Search Results (314)

Mobile manipulator robots have an increasing number of applications in industry because they extend the workspace of a fixed base manipulator mounted on a mobile platform, making it important to further investigate their control and optimization. This paper presents an implementation proposal for a coupled base–arm dynamics computed torque controller (CTC) for trajectory tracking of a differential-drive mobile manipulator, which considers the dynamics of the fixed base manipulator and the mobile base in a coupled way and compares its performance with that of a Proportional Derivative (PD) controller. Both controllers are tuned using Particle Swarm Optimization (PSO) with a cost function that aims to simultaneously reduce the control energy and the end-effector tracking error for different types of trajectories, and they operate in discrete time, thus accounting for inherent process delays. Simulation and laboratory implementation results show the superior performance of the CTC in both cases: in simulation, the average end-effector positioning error is reduced by 51.55% and the average RMS power by 46.44%; in the laboratory experiments, the average end-effector positioning error is reduced by 43.29% and the average RMS power by 53.49%, even in the presence of possible model uncertainties and system disturbances. Full article

This paper investigates an optimal decision-making and optimization framework for networked systems operating under the coupled effects of stochastic transmission delays, packet dropouts, and input delays, which is a critical unresolved challenge in data-driven intelligent systems deployed over shared communication networks. Such uncertainty-aware optimization problems exhibit strong similarities to modern recommender and decision support systems, where multiple performance criteria must be balanced under dynamic and resource-constrained environments while addressing the disruptive impact of coupled network-induced uncertainties. By explicitly modeling stochastic transmission delays and packet losses in the sensor to controller channel, together with input delays in the actuation loop, the problem is formulated as a stochastic optimal control task with multi-stage decision coupling that captures the interdependency of communication uncertainties and system performance. An optimal feedback policy is derived based on a discrete time Riccati recursion explicitly quantifying and mitigating the cumulative impact of network-induced uncertainties on the expected performance cost, which is a capability lacking in existing frameworks that treat uncertainties separately. Numerical simulations using realistic traffic models validate the effectiveness of the proposed framework. The results demonstrate that the proposed decision optimization approach offers a principled foundation for uncertainty-aware optimization with potential applicability to data-driven recommender and intelligent decision systems where coupled uncertainties and multi-criteria trade-offs are pervasive. Full article

Autonomous-rail Rapid Transit (ART) systems operate on standard roadways while maintaining dedicated right-of-way privileges. Owing to their sustainability, punctual operation, and cost efficiency, ART systems have emerged as a promising solution for medium-capacity urban transit. However, the exclusive lane usage for ART systems frequently leads to inefficient lane utilization, thereby intensifying congestion for non-ART vehicles. This study proposes a moving-block-based lane-sharing strategy for ART with a leading eco-driving approach. First, dynamic lane-access rules are introduced, allowing non-ART vehicles to temporarily use the ART lane without forced clearance or signal coordination. Second, a modified eco-driving trajectory optimization algorithm is constructed on a discrete time–space–state network, allowing the ART trajectory to be obtained through an efficient graph-search procedure while simultaneously guiding following vehicles toward energy-efficient driving patterns. Finally, simulation experiments are conducted to evaluate the impacts of traffic demand, arrival interval, and non-ART vehicles’ compliance rate on system performance. The results demonstrate that the proposed strategy significantly reduces delay and energy consumption for non-ART vehicles by 72.6% and 24.6%, respectively, without compromising ART operations efficiency. This work provides both technical insights and theoretical support for the efficient management of ART systems and the sustainable development of urban transportation. Full article

Compared to conventional solid-fueled reactors, the liquid fuel transport in molten salt reactors (MSRs) leads to a strong coupling between thermal-hydraulics and neutronics. To enable system-level analysis of MSR, this study focuses on the main loop of the Molten Salt Reactor Experiment (MSRE). A system model is developed using the open-source, multiphysics modeling platform Modelica/TRANSFORM. The model is validated against ORNL experimental data under various conditions, including zero-power pump start/stop, natural circulation. In addition, the xenon transport behavior is compared with predictions from a two-region analytical model. Results indicate that the number of discretized core nodes significantly influences the estimation of delayed neutron precursor (DNP) losses due to fuel circulation. The applicability of the ANSI/ANS-5.1 decay heat model, originally developed for light water reactors, is confirmed to be conservative when applied to MSRE conditions. Finally, natural circulation behavior with decay heat transport is further analyzed. Full article

We present a comprehensive mathematical analysis of a within-host dual-target HIV dynamics model, which explicitly incorporates the virus’s interactions with its two primary cellular targets: CD4⁺ T cells and macrophages. The model is formulated as a system of five nonlinear delay differential equations, integrating three distinct discrete time delays to account for critical intracellular processes such as the development of productively infected cells and the maturation of new virions. We first establish the model’s biological well-posedness by proving the non-negativity and boundedness of solutions, ensuring all trajectories remain within a feasible region. The basic reproduction number, ${\mathcal{R}}_0^d$, is derived using the next-generation matrix method and serves as a sharp threshold for disease dynamics. Analytical results demonstrate that the infection-free equilibrium is globally asymptotically stable (GAS) when ${\mathcal{R}}_0^d\le 1$, guaranteeing viral eradication from any initial state. Conversely, when ${\mathcal{R}}_0^d>1$, a unique endemic equilibrium emerges and is proven to be GAS, representing a state of chronic infection. These global stability properties are rigorously established for both the non-delayed and delayed systems using carefully constructed Lyapunov functions and functionals, coupled with LaSalle’s invariance principle. A sensitivity analysis identifies viral production rates ($p_1,p_2$) and infection rates (${\beta }_1,{\beta }_2$) as the most influential parameters on ${\mathcal{R}}_0^d$, while the viral clearance rate (m) and maturation delay (${\tau }_3$) have a suppressive effect. The model is extended to evaluate antiretroviral therapy (ART), revealing a critical treatment efficacy threshold ${ϵ}^{cr}$ required to suppress the virus. Numerical simulations validate all theoretical findings and further investigate the dynamics under varying treatment efficacies and maturation delays, highlighting how these factors can shift the system from persistence to clearance. This study provides a rigorous mathematical framework for understanding HIV dynamics, with actionable insights for designing targeted treatment protocols aimed at achieving viral suppression. Full article

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems. Full article

As a multi-switching power electronic circuit with complex variable topology, the three-level active neutral point clamped (ANPC) converter is a complex system with strong coupling and low linearity. It has numerous high-speed switching devices, a large number of switch states, and a high matrix dimension. Modeling each switch will undoubtedly further increase the circuit size. While in real-time simulation, updating all states of the model to produce outputs within a single time step results in a significant computational load, causing an increasing consumption of FPGA hardware resources as the number of switches and circuit size grow. In order to solve this problem, the current common practice is to decompose the entire complex power electronic system into smaller serial subsystems for modeling. The overall modeling approach for small circuits can be achieved, but when the size of the circuit increases, the overall modeling complexity and difficulty are increased or even impossible to achieve. Decoupling power electronic circuits with this decomposition into subsystem modeling not only reduces the matrix dimension and simplifies the modeling process, but also improves the computational efficiency of the real-time simulator. However, this inevitably generates simulation delays between different subsystems, leading to numerical oscillations. In an effort to overcome this challenge, this paper adopts the method of parallel computation after subsystem partitioning. There is no one-beat delay between different subsystems, and there is no loss of accuracy, which can improve the numerical stability of the modeling and can effectively reduce the step length of real-time simulation and alleviate the problem of real-time simulation resource consumption. In addition, to address the problems of low accuracy due to the traditional forward Euler method as a solver and the possibility of significant errors at some moments, this paper uses a modified prediction correction method to solve the discrete mathematical model, which provides higher accuracy as well as higher stability. And, different from the traditional control method, this paper uses an improved FCS-MPC strategy to control the switching transients of the ANPC model, which achieves a very good control effect. Finally, a simulation step size of less than 60 ns is successfully realized by empirical demonstration on the Speedgoat test platform. Meanwhile, the accuracy of our model can be objectively evaluated by comparing it with the simulation results of the Matlab Simpower system. Full article

Temporal variability in online streams arises in information systems where heterogeneous modalities exhibit varying latencies and delay distributions. Efficient synchronization strategies help to establish a reliable flow and ensure a correct delivery. This work establishes a formal modeling foundation for addressing temporal dynamics in streams with multimodality using a discrete-event system specification framework. This specification captures different latencies and interarrival dynamics inherent in multimodal flows. The framework also incorporates a Markov variant to account for variations in delay processes, thereby capturing timing uncertainty in a single modality. The proposed models are modular, with built-in mechanisms for diverse temporal integration, thereby facilitating heterogeneity in information flows and communication. Various structural and behavioral forms can be flexibly represented and readily simulated. The devised experiments demonstrate, across several model permutations, the time-series behavior of individual stream components and the overall composed system, highlighting performance metrics in both, quantifying composability and modular effects, and incorporating learnability into the simulation of multimodal streams. The primary motivation of this work is to enhance the degree of fitting within formal simulation frameworks and to enable adaptive, learnable distribution modeling in multimodal settings that combine synthetic and real input data. We demonstrate the resulting errors and degradation when replacing real sensor data with synthetic inputs at different dropping probabilities. Full article

Port congestion, particularly excessive truck turn time (TTT), disrupts supply chains, increases costs, and contributes to environmental impacts. This study evaluates the potential of integrating autonomous vehicles (AVs) into port operations to reduce TTT, using the Port of Montreal’s Viau Terminal as a case study. A discrete event simulation (DES) with agent-based logic was developed to model landside processes, including gate, yard, and staging operations, while differentiating between human-driven vehicles (HDVs) and AVs. Four scenarios were tested: Baseline indicating current operations, Truck Appointment System (TAS), partial AV integration (35% AVs) with shared resources, and AVs with dedicated staging areas and cranes. Model inputs were informed by port publicly available data and validated against observed TTT metrics. Results show that TAS reduced average TTT from 88.2 to 78.37 min; partial AV integration lowered it further to 55.91 min, with AVs averaging 45.33 min; dedicated AV infrastructure yielded the lowest AV TTT (32.86 min) but slightly increased overall TTT due to HDV delays. Findings suggest that combining AV adoption with demand management and targeted infrastructure investments can substantially improve efficiency. The study offers quantitative evidence and strategic recommendations to support port authorities in planning for automation while ensuring balanced resource allocation. Full article

The efficiency of intelligent urban mobility increasingly depends on adaptive mathematical models that can optimize multimodal transportation resources under stochastic and heterogeneous conditions. This study proposes a Markovian stochastic modeling and metaheuristic optimization framework for the adaptive management of bus lane capacity in mixed connected traffic environments. The heterogeneous vehicle arrivals are modeled using a Markov Arrival Process (MAP) to capture correlated and busty flow characteristics, while the system-level optimization aims to minimize total fuel consumption through discrete lane capacity allocation. To support real-time adaptation, a Hidden Markov Model (HMM) is integrated for queue-length estimation under partial observability. The resulting nonlinear and nonconvex optimization problem is solved using Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), ensuring robustness and convergence across diverse traffic scenarios. Numerical experiments demonstrate that the proposed stochastic–adaptive framework can reduce fuel consumption and vehicle delay by up to 68% and 65%, respectively, under high saturation and connected-vehicle penetration. The findings verify the effectiveness of coupling stochastic modeling with adaptive control, providing a transferable methodology for energy-efficient and data-driven lane management in smart and sustainable cities. Full article

The stability of energy demand–supply systems is often affected by delayed feedback caused by regulatory inertia, communication lags, and heterogeneous agent responses. Conventional models typically assume discrete delays, which may oversimplify real dynamics and reduce controller effectiveness. This work addresses this limitation by introducing a novel class of nonlinear energy models with distributed delay feedback governed by gamma-distributed memory kernels. Specifically, we consider both weak (exponential) and strong (Erlang-type) kernels to capture a spectrum of memory effects. Using the linear chain trick, we reformulate the resulting integro-differential model into a higher-dimensional system of ordinary differential equations. Analytical conditions for local asymptotic stability and Hopf bifurcation are derived, complemented by Lyapunov-based global stability criteria. The related numerical analysis confirms the theoretical findings and reveals a distinct stabilization regime. Compared to fixed-delay approaches, the proposed framework offers improved flexibility and robustness, with implications for delay-aware energy control and infrastructure design. Full article

We develop and analyze a distributed-delay model for nutrient–fish–mussel dynamics in multitrophic aquaculture systems. Extending the classical discrete-delay framework, we incorporate gamma-distributed kernels to capture the time-distributed nature of nutrient assimilation, yielding a more realistic and analytically tractable representation. These kernels introduce a form of temporal symmetry in the system’s memory, where past nutrient levels influence present dynamics in a balanced and structured way. Using the linear chain trick, we reformulate the integro-differential equations into ordinary differential systems for both weak and strong memory scenarios. We derive conditions for local stability and Hopf bifurcation, and establish global stability using Lyapunov-based methods. Numerical simulations confirm that increased delay can destabilize the system, leading to oscillations, while stronger memory mitigates this effect and enhances resilience. Bifurcation diagrams, time series, and phase portraits illustrate how memory strength governs the system’s dynamic response. This work highlights how symmetry in memory structures contributes to system robustness, offering theoretical insights and practical implications for the design and management of ecologically stable aquaculture systems. Full article

We propose a hybrid Caputo–Lagrange Discretization Method (CLDM) for the fractional-order modeling of glucose–insulin dynamics. The model incorporates key physiological mechanisms such as glucose suppression, insulin activation, and delayed feedback with memory effects captured through Caputo derivatives. Analytical results establish positivity, boundedness, existence, uniqueness, and Hyers–Ulam stability. Numerical simulations confirm that the proposed method improves accuracy and efficiency compared with the Residual Power Series Method and the fractional Runge–Kutta method. Sensitivity analysis highlights fractional order $\theta$ as a biomarker for metabolic memory. The findings demonstrate that CLDM offers a robust and computationally efficient framework for biomedical modeling with potential applications in diabetes research and related physiological systems. Full article

Understanding the diversity of star formation histories (SFHs) of galaxies is key to reconstructing their evolutionary paths. Traditional models often assume parametric forms such as delayed-$\tau$ or exponentially declining models, which may not reflect the actual variety of formation processes. We aim to assess what types of SFHs are consistent with the observed present-day star formation rates (${\mathrm{SFR}}_0$) and time-averaged star formation rates ($⟨\mathrm{SFR}⟩$) of galaxies in the Local Volume, without assuming any fixed functional form. We construct a non-parametric framework by generating large ensembles of randomized SFHs for each galaxy in the sample. For each SFH, we compute its predicted stellar mass and present-day SFR and retain only those consistent with the observed values within a 20% tolerance. We then infer the statistical distribution of power-law slopes $\eta$ (fitted as $\mathrm{SFR}\left(t\right)\propto {(t-t_{\mathrm{start}})}^{\eta }$) and 50% stellar mass formation times $t_{50}$. Across the full sample of 555 galaxies, we find that ≈70% have flat SFHs ($\left|\eta \right|\le 0.01$), ≈24% are mildly declining ($\eta <-0.01$), and ≈6% are rising ($\eta >0.01$). In the low-mass bin ($M_{★}<3\times {10}^9{M}_{\odot }$), rising SFHs slightly increase (≈7%) but remain a minority as the majority have flat SFHs. Both $\eta$ and $t_{50}$ correlate strongly with the SFR ratio (Spearman $\rho >0.75$, $p\ll {10}^{-16}$), indicating that the shape and timing of star formation are primarily governed by this ratio. The $t_{50}$ distribution shows sharp spikes near 7.74 and 7.86 Gyr, which we attribute to grid discretization combined with filtering, rather than a physical bimodality. Our results confirm that strongly declining SFH templates are disfavored in the Local Volume: most systems are consistent with flat long-term SFHs, with only mild decline or occasional rising. Importantly, this is demonstrated through a fully model-independent, data-driven approach, with per-galaxy uncertainties quantified using the standard error of $\eta$ and $t_{50}$ from the ensemble of accepted SFHs. Full article

Based on the perspective of multi-stage dynamic competition, this study constructs a discrete dynamic model of price competition between the “direct sales” and “resale” channels in cross-border e-commerce (CBEC) under three blockchain deployment modes. Drawing on nonlinear dynamics theory, the Nash equilibrium of the system and its stability conditions are examined. Using numerical simulations, the effects of factors such as the channel price adjustment speed, tariff rate, and commission ratio on the dynamic evolution, entropy, and stability of the system under the empowerment of blockchain technology are investigated. Furthermore, the impact of noise factors on system stability and the corresponding chaos control strategies are further analyzed. This study finds that a single-channel deployment tends to induce asymmetric system responses, whereas dual-channel collaborative deployment helps enhance strategic coordination. An increase in price adjustment speed, tariffs, and commission rates can drive the system’s pricing dynamics from a stable state into chaos, thereby raising its entropy, while the adoption of blockchain technology tends to weaken dynamic stability. Therefore, after deploying blockchain technology, each channel should make its pricing decisions more cautiously. Moderate noise can exert a stabilizing effect, whereas excessive disturbances may cause the system to diverge. Hence, enterprises should carefully assess the magnitude of disturbances and capitalize on the positive effects brought about by moderate fluctuations. In addition, the delayed feedback control method can effectively suppress chaotic fluctuations and enhance system stability, demonstrating strong adaptability across different blockchain deployment modes. Full article