Search Results (550)

This paper presents a repetitive learning control scheme to handle systems subject to both time-delay and dead-zone nonlinearities and the state-dependent input gain simultaneously. The adaptive bounding techniques are utilized to deal with the nonparametric uncertainties originated from the time-delay and the state-dependent input gain, in which the indirect learning manner is employed to avoid the appearance of the sign function, alleviating the requirement for the system information. The only prior knowledge of the proposed scheme is the lower bound of the input gain and the dead-zone slope. The desired control signal is recognized as the parametric uncertainties with a constant regressor. The derivation of the convergence analysis is provided in detail, and the boundedness of variables in the closed-loop system is guaranteed. The numerical simulation is conducted to testify the effectiveness of the presented control approach. Full article

This study addresses the stability and quasi-synchronization of fractional-order neural networks that incorporate mixed delays, system uncertainties, and external disturbances. Accordingly, a more realistic neural network model is constructed. For fractional-order neural networks incorporating mixed delays and uncertainties (FONNMDU), this study establishes a criterion for uniform asymptotic stability and proves the existence and uniqueness of the equilibrium solution. Furthermore, it investigates the global uniform stability and stability regions of fractional-order neural networks with mixed delays, uncertainties, and external disturbances (FONNMDUED). Then, to address the quasi-synchronization problem, a controller is designed and some novel sufficient conditions for achieving quasi-synchronization are established. The results show that tuning the control parameters can adjust the error bound. These findings not only enrich the theoretical foundation of fractional-order neural networks but also offer practical insights for applications in complex systems. Full article

In the context of fifth-generation (5G) communications and the dawn of sixth-generation (6G) networks, a surged societal demand on bandwidth and data rate and more stringent commercial requirements on transmission efficiency, cost, and reliability are increasingly evident and, hence, driving the maturity of reconfigurable millimeter-wave (mmW) and terahertz (THz) devices and systems, in particular, liquid crystal (LC)-based tunable solutions for delay line phase shifters (DLPSs). However, the field of LC-combined electronics has witnessed only incremental developments in the past decade. First, the tuning principle has largely been unchanged (leveraging the shape anisotropy of LC molecules in microscale and continuum mechanics in macroscale for variable polarizability). Second, LC-enabled devices’ performance has yet to be standardized (suboptimal case by case at different frequency domains). In this context, this work points out three underestimated knowledge gaps as drawn from our theoretical designs, computational simulations, and experimental prototypes, respectively. The first gap reports previously overlooked physical constraints from the analytical model of an LC-embedded coaxial DLPS. A new geometry-dielectric bound is identified. The second gap deals with the lack of consideration in the suboptimal dispersion behavior in differential delay time (DDT) and differential delay length (DDL) for LC phase-shifting devices. A new figure of merit (FoM) is proposed and defined at the V-band (60 GHz) to comprehensively evaluate the ratios of the DDT and DDL over their standard deviations across the 54 to 66 GHz spectrum. The third identified gap deals with the in-depth explanation of our recent experimental results and outlook for partial leakage attack analysis of LC phase shifters in modern eavesdropping. Full article

Modern 5G+grade low power wide area network (LPWAN) technologies such as Narrowband Internet-of-Things (NB-IoT) operate utilizing a multi-channel slotted ALOHA algorithm at the random access phase. As a result, the random access phase in such systems is characterized by relatively low throughput and is highly sensitive to traffic fluctuations that could lead the system outside of its stable operational regime. Although theoretical results specifying the optimal transmission probability that maximizes the successful preamble transmission probability are well known, the lack of knowledge about the current offered traffic load at the BS makes the problem of maintaining the optimal throughput a challenging task. In this paper, we propose and analyze a new reactive access-barring scheme for NB+IoT systems based on machine learning (ML) techniques. Specifically, we first demonstrate that knowing the number of user equipments (UE) experiencing a collision at the BS is sufficient to make conclusions about the current offered traffic load. Then, we show that through utilizing ML-based techniques, one can safely differentiate between events in the Physical Random Access Channel (PRACH) at the base station (BS) side based on only the signal-to-noise ratio (SNR). Finally, we mathematically characterize the delay experienced under the proposed reactive access-barring technique. In our numerical results, we show that by utilizing modern neural network approaches, such as the XGBoost classifier, one can precisely differentiate between events on the PRACH channel with accuracy reaching 0.98 and then associate it with the number of user equipment (UE) competing at the random access phase. Our simulation results show that the proposed approach can keep the successful preamble transmission probability constant at approximately 0.3 in overloaded conditions, when for conventional NB-IoT access, this value is less than 0.05. The proposed scheme achieves near-optimal throughput in multi-channel ALOHA by employing dynamic traffic awareness to adjust the non-unit transmission probability. This proactive congestion control ensures a controlled and bounded delay, preventing latency from exceeding the system’s maximum load capacity. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

Background: Early surgical intervention is associated with improved outcomes in hip fracture care, yet in patients using Direct Oral Anticoagulants (DOACs), surgery is frequently delayed due to concerns about increased intraoperative bleeding. Despite the increasing prevalence of hip fracture patients on DOACs, no consensus exists on optimal surgical timing. This has led to substantial practice variation between hospitals, with some operating within 24 h of last DOAC intake and others delaying surgery beyond 24 h. This study hypothesizes that early surgery within 24 h results in a non-inferior blood transfusion risk compared to delayed surgery 24 h or more after last DOAC intake in hip fracture patients on DOACs. This protocol describes the design and methodological rationale of a natural experiment. Methods and analysis: A multicenter cohort study designed as a natural experiment will be conducted across seven Dutch level 2 trauma centers, using predefined and standardized prospectively collected variables from electronic health records. Centers will adhere to distinct local surgical timing protocols, forming two cohorts: early surgery within 24 h and delayed surgery 24 h or more after last DOAC intake. Patients presenting with an isolated hip fracture who are using a DOAC and have taken their last dose within 24 h before admission will be included. The primary endpoint is postoperative blood transfusion. Secondary endpoints include additional bleeding-related outcomes, thrombotic and postoperative complications, and hospital length of stay. The primary analysis will be conducted on a per-protocol basis, with an intention-to-treat analysis performed as a supplementary assessment. Non-inferiority will be established if the upper bound of the one-sided 95% confidence interval for the risk difference does not exceed the predefined margin of 5%. Ethics and dissemination: Ethical approval was obtained from the Medical Ethics Committee United, Utrecht, The Netherlands. As this is a cohort study without altering clinical care, individual informed consent is not required. All data will be pseudonymized, and findings will be disseminated through peer-reviewed journals and scientific conferences. Registration details: Medical Ethics Committee United, Utrecht, The Netherlands, registration number W25.034. Full article

In this brief, the quasi-consensus issue is examined for fractional-order multi-agent systems (FROMASs) subject to both mixed delays and external disturbances, employing an impulsive pinning control (IPC) strategy. Unlike mainstream pinning strategies with fixed nodes or static rules, a dynamic pinning mechanism based on consensus error distances is proposed, which adaptively adjusts the set of pinned nodes at each impulsive instant. By integrating the Razumikhin method, Lyapunov stability theory, and the comparison system approach, sufficient conditions for achieving quasi-consensus of FROMASs are established. Moreover, the convergence bound of consensus errors is quantitatively estimated and shown to be explicitly determined by the intensity of external disturbances. This paper organically integrates the dynamic node pinning mechanism, FRO impulsive control, and Razumikhin stability analysis. It effectively handles mixed delays and external disturbances while significantly reducing control costs. Finally, numerical simulations show that the synchronization errors are strictly bounded within the threshold $M=13.6372$, which effectively validates both the proposed control scheme and the theoretical analysis. Full article

In this article, the problem of control of the kinematic model of an omnidirectional robot with time delay in the control input is tackled through an Active Disturbance Rejection Control (ADRC) with a disturbance predictor-based scheme, which consists in predicting the generalized forward disturbance input in order to cancel it and then using a feedforward linearization approach to control the system in trajectory tracking tasks. The novelties of the scheme are to demonstrate that using the proposed extended state disturbance estimation leads to a forward estimation following the Taylor series approximation, and, to avoid using additional pose predictions, a feedforward input as an exact linearization approach is used, in which the remaining dynamics can be lumped into the generalized disturbance input. Thus, the use of extended states in prediction improves the robustness of the predictor while increasing the prediction horizon for larger time delays. The stability of the proposal is demonstrated using the second method of Lyapunov, which shows the closed-loop estimation/tracking ultimate bound behavior. Additionally, numerical simulations and experimental tests validate the robustness of the approach in trajectory-tracking tasks. Full article

While drug-eluting cardiovascular devices, including drug-eluting stents and drug-coated balloons, have significantly reduced restenosis rates, they remain limited by delayed vascular healing, chronic inflammation, and late adverse events. These limitations reflect a fundamental mismatch between current device pharmacology, which relies on nonselective antiproliferative drugs, and the highly coordinated, cell-specific programs that orchestrate vascular repair. Extracellular vesicles (EVs), nanometer-scale membrane-bound particles secreted by virtually all cell types, provide a biologically evolved platform for intercellular communication and cargo delivery. In the cardiovascular system, EVs regulate endothelial regeneration, smooth muscle cell phenotype, extracellular matrix remodeling, and macrophage polarization through precisely orchestrated combinations of miRNA, proteins, and lipids. Here, we synthesize mechanistic insights into EV biogenesis, cargo selection, recruitment, and functional effects in vascular healing and inflammation and translate these into a formal framework for EV-inspired device engineering. We discuss how EV-based or EV-mimetic coatings can be designed to sense the local microenvironment, deliver encoded biological “instruction sets,” and function within ECM-mimetic scaffolds to couple local stent healing with systemic tissue repair. Finally, we outline the manufacturing, regulatory, and clinical trial issues that must be addressed for EV-inspired cardiovascular devices to transition from proof of concept to clinical reality. By shifting the focus from pharmacological suppression to biological regulation of healing, EV-based strategies offer a path to resolve the long-standing tradeoff between restenosis prevention and durable vascular healing. Full article

Fusarium oxysporum-induced soft rot severely threatens postharvest pitaya quality and storage life, and while vanillin shows promise in the disease management, its mechanisms for controlling pitaya decay remain incompletely understood. In this study, we systematically investigated the molecular mechanism by which vanillin inhibits soft rot in postharvest pitaya, employing physiological and biochemical characterization, bioinformatics analysis, and molecular biology techniques. Compared with control fruit on 10 d, vanillin treatment significantly reduced disease index and lesion area by 27.12% and 67.43%, respectively. Meanwhile, vanillin treatment delayed the degradation of total soluble solids (TSSs) and titratable acidity (TA) and promoted the accumulation of total phenolics and flavonoids. Additionally, vanillin enhanced the activities of defense-related enzymes, such as catalase (CAT), superoxide dismutase (SOD), phenylalanine ammonia-lyase (PAL), β-1,3-glucanase (GLU), chitinase (CHI), peroxidase (POD) and polyphenol oxidase (PPO), and increased antioxidant capacity, as evidenced by increased DPPH radical scavenging capacity and ascorbic acid content. This resulted in reduced oxidative damage, as indicated by decreased levels of malondialdehyde (MDA), H₂O₂ and O₂^•−. Yeast one-hybrid (Y1H), dual-luciferase reporter (DLR) and subcellular localization revealed that HuTGA1, a nuclear-localized transcriptional activator, specifically bound to the as-1 cis-acting element and activated expression of HuNPR1 and HuNPR5-1. Transient overexpression of HuTGA1 reduced reactive oxygen species (ROS) accumulation and upregulated related genes. These findings suggest that vanillin treatment might enhance pitaya resistance by activating the HuTGA1-HuNPR signaling module, providing insights into the molecular mechanisms underlying vanillin-induced resistance. Full article

This paper proposes a compressive-sensing (CS) acquisition algorithm for low-power, high-dynamic GNSS receivers based on low-dimensional time-domain measurements, a non-iterative compressive-domain direct-projection peak-search pipeline, and a coherence-optimized sensing-matrix design. Unlike most existing GNSS-CS acquisition approaches that rely on explicit sparse-recovery formulations (e.g., OMP/BP/LS-type iterative reconstruction) to identify the delay–Doppler support—often incurring substantial computational burden and acquisition latency—the proposed method performs peak detection directly in the compressive measurement domain and is supported by unified Gram-matrix optimization and perturbation/detection analyses. Specifically, the measurement Gram matrix is optimized on the symmetric positive-definite (SPD) manifold to obtain a diagonally dominant and well-conditioned structure with reduced inter-column correlation, thereby bounding reconstruction-induced perturbations and preserving the main correlation peak. Simulation results show that the proposed scheme retains the low online complexity characteristic of direct-projection baselines while achieving a 2–3 dB acquisition sensitivity gain, and it requires substantially fewer operations than iterative OMP-based CS acquisition schemes whose cost scales approximately linearly with the sparsity level K. The proposed framework enables robust, low-latency acquisition suitable for resource-constrained GNSS receivers in high-dynamic environments. Full article

This paper addresses the tracking control problem for stochastic nonlinear systems (SNSs) subject to distributed time-varying delays and output dead zones. A novel dynamic event-triggered control scheme is proposed by integrating the backstepping technique with a fuzzy logic system (FLS). The FLS is employed to approximate unknown nonlinear functions, while a Nussbaum-type function is incorporated to mitigate the effects of the output dead zone. The challenges posed by distributed time-varying delays are effectively overcome by constructing novel double-integral Lyapunov–Krasovskii functionals. Furthermore, the introduced dynamic event-triggering mechanism, which features a relative threshold and an adaptive parameter, significantly reduces the network communication burden while maintaining desired system performance. Based on Lyapunov stability theory, it is rigorously proven that all signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to validate the feasibility and effectiveness of the proposed control approach. Full article

This paper presents a unified framework for controllability and minimum-energy control of linear fractional differential systems with Caputo derivative order $\gamma \in (0,1)$ and fully time-varying state and control delays. An explicit mild solution representation is derived using the fractional fundamental matrix, and a new controllability Gramian is introduced. Using analytic properties of the matrix-valued Mittag-Leffler function, we prove a fractional Kalman-type theorem showing that bounded time-varying delays do not change the algebraic controllability structure determined by $(F,G,K)$. The minimum-energy control problem is solved in closed form through Hilbert space methods. Efficient numerical strategies and several examples—including delayed viscoelastic, neural, and robotic models—demonstrate practical applicability and computational feasibility. Full article

Urban welfare can deteriorate over a few weeks, yet most official indicators are only updated quarterly. This mismatch in time scales leaves city administrations effectively blind to the early stages of emerging crises, especially in areas where vulnerable residents generate few administrative or digital records. We cast urban socioeconomic monitoring as a systems problem: a six-dimensional welfare state on a spatial grid, observed through sparse delayed administrative data and noisy digital traces whose reliability declines with digital exclusion. On top of this latent state, we design a four-layer cyber–physical–social (CPSS) architecture centered on a stochastic state-space model with empirically guided couplings. This is supported by a semantic sensing layer where large language models (LLMs) convert daily geo-referenced public text into noisy welfare indicators. These signals are then fused with quarterly administrative records via an extended Kalman filter (EKF). Finally, a lightweight convex post-processing layer enforces fairness, differential privacy, and minimum representation as hard constraints. A key ingredient is a state-dependent noise model in which the LLM observation variance grows exponentially with digital exclusion. Under this model, we study finite-horizon observability and obtain an exclusion threshold beyond which several welfare dimensions become effectively unobservable over 30–60 day horizons; EKF error bounds scale with the same exponent, clarifying when semantic sensing is informative and when it is not. Finally, a 100,000-agent agent-based model of a synthetic city with daily shocks suggests that, relative to a quarterly-only baseline, the LLM-augmented fusion pipeline substantially reduces detection lags and multi-dimensional cascade failures while keeping estimation error bounded and satisfying the explicit fairness and privacy constraints. Full article

Vehicular ad hoc networks (VANETs) must simultaneously satisfy stringent reliability, latency, and sustainability targets under highly dynamic urban and highway mobility. Existing solutions typically optimise one or two dimensions (link stability, clustering, or energy) but lack an integrated, adaptive mechanism that fuses heterogeneous metrics while remaining lightweight and deployable. This paper introduces a VANET routing protocol named SYMPHONY (Synergistic Hierarchical Metric-Fusion and Predictive Hybrid Optimization for Network Yield) that operates in three coordinated layers: (i) a compact neighbourhood filtering stage that reduces forwarding scope and eliminates transient relays, (ii) a cluster layer that elects resilient cluster heads using fuzzy energy-aware metrics and backup leadership, and (iii) a global inter-cluster optimizer that blends a GA-reseeded swarm metaheuristic with a stability-aware pheromone scheme to produce multi-objective routes. Crucially, SYMPHONY employs an ultra-lightweight online weight-adaptation module (contextual linear bandit) to tune metric fusion weights in response to observed rewards (packet delivery ratio, end-to-end delay, and Green Performance Index). We evaluated the proposed routing protocol SYMPHONY versus strong modern baselines across urban and highway scenarios with varying density and resource constraints. The results demonstrate that SYMPHONY improves packet delivery ratio by up to 12–18%, reduces latency by 20–35%, and increases the Green Performance Index by 22–45% relative to the best baseline, while keeping control overhead and per-node computation within practical bounds. Full article