Search Results (19,307)

Unmanned aerial vehicle (UAV) swarms offer an efficient solution for data collection from widely distributed ground users (GUs). However, incomplete environment information and frequent changes make it challenging for standard centralized planning or pure reinforcement learning approaches to simultaneously maintain global solution quality and local flexibility. We propose a hierarchical data collection framework for heterogeneous UAV-assisted wireless sensor networks (WSNs). A small set of high-capability UAVs (H-UAVs), equipped with substantial computational and communication resources, coordinate regional coverage, trajectory planning, and uplink transmission control for numerous resource-constrained low-capability UAVs (L-UAVs) across power-Voronoi-partitioned areas using multi-agent deep reinforcement learning (MADRL). Specifically, we employ Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance H-UAVs’ decision-making capabilities and enable coordinated actions. The partitions are dynamically updated based on GUs’ data generation rates and L-UAV density to balance workload and adapt to environmental dynamics. Concurrently, a large number of L-UAVs with limited onboard resources perform self-organized data collection from GUs and execute opportunistic relaying to a remote access point (RAP) via H-UAVs. Within each Voronoi cell, L-UAV motion follows a weighted Vicsek model that incorporates GUs’ age of information (AoI), link quality, and congestion avoidance. This spatial decomposition combined with decentralized weak-swarm control enables scalability to large-scale L-UAV deployments. Experiments demonstrate that the proposed strong and weak agent MADDPG (SW-MADDPG) scheme reduces AoI by 30% and 21% compared to No-Voronoi and Heuristic-HUAV baselines, respectively. Full article

Background: Aging exerts a progressive and multifaceted impact on the microcirculatory system, undermining the structural and molecular integrity that sustains endothelial stability across both peripheral and cerebral vascular territories. A sustained shift toward oxidative imbalance, chronic low-grade inflammation, and progressive endothelial exhaustion converges to destabilize microvascular networks, linking peripheral artery disease (PAD) with heightened susceptibility to cerebral microvascular dysfunction and neurovascular decline. As redox homeostasis deteriorates, endothelial cells progressively lose barrier-selective properties, intercellular communication with pericytes weakens, and pro-thrombotic tendencies subtly emerge, creating a permissive environment for early neurovascular injury and impaired cerebrovascular resilience. Methods: This narrative review integrates mechanistic evidence derived from experimental, clinical, and translational studies examining the interplay between oxidative stress, inflammatory signaling cascades, endothelial senescence, and blood–brain barrier (BBB) disruption across peripheral and cerebral microvascular systems. A comparative framework was applied to PAD and cerebral microcirculatory pathology to identify convergent molecular drivers and systemic mechanisms underlying endothelial deterioration. Results: Accumulating evidence demonstrates that oxidative stress disrupts endothelial mitochondrial function, compromises tight junction architecture, and accelerates angiogenic failure. Concurrent inflammatory activation amplifies these alterations through cytokine-mediated endothelial activation, enhanced leukocyte adhesion, and promotion of a pro-thrombotic microenvironment. Progressive endothelial senescence consolidates these insults into a persistent state of microvascular dysfunction characterized by diminished nitric oxide bioavailability, capillary rarefaction, and compromised barrier integrity. Notably, these pathological features are shared between PAD and the aging cerebral circulation, reinforcing the concept of a unified systemic microvascular aging phenotype. Conclusions: Microvascular failure in the aging brain should be understood as an extension of systemic endothelial deterioration driven by oxidative stress, chronic inflammation, and senescence-associated vascular exhaustion. Recognizing the shared molecular architecture linking peripheral and cerebral microcirculatory dysfunction offers a strategic framework for developing targeted therapeutic interventions aimed at restoring endothelial resilience, stabilizing BBB integrity, and preserving neurovascular homeostasis in aging populations. Full article

Connecting distributed applications across multiple cloud-native domains is growing in complexity. Applications have become containerized and fragmented across heterogeneous infrastructures, such as public clouds, edge nodes, and private data centers, including emerging IoT-driven environments. Existing networking solutions like CNI plugins and service meshes have proven insufficient for providing isolated, low-latency and secure multi-cluster communication. By combining SDN control with Kubernetes abstractions, we present L2S-CES, a Kubernetes-native solution for multi-cluster layer-2 network slicing that offers flexible isolated connectivity for microservices while maintaining performance and automation. In this work, we detail the design and implementation of L2S-CES, outlining its architecture and operational workflow. We experimentally validate against state-of-the-art alternatives and show superior isolation, reduced setup time, native support for broadcast and multicast, and minimal performance overhead. By addressing the current lack of native link-layer networking capabilities across multiple Kubernetes domains, L2S-CES provides a unified and practical foundation for deploying scalable, multi-tenant, and latency-sensitive cloud-native applications. Full article

This study investigates the configurational pathways enabling women in Oman to translate entrepreneurial intentions into technology venture creation. By integrating institutional theory and resource-based view, we develop a novel framework examining how formal institutional support (FIS), informal institutional support (IIS), and digital self-efficacy (DSE) interact in Oman’s conservative context. We emphasize the significant enabling role of work–life balance resources (WLBR) and the cultural legitimacy of spousal endorsement. Our mixed-methods design utilizes survey data from 418 female IT graduates and 20 semi-structured interviews, analyzed through fuzzy-set Qualitative Comparative Analysis (fsQCA). The findings indicate that FIS predicts entrepreneurial intention (β = 0.34, p < 0.001) but not venture creation (OR = 0.85, p = 0.298), revealing a visibility gap in policy implementation. IIS predicts venture creation (OR = 1.43, p = 0.033), with spousal endorsement acting as a cultural legitimacy signal. DSE alone fails to predict venture creation but is vital when combined with WLBR. FsQCA identifies a sufficient configuration pathway characterized by the combination of spousal endorsement, domestic support, DSE, and WLBR with solution consistency of 0.93 and coverage of 0.78. WLBR is a necessary condition with necessity consistency of 0.96, demonstrating that venture creation is improbable without it. Qualitative evidence shows founders reposition conservative norms as legitimacy signals, while non-founders emphasize funding barriers despite policy awareness. We recommend that policymakers subsidize care infrastructure, leverage women-led community networks for targeted outreach, and formalize state-backed legitimacy programs that reduce kinship dependency while building autonomy-focused alternatives. Full article

The enteric nervous system (ENS) constitutes a highly organized and intricate neuronal network comprising two principal plexuses: myenteric and submucosal. These plexuses consist of neurons and enteric glial cells (EGCs). Neurons ensure innervation throughout the intestinal wall, whereas EGCs, distributed within the mucosa, contribute to epithelial barrier integrity and modulation of local inflammatory responses. The ENS orchestrates essential gastrointestinal functions, including motility, secretion, absorption, vascular regulation, and immune interactions with gut microbiota. Under physiological conditions, intestinal homeostasis involves moderate generation of reactive oxygen species (ROS) through endogenous processes such as mitochondrial oxidative phosphorylation. Cellular antioxidant systems maintain redox equilibrium; however, excessive ROS production induces oxidative stress, promoting EGCs activation toward a reactive phenotype characterized by pro-inflammatory cytokine release. This disrupts neuron–glia communication, predisposing to enteric neuroinflammation and neurodegeneration. Obesity, associated with hyperglycemia, hyperlipidemia, and micronutrient deficiencies, enhances ROS generation and inflammatory cascades, thereby impairing ENS integrity. Nevertheless, non-pharmacological strategies—including synthetic and natural antioxidants, bioactive dietary compounds, probiotics, and prebiotics—attenuate oxidative and inflammatory damage. This review summarizes preclinical and clinical evidence elucidating the interplay among the ENS, obesity-induced oxidative stress, inflammation, and the modulatory effects of antioxidant interventions. Full article

The rapid growth in demand for diverse network application services has driven the continuous development and expansion of data centers. BCubes was proposed by Microsoft Research Asia for designing modular data centers, and it is a multi-layer recursively constructed network with many advantages. This article shows that BCube is the existence of two edge-disjoint Hamiltonian cycles, abbreviated as two EDHCs, which provide two significant benefits in data center operations: (1) parallel data broadcast and (2) edge fault-tolerance in network communications. We present the following results in this paper: (1) By utilizing the network topology characteristics, we first provide construction algorithms for two EDHCs on low-dimensional BCubes. (2) Based on the algorithm and the recursive structure of BCubes, we prove that two EDHCs exist for all BCubes. (3) Considering all-to-all broadcasting using two EDHCs as transmission channels, we evaluate the performance of all-to-all broadcasting through simulations on low-dimensional BCubes. Full article

Cystinosis is a rare lysosomal storage disorder characterized by defective cystine transport and progressive multi-organ damage, with the kidney being the primary site of pathology. In addition to the traditional perspective on lysosomal dysfunction, recent studies have demonstrated that cystinosis exerts a substantial impact on cellular energy metabolism, with a particular emphasis on oxidative pathways. Mitochondria, the central hub of ATP production, exhibit structural abnormalities, impaired oxidative phosphorylation, and increased reactive oxygen species. These factors contribute to proximal tubular cell failure and systemic complications. This review highlights the critical role of energy metabolism in cystinosis and supports the emerging idea of organelle communication. A mounting body of evidence points to a robust functional and physical association between lysosomes and mitochondria, facilitated by membrane contact sites, vesicular trafficking, and signaling networks that modulate nutrient sensing, autophagy, and redox balance. Disruption of these interactions in cystinosis leads to defective mitophagy, accumulation of damaged mitochondria, and exacerbation of oxidative stress, creating a vicious cycle of energy failure and cellular injury. A comprehensive understanding of these mechanisms has the potential to reveal novel therapeutic avenues that extend beyond the scope of cysteamine, encompassing strategies that target mitochondrial health, enhance autophagy, and restore lysosome–mitochondria communication. Full article

Background: The scalp represents a distinct ecological niche within the skin, and the structure of its microbiota, together with the factors shaping it, is considered important for the maintenance of scalp health. Methods: This study systematically analyzed the bacterial and fungal community structures on the scalps of 63 healthy Chinese women aged 18–25, and examined their associations with scalp type, sensitivity, and lifestyle factors. Scalp samples were collected, questionnaire surveys were administered, scalp physiological parameters were measured, and high-throughput sequencing of 16S rRNA and ITS genes was performed. Results: The results showed that, in this unique scalp skin niche, the dominant bacterial phylum was Actinobacteria, while the dominant fungal phylum was Ascomycota. The predominant bacterial genera were Cutibacterium and Staphylococcus, and the fungal community was dominated by Malassezia. When scalp types were categorized according to sebum content, dry scalps showed enrichment of Micrococcus, Streptococcus, Delftia, Staphylococcus aureus, and Staphylococcus hominis, whereas oily scalps, on the other hand, are primarily colonized by Cutibacterium and Staphylococcus species. In addition, we observed microbial interactions under different physiological conditions. The relative abundance of Cutibacterium decreased with increasing scalp sensitivity. Higher psychological stress, insufficient sleep, and high-sugar/high-fat dietary patterns tended to coincide with shifts in the relative abundance of Malassezia, implying that these influences may act through fungal rather than bacterial components of the scalp microbiota. Scalp sensitivity showed the strongest association with β-diversity among the variables examined, although the effect size was modest and did not reach conventional significance in the multivariable PERMANOVA. Conclusions: In young women, the scalp constitutes a distinct cutaneous niche whose microbiota is jointly shaped by sebum level, barrier sensitivity, and lifestyle factors, with sensitivity emerging as one of the more influential dimensions of community variation. These findings provide guidance for future in-depth research on the scalp microbiome network and offer a foundational reference for preventing suboptimal and pathological scalp conditions. Full article

Wearable antennas for wireless sensor network (WSN) applications require circularly polarized (CP) radiation to maintain stable communication link under human body movement and complex environments. However, many existing wearable CP antennas rely on either linearly polarized (LP) or CP radiator with a single axial ratio (AR) mode combined with external polarization conversion structures, which limit the achievable axial ratio bandwidth (ARBW). In this work, an all-textile wideband CP antenna with a square-ring notched slot radiator, a 50 Ω microstrip line, and a 3 × 3 nonuniform metasurface (MTS) is proposed for 5.85 GHz WSN applications. Unlike conventional CP generation approaches, the square-ring notched slot, analyzed using characteristic mode analysis (CMA), directly excites three distinct AR modes, enabling potential wideband CP radiation. The nonuniform MTS further improves IBW performance by exciting additional surface wave resonances. Moreover, the nonuniform MTS further enhances ARBW by redirecting the incident wave into an orthogonal direction with equivalent amplitude and a 90° phase difference at higher frequency region. The proposed antenna is composed of conductive textile and felt substrates, offering flexibility for wearable applications. The proposed antenna is measured in free space, on human bodies, and fresh pork in an anechoic chamber. The measured results show a broad IBW and ARBW of 84.52% and 43.56%, respectively. The measured gain and radiation efficiency are 4.47 dBic and 68%, respectively. The simulated specific absorption rates (SARs) satisfy both US and EU standards. Full article

Dysregulated lipid metabolism constitutes the fundamental etiology underlying the global burden of obesity and its associated metabolic disorders. N⁶-methyladenosine (m⁶A) is the most abundant reversible chemical modification on messenger RNA and influences virtually every aspect of RNA metabolism. Recent studies demonstrate that m⁶A mediates regulatory networks governing lipid metabolism and contributes to the pathogenesis of multiple metabolic diseases. However, the precise roles of m⁶A in lipid metabolism and related metabolic disorders remain incompletely understood. This review positions m⁶A modification as a central epigenetic switch that governs lipid homeostasis. We first summarize the molecular components of the dynamic m⁶A regulatory machinery and delineate the mechanisms by which it controls key lipid metabolic processes, with an emphasis on adipogenesis, thermogenesis and lipolysis. Building on this, we further discuss how dysregulated m⁶A acts as a shared upstream driver linking obesity, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), and insulin resistance through tissue-specific and inter-organ communication mechanisms. We also evaluate the potential of targeting m⁶A regulators as therapeutic strategies for precision intervention in metabolic diseases. Ultimately, deciphering the complex interplay between m⁶A modification and lipid homeostasis offers a promising frontier for the development of epitranscriptome-targeted precision medicine against obesity and its associated metabolic disorders. Full article

Direction of arrival (DOA) estimation is a fundamental problem in array signal processing, with extensive applications in radar, communications, sonar, and other fields. Traditional DOA estimation methods, such as MUSIC and ESPRIT, rely on eigenvalue decomposition or spectral peak search, which suffer from high computational complexity and performance degradation under conditions of low signal-to-noise ratio (SNR), coherent signals, and array imperfections. Cylindrical arrays offer unique advantages for omnidirectional sensing due to their circular structure and three-dimensional coverage capability; however, their nonlinear array manifold increases the difficulty of estimation. This paper proposes a circular-attention residual network (CA-ResNet) for DOA estimation using uniform cylindrical arrays. The proposed approach achieves high accuracy and robust angle estimation through phase difference feature extraction, a multi-scale residual network, an attention mechanism, and a joint output module. Simulation results demonstrate that the proposed CA-ResNet method delivers superior performance under challenging scenarios, including low SNR (−10 dB), a small number of snapshots (L = 5), and multiple sources (1 to 4 signal sources). The corresponding root mean square errors (RMSE) are 0.21°, 0.45°, and below 1.5°, respectively, significantly outperforming traditional methods like MUSIC and ESPRIT, as well as existing deep learning models (e.g., ResNet, CNN, MLP). Furthermore, the algorithm exhibits low computational complexity and a small parameter size, highlighting its strong potential for practical engineering applications and robustness. Full article

This study involved a systematic literature review using bibliometric analysis to examine the evolution and current trends of Biological Remediation studies. The bibliometric analysis was used for the descriptive, intellectual, social, and conceptual network analyses, while systematic reviews were used to identify the application of the Biological Remediation. A total of 4835 papers were selected and extracted from Scopus between 2020 and 2025. The publication trends, most influential countries and articles, leading journals, collaboration networks, coupling networks, and application of the Biological Remediation in various disciplines were described. This study summarized the research agenda of the Biological Remediation field, which would be helpful for researchers and funding agencies. This article highlights four new research directions in Current Bioremediation Trends: (1) understanding the interactions between petroleum hydrocarbons and heavy metals in composite pollution systems; (2) exploring microbial community succession during bioremediation; (3) utilizing biosurfactants to enhance contaminant solubilization and biodegradation; and (4) developing integrative, multi-mechanistic remediation approaches. Full article

Contemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian neural network architecture that integrates T-regulatory-cell-inspired immunoregulation with autophagic structural optimization. The model integrates three synergistic subsystems: the Bayesian Effector Network (BEN) for uncertainty-aware inference, the Regulatory Immune Network (RIN) for Lyapunov-based inhibitory control, and the Autophagic Optimization Engine (AOE) for energy-efficient regeneration, thereby establishing a closed energy–entropy loop that attains adaptive equilibrium among cognition, regulation, and metabolism. This triadic feedback achieves meta-homeostasis, transforming learning into a process of ongoing self-stabilization instead of static optimization. Bio-RegNet routinely outperforms state-of-the-art dynamic GNNs across twelve neuronal, molecular, and macro-scale benchmarks, enhancing calibration and energy efficiency by over 20% and expediting recovery from perturbations by 14%. Its domain-invariant equilibrium facilitates seamless transfer between biological and manufactured systems, exemplifying a fundamental notion of bio-inspired, self-sustaining intelligence—connecting generative AI and biomimetic design for sustainable, living computation. Bio-RegNet consistently outperforms the strongest baseline HGNN-ODE, improving ARI from 0.77 to 0.81 and NMI from 0.84 to 0.87, while increasing equilibrium coherence $\kappa$ from 0.86 to 0.93. Full article

Hybrid Split Federated Learning (HSFL for short) in emerging 6G-enabled UAV networks faces persistent challenges in data protection, device trust management, and long-term participation incentives. To address these issues, this study introduces S-HSFL, a security-enhanced framework that embeds verifiable federated learning mechanisms into HSFL and incorporates digital-signature-based authentication throughout the device selection process. This design effectively prevents model tampering and forgery attacks, achieving a defense success rate above 99%. To further strengthen collaborative training, we develop a MAB-GT device selection strategy that integrates multi-armed bandit exploration with multi-stage game-theoretic decision models, spanning non-cooperative, coalition, and repeated games, to encourage high-quality UAV nodes to provide reliable data and sustained computation. Experiments on the Modified National Institute of Standards and Technology (MNIST) dataset under both Independent and Identically Distributed (IID) and non-IID conditions demonstrate that S-HSFL maintains approximately 97% accuracy even in the presence of 30% adversarial UAVs. The MAB-GT strategy significantly improves convergence behavior and final model performance, while incurring only a 10–30% increase in communication overhead. The proposed S-HSFL framework establishes a secure, trustworthy, and efficient foundation for distributed intelligence in next-generation 6G UAV networks. Full article

The advent of sixth-generation (6G) imposes stringent demands on wireless networks, while traditional 2D rigid reconfigurable intelligent surfaces (RISs) face bottlenecks in regulatory freedom and scenario adaptability. To address this, stacked intelligent metasurfaces (SIMs) have emerged. This paper presents a systematic review of SIM technology. It first elaborates on the SIM multi-layer stacked architecture and wave-domain signal-processing principles, which overcome the spatial constraints of conventional RISs. Then, it analyzes challenges, including beamforming and channel estimation for SIM, and explores its application prospects in key 6G scenarios such as integrated sensing and communication (ISAC), low earth orbit (LEO) satellite communication, semantic communication, and UAV communication, as well as future trends like integration with machine learning and nonlinear devices. Finally, it summarizes the open challenges in low-complexity design, modeling and optimization, and performance evaluation, aiming to provide insights to promote the large-scale adoption of SIM in next-generation wireless communications. Full article