To alleviate network congestion resulting from packet retransmission in Federated Learning (FL) systems, the User Datagram Protocol (UDP) has been adopted. However, data loss under UDP inevitably degrades FL performance, as learning is sensitive to incomplete model parameters. This paper addresses such degradation by proposing a low-rank approximation–based parameter transmission method for FL. This approach decomposes model parameters at the transmitter to extract dominant singular values, which are essential for accurate approximation of the model parameters at the receiver. Although the selective delivery of singular values and vectors reduces communication overhead, their loss would cause severe performance degradation, making it essential to employ protection mechanisms. Therefore, to protect the extracted singular values and vectors, we use Systematic Network Coding (SysNC). The SysNC with low-rank approximation can recover original information under a packet loss environment, enhancing robustness against partial loss during the transmission of model parameters. Theoretical analysis and experiments confirm its effectiveness. As an example from our experiments, with a packet loss ratio of p = 0.1, the proposed method achieves over 96% of the accuracy observed in the packet loss-free case, while reducing the end-to-end delay for model parameter transmission by approximately 50% compared to a UDP baseline.

Low-Earth orbit satellites are gradually becoming the core infrastructure of integrated aerospace communication networks, with their significant advantages of high communication rates, small transmission delay, and wide coverage. Interference with military communications in response to their security and protection needs is a current research challenge. Consequently, this paper introduces an interference technique optimized for low-Earth orbit satellite signals using a multimodal learning transformer model (OI-MLT). The proposed method incorporates symmetry-aware design by exploiting the inherent time–frequency structural characteristics of LEO satellite signals and the spatially distributed topology of interference sources. An optimized model for distributed interference sources is developed, and multimodal information of spectra and numerical values is processed in parallel through the self-attention mechanism. This approach effectively addresses the problem of dynamic matching between the interference signal and target signal in high-speed LEO scenarios, as well as high-precision interference synchronization under time-varying channels. Experimental results demonstrate that this technique enhances the precision of frequency tracking, reduces the time required for synchronization establishment, and improves the interference success rate by 27.52% on average compared with existing methods.

In the context of China's comprehensive implementation of the fundamental task of "Fostering Virtue through Education," integrating values education into university physical education (PE) and elevating it from "skill delivery" to "character shaping" has become a central educational issue of China. However, robust empirical evidence on how PE instructors' political literacy drives this process remains scarce. This study draws on survey data from PE instructors across 20 Chinese provinces and municipalities, collected from December 2025 to January 2026. Using structural equation modeling and mediation analysis, it systematically examines the internal mechanism through which teachers' political literacy influences student values development. (1) University PE instructors' political literacy is a multidimensional construct comprising political understanding, identification, faith, and practice; (2) teachers' "educative efficacy" plays a substantially mediating role between political literacy and effectiveness in fostering student values; and (3) three "transmission delays" exist within this pathway: the delay in translating political cognition into teaching practice, the delay in sublimating classroom fun into value internalization, and the delay in converting educative efficacy into educational outcomes. The accumulation of political literacy is driven by the endogenous motivation of self-efficacy, its deepening relies on external support from collaborative platforms, and its sustained development depends on the iterative momentum provided by feedback mechanisms. Consequently, this study constructs an integrated enhancement path synergizing "endogenous motivation stimulation, external environment optimization, and developmental mechanism guarantee." This research elucidates the universal mechanism of how teachers translate personal belief systems into effective teaching practices, providing practical guidance for the professional development of PE instructors in the new era.

Robust state estimation for networked systems with transmission delays via predictive event-triggered sampling

Neural encoding of speech in noise: Effects of background noise and behavioral correlates.

Healthcare systems have recently undergone a significant digital transformation, driven by the rapid growth of the Internet of Medical Things (IoMT) and smart sensing technologies. The sensing technologies generate continuous, high-speed stream of medical information that require real-time analysis and processing. Healthcare data streams are evolving over time. Recently, medical data segmentation and clustering are considered one of the most important techniques used to enhance IoMT reliability, scalability and to support the online medical decisions. Furthermore, these techniques employ bandwidth optimization by reducing the overhead and transmission delay. To date, several surveys have also been proposed in the literature. However, current challenges such as real-time processing and dynamic maintaining of wide variety of medical data streams, which raise the question of developing intelligent and adaptive analytical systems for use in the medical field. Therefore, we conduct a comprehensive survey on the recent advancements in the segmentation and clustering methods for healthcare data streams. This survey examines healthcare data streams, employing clustering and segmentation techniques to improve diagnostic accuracy and enable early disease prediction.

Underwater wireless sensor networks (UWSNs) have an important role in ocean monitoring, environmental surveillance and disaster prevention; however, their practical deployment is severely limited by the limited battery capacity, high cost of acoustic communications and difficulty of node maintenance. In particular, inefficient clustering and routing strategies result in unbalanced energy consumption, premature failures of nodes and decreased network lifetime. To solve these problems, the purpose of this paper is to design an energy-efficient and scalable clustering and multi-hop routing framework for UWSNs that can extend the network lifetime while ensuring reliable data delivery. We propose a hybrid optimization approach that is named as MPA-HGSO, where Marine Predator Algorithm (MPA) is used for cluster head selection and cluster formation while Henry Gas Solubility Optimization (HGSO) is used to optimize the multi-hop routing paths. The proposed framework is tested with extensive simulations performed in the Matlab environment in three base station deployment scenarios with a network of 300 sensor nodes deployed in a 200 × 200 m2 area. Performance is measured in terms of network lifetime, energy consumption, and end-to-end transmission delay and compared with LEACH, TEEN, MPSO, and IPSO-GWO protocols. Simulation results show that MPA-HGSO is significantly better than benchmark methods. In the central base station scenario, the proposed approach gives a First Node Die (FND) at 2151 rounds and a Half Nodes Die (HND) at 2160 rounds, as compared to 1115 and 1290 rounds for LEACH, respectively. Moreover, the average transmission delay is reduced to 140 ms, which is a reduction of up to 44% compared to conventional approaches. These results validate that the proposed MPA-HGSO framework is an effective energy consumption and network lifetime and communication efficiency balance framework, which is a promising solution for the long-term and large-scale UWSN deployments.

To meet the growing demand for autonomous decision-making and real-time optimization in industrial manufacturing, integrating Artificial Intelligence-Generated Content (AIGC) services with Industry 5.0 can enable real-time industrial intelligence. The effectiveness of a generative model is closely related to the current state of the production environment. However, existing studies often ignore the dynamic temporal relationship between generative models and production environments, especially in industrial scenarios with large model transmission delays and random AIGC task arrivals. Therefore, we define a novel metric, namely the Age of Model (AoM), to measure the freshness of generative models with respect to current industrial tasks. We then formulate an average-AoM-minimization problem that jointly considers LoRA-based fine-tuning, wireless transmission and resource allocation. To solve this problem, we propose a Hybrid-Action Multi-Agent Proximal Policy Optimization (HA-MAPPO) algorithm. The proposed algorithm follows the centralized training and decentralized execution (CTDE) paradigm and introduces a Main-Agent Priority State Strategy to support coordinated training and independent execution. In addition, a multi-head output structure is designed to handle the hybrid-action space, which includes discrete fine-tuning association decisions and continuous transmission resource allocation. Simulation results show that the proposed scheme outperforms all benchmark methods. Specifically, the cumulative rewards are improved by approximately 11.13%, 20.32%, 36.61%, and 38.78% compared with the four benchmark algorithms, respectively. These results demonstrate that the proposed scheme can significantly reduce the average AoM while providing high-quality and timely industrial AIGC services.

Abstract LEO satellites will enhance the GNSSs in future Positioning, Navigation, and Timing services. In addition to achieving high orbital accuracy of the LEO satellites, ensuring the reliability and safety of the Near-Real-Time LEO satellite Precise Orbit Determination is equally essential for maintaining integrity in LEO-augmented positioning and timing, yet it is less studied. Compared to the favorable data conditions of scientific LEO satellites in post-processing mode, GNSS observations collected by navigation-oriented LEO satellites may encounter significant discontinuities due to not only tracking problems, but also transmission delays and interruptions within potential data downlinking for NRT ground-based POD, posing challenges to its Integrity Monitoring. In particular, the Protection Level during observation gaps may encounter large increases and strongly harm the IM availability. This study investigates the algorithm calculating the NRT LEO satellite PLs under various observation gaps, followed by different lengths of observation tails based on the reduced-dynamic model. The strategy estimating (RD) and not estimating (CD) piecewise-constant stochastic accelerations in the POD process are both tested. While the former benefits the POD accuracy in complete data conditions, the latter exhibited its advantage in reducing the PLs within large data gaps. Results showed that during observation discontinuities, the RD strategy yields significantly degraded PLs in all three directions, which increase rapidly with gap duration. In the 9-hour gap case, the along-track PL exceeds 20 m, while under nominal conditions, it remains around 0.51 m. In contrast, the CD strategy achieves more stable PLs, i.e., below 2 m even during a 9-h gap, although its convergence after observation recovery is slower. Furthermore, the study derives suitable thresholds of alert limits to bound 99% of the PLs under various gap conditions, providing a reference for LEO integrity assessment under such conditions.

The security of underwater sensor networks (USNs) is challenging in ocean research. The primary objective of this work is to investigate the susceptibility of USNs to passive attacks, such as source location privacy (SLP). A trusted Sybil node-based source location scheme is introduced in this manuscript to fulfil the above purpose. The Sybil nodes and suitable fake source nodes are initially inserted into a network. The main intention is to generate false source data by using fake source nodes generated with evidence theory for data fusion, thereby disguising the traffic carried by the source information. After that, separate transmission windows were scheduled for the real and false packets to prevent data conflicts. The Sybil nodes are also used during the multi-path data routing process, which occurs from the source node to the destination node. In addition to diversifying the routes, it raises the bar for the adversary's ability to track the source information's route while it is being sent without conflict. The proposed TSN-SLP scheme outperformed SLPRRFPR, EECOR, and ARR by 21%, 53%, 50%, and 17.4%, respectively, in average packet data rate, safety time, transmission delay, and energy use, according to the experimental results.

BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies-local development, simulated cloud testing, and limited cloud usage for benchmark capture-enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows.

В работе предложен новый гибридный протокол маршрутизации AOHPR (Ad-hoc On-demand Hybrid Proactive-Reactive) для беспроводных самоорганизующихся сетей Ad-hoc. Он реализован на языке C++ в симуляторе NS-2 и сочетает проактивные и реактивные принципы маршрутизации, объединяя сильные стороны протоколов. Протокол разработан в виде класса Agent/AOHPR и интегрируется в симулятор NS2 через механизм Tcl Class. Проведено сравнительное моделирование работы протокола AOHPR с существующими протоколами маршрутизации в сетях Ad-hoc. Introduction. This paper introduces a novel hybrid routing protocol, AOHPR (Ad-hoc On-demand Hybrid Proactive-Reactive), designed for mobile ad hoc networks. The protocol integrates proactive and reactive approaches by implementing mechanisms for local proactivity, adaptive caching, and automatic route restoration. The implementation was realized in the NS-2 simulator using C++ as the Agent/AOHPR class and integrated via the Tcl Class mechanism. The objective of this work is to enhance routing efficiency in wireless ad-hoc networks under conditions of variable node density through the development and analysis of the hybrid AOHPR protocol. Simulation of the proposed protocol in NS-2. As AOHPR is a new hybrid routing protocol not included in the standard NS-2 distribution, its implementation was written in C++, comprising a header file (aohpr.h) and an implementation file (aohpr.cc). This code was integrated into the NS-2 architecture and implements the core functions described in this paper: local proactivity, reactive routing for remote destinations, adaptive caching, and an automatic route recovery mechanism. (The files aohpr.cc and aohpr.h are C++ source codes not part of the standard NS-2 build). Results.A novel hybrid routing protocol, AOHPR (Ad hoc Optimized Hybrid Proactive-Reactive), is proposed. It combines four key principles – local proactivity, a reactive mechanism, adaptive caching, and automatic recovery to improve the efficiency of routing and data transmission in wireless ad-hoc networks. To enable its simulation, the AOHPR protocol was developed in C++ as the Agent/AOHPR class and integrated into the NS-2 simulator via the Tcl Class mechanism. Simulation of the proposed protocol allowed for an evaluation of its performance, confirming its effectiveness in networks with varying node density. The new protocol was compared against existing protocols, such as AODV and OLSR, using metrics including packet delivery delay, packet delivery ratio (PDR), and packet loss ratio. In networks comprising 25–100 nodes with a mobility speed of 5 m/s, the simulation results showed: AOHPR reduces average data transmission delay, particularly in larger networks (≥ 75 nodes), achieving delays as low as 200 ms; AOHPR increases the packet delivery ratio (PDR) by up to 10% compared to AODV and up to 20% compared to OLSR, depending on the network size; AOHPR reduces packet loss by 3–20% relative to AODV and OLSR, attributed to its rapid route restoration capability upon loss of neighboring nodes.

By incorporating opportunistic coding, network throughput is enhanced, resulting in improved overall performance. However, applying this paradigm to Flying Ad-hoc Networks (FANETS) faces significant challenges due to the highly dynamic topology caused by the high-velocity mobility of UAVs, alongside the NP-hard complexity of identifying optimal coding opportunities in rapidly evolving aerial network architectures. To address these challenges, this paper proposes a novel coding-aware routing protocol based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). We formulate the routing problem as a multi-agent continuous decision-making process, employing the MADDPG algorithm to optimize routing policies in real-time through decentralized execution and centralized training. To maximize network utility, we design a comprehensive reward function that integrates coding benefits, throughput, energy distribution, and end-to-end delay, ensuring a balance between throughput maximization and the energy sustainability of individual UAV nodes. Simulation results demonstrate that the proposed protocol significantly outperforms state-of-the-art coding-aware routing protocols in terms of throughput, Packet Delivery Ratio (PDR), and transmission delay, exhibiting superior robustness in highly dynamic FANET scenarios. Notably, at a network density of 20 UAVs, MARL-CAR outperforms COPE, DCAR, TSCAR, and RLCAR in terms of coding ratio by 32.23%, 18.93%, 20.35%, and 5.5%, respectively. This research provides a scalable and intelligent networking solution for the next generation of autonomous UAV swarms and collaborative aerial missions.

The short-latency reflex (SLR), which occurs immediately after ground contact during jumping, is traditionally attributed to a muscle spindle-mediated stretch reflex, with a longer latency explained by slow muscle stretching. However, emerging evidence suggests that the bone myoregulation reflex (BMR) may provide a more physiologically parsimonious and biomechanically integrated explanation for this response. This study compared the latencies of these reflexes and assessed the mechanical stimulus transmission delay to the muscle during impact. Two experiments were performed in healthy adults. Experiment 1 measured the soleus tendon reflex (T-reflex), SLR, and BMR latencies via surface electromyography (EMG). Experiment 2 recorded delays from the mechanical stimulus to the muscle belly using intramuscular EMG. The median latencies in Experiment 1 were 35.0ms (T-reflex), 45.8ms (SLR), and 43.0ms (BMR). The SLR and BMR latencies were significantly longer than the T-reflex latencies (p = 3.6 × 10⁻11). There was no difference between the SLR and BMR. Experiment 2 showed mechanical transmission delays of 4.31ms (tendon stretch), 3.31ms (tap), and 2.83ms (whole-body vibration), without significant differences. The ~ 11ms longer SLR latency than the T-reflex cannot be explained by slow muscle stretching. Normalized soleus EMG signals during landing (feedforward) were positively correlated with the SLR amplitude (feedback) (r = 0.554, p = 0.0003). The latency characteristics of the SLR suggest that it more closely resembles the BMR than the classical stretch reflex does. It is speculated that as a bone-protective mechanism, BMR may underlie reflexive muscle contractions that deliver load-induced protective feedback during impact, potentially preserving both bone and muscle-tendon integrity.

Experimental investigation of buoyancy distribution characteristics in basement structures founded on clayey soils

ABSTRACT The digitalization of ceramic technology teaching generates sensitive multi‐modal data, including personal information and valuable cultural assets like unique ceramic patterns, requiring robust protection. This paper establishes a comprehensive, network security‐empowered algorithm system for such teaching scenarios. The system integrates multi‐modal sensitivity identification, feature fusion, hybrid encryption, and a reversible image perturbation mechanism. Key results show the sensitivity identification model achieves 0.97 accuracy. The hybrid encryption keeps transmission delay below 17.5 ms for real‐time teaching. The reversible perturbation algorithm effectively protects cultural images (e.g., Fanchang kiln celadon), with authorized restoration SSIM (Structural Similarity Index Measure) at 0.92–0.95 and unauthorized restoration PSNR (Peak Signal‐to‐Noise Ratio) reduced to 18–20 dB, hindering pattern theft. The main contribution is a unified security framework that maintains controllable risk for teaching and cultural data throughout the digital pipeline, providing a deployable foundation for the secure digital inheritance of ceramic craftsmanship.

BACKGROUNDIn tuberculosis (TB) surveillance, genomics is mainly used to identify TB patient clusters; growing clusters are commonly attributed to ongoing transmission events.AIMThis study's objective was to explore other factors, in addition to ongoing transmission, contributing to cluster expansion.METHODSThe study population included all 1,886 culture-positive TB cases diagnosed within the whole Almería province population, Spain, between January 2003 and June 2024. Cases' Mycobacterium tuberculosis strains were whole genome sequenced enabling detection of clusters (with pairwise distance between strains < 12 single nucleotide polymorphisms (SNPs)). Evolutionary analyses positioned cases within genomic networks based on SNP distribution. This allowed, together with clinical and epidemiological data, to infer why new cases (diagnosed 3.5 years prior) entered clusters.RESULTSCases' mean age was 37.3 years (standard deviation: 16.4); 71.7% (1,352/1,886) were male and 65.2% (1,230/1,886) migrants from 50 countries, with mostly Moroccan (21.6%; 407/1,886), Romanian (10%; 188/1,886), Senegalese (8.3%; 156/1,886) and Malian (5.2%; 98/1,886) nationalities. We detected 106 clusters, comprising 537 cases in total. The 106 new cases occurred within 53 clusters, including 31 growing clusters (identified pre-2021) and 22 recent clusters (that arose in 2021 and after). Ongoing transmission was responsible for cluster expansion in around one-third of growing clusters (9/31), versus two-thirds (15/22) of recent clusters. Genomic network assessments found that newly clustered cases not due to ongoing transmission, were likely driven by reactivation of past exposures, prolonged diagnostic delays or subclinical periods, or a combination of these factors.CONCLUSIONUnderstanding cluster dynamics guides case-specific management and supports TB control.

Time Sensitive Networking (TSN) is fundamental for the reliable, low-latency networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome by using Target Wake Time (TWT), which enables the access point to instruct Wi-Fi stations to wake up and transmit in non-overlapping TWT Service Periods (SPs), and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), with the objective to schedule TWT SPs that maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, due to TASP being NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Using a TWT simulator based on ns-3, we compare TASPER to several baselines, including HSA, a state-of-the-art solution originally designed for WirelessHART networks. We demonstrate that TASPER obtains up to 24.97% lower mean transmission rejection cost and saves up to 14.86% more energy compared to the leading baseline, ShortestFirst, in a challenging, large-scale scenario. Additionally, when compared to HSA, TASPER also reduces the energy consumption by 34% and reduces the mean rejection cost by 26%. Furthermore, we validate TASPER on our IIoT testbed, which comprises 10 commercial TWT-compatible stations, observing that our solution admits more transmissions than the best baseline strategy, without violating any AoI deadline.

Ever-increasing demands for lower power loss, shorter transmission delay, and higher communication capacity have been a challenge in optical networks-on-chips (ONoCs). To address this, various multiplexing technologies have been proposed and applied to optical interconnection networks. Among these, Mode-Division Multiplexing (MDM) technology stands out for its ability to significantly enhance network throughput and reduce communication delays by simultaneously transmitting multiple-mode optical signals through a multi-mode waveguide, making it a compelling research area. In this paper, we propose a flexible and scalable multi-mode optical switching element (MOSE) that adjusts transmitted optical signals by modifying its structure. A multi-mode optical router (MOR) based on the proposed MOSE is introduced, followed by the design of MDM-based optical mesh networks-on-chip (MMONoCs) that support parallel transmission of multiple TE-polarization mode optical signals. Additionally, loss and crosstalk models for multi-mode optical devices, including MOSE and MOR, are systematically established. In conclusion, the loss, OSNR, and BER for three TE-polarization modes (TE0, TE1, and TE2) at different network scales are analyzed using MOR as a specific example. The findings indicate that the scalability of MMONoC and its communication quality are mainly influenced by mode crosstalk noise. Furthermore, network performance metrics, including End-to-End (ETE) delay and throughput, are discussed based on two-mode (TE0, TE1) and three-mode (TE0, TE1, and TE2) optical signals at 1550 nm in 4 × 4 and 5 × 5 optical mesh networks. Simulation results demonstrate that MMONoC exhibits significant improvements in ETE delay and throughput compared to traditional single-mode optical modes.

Abstract This paper proposes a novel predefined-time sliding mode synchronization control strategy and applies it to the synchronization control and chaotic communication encryption of a newly designed 4D hyperchaotic memristive entangled system. The 4D system is constructed by integrating the chaotic entanglement mechanism with the nonlinear characteristics of a cubic magnetron memristor, and its hyperchaotic properties and physical realizability are verified through dynamic analysis and the implementation of an equivalent analog circuit. On this basis, a predefined-time sliding mode controller is designed, which achieves fast system synchronization within a preset time and effectively suppresses chattering via adaptive parameter adjustment. Numerical simulations and chaotic communication encryption experiments validate the strategy’s effectiveness and robustness, results show that even under internal uncertainties, transmission delays, and external disturbances, the strategy still enables the hyperchaotic system to synchronize within the predefined time and realize signal recovery in encryption.