Search Results (148)

Frame Replication and Elimination for Reliability (FRER) in Time-Sensitive Networking (TSN) enhances fault tolerance by duplicating critical traffic across disjoint paths. However, always-on FRER configurations introduce persistent redundancy overhead, even under nominal network conditions. This paper proposes a predictive FRER activation framework that anticipates faults using a Key Performance Indicator (KPI)-driven bidirectional Long Short-Term Memory (BiLSTM) model. By continuously analyzing multivariate KPIs—such as latency, jitter, and retransmission rates—the model forecasts potential faults and proactively activates FRER. Redundancy is deactivated upon KPI recovery or after a defined minimum protection window, thereby reducing bandwidth usage without compromising reliability. The framework includes a Python-based simulation environment, a real-time visualization dashboard built with Streamlit, and a fully integrated runtime controller. The experimental results demonstrate substantial improvements in link utilization while preserving fault protection, highlighting the effectiveness of anticipatory redundancy strategies in industrial TSN environments. Full article

Many Time-Sensitive Networking (TSN) workloads require deterministic service across heterogeneous links, yet commodity WLANs are contention-based. Although IEEE 802.11be introduced Restricted Target Wake Time (r-TWT) for prioritized access, its ability to robustly guarantee determinism in mixed deployments with legacy devices remains unverified. We propose a standards-aligned scheme that composes r-TWT, Quiet Time Period (QTP), and an optional Randomized Enqueue (RE) policy. These three mechanisms act in concert to protect the Scheduled Traffic (ST) service period (SP) while minimizing the impact on Non-Scheduled Traffic (NST). To analyze how the proposed scheme impacts existing WLANs, we focus the analysis on how the scheme reshapes the contention period (CP)—where opportunistic capacity is realized—by modeling SP/CP timing with renewal theory and embedding it into an EDCA Markov chain. Simulation results confirm that the proposed scheme protects ST determinism: ST throughput remains pinned to the ceiling with zero observed outage and bounded delay across a wide range of station counts. The proposed scheme minimizes NST throughput degradation in the system-peak throughput range (8–12 stations). Full article

Split-thickness skin grafting (STSG) is the standard of care for skin replacement therapy. While STSG is a well-established technique, it has several limitations at both the donor and recipient sites. Full-thickness skin column (FTSC) grafting is an alternative approach that involves the orthogonal harvesting of small skin columns containing the epidermis, dermis, and associated skin appendages. Peptides have been shown to promote wound repair through various reparative and regenerative mechanisms. In this study, we aimed to evaluate the extent to which FTSCs and the matrix-derived peptide TSN6, individually or in combination, influenced the rate and quality of healing, as assessed by metrics such as epithelialization, epithelial thickness, and the presence of adnexal structures. TSN6 peptide and its scrambled form was synthetized in a laboratory and mixed with a carboxymethylcellulose (CMC) hydrogel. Up to 16 standardized full-thickness excisional wounds (∅ 5 cm) were created on the dorsum of two anesthetized pigs. FTSC biopsies (∅ 1.5 mm) were harvested from donor sites located on the rump of the pig at a ratio of up to eight 1.5 mm-diameter skin columns/1 cm². Subsequently, the wounds were randomized to receive either (1) FTSC + TSN6, (2) FTSC + scrambled peptide, (3) FTSC alone, and (4) blank CMC hydrogel. Healing was monitored for 14 or 28 days. After euthanasia, the wounds were excised and processed for histology. Additionally, non-invasive imaging systems were utilized to assess wound healing. By day 14, wounds treated with FTSC or FTSC + TSN6 were significantly more re-epithelialized compared to those treated with blank CMC hydrogel. By day 28, all FTSC-transplanted wounds were fully re-epithelialized. Notably, wounds treated with FTSC + TSN6 exhibited improved healing quality, characterized by a thicker neo-epidermis and increased rete ridges at day 28 post-transplantation. All FTSC-transplanted wounds healed better than the untransplanted controls. The TSN6 peptide further improved healing quality when applied in combination with FTSCs, particularly by enhancing epidermal maturation. Full article

In the era of Industry 5.0, future wireless networks are increasingly shifting from traditional symmetric architectures toward heterogeneous and asymmetric paradigms, driven by the demand for diversified and dynamic services. This architectural evolution gives rise to complex and asymmetric flows, such as the coexistence of periodic and burst flows with varying latency, jitter, and deadline constraints, posing new challenges for deterministic transmission. Traditional time-sensitive networking (TSN) is well-suited for periodic flows but lacks the flexibility to effectively handle dynamic, asymmetric traffi. To address this limitation, we propose a two-stage asymmetric flow scheduling framework with dynamic deadline control, termed A-TSN. In the first stage, we design a Deep Q-Network-based Dynamic Injection Time Slot algorithm (DQN-DITS) to optimize slot allocation for periodic flows under varying network loads. In the second stage, we introduce the Dynamic Deadline Online (DDO) scheduling algorithm, which enables real-time scheduling for asymmetric flows while satisfying flow deadlines and capacity constraints. Simulation results demonstrate that our approach significantly reduces end-to-end latency, improves scheduling efficiency, and enhances adaptability to high-volume asymmetric traffic, offering a scalable solution for future deterministic wireless networks. Full article

This paper presents a novel metascheduling algorithm to enhance communication efficiency in off-chip time-triggered multi-processor system-on-chip (MPSoC) platforms, particularly for safety-critical applications in aerospace and automotive domains. Time-triggered communication standards such as time-sensitive networking (TSN) and TTEthernet effectively enable deterministic and reliable communication across distributed systems, including MPSoC-based platforms connected via Ethernet. However, their dependence on static resource allocation limits adaptability under dynamic operating conditions. To address this challenge, we propose an offline metascheduling framework that generates multiple precomputed schedules corresponding to different context events. The proposed algorithm introduces a selective communication strategy that synchronizes context information exchange with key decision points, thereby minimizing unnecessary communication while maintaining global consistency and system determinism. By leveraging knowledge of context event patterns, our method facilitates coordinated schedule transitions and significantly reduces communication overhead. Experimental results show that our approach outperforms conventional scheduling techniques, achieving a communication overhead reduction ranging from 9.89 to 32.98 times compared to a two-time-unit periodic sampling strategy. This work provides a practical and certifiable solution for introducing adaptability into Ethernet-based time-triggered MPSoC systems without compromising the predictability essential for safety certification. Full article

The evolution of the internet towards supporting time-critical applications, such as industrial cyber-physical systems (CPSs) and autonomous systems, has created an urgent demand for networks capable of providing deterministic, low-latency communication. Autonomous vehicles represent a particularly challenging use case within this domain, requiring both reliability and determinism for massive data streams—a requirement that traditional Ethernet technologies cannot satisfy. This paper addresses this critical gap by proposing a comprehensive scheduling framework based on Time-Aware Shaping (TAS) within the Time-Sensitive Networking (TSN) standard. The framework features two key contributions: (1) a novel baseline scheduling algorithm that incorporates a sub-flow division mechanism to enhance schedulability for high-bandwidth streams, computing Gate Control Lists (GCLs) via an iterative SMT-based method; (2) a separate heuristic-based computation acceleration algorithm to enable fast, scalable GCL generation for large-scale networks. Through extensive simulations, the proposed baseline algorithm demonstrates a reduction in end-to-end latency of up to 59% compared to standard methods, with jitter controlled at the nanosecond level. The acceleration algorithm is shown to compute schedules for 200 data streams in approximately one second. The framework’s effectiveness is further validated on a real-world TSN hardware testbed, confirming its capability to achieve deterministic transmission with low latency and jitter in a physical environment. This work provides a practical and scalable solution for deploying deterministic communication in complex autonomous and cyber-physical systems. Full article

Background: Fructus Meliae Toosendan (FMT) is a traditional Chinese medicine used to treat ascariasis; however, its reported hepatotoxicity limits its application. Toosendanin (TSN), as a principal active component, is recognized as the primary toxic ingredient responsible for FMT-induced hepatotoxicity, but the underlying mechanisms remain elusive. Methods: HepG2 cells were treated with TSN and analyzed using Western blotting and qPCR assays for related gene transcription and protein expression. Lipid peroxidation and ferroptosis markers were measured. Balb/c and C57BL/6 mice received various doses of TSN administration, and their liver function was assessed with serum biochemistry and histopathology. Network pharmacology and oxidative lipidomics were performed to identify key targets and metabolites. Results: TSN triggered ferroptosis both in vitro and in vivo, accompanied by the elevated expression of 5-lipoxygenase (ALOX5) and its downstream metabolites. The ALOX5 level modulated hepatocyte sensitivity to TSN-induced ferroptotic damage. An ALOX5 knockdown alleviated TSN-induced liver injury and ferroptosis in vivo. Conclusions: Our study demonstrated that TSN induces hepatotoxicity by facilitating ALOX5-mediated lipid peroxidation, thereby sensitizing cells to ferroptosis. Full article

Despite the abundant expression of the microRNA-degrading Translin (TN)/Trax (TX) complex in midbrain dopaminergic (DA) neurons and its implication in neuropsychiatric disorders, its cell-autonomous roles in metabolic and behavioral responses remain unclear. To address this, we generated DA neuron-specific conditional knockout (cKO) mice for Tsn (TN) or Tsnax (TX) using DAT-Cre. Immunostaining confirmed efficient TX loss in Tsnax cKO DA neurons without affecting TN, while Tsn deletion abolished TX expression, revealing asymmetric protein dependency. Body composition analysis showed no alterations in adiposity in either cKO model. Locomotor responses to acute or repeated administration of cocaine (20 mg/kg) or amphetamine (2.5 mg/kg) were unchanged in Tsn or Tsnax cKO mice. Furthermore, amphetamine-induced conditioned place preference (1 mg/kg) was unaffected. These results demonstrate that the TN/TX complex within DA neurons is dispensable for regulating adiposity, psychostimulant-induced locomotion (both acute and sensitized), or amphetamine reward-related behavior, suggesting its critical functions may lie outside these specific domains. Full article

Time-Sensitive Networking (TSN) is an advance Ethernet paradigm designed to provide low delay, low jitter, and deterministic transmission time. The Cycling Queuing and Forwarding (CQF) mechanism is introduced in TSN as a scheduler to achieve precise communication. Multi-CQF, as an extension of CQF, supports the transmission of various traffic types by assigning different cycle lengths to each queue group. In its original form, Multi-CQF-based scheduling algorithms do not account for flow sorting, leading to increased transmission delays and reduced network efficiency as a network dynamically changes. To enhance the performance of Multi-CQF, this paper initially utilizes queuing theory to analyze and manage traffic, providing foundation solutions. Subsequently, Mixed Integer Programming (MIP) and the Variable Neighborhood Search Genetic Algorithm (VNS-GA) are employed to optimize transmission delay in small- and large-traffic TSN networks, respectively. MIP quickly seeks out the optimal scheduling solution for small-traffic TSN networks using branch-and-bound and linear programming techniques, while the VNS-GA improves efficiency and performance for large-traffic ones by continuously adjusting the search neighborhood strategy. Comparing with other existing schemes, computer simulation reveals that MIP reduces delay by approximately 13% on average in small-traffic TSN networks, while the VNS-GA achieves an average delay reduction of 7% in large-traffic ones. Full article

Multi-vendor interoperability is essential for the stable operation, scalability, and successful market adoption of Time-Sensitive Networking (TSN). Conformance tests address protocol conformance. Informal interoperability testing and plugfests help to improve the quality and interoperability of specific implementations, and of the underlying international standard documents. This paper presents three findings related to time synchronization in a multi-vendor TSN system. Differing interpretations of released standards and inconsistent setting of relevant system parameters resulted in undesirable behavior impacting the performance of the complete TSN system. The findings relevant to the standards themselves have been submitted to IEEE as maintenance items or are already being considered in work in progress at IEEE. In addition to interoperability testing, the importance of consistent system engineering and industry-specific TSN profiles are identified as important ingredients for successful implementation of TSN-based systems. Full article

As Industry 5.0 progresses, the demand for zero-touch configuration in industrial automation and smart manufacturing is increasing. This paper proposes a dynamic configuration management framework for Time-Sensitive Networking (TSN), aiming to address the challenges of flexibility and adaptability in dynamic network environments. A zero-touch configuration model is presented for TSN by incorporating a Delay-Aware Shortest Path Search (DASPS) algorithm to improve scheduling success rates. Simulation results demonstrate the ability of the framework to reconfigure networks within 2.67 milliseconds. The DASPS algorithm achieves a scheduling success rate of 70.22% for 1000 TSN flows, in contrast to only 22.23% achieved by the Shortest Path Search (SPS) algorithm. The proposed model effectively adapts to dynamic network changes, guaranteeing real-time data transmission. To further evaluate system adaptability, path entropy is introduced as a metric to quantitatively assess the balance of scheduling outcomes under topological changes. In the event of link failures, path entropy experiences a sharp decline but rapidly recovers after reconfiguration, demonstrating the system’s strong self-healing capability. Full article

Routing and scheduling in Time-Sensitive Networking (TSN) is an NP-hard problem. In this paper, we propose a novel routing and scheduling approach for TSN based on evolutionary algorithm. Specifically, we introduce a flow grouping method that leverages the greatest common divisor to optimize flow aggregation. On this basis, we develop a flow routing strategy that employs a genetic algorithm, where the evaluation function considers not only flow combinability but also path length and network load. By exploiting the non-combinable properties of flows, we effectively reduce the search space for the genetic algorithm. Furthermore, we design a scheduling method based on differential evolution algorithms tailored to TSN’s requirements of zero jitter and no frame loss. We propose a gene coding method and rigorously prove its correctness, which significantly reduces the search space of the differential evolution algorithm. The experimental results demonstrate that our approach enables more flows to traverse along the shortest path compared to both k-shortest path methods and integer linear programming approaches, while achieving a faster execution time in large-scale scheduling scenarios. Full article

Background/Objectives: The automatic identification of coronary artery dominance holds critical importance for clinical decision-making in cardiovascular medicine, influencing diagnosis, treatment planning, and risk stratification. Traditional classification methods rely on the manual visual interpretation of coronary angiograms. However, current deep learning approaches typically classify right and left coronary artery angiograms separately. This study aims to develop and evaluate an integrated video-based deep learning framework for classifying coronary dominance without distinguishing between RCA and LCA angiograms. Methods: Three advanced video-based deep learning models—Temporal Segment Networks (TSNs), Video Swin Transformer (VST), and VideoMAEv2—were implemented using the MMAction2 framework. These models were trained and evaluated on a large dataset derived from a publicly available source. The integrated approach processes entire angiographic video sequences, eliminating the need for separate RCA and LCA identification during preprocessing. Results: The proposed framework demonstrated strong performance in classifying coronary dominance. The best test accuracies achieved using TSNs, Video Swin Transformer, and VideoMAEv2 were 87.86%, 92.12%, and 92.89%, respectively. Transformer-based models showed superior accuracy compared to convolution-based methods, highlighting their effectiveness in capturing spatial–temporal patterns in angiographic videos. Conclusions: This study introduces a unified video-based deep learning approach for coronary dominance classification, eliminating manual arterial branch separation and reducing preprocessing complexity. The results indicate that transformer-based models, particularly VideoMAEv2, offer highly accurate and clinically feasible solutions, contributing to the development of objective and automated diagnostic tools in cardiovascular imaging. Full article

With the rapid development of intelligent driving technology, in-vehicle bus networks face increasingly stringent requirements for real-time performance and data transmission. Traditional bus network technologies such as LIN, CAN, and FlexRay are showing significant limitations in terms of bandwidth and response speed. In-Vehicle Ethernet, with its advantages of high bandwidth, low latency, and high reliability, has become the core technology for next-generation in-vehicle communication networks. This study focuses on bandwidth waste caused by guard bands and the limitations of Frame Pre-Emption in fully utilizing available bandwidth in In-Vehicle Ethernet. It aims to optimize TSN scheduling mechanisms by enhancing scheduling flexibility and bandwidth utilization, rather than modeling system-level vehicle functions. Based on the Time-Sensitive Networking (TSN) protocol, this paper proposes an innovative Adaptive Frame Segmentation (AFS) algorithm. The AFS algorithm enhances the performance of In-Vehicle Ethernet message transmission through flexible frame segmentation and efficient message scheduling. Experimental results indicate that the AFS algorithm achieves an average local bandwidth utilization of 94.16%, improving by 4.35%, 5.65%, and 30.48% over Frame Pre-Emption, Packet-Size Aware Scheduling (PAS), and Improved Qbv algorithms, respectively. The AFS algorithm demonstrates stability and efficiency in complex network traffic scenarios, reducing bandwidth waste and improving In-Vehicle Ethernet’s real-time performance and responsiveness. This study provides critical technical support for efficient communication in intelligent connected vehicles, further advancing the development and application of In-Vehicle Ethernet technology. Full article

The development of low-orbit satellite communication networks marks the beginning of a new era in global communication. However, in the context of large-scale LEO satellite communication scenarios, the traditional adjacent connection transmission method limits the advantages of low latency in optical communication. Multi-hop transmission increases the number of hops and propagation distance, thereby affecting time-sensitive business transmissions. Therefore, based on the design of optical interconnect parallel subnetworks, this paper proposes a scheduling strategy for time-sensitive business transmissions between LEO satellites. Firstly, this strategy integrates the gate control scheduling mechanism from Time-Sensitive Networking (TSN) transmission in the interconnect parallel subnetwork scenario. Secondly, considering issues like queuing after subnetwork division, excessive burden, and algorithm complexity, mathematical problem abstraction modeling is applied to subsequent route scheduling, with reinforcement learning used to solve the problem. Through simulation experiments, it has been observed that compared to SPF (Shortest Path First) and ELB (Equal Load Balance), this approach can effectively enhance the control capability of end-to-end latency for TSN services in long-distance transmissions within Low Earth Orbit mega-constellations. The integration of reinforcement learning decision algorithms also reduces the complexity compared to traditional constraint-solving algorithms, ensuring a certain level of practicality. Overall, this solution can enhance the communication efficiency and performance of time-sensitive services between satellite constellations. By integrating time-sensitive network transmission technologies into optically interconnected subnets, further exploration and realization of low-latency and controllable latency satellite communication networks can be pursued. Full article