Search Results (61)

Cryptocurrencies play a central role in modern financial markets; however, geopolitical tensions and environmental concerns raise critical questions about their stability and informational efficiency. This study distinguishes between green cryptocurrencies (GCs), based on low-energy validation mechanisms, and dirty cryptocurrencies (DCs), which rely on energy-intensive protocols, to examine their behaviour under geopolitical stress. The objective of this paper is to assess how information dynamics, market resilience, and efficiency differ between GCs and DCs during periods of heightened geopolitical uncertainty, with particular focus on the Russia–Ukraine war. Using daily data from 28 April 2019 to 5 October 2023, we employ advanced information-theoretic measures, including mutual information, the rolling local nearest-neighbour entropy estimator (RLNNEE), and approximate entropy. The results show that DCs exhibit stronger information dominance than GCs, with this gap widening during the conflict. In contrast, GCs display lower but more stable mutual information, indicating greater informational resilience. Approximate entropy further reveals a decline in market complexity during the war period. Overall, the findings highlight the relevance of entropy-based tools for evaluating stability and risk in cryptocurrency markets facing geopolitical shocks. Full article

An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes. Full article

Chatter is a self-excited vibration that limits productivity, accelerates roll wear and compromises strip surface quality in high-speed tandem cold-rolling. This work presents a predictive hybrid model that couples the strip-deformation physics to the structural dynamics of a five-stand, 4-high mill, providing a fast decision tool for process optimization and real-time control. The model represents each stand as a four-degree-of-freedom mass–spring–damper system whose parameters are extracted from manufacturing automation datasheets and roll-gap sensing. Linearization about the nominal point yields analytical sensitivity matrices that close the electromechanical loop; the delay between stands is also included in the model. Implemented in MATLAB/Simulink, the computational model, based on data provided by Danieli & C. Officine Meccaniche S.p.A., reproduces the onset of chatter for two types of steel. The framework therefore supports automation-ready scheduling, active vibration mitigation and design-space exploration for next-generation mechatronic cold-rolling systems. Full article

This study addresses the issues with traditional rolling protection bearings in vertical magnetic levitation flywheel energy storage systems (FESSs), which are prone to impact, wear, and temperature rise under abnormal conditions, such as drops. It designed a permanent magnet axial protection bearing based on a Halbach array, utilizing N42SH permanent magnet material. The five-layer Halbach array achieved a maximum axial magnetic force of 86 KN and a maximum air gap magnetic flux density of 2.2 T, meeting the application requirements. Simulation results, combined with rotor drop dynamics and thermal analysis, show that under an 8000 rpm drop condition, the permanent magnet bearing reduces radial and axial contact forces by approximately 60% and 54%, respectively, and wear by around 70%. Additionally, the maximum system temperature decreases from 109 °C to 74 °C, with a 32% reduction in temperature rise. Friction experimental analysis indicates that low frequency, low load, and moderate temperatures improve friction stability and reduce wear. Overall, the permanent magnet axial protective bearing effectively mitigates drop impact, reduces friction heat and wear, and enhances the safety and reliability of the flywheel energy storage system under abnormal working conditions, providing valuable theoretical support and a design reference for engineering applications. Full article

In this study, a novel and efficient solid-state additive manufacturing technique, friction rolling additive manufacturing (FRAM), was employed to fabricate an aluminum alloy ring component, significantly reducing process complexity and mitigating solidification defects typical of melt-based techniques. However, previous studies on FRAM have primarily focused on the microstructural characteristics and mechanical properties of flat components, with limited attention paid to ring-shaped components. Owing to the unique geometric constraints imposed during the forming process, ring components exhibit markedly different microstructural evolution and defect formation mechanisms compared with flat counterparts, and these mechanisms remain insufficiently and systematically understood. To address this knowledge gap, the coupled Eulerian–Lagrangian (CEL) method was introduced for the first time to numerically simulate the temperature distribution and residual stress evolution during the FRAM process of ring-shaped components. In addition, tracer particles were incorporated into the simulations to analyze the material flow behavior, thereby systematically elucidating the forming behavior and microstructural evolution characteristics under geometric constraint conditions. Moreover, scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) were employed to systematically characterize the microstructural evolution and defect morphology. The CEL numerical simulations exhibited good consistency with the experimental observations, demonstrating the reliability and accuracy of the simulation method. The results showed that the peak temperatures were primarily concentrated at the advancing side of the rotation tool, and the temperature on the outer diameter side of the ring was consistently higher than that on the inner diameter side. The lack of shoulder friction on the inner side led to an increased heat dissipation rate, thereby resulting in higher residual stress compared to other regions. The particle analysis revealed that, due to ring geometry, material flow varied across radial regions, resulting in distinct microstructures. Further EBSD analysis revealed that, after the rotating tool passed, the material first developed a preferential orientation with {111} planes parallel to the shear direction, and with more layers, dynamic recrystallization produced an equiaxed grain structure. This study provides a theoretical basis and process reference for the application of the FRAM technique in the manufacturing of large ring components. Full article

Autonomous driving, a transformative automotive technology, is currently a major research focus. Trajectory planning, one of the three core technologies for realizing autonomous driving, plays a decisive role in the performance of autonomous driving systems. The key challenge lies in planning an optimal trajectory based on real-time environmental information, yet significant research gaps remain, particularly for dynamic driving scenarios. To address this, our study investigates lane-changing trajectory planning in dynamic scenarios based on model predictive control (MPC) theory and proposes a novel dynamic lane-changing trajectory planning method. First, kinematic models for both the host vehicle and surrounding vehicles are established. Then, following the core components of MPC theory, we construct a prediction model, define an objective function, and formulate constraints for the rolling optimization step. Finally, the optimal control sequence derived from the optimization is processed using a least-squares fitting method to generate a lane-changing trajectory that demonstrates real-time adaptability in dynamic environments. The proposed method is validated through simulation studies of three typical driving conditions on a co-simulation platform. The results confirm that the planned trajectory exhibits excellent dynamic real-time adaptability, thereby contributing a foundation for achieving full-scenario autonomous driving. Full article

Rolling element bearing failure in industrial robots can cause system downtime, high repair costs, and significant economic losses. Traditional fault diagnosis methods assume that training and testing data follow the same distribution, requiring extensive historical data, which is often impractical in dynamic operational environments. Digital twin and transfer learning technologies offer a new approach for intelligent fault diagnosis, addressing these limitations. This paper combines model knowledge and data-driven approaches using digital twin and transfer learning for bearing fault diagnosis. First, a dynamic twin model of the bearing is developed using MATLAB/Simulink (R2018a), simulating fault data under various operating conditions that are difficult to obtain in real-world scenarios. A multi-level construal neural network algorithm is then proposed to minimize cumulative errors in data preprocessing. The digital twin technology generates a balanced dataset for pre-training the model, which is subsequently applied to real-time fault diagnosis in industrial robot bearings via transfer learning, bridging the gap between virtual and physical entities. Experimental results demonstrate the feasibility of the method, with a diagnostic accuracy of 96.95%, marking a 15% improvement over traditional convolutional neural network methods without digital twin enhancement. Full article

Despite the recognized importance of spiral curve implementation in highway design, several design manuals permit spiral omission depending on the geometric layout and the performance characteristics of road users. A critical safety issue associated with these methodologies arises from the potential exceedance of the maximum allowable side friction coefficient and the design utilization factor on a circular arc, particularly under wet pavement conditions. The present study aims to address a gap in geometric design manuals and the international literature by optimizing the superelevation design of the runoff section in the tangent-to-curve transition for circular arcs in mountainous terrain with a maximum design superelevation rate of up to 5%. The proposed methodology is supported by an analysis based on fundamental vehicle dynamics equilibrium equations, aiming to resolve concerns among practitioners regarding the elimination of superelevation rate transitions within the circular arc itself, with particular focus on the evaluation of the utilization factor and the applicability in icy conditions. The comparative evaluation of the demanded utilization factors resulting from this method and those defined by existing guidelines, along with the safety levels it maintains under icy conditions (i.e., compound slope up to 10%), encourages its immediate implementation in circular arcs with a design superelevation rate up to 5%, as well as further investigation into the potential application of this method in circular arcs with a design superelevation rate greater than 5% in mountainous and rolling terrains. Full article

This study presents a systematic analysis of the stability and roll characteristics of an Oscillating Water Column (OWC) wave energy buoy. By integrating theoretical derivation and AQWA simulation, the research identifies thirteen possible heeling states of OWC buoy, focusing on five representative states applicable to the current design. A novel segmented-integration model is proposed to compute the centre of buoyancy and righting moment for the hollow-annular OWC buoy, accurately capturing the evolution of static and dynamic stability across heel angles from 0° to 90°. Results show that the buoy has an initial metacentric height of 0.33 m, a maximum righting arm of 0.713 m, a limiting static heel angle of 77°, and a minimum capsizing moment of 22,887 N·m—all significantly exceeding regulatory requirements. The roll natural period ranges from 5.8 to 7.7 s, with a tuning factor above 1.3, effectively avoiding resonance with typical wave periods in the target sea area. The buoy demonstrates excellent dynamic stability and capsize resistance. This study fills a gap in OWC buoy stability analysis and provides a practical guidance for the safe design of wave energy devices. Full article

Aiming at the trajectory tracking control problem caused by the coupling of strong nonlinearity, parameter uncertainty and unknown disturbances in rigid robotic arms, this paper proposes an adaptive robust model predictive control (APRMPC) scheme. This study aims to fill the gap in the existing literature by proposing a dedicated control framework capable of simultaneously and effectively handling parameter uncertainty, unmodeled dynamics, and external disturbances, while ensuring constraint satisfaction. Firstly, a dynamic model of a three-degree-of-freedom robotic arm was established based on the Lagrange equation; secondly, this paper designs a deep integration mechanism of adaptive law and robust predictive control: by designing a parameter adaptive algorithm to estimate the system uncertainty online and feedforward compensate it to the predictive model, the impact of model mismatch is significantly reduced; meanwhile, for the estimated residuals and unknown disturbances, feedback gain was introduced and the control input was designed based on the robust invariant set theory, achieving unified parameter identification, disturbance suppression and rolling optimization within a single framework. This paper strictly proves the feasibility and stability of the control scheme. Finally, the simulation experiments based on MATLAB show that, compared with the traditional MPC and PID methods, the APRMPC algorithm can achieve higher accuracy and stronger robustness in trajectory tracking under various working conditions, effectively resolving the inherent contradiction between the weak robustness of the traditional MPC and the large buffering of sliding mode control, and verifying the value of the proposed scheme in filling the gap in related literature. Full article

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions. Full article

Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today’s sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks. Full article

Cross-docking has emerged as a critical logistics strategy to reduce lead times, lower inventory levels, and enhance supply chain responsiveness. However, in high-throughput terminals, efficient coordination of inbound and outbound trailers remains a complex task, especially under uncertain and dynamically changing conditions. We propose a practical framework that helps logistics terminals assign trailers to docks in real time. It links live sensor data with a mathematical optimization model, so that the system can quickly adjust trailer plans when traffic or workload changes. Real-time data from IoT sensors, GPS, and operational records are preprocessed, enriched with predictive analytics, and used as input for a Mixed-Integer Linear Programming (MILP) model solved in rolling horizons. This enables the continuous reallocation of inbound and outbound trailers, ensuring synchronized flows and balanced dock utilization. Numerical experiments compare the adaptive approach with conventional first-come-first-served scheduling. Results show that average inbound dock utilization improves from 68% to 71%, while the share of periods with full utilization increases from 33.3% to 41.4%. Outbound utilization also rises from 57% to 62%. Moreover, trailer delays are significantly reduced, and the overall makespan shortens from 45 to 40 time slots. These findings confirm that adaptive, real-time trailer assignment can enhance efficiency, reliability, and resilience in cross-docking operations. The proposed framework thus bridges the gap between static optimization models and the operational requirements of modern, high-throughput logistics hubs. Full article

This paper presents a comprehensive investigation of sensitivity-determining factors in dual-backplate capacitive MEMS microphones through analytical modeling, finite element analysis (FEM), and experimental validation. The study focuses on three critical design parameters: backplate perforation density, membrane tension, and electrode gap spacing. A lumped parameter model (LPM) and FEM simulations are employed to characterize the dynamic behavior and frequency response of the microphone. Simulation results demonstrate that reducing the backplate hole diameter or hole count amplifies squeeze-film damping, inducing nonlinear effects and anti-resonance dips near the fundamental frequency (f0) while mitigating low-frequency roll-off (<100 Hz). Membrane tension exhibits a nonlinear relationship with sensitivity, stabilizing at high tension (>7000 N/m) but risking pull-in instability at low tension (<1500 N/m). Smaller electrode gaps enhance sensitivity but are constrained by pull-in voltage limitations. The FEM model achieves higher accuracy (≤2 dB error) than LPM in predicting low-frequency response anomalies. This work provides systematic guidelines for optimizing dual-backplate MEMS microphone designs, balancing sensitivity, stability, and manufacturability. Full article

To address the issue of uneven load and premature failure in ball–column composite bearings caused by ring deformation, this study develops a mechanical analysis model, considering ring deformation based on flexible ring theory and rolling bearing design. It systematically examines radial deflection of the ring and how key parameters affect load distribution and stress. The results demonstrate that the elastic deformation of the collar redistributes the load, reduces the roller column’s load-carrying efficiency, and disrupts the optimal load distribution mode. Increasing the number of loaded rolling elements significantly improves the load uniformity, reduces the peak contact stress, and enhances the overall load-carrying performance. By optimizing the clearance matching across three bearings rows, a load-adaptive gradient bearing mechanism is realized by dynamically transferring, 70–90% of the heavy-load optimal distribution. These findings address the domestic research gaps and offer theoretical support for the performance prediction and optimal design of integrated ball–column composite bearings. Full article