Search Results (836)

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

Industrial Internet of Things (IIoT) networks, Microsoft Azure Internet of Things (IoT) Hub, and device twins (DvT) are increasingly recognized as core enablers of adaptive, data-driven manufacturing. This paper proposes a hierarchical IIoT framework that integrates industrial IoT networking, DvT for asset-level virtualisation, system-level digital twins (DT) for cell orchestration, and cloud-native services to support the digital transformation of brownfield, programmable logic controller (PLC)-centric modular automation (MA) environments. Traditional PLC/supervisory control and data acquisition (SCADA) paradigms struggle to meet interoperability, observability, and adaptability requirements at scale, motivating architectures in which DvT and IoT Hub underpin real-time orchestration, virtualisation, and predictive-maintenance workflows. Building on and extending a previously introduced conceptual model, the present work instantiates a multilayered, end-to-end design that combines a federated Message Queuing Telemetry Transport (MQTT) mesh on the on-premises side, a ZigBee-based backup mesh, and a secure bridge to Azure IoT Hub, together with a systematic DvT modelling and orchestration strategy. The methodology is supported by a structured analysis of relevant IIoT and DvT design choices and by a concrete implementation in a nine-cell MA laboratory featuring a robotic arm predictive-maintenance scenario. The resulting framework sustains closed-loop monitoring, anomaly detection, and control under realistic workloads, while providing explicit envelopes for telemetry volume, buffering depth, and latency budgets in edge-cloud integration. Overall, the proposed architecture offers a transferable blueprint for evolving PLC-centric automation toward more adaptive, secure, and scalable IIoT systems and establishes a foundation for future extensions toward full DvT ecosystems, tighter artificial intelligence/machine learning (AI/ML) integration, and fifth/sixth generation (5G/6G) and time-sensitive networking (TSN) support in industrial networks. Full article

Numerical modelling of airflow in underground mines is gaining importance in modern ventilation system design and safety assessment. Computational Fluid Dynamics (CFD) simulations enable detailed analyses of air movement, contaminant dispersion, and heat transfer, yet their reliability depends strongly on the accuracy of the geometric representation of excavations. Raw point cloud data obtained from laser scanning of underground workings are typically irregular, noisy, and contain discontinuities that must be processed before being used for CFD meshing. This study presents a methodology for automatic segmentation and regularization of large-scale point cloud data of underground excavation systems. The proposed approach is based on skeleton extraction and trajectory analysis, which enable the separation of excavation networks into individual tunnel segments and crossings. The workflow includes outlier removal, alpha-shape generation, voxelization, medial-axis skeletonization, and topology-based segmentation using neighbor relationships within the voxel grid. A proximity-based correction step is introduced to handle doubled crossings produced by the skeletonization process. The segmented sections are subsequently regularized through radial analysis and surface reconstruction to produce uniform and watertight models suitable for mesh generation in CFD software (Ansys 2024 R1). The methodology was tested on both synthetic datasets and real-world laser scans acquired in underground mine conditions. The results demonstrate that the proposed segmentation approach effectively isolates single-line drifts and crossings, ensuring continuous and smooth geometry while preserving the overall excavation topology. The developed method provides a robust preprocessing framework that bridges the gap between point cloud acquisition and numerical modelling, enabling automated transformation of raw data into CFD-ready geometric models for ventilation and safety analysis of complex underground excavation systems. Full article

Accurate distance perception and collision reasoning are crucial for robotic manipulation in the confined interior of tokamak vacuum vessels. Traditional mesh- or voxel-based methods suffer from discretization artifacts, discontinuities, and heavy memory requirements, making them unsuitable for continuous geometric reasoning and optimization-based planning. This paper presents an Occupancy-Aware Neural Distance Perception (ONDP) framework that serves as a compact and differentiable geometric sensor for manipulator obstacle avoidance in reactor-like environments. To address the inadequacy of conventional sampling methods in such constrained environments, we introduce a Physically-Stratified Sampling strategy. This approach moves beyond heuristic adaptation to explicitly dictate data distribution based on specific engineering constraints. By injecting weighted quotas into critical safety buffers and enforcing symmetric boundary constraints, we ensure robust gradient learning in high-risk regions. A lightweight neural network is trained directly in physical units (millimeters) using a mean absolute error loss, ensuring strict adherence to engineering tolerances. The resulting model achieves approximately 2–3 mm near-surface accuracy and supports high-frequency distance and normal queries for real-time perception, monitoring, and motion planning. Experiments on a tokamak vessel model demonstrate that ONDP provides continuous, sub-centimeter geometric fidelity. Crucially, benchmark results confirm that the proposed method achieves a query frequency exceeding 15 kHz for large-scale batches, representing a 5911× speed-up over mesh-based queries. This breakthrough performance enables its seamless integration with trajectory optimization and model-predictive control frameworks for confined-space robotic manipulation. Full article

High-speed silicon (Si) photonic transmitters operating in the O-band require higher on-chip optical power to support advanced modulation formats and ever-increasing line rates. A straightforward approach is to operate laser diodes at higher output power or employ more specialized sources, but this raises cost and exacerbates nonlinear effects such as self-phase modulation, two-photon absorption, and free-carrier generation in high-index-contrast Si waveguides. This paper proposes a low-cost 4 × 1 tree-cascade multimode interference (MMI) power combiner on a Si-on-insulator platform at 1310 nm wavelength that enables coherent power scaling while remaining fully compatible with standard commercial O-band lasers. The device employs adiabatic tapers and low-loss S-bends to ensure uniform field evolution, suppress local field enhancement, and mitigate nonlinear phase accumulation. The optimized layout occupies a compact footprint of 12 µm × 772 µm and achieves a simulated normalized power transmission of 0.975 with an insertion loss of 0.1 dB. Spectral analysis shows a 3 dB bandwidth of 15.8 nm around 1310 nm, across the O-band operating window. Thermal analysis shows that wavelength drift associated with ±50 °C temperature variation remains within the device bandwidth, ensuring stable operation under realistic laser self-heating and environmental changes. Owing to its broadband response, fabrication tolerance, and compatibility with off-the-shelf laser diodes, the proposed combiner is a promising building block for O-band transmitters and photonic neural-network architectures based on cascaded splitter and combiner meshes, while preserving linear transmission and enabling dense, large-scale photonic integration. Full article

We investigated diffusion of thin and thick nanorods with varying lengths and rigidities in cross-linked polymer networks using coarse-grained molecular dynamics (CGMD) simulations. Our results show that the translational diffusion of nanorods slows down with power scaling laws as their length increases, exhibiting a non-monotonic dependence on rigidity of thin nanorods, and decreases with the rigidity of thick nanorods. The sub-diffusion of nanorods is observed at short time scales, which becomes more pronounced for rigid nanorods. The nanorods show anisotropic diffusion behavior with favoring motion along their major axes in cross-linked networks, and the anisotropy enhances by increasing either rigidity or length of nanorods, especially for thick nanorods. The sub-diffusion behavior of nanorods is primarily due to the strong heterogeneity of motions perpendicular to major axes of nanorods, and the time scales of this heterogeneous diffusion increase with the length and rigidity of nanorods. In rotational dynamics, nanorods with higher rigidity rotate more slowly, and the effect is more evident in longer nanorods. The rotational diffusion coefficient follows a power scaling law with the rigidity of nanorods, when the effective length of a nanorod exceeds the mesh size of cross-linked network. The rotations of nanorods also display heterogeneous dynamics, in which the time scale of heterogeneous rotation increase with rigidity, and such heterogeneity is more pronounced in softer nanorods. Overall, our work elucidates the microscopic mechanisms governing both translational and rotational diffusion of nanorods in cross-linked networks. Full article

Hamiltonian cycle problems play a central role in graph theory and have wide-ranging applications in network-on-chip architectures, interconnection networks, and large-scale parallel systems. When a network contains faulty nodes or faulty links, the feasibility of certain paths becomes restricted, making the construction of Hamiltonian cycles substantially more difficult and increasingly important for ensuring reliable communication. A dimension-balanced Hamiltonian cycle is a special type of cycle that maintains an even distribution of edges across multiple dimensions of a network. Its directed counterpart extends this idea to symmetric directed networks by balancing the number of edges used in each positive and negative direction. Such cycles are desirable because they support uniform traffic distribution and reduce communication contention in practical systems. Previous research has examined the existence of directed dimension-balanced Hamiltonian cycles in directed toroidal mesh networks and has shown that some configurations permit directed dimension-balanced Hamiltonian cycles while others do not. Building on this foundation, this paper investigates the fault-tolerant properties of such networks by analyzing whether directed dimension-balanced Hamiltonian cycles still exist when a single vertex (node) or a single edge (link) is faulty. Our results extend the current understanding of Hamiltonian robustness in symmetric directed networks. Full article

In this study, a two-dimensional gamma-ray source localization system using a mesh array of plastic scintillating fibers and an artificial neural network is presented. The system covers a 200 cm by 100 cm area using SCSF-78 multi-cladded fibers. A novel U-shaped fiber topology connects both fiber ends to one side, requiring only two data-acquisition systems. Silicon photomultiplier arrays measure fast time-of-flight under optimized operating conditions to maximize signal yield. An independent artificial neural network model map measured time-of-flight values to spatial coordinates, compensating for systematic non idealities. Performance was validated using a Cesium-137 source at 20 random test positions. The artificial neural network method achieved a mean full-scale error of 4.6%. This demonstrated a 79.34% accuracy improvement over direct theoretical calculation, which had a mean full-scale error of 22.5%. The system showed consistent performance, achieving a two-dimensional standard deviation of 0.492 cm during repeatability assessment. This methodology provides a practical, efficient approach to two-dimensional radiation source localization suitable for real time monitoring and contamination mapping. Full article

This paper explores the application of mobile sensor networks in cow herds, focusing on the challenge of achieving local communication under minimal computational constraints such as restricted locality, limited memory, and implicit coordination. To address this, we propose a high connectivity based sensor network scheme that enables individual sensors to self-organize and dynamically adapt to topological variations caused by cow movements. In this scheme, each sensor acquires local distribution data from neighboring sensors, identifies those with high connectivity, and forms a local network with a star topology. The overlap of these local networks results in a globally interconnected mesh topology. Furthermore, information exchanged through broadcasting and overhearing allows each sensor to incrementally update and adapt to dynamic changes in its local network. To validate the proposed scheme, a custom wireless sensor tag was developed and mounted on the necks of individual cows for experimental testing. Furthermore, large-scale simulations were performed to evaluate performance in herd environments. Both experimental and simulation results confirmed that the scheme effectively maintains network coverage and connectivity under dynamic herd conditions. Full article

We present an experimental demonstration of a daylight-capable Optical Amplify-and-Forward (OAF) relaying system designed to support flexible and high-capacity network topologies. The proposed architecture integrates fiber-based infrastructure with OAF Free Space Optics (FSO) relaying, enabling bidirectional optical communication over 460 m (x2) using SFP-compatible schemes, while addressing Non-Line-of-Sight (NLOS) constraints and fiber disruptions. This work achieves a Bit Error Rate (BER) below the Hard-Decision Forward Error Correction (HD-FEC) limit, validating the feasibility of high-speed urban FSO links. By leveraging low-cost fiber-coupled optical terminals, the system transmits single-carrier 120 Gbps Intensity Modulation/Direct Detection (IM/DD) signals using NRZ (Non-Return-to-Zero) and PAM4 (4-Pulse Amplitude Modulation) modulation formats. Operating entirely in the optical C-Band domain, this approach ensures compatibility with existing infrastructure, supporting scalable mesh FSO deployments and seamless integration with hybrid Radio Frequency (RF)/FSO systems. Full article

Accurate sea surface temperature (SST) forecasting in coastal upwelling systems requires predictive models capable of representing complex oceanic geometries. This work revisits grid-to-mesh coupling strategies in Graph Neural Networks (GNNs) and analyzes how mesh topology and connectivity influence prediction accuracy and artifact formation. This standard coupling process is a significant source of discretization errors and spurious numerical artifacts that compromise the final forecast’s accuracy. Using daily Copernicus SST and 10 m wind reanalysis data from 2000 to 2020 over the Canary Islands and the Northwest African region, we evaluate four mesh configurations under varying grid-to-mesh connection densities. We analyze two structured meshes and propose two new unstructured meshes for which their nodes are distributed according to the bathymetry of the ocean region. The results show that forecast errors exhibit geometric patterns equivalent to order-k Voronoi tessellations generated by the k-nearest neighbor association rule. Bathymetry-aware meshes with $k=3$ and $k=4$ grid-to-mesh connections significantly reduce polygonal artifacts and improve long-term coherence, achieving up to 30% lower $\mathrm{RMSE}$ relative to structured baselines. These findings reveal that the underlying geometry, rather than node count alone, governs error propagation in autoregressive GNNs. The proposed analysis framework provides a clear understanding of the implications of grid-to-mesh connections and establishes a foundation for artifact-aware, geometry-adaptive learning in operational oceanography. Full article

Prolonged Grief Disorder (PGD) is marked by enduring and disruptive grief symptoms following the death of a significant other. Although PGD has been recognized as a distinct psychopathological entity within the trauma dimension in the DSM-5-TR, its neurobiological underpinnings remain not fully defined. A systematic literature review was conducted up to September 2025 following PRISMA 2020 guidelines. PubMed, Scopus, Embase and Web of Science were searched using a comprehensive strategy combining MeSH terms and free-text keywords. Eligible studies included human participants, validated grief assessment tools and biomarker assessments. Out of 2140 initial records, 12 studies published between 1989 and 2022 met inclusion criteria. Investigated neuro–psycho–endocrine systems included the hypothalamic–pituitary–adrenal (HPA) axis, catecholamines, oxytocin, endocannabinoids and immune/inflammatory markers. Key findings in pathological grief reactions included altered cortisol rhythms, elevated oxytocin levels, increased pro-inflammatory cytokines and immune system dysregulation. Results are limited by heterogeneity in study designs, small sample sizes, inconsistent use of diagnostic criteria prior to DSM-5-TR and lack of control for psychiatric comorbidities. This review highlights emerging biological correlates of PGD, particularly those involving the stress response, reward-attachment networks and immune/inflammatory pathways. Further standardized, longitudinal research is essential to gain a more defined picture of PGD, to clarify causal mechanisms and to guide targeted therapeutic interventions. Full article

As industrialized construction and smart building continue to advance, rebar-tying robots place higher demands on the real-time and accurate recognition of rebar intersections and their tying status. Existing deep learning-based detection methods generally rely on heavy backbone networks and complex feature-fusion structures, making it difficult to deploy them efficiently on resource-constrained mobile robots and edge devices, and there is also a lack of dedicated datasets for rebar intersections. In this study, 12,000 rebar mesh images were collected and annotated from two indoor scenes and one outdoor scene to construct a rebar-intersection dataset that supports both object detection and instance segmentation, enabling simultaneous learning of intersection locations and tying status. On this basis, a lightweight improved YOLOv8-based method for rebar intersection detection and segmentation is proposed. The original backbone is replaced with ShuffleNetV2, and a C2f_Dual residual module is introduced in the neck; the same improvements are further transferred to YOLOv8-seg to form a unified lightweight detection–segmentation framework for joint prediction of intersection locations and tying status. Experimental results show that, compared with the original YOLOv8L and several mainstream detectors, the proposed model achieves comparable or superior performance in terms of mAP@50, precision and recall, while reducing model size and computational cost by 51.2% and 58.1%, respectively, and significantly improving inference speed. The improved YOLOv8-seg also achieves satisfactory contour alignment and regional consistency for rebar regions and intersection masks. Owing to its combination of high accuracy and low resource consumption, the proposed method is well suited for deployment on edge-computing devices used in rebar-tying robots and construction quality inspection, providing an effective visual perception solution for intelligent construction. Full article

We use deep Physics-Informed Neural Networks (PINNs) to simulate stratified forced convection in plane Couette flow. This process is critical for atmospheric boundary layers (ABLs) and oceanic thermoclines under global warming. The buoyancy-augmented energy equation is solved under two boundary conditions: Isolated-Flux (single-wall heating) and Flux–Flux (symmetric dual-wall heating). Stratification is parameterized by the Richardson number $(R_i\in [-1,1\left]\right),$ representing $\pm 2 °\mathrm{C}$ thermal perturbations. We employ a decoupled model (linear velocity profile) valid for low-Re, shear-dominated flow. Consequently, this approach does not capture the full coupled dynamics where buoyancy modifies the velocity field, limiting the results to the laminar regime. Novel contribution: This is the first deep PINN to robustly converge in stiff, buoyancy-coupled flows ($\mid R_i\mid \le 1$) using residual connections, adaptive collocation, and curriculum learning—overcoming standard PINN divergence (errors $>28\%$). The model is validated against analytical ($R_i=0$) and RK4 numerical ($R_i\ne 0$) solutions, achieving $L_2$ errors $\le 0.009\%$ and $L_{\mathrm{\infty }}$ errors $\le 0.023\%$. Results show that stable stratification $(R_i>0)$ suppresses convective transport, significantly reduces local Nusselt number ($Nu$) by up to $100\%$ (driving $Nu$ towards zero at both boundaries), and induces sign reversals and gradient inversions in thermally developing regions. Conversely, destabilizing buoyancy $(R_i<0)$ enhances vertical mixing, resulting in an asymmetric response: $Nu$ increases markedly (by up to $140\%$) at the lower wall but decreases at the upper wall compared to neutral forced convection. At $5–10\times$ lower computational cost than DNS or RK4, this mesh-free PINN framework offers a scalable and energy-efficient tool for subgrid-scale parameterization in general circulation models (GCMs), supporting SDG 13 (Climate Action). Full article

Reliable navigation in cooperative unmanned aerial vehicle (UAV) networks requires adaptively managing measurement degradations within Kalman-filter-based estimation frameworks. This paper introduces a learning-based Kalman approach for real-time detection of degraded measurements in mesh-network-based multi-UAV navigation. The method incorporates a data-driven pre-filtering module that assesses measurement reliability prior to the Kalman update, thereby improving the robustness of the estimation process under communication-induced degradations. Within this approach, four measurement fault detection strategies—Innovation Filter (IF), Deep Q-Network (DQN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM)—were implemented and comparatively evaluated through Monte Carlo simulations combining inertial sensors, time-of-arrival, and Doppler-based inter-agent observations. Additional statistical analyses, including ±1σ error bars and a Wilcoxon rank-sum test, were conducted to verify the significance of the performance differences among the methods. The results show that the proposed approach significantly enhances navigation reliability, particularly under degraded or intermittent GNSS and communication conditions. The MLP-based configuration achieved the best balance between fault-detection accuracy and overall filter consistency. These findings confirm the effectiveness of learning-augmented Kalman filtering architectures for robust and scalable cooperative UAV navigation. Full article