Search Results (167)

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article

To enhance riding comfort for drivers of heavy-duty vehicles, this paper introduces a novel adaptive prescribed performance control (APPC) for active seat suspension systems. The model incorporates dynamic friction and hysteresis damping effects to capture the complex behavior of the seat suspension. The accuracy of the proposed model is validated through experimental data. The controller utilizes a prescribed performance function (PPF) to regulate the dynamic response of the system, combined with an adaptive backstepping control (ABC) method to account for system uncertainties, such as variations in driver weight, friction, suspension stiffness, and damping coefficients. A set of parameter estimators, governed by innovative adaptive laws, compensates for estimation errors. Furthermore, the stability of the controlled system is rigorously demonstrated. Both simulation and experimental tests, including bump and random excitation tests, are conducted to assess the controller performance in both time and frequency domains. The results confirm that the proposed controller effectively mitigates vibrations in the driver–seat system and demonstrates robustness against system uncertainties. Full article

Hub motor-driven vehicles (HMDVs) suffer from poor handling and stability due to an increased unsprung mass and unbalanced radial electromagnetic forces. Although traditional ground-hook control reduces the dynamic tire load, it severely worsens the body acceleration. This paper presents a generalized ground-hook control strategy based on impedance transfer functions to address the parameter redundancy in structural methods. A quarter-vehicle model with a switched reluctance motor wheel hub drive was used to study different orders of generalized ground-hook impedance transfer function control strategies for dynamic inertial suspension. An enhanced fish swarm parameter optimization method identified the optimal solutions for different structural orders. Analyses showed that the third-order control strategy optimized the body acceleration by 2%, reduced the dynamic tire load by 8%, and decreased the suspension working space by 22%. This strategy also substantially lowered the power spectral density for the body acceleration and dynamic tire load in the low-frequency band of 1.2 Hz. Additionally, it balanced computational complexity and performance, having slightly higher complexity than lower-order methods but much less than higher-order structures, meeting real-time constraints. To address time-domain deviations from generalized ground-hook control in semi-active systems, a dynamic compensation strategy was proposed: eight topological structures were created by modifying the spring–damper structure. A deviation correction mechanism was devised based on the frequency-domain coupling characteristics between the wheel speed and suspension relative velocity. For ride comfort and road-friendliness, a dual-frequency control criterion was introduced: in the low-frequency range, energy transfer suppression and phase synchronization locking were realized by constraining the ground-hook damping coefficient or inertance coefficient, while in the high-frequency range, the inertia-dominant characteristic was enhanced, and dynamic phase adaptation was permitted to mitigate road excitations. The results show that only the T0 and T5 structures met dynamic constraints across the frequency spectrum. Time-domain simulations showed that the deviation between the T5 structure and the third-order generalized ground-hook impedance model was relatively small, outperforming traditional and T0 structures, validating the model’s superior adaptability in high-order semi-active suspension. Full article

Plasma amino acid (PAA) concentration in horses vary according to the exercise type. This study evaluated the changes in PAA levels and the associated metabolites, urea and ammonia, following short-duration, high-intensity cross-country exercise in eventing horses. Twenty eventing horses participated in 55 rides at 14 international competitions (2* to 4* levels) across five venues in Germany and Poland. Blood samples were collected at four timepoints: before exercise (TP0), at 10 min (TP1), and at 30 min (TP2) post-exercise, as well as in the morning on the day after the competition (TP3). A total of 23 different PAAs and two metabolites (ammonia and urea) were analyzed. PAA concentration difference over time was assessed by a mixed ANOVA. Significant fluctuations were observed in 18/25 parameters. For 21/23 PAAs, levels increased at TP1 and/or TP2, while cysteine concentrations decreased. Concentrations returned to pre-competition levels for 21/23 PAAs by TP3. Proline levels remained elevated (p = 0.002), while those of glycine significantly decreased (p = 0.027) at TP3. Plasma ammonia and urea levels increased at TP1, TP2 and TP3. This study provides foundations for supplementation strategies and can inform future works exploring PAAs’ role in performance and training adaptation in eventing horses and their potential as performance-related biomarkers. Full article

Accurate voltage information is important for ensuring the safe operation of power systems and their performance evaluation. However, as distributed energy sources become more prevalent, the levels of harmonics and DC components in the power grid are increasing notably, resulting in voltage waveform distortion and a breakdown of waveform symmetry. As a result, traditional voltage parameter detection methods are unable to obtain the voltage information accurately. To address this issue, this paper proposed a novel approach that leverages adaptive estimation to accurately detect voltage parameters under grid voltage distortion conditions. More importantly, the proposed method has the ability to extract the harmonics and the DC component without steady-state error and exhibits a fast dynamic response. With this approach, the amplitude of the grid voltage can be derived in 4.2 ms when the grid voltage is undistorted. In the presence of low-order harmonics, the amplitude of the grid voltage can be accurately derived in 10.7 ms. Finally, simulation results and experimental results are respectively used for model validation and functionality validation. Full article

A machine learning framework is developed to interpret vehicle subsystem status from sensor data, providing actionable insights for adaptive control systems. Using the vehicle’s suspension as a case study, inertial data are collected from driving maneuvers, including braking and cornering, to seed a prototype XGBoost classifier. The classifier then pseudo-labels a larger exemplar dataset acquired from street and racetrack sessions, which is used to train an inference model capable of robust generalization across both regular and performance driving. An overlapping sliding-window grading approach with reverse exponential weighting smooths transient fluctuations while preserving responsiveness. The resulting real-time semantic mode predictions accurately describe the vehicle’s current dynamics and can inform a model predictive control system that can adjust suspension parameters and update internal constraints for improved performance, ride comfort, and component longevity. The methodology extends to other components, such as braking systems, offering a scalable path toward fully self-optimizing vehicle control in both conventional and autonomous platforms. Full article

Auxiliary transport vehicles are essential components of the underground mine auxiliary transportation system, primarily used for tasks such as personnel and material transportation. However, the underground environment is complex, and unstructured roads exhibit significant randomness. Traditional passive hydropneumatic suspension systems struggle to strike a balance between ride comfort and stability, resulting in insufficient adaptability of auxiliary transport vehicles in such challenging underground conditions. To address this issue, this paper proposes a multi-mode hydropneumatic suspension control strategy based on the identification of road surface grades in underground mines. The strategy dynamically adjusts the controller’s parameters in real time according to the identified road surface grades, thereby enhancing vehicle adaptability in complex environments. First, the overall framework of the active suspension control system is constructed, and models of the hydropneumatic spring, vehicle dynamics, and road surface are developed. Then, a road surface grade identification method based on Long Short-Term Memory networks is proposed. Finally, a fuzzy-logic-based sliding mode controller is designed to dynamically map the road surface grade information to the controller’s parameters. Three control objectives are set for different road grades, and the multi-objective optimization of the sliding mode’s surface coefficients and fuzzy-logic-based rule parameters is performed using the Hiking Optimization Algorithm. This approach enables the adaptive adjustment of the suspension system under various road conditions. The simulations indicate that when contrasted with conventional inactive hydropneumatic suspensions, the proposed method reduces the sprung mass’s acceleration by 21.2%, 18.86%, and 17.44% on B-, D-, and F-grade roads, respectively, at a speed of 10 km/h. This significant reduction in the vibrational response validates the potential application of the proposed method in underground mine environments. Full article

Existing studies on platoon trajectory optimization of connected and automated vehicles face challenges in balancing computational efficiency, privacy, and safety. This study proposes a distributed optimization method that decomposes the platoon trajectory planning problem into independent individual vehicle tasks while ensuring safe inter-vehicle following gaps and maximizing travel efficiencyand ride comfort. The individual vehicle problems independently optimize their trajectory to improve computational efficiency, and only exchange dual variables related to safe following gaps to preserve privacy. Simulation experiments were conducted under single-platoon scenarios with different simulation horizons, as well as multi-platoon and platoon-merging scenarios, to analyze the control performance of the distributed method in contrast to the centralized method. Simulation results demonstrate that the mean computation time is reduced by 50% and the fuel consumption is decreased by 4% compared to the centralized method while effectively maintaining the safe inter-vehicle following gaps. The distributed method shows its scalability and adaptability for large-scale problems. Full article

The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors influence spatial variations in ride-hailing demand using multi-source data from Haikou, China. A multi-scale geographically weighted regression (MGWR) model is employed to address spatial scale heterogeneity. To more accurately capture environmental features around sampling points, the DeepLabv3+ model is used to segment street-level imagery, with extracted visual indicators integrated into the regression analysis. By combining multi-scale geospatial data and computer vision techniques, the study provides a refined understanding of the spatial dynamics between ride-hailing demand and urban form. The results indicate notable spatiotemporal imbalances in demand, with varying patterns across workdays and holidays. Key factors, such as distance to the city center, bus stop density, and street-level features like greenery and sidewalk proportions, exert significant but spatially varied impacts on demand. These findings offer actionable insights for urban transportation planning and the design of more adaptive mobility strategies in contemporary cities. Full article

This paper proposes a dynamic weight model predictive control (DWMPC) strategy for adaptive cruise control (ACC) in pure electric vehicles, aiming to enhance robustness, energy efficiency, and ride comfort under complex traffic conditions. Unlike conventional MPC with static weights, the proposed method integrates a fuzzy inference system that evaluates driving urgency based on real-time spacing and velocity errors. The resulting emergency coefficient is mapped through a nonlinear function to dynamically adjust the velocity tracking weight in the MPC cost function. Additionally, a four-mode coordination mechanism adaptively modifies acceleration and jerk penalties according to risk levels, enabling balanced responses between safety and comfort. A composite performance evaluation index (PEI) is formulated to quantitatively assess energy consumption, ride comfort, spacing accuracy, and emergency responsiveness. Simulation results under WLTC and typical urban driving scenarios demonstrate that DWMPC outperforms fixed-weight MPC and PI controllers, reducing energy consumption by 6.5%, jerk by 42.9%, and response time by 41.8% while improving coordination in speed tracking, inter-vehicle distance regulation, and energy-efficient control. Full article

The proliferation of next-generation renewable energy systems has driven widespread adoption of electronic devices and nonlinear loads, causing grid distortion that degrades waveform quality in grid-forming (GFM) converters. Additionally, unbalanced grid faults exacerbate overcurrent risks and transient stability challenges when employing conventional virtual impedance strategies. While existing studies have separately examined these challenges, few have comprehensively addressed non-ideal grid conditions. To bridge this gap, a novel control strategy is proposed that reshapes the output current waveforms and enhances transient stability in GFM converters under such conditions. First, a sliding mode controller with an improved composite reaching law to achieve rapid reference tracking while eliminating chattering is designed. Second, a multi-quasi-resonance controller incorporating phase compensation is introduced to suppress harmonic distortion in the converter output current. Third, an individual-phase fuzzy adaptive virtual impedance strategy dynamically reshapes the current amplitude during unbalanced faults and improves the system’s transient stability. Validated through PSCAD/EMTDC simulations and hardware-in-the-loop experiments, the proposed strategy demonstrates superior transient stability and fault ride-through capability compared to state-of-the-art methods, ensuring reliable GFM converter operation under severe harmonic and unbalanced grid conditions. Full article

In the field of active safety technology for intelligent connected vehicles (ICVs), the reliability and safety of the Automatic Emergency Braking (AEB) system is recognized as critical to driving safety. However, existing evaluation methods have been constrained by the inadequacy of static weight assessments in adapting to diverse driving conditions, as well as by the disconnect between conventional evaluation frameworks and experimental validation. To address these limitations, a comprehensive Vehicle-in-the-Loop (VIL) evaluation system based on the dynamic weight analytic hierarchy process (DWAHP) was proposed in this study. A two-tier dynamic weighting architecture was established. At the criterion level, a bivariate variable–weight function, incorporating the vehicle speed and road surface adhesion coefficient, was developed to enable the dynamic coupling modeling of road environment parameters. At the scheme level, a five-dimensional indicator system—integrating braking distance, collision speed, and other key metrics—was constructed to support an adaptive evaluation model under multi-condition scenarios. By establishing a dynamic mapping between weight functions and driving condition parameters, the DWAHP methodology effectively overcame the limitations associated with fixed-weight mechanisms in varying operating conditions. Based on this framework, a dedicated AEB system performance test platform was designed and developed. Validation was conducted using both VIL simulations and real-world road tests, with a Volvo S90L as the test vehicle. The experimental results demonstrated high consistency between VIL and real-world road evaluations across three dimensions: safety (deviation: 0.1833/9.5%), reliability (deviation: 0.2478/13.1%), and riding comfort (deviation: 0.05/2.7%), with an overall comprehensive score deviation of 0.0707 (relative deviation: 0.51%). This study not only verified the technical advantages of the dynamic weight model in adapting to complex driving environments and analyzing multi-parameter coupling effects but also established a systematic methodological framework for evaluating AEB system performance via VIL. The findings provide a robust foundation for the testing and assessment of AEB system, offer a structured approach to advancing the performance evaluation of advanced driver assistance systems (ADASs), facilitate the safe and reliable validation of ICVs’ commercial applications, and ultimately contribute to enhancing road traffic safety. Full article

Variable Speed AC-excited Pumped Storage Units (VSACPSUs) demonstrate advantages in flexibility, high efficiency, and fast response, and they play a crucial regulatory role in power systems with increasing renewable energy penetration. Typically connected to weak grids, conventional low-voltage ride-through (LVRT) control methods for these units suffer from single control objectives, poor adaptability, and neglect of electromechanical coupling characteristics. To address these limitations, this paper proposes a multi-objective optimization strategy considering electromechanical coupling under a grid voltage fault. Firstly, a positive/negative-sequence mathematical model of doubly-fed machines is established. Based on stator winding power expressions, the operational characteristics under a grid fault are analyzed, including stator current imbalance as well as oscillation mechanisms of active power, reactive power, and electromagnetic torque. Considering the differences in rotor current references under different control objectives, a unified rotor current reference expression is constructed by introducing a time-varying weighting factor according to expression characteristics and electromechanical coupling properties. The weighting factor can be dynamically adjusted based on operating conditions and grid requirements using turbine input power, grid current unbalance, and voltage dip depth as key indicators to achieve adaptive control optimization. Finally, a multi-objective optimization model incorporating coupling characteristics and operational requirements is developed. Compared with conventional methods, the proposed strategy demonstrates enhanced adaptability and significantly improved low-voltage ride-through performance. Simulation results verify its effectiveness. Full article

This study introduces a novel magnetorheological (MR) damper for semi-active vehicle suspension systems that enhance ride comfort and handling stability. The proposed damper integrates reverse and normal damping modes, enabling independent control of rebound and compression strokes through an external MR valve. This configuration supports four damping modes—Soft/Soft, Hard/Soft, Soft/Hard, and Hard/Hard—allowing adaptability to varying driving conditions. Magnetic circuit optimization ensures rapid damping force adjustments (≈10 ms), while a semi-active control algorithm incorporating skyhook logic, roll, dive, and squat control strategies was implemented. Experimental validation on a mid-sized sedan demonstrated significant improvements, including a 30–40% reduction in vertical acceleration and pitch/roll rates. These enhancements improve vehicle safety by reducing body motion during critical maneuvers, potentially lowering accident risk and driver fatigue. In addition to performance gains, the simplified MR damper architecture and modular control facilitate easier integration into diverse vehicle platforms, potentially streamlining vehicle design and manufacturing processes and enabling cost-effective adoption in mass-market applications. These findings highlight the potential of MR dampers to support next-generation vehicle architectures with enhanced adaptability and manufacturability. Full article

Vehicle congestion and the environmental problems associated with the increasing vehicle fleet have led stakeholders to create solutions to these problems. Park-and-Ride (P&R) facilities are provided as a solution for public transportation to avoid increasing vehicular flow and using private vehicles. However, the optimal location of these facilities is still a challenge to be considered. Therefore, this article aims to present a systematic review of the mathematical models applied for P&R localization, using the PRISMA protocol to ensure a comprehensive analysis. A total of 44 articles between 2002 and 2025 were identified into four categories: decision support models, econometric models, optimization models, and other models. The review also examines the term distribution of urban contexts where the mathematical models are applied, distinguishing between Global North versus Global South urban contexts. The results showed the efficiency of mathematical models within the decision support models category due to their integration with multiple criteria. The econometric models analyze factors influencing user behavior, while the optimization models improve and optimize the efficiency of transport networks despite facing computational challenges. Finally, other models, such as multilevel programming and fuzzy logic, offer adaptive solutions for highly variable urban environments. The primary contribution of this study is its comprehensive application of the mathematical models used for the location of P&R facilities. This offers a systematic approach for anticipating future urban situations, developing supporting policies, and analyzing their effects. Full article