masks were evaluated across four Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) subsets under controlled synthetic sensor unavailability. Trajectory metrics use a 125-cycle label cap and equal engine weighting. At the prespecified FD001 40% Missing Completely at Random endpoint, RUL-DARNet attained an RMSE of 17.474 ± 1.410 cycles, compared with 19.023 ± 0.706 for SD-only. Adding MA after SD reduced RMSE by 1.549 cycles in nine of ten seeds after Holm correction. Benefits weakened or reversed under value-related missingness, multiple operating conditions, and several trajectory-level outages. Training-stage exposure accounts for most of the observed robustness, while mask-aware reweighting provides a smaller, conditional benefit within the tested C-MAPSS protocols when availability labels are reliable and the remaining channels retain degradation information. Full article

Unmanned aerial vehicle (UAV)-assisted wireless-powered communication networks (WPCNs) have emerged as a promising solution for energy-constrained Industrial Internet of Things systems, where ground sensor nodes are often deployed in harsh and hard-to-reach environments. However, efficient UAV-assisted data collection remains challenging due to limited UAV onboard energy, realistic propulsion consumption, and varying quality-of-service requirements of industrial nodes. This paper investigates an energy efficiency maximization problem in a UAV-assisted WPCN by jointly optimizing the UAV trajectory, hovering altitude, and hybrid TDMA/NOMA resource allocation. To solve the resulting high-dimensional and highly coupled problem, we propose a deep reinforcement learning-driven Hierarchical Energy-Efficient Data Collection scheme, which we name DRL-HEEC. Specifically, a Double Deep Q-Network is employed at the upper level to optimize the UAV trajectory with altitude state inheritance, while a lower-level optimization engine based on the Dinkelbach method, block coordinate descent, and successive convex approximation is developed to handle heterogeneous resource allocation. Simulation results show that the proposed DRL-HEEC scheme outperforms other baselines. In particular, DRL-HEEC improves the system energy efficiency by approximately $9\%$ to $12\%$ compared to other reinforcement learning-based algorithms while ensuring QoS satisfaction on the part of emergency nodes. Full article

Micro-vibrations of satellite platforms can reduce the pointing accuracy of space optical communication links, leading to a reduction in link margin and even link interruption. Traditional methods rely on accelerometers to obtain vibration labels, leading to hardware deployment difficulties and additional energy overhead in space or power constrained scenarios. Here, we propose a physics-encoded self-supervised vibration sensing model that directly recovers vibration signals from time-series images of the lunar surface without external sensors. The model consists of an optical flow module, a convolutional network, and a memory network, formulating vibration sensing as a physical inversion problem constrained by an image reconstruction process. This approach extracts the textural features of the lunar surface and the temporal dynamics characteristics of platform vibrations, achieving end-to-end high-precision vibration sensing. In simulation experiments, it attains excellent performance, with a coefficient of determination ($R^2$) of 0.9975, a mean absolute error (MAE) of 0.0727, and a root mean square error (RMSE) of 0.0821. Furthermore, experiments on a real vibration platform validate the engineering applicability of the model, achieving an $R^2$ of 0.9910, an MAE of 0.1180, and an RMSE of 0.1463, with predicted values closely matching the ground truth. The proposed model exhibits sub-pixel-level accuracy and excellent generalization capability in both simulated and real-world experimental scenarios, providing an effective solution for visual sensing of micro-vibrations on satellite platforms in space optical communication. Full article

Monumental heritage sites must be protected as rigorously as critical infrastructures, but under aesthetic and architectural constraints that limit where protection technologies can be installed; the purpose of this study is to reconcile effective security with minimal impact on the historical fabric. The paper presents the integrated security and safety system realized for the Royal Palace and Gardens of Caserta, a UNESCO World Heritage Site visited by around one million people per year. This system includes an Internet of Things/Internet of Everything framework integrating a 3D supervision platform, a resilient park-wide network, video surveillance, emergency communications, an artificial-intelligence engine and visitor services. Perimeter video-surveillance design is formulated as a constrained multi-objective optimization problem (coverage, camera count, overlap, reuse of existing installation points) solved with a purpose-built genetic algorithm and characterized through 311 optimization runs and 216 baseline runs on synthetic perimeter instances. The algorithm reached 95–98% perimeter coverage while reusing 95–100% of existing installation points, converging within 120–410 generations. Two greedy baselines were respectively quantified: the price of the aesthetic objectives (a 29–41% camera overhead with respect to a coverage-only design) and the specific contribution of the joint optimization (an order-of-magnitude reduction in coverage redundancy at equal reuse of existing installation points). Sensitivity analysis exposed the coverage–cost trade-off. The framework and method provide a reproducible, quantitatively characterized approach to minimally invasive heritage security design that is transferable to comparable sites. Full article

By combining digital sensing, network connectivity, data-driven analyses, cloud services, and automated controls, smart farming has been increasingly adopted in agricultural production. Although these technologies have improved the precision and efficiency of farm management, they also increase cybersecurity exposure as agricultural facilities are connected to external networks, platforms, and remote-control environments. This review seeks to clarify why cybersecurity should be considered a fundamental requirement in smart farming and details the major system components, cybersecurity risks, and network design considerations required for secure operation. This review first explains the concept and application scope of smart farming, and then examines how sensors, communication networks, gateways, control systems, data platforms, user interfaces, cloud infrastructure, and physical support systems contribute to farm management and cybersecurity exposure. The review also emphasizes that smart farming differs from ordinary information systems because digital data and control commands can directly affect physical processes, such as irrigation, ventilation, heating, nutrient supply, and livestock management. Based on these cyber-physical characteristics, the review summarizes the key architectural considerations for reducing cybersecurity risks, including network segmentation, data and command flow mapping, gateway and wireless security, remote access management, cloud access control, device inventory, logging, monitoring, resilience, and local fail-safe operation. Overall, ensuring cybersecurity in smart farming requires an integrated approach that protects not only data and accounts but also the reliability and continuity of agricultural production. Full article

Next-generation Intelligent Transportation Systems (ITSs) require ultra-reliable, low-latency Vehicle-to-Everything (V2X) communication frameworks that support safety-critical vehicular services. Conventional centralized, monolithic architectures suffer from excessive transmission latency, limited scalability, and authentication overheads that are ill-suited to the highly dynamic and dense vehicular environment. This paper presents a secure and low-latency edge–cloud architecture for intelligent V2X communications based on a lightweight microservice-driven design paradigm. A formal latency-constrained model is presented to ensure that the end-to-end delay satisfies tight real-time constraints. The proposed framework is lightweight and includes HMAC-based authentication, nonce-based replay protection, timestamp validation, and short-lived encrypted session tokens in a stateless architecture using the Laravel framework deployed at the edge layer. Security validation is performed at edge gateways, and asynchronous SQLite-backed job queues support non-blocking telemetry processing and scalable service orchestration. Experimental evaluation shows that the edge-based deployment achieves a mean response time of 2.58 ms with small variance under repeated request conditions, while centralized processing exhibits significantly higher latency. The results demonstrate that secure authentication and telemetry exchange can be achieved without breaching strict latency requirements. The proposed solution creates a deployable, scalable, and security-aware foundation for next-generation V2X ecosystems and Intelligent Transportation Systems (ITSs) in real time. Full article

This study contributes to the design of a bidirectional communication architecture based on the ISA-95 standard, which is integrated with an IoT gateway network to digitize plant-floor data, a manufacturing execution system (MES) with local backup and automatic inventory updates using YOLOv5-based computer vision, and a cloud instance for product order management. According to interoperability metrics, the results demonstrate the feasibility of developing digital scalability in an architecture composed of heterogeneous devices, maintaining interconnectivity throughout the entire value chain. Full article

Nomophobia, fear of missing out (FoMO), mindful attention, and happiness are interconnected psychological constructs, yet their interdependencies remain poorly understood, particularly in Arab-speaking contexts. Using network analysis (EBICglasso), this cross-sectional study examined these associations in 462 Omani college students across a 56-node, four-community network. Centrality indices (strength, expected influence) and bridge expected influence were calculated, with stability assessed via bootstrapping and gender differences examined using the Network Comparison Test. Results revealed strong within-community connections and notable cross-community links. Mindful attention showed positive associations with nomophobia and FoMO but negative associations with happiness. Happiness and mindful attention emerged as the most central constructs, while FoMO and mindful attention showed the strongest bridge associations across network communities. Stability coefficients exceeded the recommended 0.50 threshold, and no significant gender differences emerged in network structure or global strength. These findings highlight the complexity of the associations among mindful attention, nomophobia, FoMO, and happiness and suggest that the role of mindful attention may vary across contexts. The identified central and bridge constructs warrant further investigation as potential areas of focus in future digital well-being research. Full article

The transition toward Open Radio Access Network (O-RAN) architecture has enabled unprecedented intelligence and flexibility in 5G and 6G network slicing. However, a fundamental challenge remains in managing the tension between radio unit energy efficiency and the strict Service Level Agreement (SLA) requirements of Ultra-Reliable Low-Latency Communication (URLLC) slices, particularly under highly dynamic traffic conditions. Existing O-RAN approaches suffer from a timescale conflict where Non-Real-Time (Non-RT) policy planners optimize for long-term energy but fail to react to rapid traffic surges, while Near-Real-Time (Near-RT) controllers prioritize reliability at the cost of significant energy over-provisioning. To address this, we propose H-RLS, a hierarchical multi-timescale framework that decouples control into a Non-RT Proximal Policy Optimization (PPO) agent for strategic, energy-aware policy planning and a Near-RT Recursive Least Squares (RLS)-assisted xApp. By predicting millisecond-level delay risks, the xApp acts as a mathematically constrained safety net, applying bounded tactical adjustments when critical SLA violations are detected. Extensive evaluations across dynamic traffic transitions demonstrate that H-RLS maintains zero SLA violations. By actively preventing resource over-provisioning, the framework achieves the lowest composite Energy-SLA cost across all tested regimes, significantly minimizing dynamic power consumption while preserving Enhanced Mobile Broadband (eMBB) service integrity. Full article