Search Results (3,311)

Vehicular communication networks demand highly efficient and accurate channel estimation to ensure reliable data exchange in high mobility scenarios. The IEEE 802.11p standard is widely regarded as the foundation of the Vehicle-to-Vehicle (V2V) communication channel; however, it is constrained by limited pilot resources and a fixed pilot structure, which degrade the performance and effectiveness of traditional estimation techniques, particularly in dynamic environments. Recent advances in deep learning offer significant potential for addressing these issues by improving estimation accuracy and modelling complex channel dynamics. Though deep learning-based methods introduce trade-offs in computational complexity and accuracy, these are crucial constraints in latency-sensitive V2V scenarios. This article presents a comprehensive review of deep learning-based channel estimation techniques, analysing methods for the IEEE 802.11p standard and critically examining their limitations in both classical and deep learning-based approaches. Additionally, the article highlights improvements introduced by IEEE 802.11bd, which features an enhanced pilot structure and advanced modulation schemes, providing a more robust framework for adaptive, efficient channel estimation. By identifying future research pathways that balance delay, complexity, and accuracy, an intelligent and effective transportation system can be established. Full article

Sine and cosine wave synthesis is utilized for generating sinusoidal-like values in the digital domain. While this task is commonly handled through software, dedicated hardware like Direct Digital Synthesis (DDS) is also available. However, both methods rely on memory resources, such as look-up tables and Read-Only Memories (ROMs), which face latency limitations related to additional memory access times on top of additional $Si$ area. With the advent of real-time arithmetic for sine wave approximation, this paper presents a digital module that employs iterative multiply-accumulate (MAC) operations for sine and cosine synthesis. To support the integration of this module into Systems-on-Chip (SoCs), Field-Programmable Gate Arrays (FPGAs), and standalone Application-Specific Integrated Circuits (ASICs), a comprehensive figure of merit (FoM) comparison against various ROM-less methods is provided. When implemented on a Xilinx (AMD) XC7A100T-3CSG324 FPGA, the proposed architecture compared to other ROM-less solutions like the Taylor approximation, achieves 80.80% lower resource utilization, 80.89% reduced propagation delay, and 36.66% higher accuracy in sine and cosine wave approximation, both operating as 32-bit systems with one sample per clock cycle. Furthermore, the proposed sine accelerator, accompanying control and communication IPs, and custom firmware were deployed on an FPGA-based function generator platform and experimentally validated. Full article

High-frequency (HF) sensor networks play an irreplaceable role in remote sensing and emergency communications but suffer severely from ionospheric multipath interference, which degrades Time-of-Arrival (TOA) estimation accuracy. Conventional methods, such as the Generalized Cross-Correlation (GCC) and standard Delay-Locked Loops (DLL), often treat multipath components as noise, leading to significant measurement bias in dynamic environments. To address this, we propose a Multipath Separation and Independent Tracking (MSIT) architecture. This framework transforms multipath interference into valuable observables by establishing a closed-loop synergy: a Maximum Likelihood Estimation (MLE)-based module iteratively separates signal components, while parallel tracking loops update phase and delay parameters. Additionally, a super-resolution MUSIC algorithm is employed for initialization to resolve sub-chip multipath components. Simulations demonstrate that under disturbed channel conditions, the MSIT method achieves a mean delay estimation error reduction of about two orders of magnitude relative to the GCC method. Furthermore, field experiments on the Xi’an–Ürümqi link demonstrate its capability to stably resolve and track multiple propagation paths in real-world environments. This approach significantly enhances the measurement precision and reliability of HF sensing systems. Full article

Background: Continuity of care is a core component of high-quality, patient-centered health systems and a central domain of nursing practice, particularly for older adults and people living with chronic and complex conditions. Yet discontinuities remain common during transitions between hospital and community care, contributing to fragmented communication, delayed follow-up, negative patient experiences, and avoidable harm. Methods: Literature was identified through iterative searches in PubMed and CINAHL (2002–2024), complemented by citation tracking of seminal frameworks and reference-list screening. Sources were prioritized for conceptual frameworks and empirical studies/reviews addressing hospital-to-community transitions, patient experience, and nursing-relevant strategies to strengthen continuity. Results: Across the reviewed literature, continuity was most frequently conceptualized as informational, management, and relational continuity. Most empirical studies and reviews highlighted discharge information-transfer failures and unclear post-discharge responsibility as recurrent drivers of discontinuity, particularly among older adults and people with complex needs. Evidence also suggests that interventions combining structured discharge processes with proactive post-discharge follow-up and a consistent point of contact (often nurse-led) are associated with improved patient experience and fewer early post-discharge complications in high-risk groups. Patient-reported instruments (e.g., PCCQ and CAHPS-derived domains) complement administrative indicators by capturing continuity as lived experience. Limitations: As a narrative review, findings reflect interpretative synthesis rather than systematic evidence aggregation. Conclusions: Continuity of care should be understood as both a structural and relational process; strengthening it likely requires multi-level strategies that address information transfer, accountability, and sustained therapeutic relationships across care transitions. Full article

Introduction: Front-line therapy with Azacitidine (AZA) + Venetoclax (Ven) improved overall survival (OS) and remissions in acute myeloid leukemia (AML) patients ineligible for standard induction. Less is known about the outcome of AML treated with AZA + Ven in the “real world”. Methods: We assessed the comparative pattern of administration, tolerability, efficacy and safety of AZA vs. AZA + Ven administered at our cancer centre. We retrospectively reviewed all patients treated with AZA alone or AZA + Ven. Patients who received less than one cycle or proceeded with consolidative stem cell transplant were excluded. Results: A total of 53 patients, median age 77 years, received AZA, and 23 patients, median age 73 years, received AZA + Ven. Among those, 69% and 47.8% were ≥75 years old, respectively. Only 52% received Ven doses above 200 mg. Mean time on therapy was 13.1 months in AZA vs. 5.9 months in AZA + Ven. Treatment delays occurred in 22.6% of AZA and 34.8% of AZA + Ven patients, primarily due to infections and cytopenias. Neutropenia grade 3/4 occurred in 28.3% of AZA vs. 56.5% of AZA + Ven patients. Thrombocytopenia grade 3/4 occurred in 15.1% of AZA and 51.2% of AZA + Ven patients. Anemia grade 3/4 occurred in 5.7% of AZA vs. 30.4% of AZA + Ven patients. Moreover, 69.8% of AZA and 69.5% AZA + Ven patients reached stable disease/partial and complete remission. Median overall survival (OS) was similar: 18 months in AZA vs. 14 months in the AZA + Ven group, p = 0.905. Conclusions: In a community setting, the addition of Venetoclax to AZA did not improve overall survival or disease control, mainly due to low tolerability and higher toxicity. However, these results should be interpreted cautiously due to a significant imbalance in the cytogenetic risk profiles and lower tolerability in the combined group. This suggests the need for a larger study with adjusted analyses. Full article

Time delays are intrinsic to energy systems, arising from transport phenomena, communication latency, and control dynamics; however, their accurate modeling remains challenging, particularly under variable operating conditions. The most common delays are constant over time and are easy to model and simulate. However, simulation tools of time-varying delay systems rely on signal-delay representations that fail to enforce conservation laws, leading to unphysical results in applications involving mass or energy transport. This study develops a physically consistent mathematical framework for time-varying transfer delays that explicitly couples kinematic evolution with conservation principles through a dynamic gain term. A systematic classification is introduced, distinguishing between signal delays (information transfer) and transfer delays (physical transport), further categorized by the source of variability in time delay into Types R (variable extraction), W (variable supply), and M (variable medium). The proposed formulation was implemented in Simulink through newly developed functional blocks supporting all delay variants and validated against representative heat transport scenarios. Comparative analysis demonstrates that standard signal-delay models violate energy conservation by generating spurious energy, whereas the proposed transfer-delay formulation preserves physical consistency under variable-flow conditions. The framework provides a rigorous foundation for accurate modeling of district heating networks, renewable energy integration with power-to-gas systems, thermal storage, and smart grid communications, supporting the development of reliable control strategies essential for the ongoing energy transition. Full article

The Esox lucius is a high-quality fish species endemic to northern Xinjiang, having developed into a regional specialty industry with significant market value. However, during storage, it is prone to microbial growth that elevates biogenic amine levels, posing potential food safety risks. Therefore, this study systematically evaluated the effects of protamine—extracted from Esox lucius byproducts and used as a natural preservative—on the succession of microbial communities and biogenic amine accumulation in fish muscle under storage conditions of 4 °C, −3 °C, and −18 °C. A detection method for biogenic amines was also established. Results revealed characteristic changes in fish muscle microbial community α-diversity over storage time. Protamine treatment significantly delayed increases in total colony counts and microbial succession processes without altering the final dominant microbial community composition. By optimizing ultrasonic-assisted extraction and derivatization steps, an analytical method suitable for detecting eight biogenic amines in fish muscle matrices was established. Results indicate that protamine effectively inhibits the accumulation of all eight biogenic amines, with the 1% treatment group showing the most significant effect (p < 0.05). This study not only provides basis for the precise application of protamine in seafood preservation but also offers guidance for the resource utilization of aquatic by-products. Full article

Nowadays, the emergence of Foundation Models as a Service enables mobile users to access powerful capabilities such as inference and fine-tuning on demand and without incurring local computational overhead. This paper introduces a software-aware offloading framework for FMaaS that allows edge nodes to forecast software aging and prevent service degradation. Each node employs a lightweight Echo State Network to predict its software age, with tasks dynamically assigned based on communication cost, inference delay, and forecast reliability. Simulation results including ablation studies confirm the effectiveness of software age forecasting in reducing task failures and improving session continuity. Full article

As a next-generation immersive service, holographic video enables users to move freely within a virtual world. This imposes stringent requirements on wireless networks. Given the massive bandwidth capacity inherent to visible light, visible light communication (VLC) can effectively meet the transmission requirements of holographic video and is an ideal wireless technology for next-generation indoor immersive services. However, VLC channels are highly dependent on Line-of-Sight (LoS) links. Due to user mobility, traditional VLC systems relying on fixed-orientation Photodetectors (PDs) often suffer from severe channel fading, which significantly degrades the transmission performance. In this paper, we propose an indoor VLC holographic video transmission architecture supporting rotatable PDs, utilizing rotatable PDs mounted on Head-Mounted Displays (HMDs) to assist in holographic video transmission. To minimize the total transmission delay of all users, we address the holographic video transmission problem by jointly optimizing the transmit power allocation of VLC Access Points (APs) and the pitch and roll angles of the users’ PDs. By formulating the problem as a Markov Decision Process (MDP), we address it using a novel Deep Reinforcement Learning (DRL) strategy leveraging the Soft Actor–Critic (SAC) architecture. Simulation results demonstrate that the proposed scheme reduces the overall latency by up to 29.6% compared to the benchmark schemes. Furthermore, the convergence speed of the algorithm is improved by 35% compared to traditional deep reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG). Full article

Vehicular Ad-hoc Networks (VANETs) must sustain heterogeneous real-time content services, yet static roadside-unit (RSU) roles lead to congestion, coverage voids, and inefficient content delivery under bursty, concurrent demand. To address this issue, we propose a PSO-Based dynamic RSU role assignment framework named PDRA that dynamically adapts roles, coverage, and replication of RSU to current network conditions. A telemetry-based suitability estimator aggregates location, link stability, resource availability, traffic load, and content sensitivity at each RSU and feeds a Particle Swarm Optimization routine that assigns RSUs to Leader/Helper/Inactive roles while enforcing spatial separation between Leaders. An adaptive sectoring mechanism then resizes each cluster RSU’s communication scope—contracting under overload to protect local latency and expanding during slack to assist neighbors—thereby suppressing void areas and balancing service density. On top of the physical layer of RSUs, Leader RSUs cooperatively form a virtual Replication Layer that maintains global visibility of load and content locality to steer requests and replicate popular data near demand, reducing backhaul reliance. Finally, a load- and energy-aware reconfiguration policy orchestrates staged assist/offload, selective Helper activation, PSO-based Leader reassignment, and sleep scheduling for underutilized RSUs, preserving resilience and sustainability. NS-3 urban scenarios corroborate that PDRA improves packet delivery, lowers end-to-end delay, reduces backhaul traffic, and increases fairness over strong baselines. By jointly optimizing role assignment, coverage control, and replication, PDRA offers a scalable and robust solution for VANET content delivery under dynamic, multi-user conditions. Full article

Vehicle networking is a new paradigm in wireless technology that facilitates communication between vehicles in close proximity and in-vehicle internet access. This technology paves the way for a variety of safety, convenience and entertainment applications, including safety message exchange, real-time traffic information sharing and public internet access. The overall goal of vehicular networks is to create an efficient, safe and convenient environment for vehicles on the road. This paper presents a Sensitive Node Election (SNE) algorithm adapted to routing protocols in certain opportunistic network environments. The algorithm focuses on selecting the best agent for communication using an innovative approach for message forwarding. Quality of Service (QoS) metrics targeted for optimization include network end-to-end throughput and packet delivery, with the aim of improving the overall performance of the network. Our algorithm includes a stochastic rebroadcasting scheme that takes into account parameters, such as vehicle density, distance between vehicles and transmission distance, and adapts to various network conditions. Furthermore, the SNE algorithm uses a metric based on transmission distance and can dynamically adapt to application requirements, such as prioritization. It provides high throughput and minimizes delay. The results demonstrate the effectiveness of this approach in improving QoS in various vehicular ad hoc network (VANET) simulations and influencing the neural network ensemble (NNE Algorithm). Full article

Compact multi-channel airborne radar stations increasingly rely on an LED-based visible light communication (VLC) service link under radio-frequency spectrum restrictions and strict end-to-end delay constraints. Despite the directional nature of optical links, the VLC channel remains vulnerable to active optical interference and signal injection; furthermore, when an AI-enabled integrity monitor is embedded into the receiver, the AI decision layer becomes a direct target of evasion and online poisoning. This paper proposes a lightweight, interpretable AI-based attack detection architecture in which a Poisson photon-counting observation model is used to form physically consistent features over the preamble and control-sequence interval, while the final decision is produced by an AI ensemble combining a monotonic logistic detector and a one-class detector. The considered threat profile includes sustained illumination and synchronized flashes (jamming/blinding), spoofing via false preambles, replay of recorded fragments, and online data poisoning during self-calibration. The adequacy of solutions is assessed using the detection probability P_D (ensemble: P_D ≥ 0.90 for DC-jamming mean-count increment Δλ_DC ≈ 7.56, pulsed-interference mean-count increment Δλ_pulse ≈ 12.89, and spoofing signal-scaling factor α ≈ 1.02), the false-alarm probability P_FA = 0.045, and the per-packet end-to-end latency (bounded by the observation-window duration LΔT = 20 μs, where window length L = 20 and interval duration ΔT = 1 μs), which confirms real-time CPU operation without GPU acceleration. Full article

Traditional electronic phased array radars are constrained by electronic bottlenecks, resulting in inherent limitations including large form factor, fixed operational parameters, and narrow instantaneous bandwidth, which fail to meet the stringent requirements of next-generation high-performance radar systems. Optical true time delay (OTTD) technology based on integrated optical waveguides emerges as a core solution for realizing broadband, compact optically controlled beamforming systems. Traditional silicon-based waveguides suffer from severe mode competition (delay jitter > ±0.05 ps), energy leakage (transmission loss > 0.5 dB/cm) and large beamforming angle fluctuation (>0.3°) in OTTD systems, failing to meet the picosecond-level delay accuracy and broadband beam squint-free requirements of next-generation phased array radars. Thus, a customized mode-selective waveguide design for OTTD systems is urgently required. To address these critical challenges, this study proposes an OTTD-customized mode-selective integrated optical waveguide design tailored for OTTD systems, with three distinct innovations: (1) A systematic OTTD-oriented mode classification and selection methodology is established—instead of a conventional single-mode design, the fundamental TE₀ mode is identified as the optimal operating mode through Finite-Difference Time-Domain (FDTD) simulation, (95% TE polarization fraction and 2.0553 effective refractive index at 1548.39 nm, which cannot be achieved by other guided modes for OTTD applications). (2) The wavelength-dependent transmission characteristics of the TE₀ mode are quantitatively characterized, revealing a linear correlation between the effective refractive index (2.05–2.10) and wavelength (1500–1550 nm), alongside a controllable group delay range of 1.4315–1.4395 ps—this precise linear model fills the gap of lacking OTTD-specialized delay calibration theory in conventional waveguide research. (3) An OTTD-optimized practical mode selection criterion for OTTD applications is proposed by modifying the standard guided-vs-leaky condition for asymmetric waveguides: the effective refractive index of the operating mode must exceed the substrate refractive index with a fabrication tolerance margin (n_eff > 1.44 ± 0.02 for SiO₂ substrate) to mitigate leakage and adapt to OTTD picosecond-level delay precision. This criterion is validated through system-level beamforming experiments (rather than only device-level simulation), and the designed waveguide achieves a mode suppression ratio (MSR) of >30 dB for leakage modes and a transmission loss of <0.2 dB/cm, which is significantly superior to conventional single-mode waveguides in OTTD systems. Experimental results indicate that the angle fluctuation of the beamforming system is less than 0.08°, which is significantly superior to the 0.3° fluctuation observed in traditional silicon waveguide OTTD systems. This work provides a technical solution for improving the performance of optical phased array radar and laser radar and has broad engineering application prospects in microwave photonics and optical communication fields. Full article

Ergonomic load in human–autonomy teams is commonly treated as a static score or a post-hoc audit, even though modern sensing and communication enable real-time regulation of operator effort. We model ergonomic load as a dissipative dynamical state inferred online from multimodal effort proxies and task context, and couple it to autonomy through load-dependent gain moderation and compliance shaping. The method is evaluated on public human–swarm and human–robot interaction traces together with effort-proximal wearable and myographic datasets using a unified, windowed pipeline and controlled stress tests that emulate latency, downsampling, packet loss, and channel dropouts. On a large human–swarm benchmark, the estimator achieves strong discrimination and calibration for rare high-load events (up to AUROC $0.87$, AUPRC $0.41$, ECE $0.031$ at $q=0.90$) and degrades predictably under delay, with a knee around 300–$400\mathrm{ms}$ (AUROC $0.87\to 0.80$, ECE $0.031\to 0.061$ at $500\mathrm{ms}$). Embedding the estimate in the adaptation schedule reduces overload incidence and oscillatory redistribution while preserving coordination proxies in surrogate closed-loop simulation: overload time drops from $7.8\%$ to $4.1\%$ (relative reduction $\approx 47\%$) with throughput maintained near baseline ($1.00\to 0.97$) and oscillation power reduced ($0.26\to 0.14$) under nominal timing. These results provide a reproducible pathway for making ergonomics a control-relevant feedback signal, together with explicit operational constraints on estimator calibration (target ECE $\le 0.05$) and end-to-end latency (effective $\tau \le 300\mathrm{ms}$) required to avoid regime switching and maintain stable, interpretable adaptation. Full article

The primary challenges in routing optimization include adapting to dynamic environments with frequent node and link changes. Handling the computational complexity of large-scale networks and balancing communication resources in multi-objective optimization are also key difficulties. Traditional methods focus on optimizing a single dimension, which limits their effectiveness in dynamic network environments. They often fail to capture the full network state, making it difficult to adapt to changes in topology or traffic. This lack of a comprehensive view leads to poor resource balance and suboptimal performance as network conditions change. To address these challenges, we propose an Adaptive Routing algorithm with joint optimization of Multi-dimensional network Resources (AR-MRs) based on graph neural networks (GNNs). This algorithm optimizes multiple network resources simultaneously and effectively tackles the issues of incomplete resource consideration and insufficient balance prevalent in existing routing methods. As a result, overall network performance and reliability are enhanced. Additionally, we innovatively design a resource-adaptive module based on GNN. By leveraging GNN, complex relationships between network links and nodes are captured. This module thoroughly analyzes network states and dynamically adjusts resource allocation. Balanced optimization across multiple resource dimensions is thereby ensured. The effectiveness of the algorithm was validated through deployment in a simulation environment. Simulation results indicate that, compared to existing solutions, this approach significantly reduces end-to-end communication delay, decreases bit error rate, and enhances packet transmission efficiency. Full article