Search Results (34,542)

Swarm coverage by unmanned underwater vehicles (UUVs) is essential for inspection, environmental monitoring, and search operations, but remains challenging in three-dimensional domains under limited sensing and communication. Pheromone-based stigmergic coordination provides a low-bandwidth alternative to explicit communication, yet conventional single-field models are susceptible to depth-dependent sensing inconsistencies and multi-source signal interference. This paper introduces a dual-trail stigmergic coordination framework in which a virtual pheromone field encodes short-term motion cues while an auxiliary coverage trail records the accumulated exploration effort. UUV motion is guided by the combined gradients of these two fields, enabling more consistent behavior across depth layers and mitigating ambiguities caused by overlapping pheromone sources. At the macroscopic level, swarm evolution is modeled by a coupled system of partial differential equations (PDEs) describing vehicle density, pheromone concentration, and coverage trail. A Lyapunov functional is constructed to derive sufficient conditions under which perturbations around the uniform coverage equilibrium decay exponentially. Numerical simulations in three-dimensional underwater domains demonstrate that the proposed framework reduces coverage holes, limits redundant overlap, and improves robustness with respect to a single-pheromone baseline and a potential-field-based controller. These results indicate that dual-field stigmergic control is a promising and scalable approach for UUV coverage in constrained underwater environments. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers could break the classical cryptographic schemes used in current UAV networks. This situation underscores the need for quantum-resilient, privacy-preserving security frameworks. This paper proposes a quantum-resilient federated learning framework for multi-layer cyber anomaly detection in UAV systems. The framework combines a hybrid deep learning architecture. A Variational Autoencoder (VAE) performs unsupervised anomaly detection. A neural network classifier enables multi-class attack categorization. To protect sensitive UAV data, model training is conducted using federated learning with differential privacy. Robustness against malicious participants is ensured through Byzantine-robust aggregation. Additionally, CRYSTALS-Dilithium post-quantum digital signatures are employed to authenticate model updates and provide long-term cryptographic security. Researchers evaluated the proposed framework on a real UAV attack dataset containing GPS spoofing, GPS jamming, denial-of-service, and simulated attack scenarios. Experimental results show the system achieves 98.67% detection accuracy with only 6.8% computational overhead compared to classical cryptographic approaches, while maintaining high robustness under Byzantine attacks. The main contributions of this study are: (1) a hybrid VAE–classifier architecture enabling both zero-day anomaly detection and precise attack classification, (2) the integration of Byzantine-robust and privacy-preserving federated learning for UAV security, and (3) a practical post-quantum security design validated on real UAV communication data. Full article

The rapid evolution of wireless communication toward Fifth Generation (5G) networks has enabled unprecedented performance improvement in terms of data rate, latency, reliability, sustainability, and connectivity. Recent years have witnessed an excessive deployment of new 5G networks worldwide. This deployment lead to an exponential growth in traffic flow and a massive number of connected devices requiring a new generation of energy-hungry base stations (BSs). This results in increased power consumption, higher operational costs, and greater environmental impact, making energy efficiency (EE) a critical research challenge. This paper presents a comprehensive survey of EE optimization strategies in 5G networks. It reviews the transition from traditional methods such as resources allocation, energy harvesting, BS sleep modes, and power control to modern artificial intelligence (AI)-driven solutions employing machine learning, deep reinforcement learning, and self-organizing networks (SON). Comparative analyses highlight the trade-offs between energy savings, network performance, and implementation complexity. Finally, the paper outlines key open issues and future directions toward sustainable 5G and beyond-5G (B5G/Sixth Generation (6G)) systems, emphasizing explainable AI, zero-energy communications, and holistic green network design. Full article

As global agriculture seeks to reconcile the dual imperatives of food security and environmental sustainability, this study examines the role of Internet access in promoting green agricultural production, specifically by reducing fertilizer and pesticide use. Using a panel dataset from 16 rural fixed observation points in Henan Province from 2009 to 2022, we find that Internet access significantly lowers per-unit farmland expenditures on fertilizers and pesticides by 6.0% and 7.3%, respectively. Mechanism analysis reveals that these positive effects operate through three main channels: improved information accessibility delivers timely agricultural data and guides input decisions; enhanced technical learning efficiency reduces barriers to adopting green technologies; and stronger market connectivity via e-commerce platforms shortens supply chains and provides price incentives. Heterogeneity analysis further identifies more pronounced effects among farmers with higher human capital (higher education, better health, younger age), higher production capital (greater mechanization, larger farmland, stronger decision-making capacity), lower livelihood capital (lower income, lower consumption, less communication expenditure), and higher spatial capital (residing in urban suburbs, poverty registration villages, and traditional villages). This study provides micro evidence for digital technology to empower sustainable agricultural development and provides policy implications for building a sustainable agri-food system. Full article

Providing effective post-disaster housing remains a globally complex challenge shaped by interrelated constraints, including environmental sustainability, socio-cultural compatibility, logistical capacity, and economic feasibility. Contemporary responses therefore require housing solutions that extend beyond rapid deployment to incorporate flexibility, adaptability, and long-term spatial transformation. In this context, this study advances a design-oriented, computational framework that positions parametric design at the core of post-disaster housing production within the broader digital transformation of the construction sector. The research proposes an adaptive parametric–modular housing system in which standardized architectural units are governed by a rule-based aggregation logic capable of generating context-responsive spatial configurations across multiple scales and typologies. The methodology integrates a qualitative synthesis of global post-disaster housing literature with a quantitative computational workflow developed in Grasshopper for Rhinoceros 3D (version 8). Algorithmic scripting defines a standardized spatial grid and parametrically regulates key building components structural systems, façade assemblies, and site-specific environmental parameters, enabling real-time configuration, customization, and optimization of housing units in response to diverse user needs and varying climatic, social, and economic conditions while maintaining constructability. The applicability of the framework is examined through a case study of the Düzce Permanent Housing context, where limitations of existing post-disaster stock, such as spatial rigidity, restricted growth capacity, and fragmented public-space integration, are contrasted with alternative settlement scenarios generated by the proposed system. The findings demonstrate that the framework supports multi-scalar and multi-typological reconstruction, extending beyond individual dwellings to include public, service, and open-space components. Overall, the study contributes a transferable computational methodology that integrates modular standardization with configurational diversity and user-driven adaptability, offering a sustainable pathway for transforming temporary post-disaster shelters into permanent, resilient, and socially integrated community assets. Full article

This work addresses the increasing complexity of urban mobility by proposing an intelligent optimization and resource-allocation framework for Vehicle-to-Infrastructure (V2I) communications. The model integrates a macroscopic mobility analysis, an Integer Linear Programming (ILP) formulation for optimal Road-Side Unit (RSU) placement, and a Smart Generic Network Controller (SGNC) based on Q-learning for dynamic radio-resource allocation. Simulation results in a realistic georeferenced urban scenario with 380 candidate sites show that the ILP model activates only 2.9% of RSUs while guaranteeing more than 90% vehicular coverage. The reinforcement-learning-based SGNC achieves stable allocation behavior, successfully managing 10 antennas and 120 total resources, and maintaining efficient operation when the system exceeds 70% capacity by reallocating resources dynamically through the $\lambda$-based alert mechanism. Compared with static allocation, the proposed method improves resource efficiency and coverage consistency under varying traffic demand, demonstrating its potential for scalable V2I deployment in next-generation intelligent transportation systems. Full article

Optical Fiber Composite Overhead Ground Wire (OPGW) cables serve dual functions in power systems, lightning protection and critical communication infrastructure for real-time grid monitoring. Accurate OPGW identification during UAV inspections is essential to prevent miscuts and maintain power-communication functionality. However, detecting small, twisted OPGW segments among visually similar ground wires is challenging, particularly given the computational and energy constraints of edge-based UAV platforms. We propose OPGW-DETR, a lightweight detector based on the D-FINE framework, optimized for low-power operation to enable reliable detection. The model incorporates two key innovations: multi-scale convolutional global average pooling (MC-GAP), which fuses spatial features across multiple receptive fields and integrates spectrally motivated features for enhanced fine-grained representation, and a hybrid gating mechanism that dynamically balances global and spatial features while preserving original information through residual connections. By enabling real-time inference with minimal energy consumption, OPGW-DETR addresses UAV battery and bandwidth limitations while ensuring continuous detection capability. Evaluated on a custom OPGW dataset, the S-scale model achieves 3.9% improvement in average precision (AP) and 2.5% improvement in AP${}_{50}$ over the baseline. By mitigating misidentification risks, these gains improve communication reliability. As a result, uninterrupted grid monitoring becomes feasible in low-power UAV inspection scenarios, where accurate detection is essential to ensure communication integrity and safeguard the power grid. Full article

The emergence and dissemination of antimicrobial resistance (AMR) are driven by complex, interconnected mechanisms involving microbial communities, environmental factors, and human activities, with climate change playing a pivotal and accelerating role. Rising temperatures, altered precipitation patterns, and other environmental disruptions caused by climate change create favorable conditions for bacterial growth and enhance the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Thermal stress and environmental pressures induce genetic mutations that promote resistance, while ecosystem disturbances facilitate the stabilization and spread of resistant pathogens. Moreover, climate change exacerbates public and animal health risks by expanding the range of infectious disease vectors and driving population displacement due to extreme weather events, further amplifying the transmission and evolution of resistant microbes. Livestock agriculture represents a critical nexus where excessive antibiotic use, environmental stressors, and climate-related challenges converge, fueling AMR escalation with profound public health and economic consequences. Environmental reservoirs, including soil and water sources, accumulate ARGs from agricultural runoff, wastewater, and pollution, enabling resistance spread. This review aims to demonstrate how the Mediterranean’s strategic position makes it an ideal living laboratory for the development of integrated “One Health” frameworks that address the mechanistic links between climate change and AMR. By highlighting these interconnections, the review underscores the need for a unified approach that incorporates sustainable agricultural practices, climate mitigation and adaptation within healthcare systems, and enhanced surveillance of zoonotic and resistant pathogens—ultimately offering a roadmap for tackling this multifaceted global health crisis. Full article

The gut microbiota has emerged as a central regulator of the gut–brain axis, profoundly influencing neural, immune, and metabolic homeostasis. Increasing evidence indicates that disturbances in microbial composition and function contribute to the onset and progression of neurodegenerative diseases (NDs) through mechanisms involving neuroinflammation, oxidative stress, and impaired neurotransmission. Gut dysbiosis is characterized by a loss of microbial diversity, a reduction in beneficial commensals, and an enrichment of pro-inflammatory taxa. These shifts alter intestinal permeability and systemic immune tone, allowing microbial metabolites and immune mediators to affect central nervous system (CNS) integrity. Metabolites such as short-chain fatty acids (SCFAs), tryptophan derivatives, lipopolysaccharides (LPS), and trimethylamine N-oxide (TMAO) modulate blood–brain barrier (BBB) function, microglial activation, and neurotransmitter synthesis, linking intestinal imbalance to neuronal dysfunction and cognitive decline. Disruption of this gut–brain communication network promotes chronic inflammation and metabolic dysregulation, key features of neurodegenerative pathology. SCFA-producing and tryptophan-metabolizing bacteria appear to exert neuroprotective effects by modulating immune responses, epigenetic regulation, and neuronal resilience. The aim of this work was to comprehensively explore the current evidence on the bidirectional communication between the gut microbiota and the CNS, with a focus on identifying the principal molecular, immune, and metabolic mechanisms supported by the strongest and most consistent data. By integrating findings from recent human studies, this review sought to clarify how microbial composition and function influence neurochemical balance, immune activation, and BBB integrity, ultimately contributing to the onset and progression of neurodegenerative processes. Collectively, these findings position the gut microbiota as a dynamic interface between the enteric and CNS, capable of influencing neurodegenerative processes through immune and metabolic signaling. Full article

This paper provides a comprehensive overview of mobile network softwarization, emphasizing the technological foundations and its transformative impact on the energy efficiency of modern and future mobile networks. In the paper, a detailed analysis of communication concepts known as software-defined networking (SDN) and network function virtualization (NFV) is presented, with a description of their architectural principles, operational mechanisms, and the associated interfaces and management frameworks that enable programmability, virtualization, and centralized control in modern mobile networks. The study further explores the role of cloud computing, virtualization platforms, distributed SDN controllers, and resource orchestration systems, outlining how they collectively support mobile network scalability, automation, and service agility. To assess the maturity and evolution of mobile network softwarization, the paper reviews contemporary research directions, including SDN security, machine-learning-assisted traffic management, dynamic service function chaining, virtual network function (VNF) placement and migration, blockchain-based trust mechanisms, and artificial intelligence (AI)-enabled self-optimization. The analysis also evaluates the relationship between mobile network softwarization and energy consumption, presenting the main SDN- and NFV-based techniques that contribute to reducing mobile network power usage, such as traffic-aware control, rule placement optimization, end-host-aware strategies, VNF consolidation, and dynamic resource scaling. Findings indicate that although fifth-generation (5G) mobile network standalone deployments capable of fully exploiting softwarization remain limited, softwarized SDN/NFV-based architectures provide measurable benefits in reducing network operational costs and improving energy efficiency, especially when combined with AI-driven automation. The paper concludes that mobile network softwarization represents an essential enabler for sustainable 5G and future beyond-5G systems, while highlighting the need for continued research into scalable automation, interoperable architectures, and energy-efficient softwarized network designs. Full article

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents can formulate strategies without information about other agents’ actions so that system-level performance remains robust in the presence of communication failures.First, we use an algorithmic approach to study the scenario in which all agents lose the ability to communicate with one another, have a symmetric set of resources to choose from, and select actions independently according to a probability distribution over the resources. We show that the distribution that maximizes the expected system-level objective under this approach can be computed by solving a convex optimization problem, and we introduce a novel polynomial-time heuristic based on subset selection. Further, both of the methods are guaranteed to be within $1-1/e$ of the system’s optimal in expectation. Second, we use a learning-based approach to study how a system designer can employ neural networks to approximate optimal agent strategies in the presence of communication failures. The neural network, trained on system-level optimal outcomes obtained through brute-force enumeration, generates utility functions that enable agents to make decisions in a distributed manner. Empirical results indicate the neural network often outperforms greedy and randomized baseline algorithms. Collectively, these findings provide a broad study of optimal agent behavior and its impact on system-level performance when the information available to agents is extremely limited. Full article

Transanal Irrigation (TAI) and Colon Hydrotherapy (CHT) represent emerging therapeutic options that may complement first-line interventions or serve as rescue treatments for chronic constipation and fecal incontinence. Their clinical utility depends on patient characteristics, specific therapeutic goals, device features, and probe type, as well as the procedural setting. This review presents the various pathophysiological contexts in which these techniques can be applied, analyzing their specific characteristics and potential pros and cons. Moreover, these interventions are also considered within a Psycho-Neuro-Endocrino-Immunological (PNEI) framework, given the potential influence of intestinal function and microbiota modulation on the bidirectional communication pathways linking the enteric nervous system, neuroendocrine regulation, immune activity, and global patient well-being. Since there is not yet enough scientific data on this topic, future research should prioritize randomized controlled trials comparing these techniques with other standard treatments (e.g., laxatives or dietary fiber) in defined patient populations. Longitudinal studies will also be essential to clarify long-term safety, potential effects on microbiota, and both risks and benefits. Standardization of technical procedures also remains a critical need, especially regarding professional competencies, operating parameters (e.g., instilled volumes and pressure ranges), and reproducible protocols. Moreover, future investigations should incorporate objective outcome measures, as colonic transit time, stool form and frequency, indices of inflammation or intestinal wall integrity, and changes to microbiome composition. In conclusion, TAI and CHT have the potential to serve as important interventions for the treatment and prevention of chronic constipation and intestinal dysbiosis, as well as their broader systemic correlates, in the setting of bio-integrated medicine. Full article

Intramuscular fat (IMF) significantly impacts meat quality. Exosomes have attracted increasing attention for their regulatory roles in muscle-adipose tissue crosstalk; however, their precise mechanisms remain largely unclear. Based on this, this study aimed to establish a muscle-adipose co-culture system to better simulate the in vivo physiological environment. Using exosomal miRNAs as molecular links, we investigated how bovine muscle satellite cells influence lipid accumulation and adipogenesis in preadipocytes. We established a co-culture system of bovine muscle satellite cells and preadipocytes and found that co-culture significantly inhibited lipid droplet accumulation and adipogenesis in preadipocytes. Therefore, we hypothesized that exosomes derived from bovine muscle satellite cells regulate the adipogenic differentiation of bovine preadipocytes through intercellular communication and that specific exosomal miRNAs play pivotal roles in this regulatory process. We successfully isolated and identified muscle-derived (Mu-EXO), adipose-derived (Ad-EXO), and co-culture exosomes (Co-EXO). High-throughput sequencing revealed the differential expression profiles of miRNAs. Notably, the bovine-specific miRNA bta-miR-2904, annotated in miRBase v22 with limited cross-species conservation, was significantly enriched in Mu-EXO and Co-EXO compared with Ad-EXO. Further functional experiments demonstrated that overexpression of bta-miR-2904 markedly inhibited lipid droplet accumulation, triglyceride content, and the expression of adipogenesis-related genes in preadipocytes; inhibition had opposite effects. Our results demonstrate that bovine muscle-derived exosomal miR-2904 inhibits lipid accumulation and adipogenesis in preadipocytes. These results establish a theoretical basis for understanding skeletal muscle-adipose crosstalk and offer a novel molecular target for regulating intramuscular fat deposition in beef cattle. Full article

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. Full article

With the development of 6G technology, conventional wireless communication systems are increasingly unable to meet stringent performance requirements in complex and dynamic environments. Therefore, integrated sensing and communication (ISAC), which enables efficient spectrum sharing, has attracted growing attention as a promising solution. This paper provides a comprehensive survey of reconfigurable intelligent surface (RIS)-unmanned aerial vehicle (UAV)-assisted ISAC systems. It first introduces a four-dimensional quantitative evaluation framework grounded in information theory. Then, we provide a structured overview of coordination mechanisms between different types of RIS and UAV platforms within ISAC architectures. Furthermore, we analyze the application characteristics of various multiple access schemes in these systems. Finally, the main technical challenges and potential future research directions are discussed and analyzed. Full article