Search Results (565)

This work develops an energy-based reachability framework for linear fractional-order dynamical systems governed by Caputo derivatives of order $\alpha \in (0,1)$ in the presence of time-dependent delays acting on both the state and control channels. By combining a controllability Gramian formulation with a delay-independent algebraic characterization, explicit quantitative descriptions of reachability under finite energy constraints are obtained. It is shown that the set of terminal states attainable with bounded control energy admits a geometric characterization in terms of a Gramian-induced ellipsoidal region centered at the uncontrolled terminal state. In addition, the minimum eigenvalue of the controllability Gramian is identified as an energy-based controllability margin that provides certified reachability guarantees. Stability and sensitivity properties of the associated minimum-energy control law with respect to perturbations in the terminal target are also established. The theoretical developments are supported by implementable numerical procedures and illustrative examples that demonstrate the computation of the controllability Gramian, its spectral characteristics, and the resulting minimum-energy control inputs. Full article

Against the backdrop of promoting green buildings and a circular economy, the development of efficient, sustainable, and low-carbon cementitious materials is of great significance for reducing resource consumption and carbon emissions. In this study, plant ash (PA) was used as a partial cement replacement, and a series of alkali-activated composite cementitious materials (APAG) were prepared by regulating the dosages of PA and alkali activator (AA). The evolution of their workability, hydration behavior, and mechanical properties was systematically investigated. The results show that the incorporation of PA effectively delayed the setting process of the system; compared with P0, the initial and final setting times of P20 increased by approximately 302% and 100%, respectively, thereby mitigating the excessively rapid early-age reaction of the alkali-activated system while causing only a slight reduction in flowability. In contrast, the addition of AA shortened the setting time of APAG and led to a gradual decrease in fluidity. When the PA dosage was 20% and the AA dosage was 4%, APAG achieved a 28 d compressive strength of 57.8 MPa while maintaining good workability. Further analysis revealed a strong linear correlation between compressive strength and chemically bound water content under different PA and AA dosages, indicating that the reaction degree is a key factor governing macroscopic mechanical performance. Microstructural characterization confirmed that the incorporation of PA and AA significantly altered the reaction pathways and the morphology of hydration products, providing a reasonable microstructural explanation for the evolution of macroscopic properties. These findings provide valuable insights into the high-value utilization of biomass waste and the broader application of green cementitious materials. Full article

Hierarchical federated learning (HFL) combined with the Mobile Edge Computing (MEC) paradigm has attracted extensive research interest in the Industrial Internet of Things (IIoT) due to its ability to deploy computational resources near edge devices and effectively reduce communication overhead. However, in real-world applications, the dynamic participation of edge devices and their diverse training objectives can lead to instability in model convergence, affecting overall system performance. To address this challenge, this paper proposes a device selection strategy based on task completion probability to determine participating devices dynamically in each training round. Furthermore, to balance system resource consumption and model performance, we formulate an optimization objective to minimize the loss function under resource constraints. By leveraging theoretical analysis, we reformulate the objective as a loss upper bound minimization problem related to resource allocation, which is subsequently decomposed into multiple subproblems for iterative solving. Simulation results demonstrate that the proposed method achieves superior resource efficiency and training stability. Compared to the state-of-the-art HFL method, DSRA-HFL reduces the average training delay by approximately 18% and energy consumption by 22% under dynamic conditions, while maintaining a competitive model accuracy. This validates the effectiveness of our joint optimization strategy in practical IIoT scenarios. Full article

In this work, we consider a simple bistable motif constituted by a self-enhancing Transcription Factor (TF) and its mRNA with non-instantaneous dynamics. In particular, we mainly numerically investigated the impact of bounded stochastic perturbations of Sine–Wiener type affecting the degradation rate/binding rate constant of the TF on the phase-like transitions of the system. We show that the intrinsic exponential delay in the TF positive feedback, due to the presence of a mRNA with slow dynamics, deeply affects the above-mentioned transitions for long but finite times. We also show that, in the case of more complex delays in the feedback and/or in the translation process, the impact of the extrinsic stochasticity is further amplified. We also briefly investigate the power-law behavior (PLB) of the averaged energy spectrum of the TF by showing that, in some cases, the PLB is simply due to the filtering nature of the motif. A similar analysis can also be applied to biological models having a qualitatively similar structure, such as the well-known Capasso and Paveri–Fontana model of cholera spreading. Full article

Aircraft routing networks are complex systems vulnerable to cascading delays triggered by weather disruptions and airspace constraints. This paper proposes a Distributionally Robust Aircraft Routing (DRAR) model for systemic risk assessment. Conventional robust or stochastic optimization methods often rely on specific assumptions about delay distributions (e.g., fixed probability distributions or scenario sets). However, due to the suddenness and multi-source nature of flight delays, their true distribution is difficult to accurately characterize, limiting the effectiveness of these methods in real-world uncertain conditions. By constructing a Wasserstein-metric ambiguity set, the proposed model captures distributional uncertainty without assuming fixed probabilities, thereby handling delay risks more robustly. The study incorporated chance constraints to bound extreme delay probabilities and reformulated the model as a tractable mixed-integer program. Experiments on real airline data demonstrate that DRAR outperforms traditional benchmarks, reducing propagation delays by 4–6%, volatility by 7–9%, and extreme delay risks by up to 15.7%. Thus, the model provides a practical tool for aviation decision-makers: airlines can leverage it to optimize aircraft scheduling and routing, systematically mitigate delay propagation risk, control the probability of extreme delays, and consequently reduce indirect operational costs arising from crew overtime and airport scheduling conflicts, thereby enhancing overall resource efficiency and operational resilience. These results validate DRAR as an effective tool for controlling tail risks and ensuring sustainable operations in uncertain aviation environments. Full article

In this paper, an observer-based proportional-integral-derivative (PID) controller is designed for a class of uncertain nonlinear systems with integral measurements, denial-of-service (DoS) attacks and bounded stochastic noises under a binary encoding scheme (BES). Parameter uncertainty is involved with a norm-bounded multiplicative expression. Integral measurements are considered to reflect the delayed signal collection of sensor. For communication, BES is put into use in the signal transmission process from the sensor to the observer and from the controller to the actuator. Random bit flipping is described that may take place caused by channel noises, whose impact is described by a stochastic noise. Randomly occurring DoS attacks are taken account of that may appear due to the shared network, which block the transmitted signals totally. Three sets of Bernoulli-distributed random variables are adopted to reveal the random occurrence of uncertainties, bit flipping and DoS attacks. The aim of this paper is to design an observer-based PID controller which guarantees that the closed-loop system reaches exponential ultimate boundedness in mean square (EUBMS). By virtue of Lyapunov stability theory, stochastic analysis technique and matrix inequality method, a sufficient condition is developed for designing the observer-based PID controller such that the closed-loop system achieves EUBMS performance, and the ultimate upper bound of the controlled output is bounded and such a bound is minimized. The gain matrices of the observer-based controller are acquired explicitly by virtue of solving the solution to an optimized issue with several matrix inequality constraints. Two simulation examples are given which indicate the usefulness of the proposed control method in this paper adequately. Full article

Space–Air–Ground–Sea Integrated Networks (SAGSINs) are emerging as a key enabling architecture for broadband maritime connectivity, where heterogeneous access tiers (shore, aerial, and satellite) jointly support delay-sensitive and mission-critical uplink traffic such as alarms, telemetry, and surveillance video. However, uplink resource scheduling in maritime SAGSINs remains challenging due to time-varying channels, locally bursty traffic, and intense contention, while centralized optimization is ill-suited, as global information collection is often delayed, incomplete, and inconsistent over long-haul maritime links. Therefore, this paper investigates distributed uplink scheduling in maritime SAGSINs, where maritime nodes jointly select the access tier, spectrum slice, and transmit power under interference, spectrum, deadline, and energy constraints. Specifically, we formulate the uplink resource scheduling as a cumulative value of information (VoI) maximization problem, and develop a game-theoretic distributed multi-agent reinforcement learning algorithm, termed GTMARL. Therein, maritime nodes learn transmission policies from local observations, coordinated through congestion prices broadcast by access nodes. These prices are derived from Lagrangian relaxation and act as coordination signals that align individual decisions with global objectives. To ensure stable operation, a two-timescale mechanism is adopted, where maritime nodes make fast slot-level transmission decisions, while access nodes adapt and broadcast congestion prices on a slower timescale. Extensive experiments demonstrate that GTMARL achieves up to 90% of the idealized upper bound, significantly outperforming baselines in deadline satisfaction, throughput, delay, energy efficiency and fairness under varying traffic loads and network densities. Full article

Urbanization is intensifying congestion, emissions, and unequal mobility access in cities. This study aims to operationalize sustainability objectives—efficiency, environmental externalities, and service equity—in network-wide traffic system control. We propose SERL-H, a sustainability-aware hierarchical multi-agent reinforcement learning (MARL) controller. SERL-H separates fast intersection-level actuation from slower region-level coordination under a centralized-training decentralized-execution paradigm, and employs adaptive graph attention to capture time-varying interdependencies with bounded neighborhood communication. The learning reward explicitly balances delay/throughput, emissions/fuel, and an equity regularizer based on service dispersion across user groups. In a SUMO-based city-scale simulation with 100 signalized intersections, SERL-H reduces average delay from 45 s to 29 s and average travel time from 120 s to 88 s relative to fixed-time control, while increasing throughput and lowering total emissions (4800 kg to 3950 kg). A socio-economic assessment suggests higher annualized cost savings (e.g., $50.27 M/year to $65.91 M/year) and improved environmental quality indices. We also report, as supporting evidence, an optional sustainability-enhanced spatio-temporal graph predictor (SUT-GNN) that provides reliable short-horizon forecasts during peak-hour volatility. Full article

Cold atmospheric plasma (CAP) has emerged as a promising anticancer approach because of its ability to selectively eliminate malignant cells. Among the proposed mechanisms of this selectivity, the Bauer theory emphasizes the synergistic action of plasma-derived hydrogen peroxide (H₂O₂) and nitrite (NO₂⁻), leading to the transient generation of primary singlet oxygen (¹O₂). This early event inactivates membrane-bound catalase, allowing tumor cell-derived H₂O₂ and peroxynitrite to initiate a self-amplifying cycle that produces secondary ¹O₂, as a hallmark of CAP selectivity. To test this hypothesis, in this work, we monitored extracellular dissolved oxygen (DO) dynamics in HT-29 colorectal cancer cells treated with an argon plasma jet using time-resolved phosphorescence lifetime spectroscopy. Temporal variations in DO likely reflect the cumulative effect of rapid ¹O₂ production and its reactions with cells. A delayed surge in extracellular ¹O₂ was observed specifically in dying cancer cells within the 10–20 min window predicted by the model. Intracellular ROS imaging confirmed a strong correlation between intracellular ROS, extracellular ¹O₂ dynamics, and viability loss. Together, these results provide mechanistic validation of Bauer’s redox model and suggest that early oxygen dynamics after CAP exposure can serve as predictive markers for treatment efficacy in plasma or photodynamic therapies. Full article

In this paper, a complete analysis is presented to study a reaction–diffusion system of general gene expression with two time delays and with Neumann boundary conditions. The global existence of a unique strong solution and the existence of an attractor are established. Using delays as bifurcation parameters, we obtain critical values so that the Hopf bifurcation occurs at a unique equilibrium point. Numerical simulations are provided to illustrate both the stability of the equilibrium point and the emergence of bifurcations. For steady-state solutions, the Maximum Principle is used to obtain the bounds of positive solutions. The conditions for the system to have constant solutions are also investigated. Full article

The dynamic response accuracy of suspension servo actuators directly determines the vibration-reduction performance of active-suspension systems. However, during long-term service, the system is prone to the influence of compound-degradation faults, such as internal leakage and time delay, leading to a significant decline in control performance. To address this issue, this paper proposes a collaborative control framework combining model-free adaptive control with a differential term of tracking error (DE-MFAC) and an improved particle swarm optimization (IPSO) algorithm. Firstly, to overcome the limitations of traditional model-free adaptive control (MFAC), a DE-MFAC strategy is constructed by implicitly handling the time-delay term and introducing the differential term of tracking error and dynamic weight factor into the performance index. Secondly, to enhance the parameter-tuning effect, the traditional particle swarm optimization (PSO) algorithm is improved (IPSO) by incorporating a dynamic inertia weight and an out-of-bounds random reflection mechanism, thereby strengthening the global optimization capability. On this basis, a suspension servo actuator system model incorporating internal leakage and time-delay faults is established based on the co-simulation platform of Simulink and AMESim, and the proposed method is validated. The simulation results show that, compared with the optimized traditional MFAC, the DE-MFAC tuned by IPSO exhibits superior position-tracking accuracy, faster response speed, and stronger overshoot-suppression capability under various compound-fault conditions. Further analysis indicates that the Integral of Absolute Cubic Error (IACE) function, due to its higher sensitivity to large deviations, can more effectively suppress overshoot and is suitable for engineering scenarios with strict requirements on dynamic performance. In addition, the optimization of control parameters using the IPSO algorithm can effectively compensate for the performance degradation caused by degradation faults, providing a feasible technical approach for extending the service life of actuators through adaptive adjustment. Full article

Delays during turnaround operations are a significant source of operational inefficiency for airlines. They reduce airline profit margins by resulting in rescheduled flights and missed connections for passengers. This research paper presents the findings of an approach developed within the INTACT research project (subsequently called “the INTACT system”). The INTACT system aims to achieve reduced delays during turnaround operations and therefore increase their operational efficiency by introducing new technologies. A simulation study, including 350 simulated days, was conducted to assess the impact of three of INTACT’s abilities: (1) the localization of wheelchairs for passengers, (2) the assessment of what trolleys are onboard and how many trolley items are needed, and (3) visual observations of cabin failures and communication back to the destination airport. Results show that the implementation of these technologies leads to a statistically significant average delay reduction of 3 min per turnaround. Under the modeled schedule constraints in the discrete-event simulation, this reduction shifts the distribution of feasible daily flight counts, resulting in an average increase of 0.11 flights/day (38 additional completed flights over 350 simulated days) relative to the full-delay scenario. In addition, the cost–benefit analysis shows that the INTACT system saves an average of $966.95 in turnaround costs and gains $2714.29 in additional revenue per day and per aircraft. With estimated initial investment costs of around 2 million dollars, the payback period is only 1.5 years. During this study, gross additional revenue was reported as an upper-bound estimate; net operational benefit depends on airline-specific variable operating costs. The INTACT system can help to improve turnaround operation issues while providing positive economic performance for stakeholders in the industry. Full article

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions ($\times 10$ and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, $C={10}^2$) achieves $R^2=0.9999$, $MAPE=0.0018$, $MAE=966.04$, and $RMSE=1589.64$ on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

Accurate and energy-efficient localization of autonomous underwater vehicles (AUVs) remains a fundamental challenge due to the complex, bandwidth-limited, and highly dynamic nature of underwater acoustic environments. This paper proposes a fully adaptive deep reinforcement learning (DRL)-driven localization framework for AUVs operating in Underwater Acoustic Sensor Networks (UAWSNs). The localization problem is formulated as a Markov Decision Process (MDP) in which an intelligent agent jointly optimizes beacon selection and transmit power allocation to minimize long-term localization error and energy consumption. A hierarchical learning architecture is developed by integrating four actor–critic algorithms, which are (i) Twin Delayed Deep Deterministic Policy Gradient (TD3), (ii) Soft Actor–Critic (SAC), (iii) Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and (iv) Distributed DDPG (D2DPG), enabling robust learning under non-stationary channels, cooperative multi-AUV scenarios, and large-scale deployments. A round-trip time (RTT)-based geometric localization model incorporating a depth-dependent sound speed gradient is employed to accurately capture realistic underwater acoustic propagation effects. A multi-objective reward function jointly balances localization accuracy, energy efficiency, and ranging reliability through a risk-aware metric. Furthermore, the Cramér–Rao Lower Bound (CRLB) is derived to characterize the theoretical performance limits, and a comprehensive complexity analysis is performed to demonstrate the scalability of the proposed framework. Extensive Monte Carlo simulations show that the proposed DRL-based methods achieve significantly lower localization error, lower energy consumption, faster convergence, and higher overall system utility than classical TD3. These results confirm the effectiveness and robustness of DRL for next-generation adaptive underwater localization systems. Full article

This paper concentrates on fractional-order fuzzy cellular neural networks (FOFCNNs) with bounded uncertainties and transmission delays. To better capture real-world dynamic behaviors, the fuzzy AND and OR operators are employed to construct drive-response systems. For the fixed-time synchronization task of the systems, a novel multi-module feedback controller incorporating three functional terms is designed. These terms aim to eliminate delay effects, ensure fixed-time convergence, and reduce parameter conservativeness. Leveraging the properties of fractional-order operators and our multi-module control scheme, new synchronization criteria of the studied drive-response systems can be established within a predefined time. An upper bound on the settling time is derived, depending on the system size and control parameters, but independent of the initial conditions. A significant corollary is derived for the case of no uncertainties under the nonlinear controller. Numerical experiments discuss the impact of uncertainties and delays on synchronization, and confirm the validity of the results presented in this study. Full article