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This paper presents a novel structure of multi-cell tube filled with auxetic structures with the aim of improving the crashworthiness performances. The energy absorbing performances of the multi-cell tubes filled with auxetic structures are investigated by finite element analysis and validated experimentally. Compared with other typical energy absorbers, the multi-cell tubes filled with auxetic structures have better energy absorption performance. A parametric study is performed to analyze the effects of beam thickness, beam angle, and tube thickness on the crashworthiness performances. It is found that the tube and beam thicknesses and beam angles have great effects on the energy absorbing performances. An analytical model is also established to predict the energy absorption of multi-cell tubes filled with auxetic structures under axial impact loadings. The analytical predictions agree well with the numerical simulations indicating the analytical model is accurate to predict the energy absorption performances of the proposed structures. [ABSTRACT FROM AUTHOR]

An integrated process by coupling hydrothermal reactions, including CO2 reduction and H2O dissociation with metals, is proposed. The hydrogen could be rapidly produced under hydrothermal conditions, owing to the special characteristics of high temperature water, generating metal hydrides as intermediates. Hydrogen production from the H2O dissociation under hydrothermal conditions is one of the most ideal processes due to its environmentally friendly impact. Recent experimental and theoretical studies on the hydrothermal reduction of CO2 to value-added products by in situ generated metal hydrides are introduced, including the production of formic acid, methanol, methane, and long-chain hydrocarbons. These results indicate that this process holds promise in respect to the conversion of CO2 to useful chemicals and fuels, and for hydrogen storage, which could help alleviate the problems of climate change and energy shortage. [ABSTRACT FROM AUTHOR]

The accurate estimation of the mass and center of gravity (CG) position is key to vehicle dynamics modeling. The perturbation of key parameters in vehicle dynamics models can result in a reduction of accurate vehicle control and may even cause serious traffic accidents. A dual robust embedded cubature Kalman filter (RECKF) algorithm, which takes into account unknown measurement noise, is proposed for the joint estimation of mass and CG position. First, the mass parameters are identified based on directly obtained longitudinal forces in the distributed drive electric vehicle tires using the whole vehicle longitudinal dynamics model and the RECKF. Then, the CG is estimated with the RECKF using the mass estimation results and the vertical vehicle model. Finally, different virtual tests show that, compared with the cubature Kalman algorithm, the RECKF reduces the root mean square error of mass and CG by at least 7.4%, and 2.9%, respectively. [ABSTRACT FROM AUTHOR]

In this study, the impact of NaOH on the hydrothermal oxidation of guaiacol was investigated. It was found that NaOH significantly accelerated the production of formic acid and acetic acid with H2O2 or CuO as the oxidant. With the strong oxidant, H2O2, the highest acetic acid yield (15.73%) and formic acid (5.64%) were obtained at 300 °C for 90 s with NaOH 1.0 mol·L−1 and a 100% H2O2 oxygen supply. In comparison, with CuO as the oxidant, the highest values of acetic acid (13.42%) and formic acid (4.21%) were acquired at 250 °C for 6 h with NaOH 1.0 mol·L−1. Formic acid and acetic acid were generated through the oxidation of intermediates, such as levulinic acid, fumaric acid, maleic acid, etc. These results demonstrated that NaOH catalytic hydrothermal oxidation has potential for the production of value-added chemicals from biomass materials. When CuO is used as the oxidant, this process could also be used as a green method for copper smelting along with the utilization of lignin biomass. [ABSTRACT FROM AUTHOR]

Production of energy and chemicals from biomass resources has been regarded as one promising method to address the challenge of global warming. In this research, production of acrylic acid from fumaric acid, one of the biomass-derived building blocks, is proposed. CuO was employed as a solid oxidant, which showed excellent activity and selectivity for the production of acrylic acid, and water played an essential role in acting as not only a solvent but also a catalyst in this process. An optimum acrylic acid yield of 76.4% was successfully obtained after the reaction of fumaric acid with CuO at 300 °C for only 60 s.This research provides a green and highly efficient way to produce value-added chemicals from biomass-derived building blocks, and thus is promising for practical application. [ABSTRACT FROM AUTHOR]

The characteristics of the drought propagation from meteorological drought (MD) to agricultural drought (AD) differ in various climatic and underlying surface conditions. However, how these factors affect the process of drought propagation is still unclear. In this study, drought propagation and influencing factors were investigated in an arid region of Northeast Asia (ARNA) during 1982–2014. Based on run theory, the drought characteristics were detected using the standardized precipitation index (SPI) and standardized soil moisture index (SMI), respectively. The propagation time from MD to AD was investigated, and the influence factors were identified. Results demonstrated that five clusters (C1–C5) based on land cover distribution were further classified by the K-means cluster algorithm to discuss the spatial and seasonal propagation variation. MD and AD in ARNA became more severe during the study period in all five clusters. The propagation times from MD to AD in all five clusters were shorter (1–3 months) in summer and autumn and longer (5–12 months) in spring and winter. This result suggested that the impact of vegetation on the seasonal drought propagation time was more obvious than that of the spatial drought propagation time. Precipitation and vegetation were the major impactors of AD in spring, summer and autumn (p < 0.05). The impact of precipitation on AD was more noticeable in summer, while vegetation mainly influenced AD in spring and autumn. The research also found that drought propagation time had a negative relationship (p < 0.05) with precipitation, evapotranspiration, soil moisture and NDVI in this region, which indicated that a rapid hydrological cycle and vegetation can shorten the propagation time from MD to AD. This study can help researchers to understand the drought propagation process and the driving factors to enhance the efficiency of drought forecasting. [ABSTRACT FROM AUTHOR]

Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring. [ABSTRACT FROM AUTHOR]

Lightweight design of leaf springs is conducive to reducing fuel consumption and improving vehicle comfort. The weight of leaf spring can be reduced obviously by using composite material. Stiffness and damping are the key factors that affect the properties of the leaf spring. The influence of the glass fiber laying angle and volume content on the stiffness and damping of the composite leaf spring was analyzed through experiment and simulation. The results show that the stiffness and damping properties of the leaf springs are related to the fiber laying angle and the fiber volume content. When the volume content and the number of layers are constant, the stiffness shows a nonlinear decreasing relationship with the laying angle, and the damping coefficient increases linearly with the laying angle. When the laying angle and the number of layers are constant, the stiffness increases linearly with the fiber volume content; the damping coefficient has a nonlinear decreasing relationship with the fiber volume content. The type of research can provide theoretical basis and reference for the design, analysis and optimization of composite leaf spring. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have received extensive attention as an environmentally friendly and sustainable mode of transportation. To address "range anxiety" issues, extended-range electric vehicles (EREVs) have gradually gained popularity as a solution. However, current research on energy management strategies (EMS) for EVs often overlooks the energy consumption of the air conditioning (AC) system, resulting in suboptimal energy allocation. Therefore, this study focuses on the extended-range electric bus (EREbus), an extended-range electric bus, and incorporates the AC system into its EMS, enabling coordinated optimization with the powertrain system. First, the study embeds a control-oriented cabin thermal management model based on the powertrain model. Next, representations transfer-based reinforcement learning (RTRL) transfers the learned policy representations from the AC-off state to the EMS in the AC-on state. Furthermore, the study analyzes the impact of different representation transfers on powertrain performance and thermal comfort. The results demonstrate that the proposed EMS can improve the convergence rate and stability of training. Compared to direct learning methods, RTRL exhibits clear advantages in reducing operating costs and improving cabin thermal comfort, achieving reductions of 8.3%–12.6 % and 5.2%–27.0 % in operating costs for driving modes with different battery levels. Moreover, setting the transfer layer appropriately promotes the utilization of the global optimal potential of RTRL. This research provides support for energy management and holds the potential for promoting the development of EVs. • A novel vehicle thermal management model achieving cabin comfort regulation by controlling the evaporator and the blower. • A transfer-based reinforcement learning method enables the new comfort-aware energy management. • A collaborative strategy was tested in an extended-range electric bus model under various of driving conditions. [ABSTRACT FROM AUTHOR]

The aim of this work is to enhance the combustion performance of a turbulent jet ignition Wankel rotary engine (TJI-WRE) fueled by hydrogen/natural gas blends (HNGB) by changing the cylinder shape and adding a turbulence blade (TB). Therefore, the test bench and numerical simulation model for the TJI-WRE are established. Further, the flow field and flame propagation of the TJI-WRE with various cylinder shapes and turbulence blades are investigated. The results demonstrate that a new vortex is formed after the jet flame is injected into the cylinder and impacts the rotor piston surface. This vortex can enhance the combustion speed of the mixture in the back of the cylinder (BOC). The cylinder shape can change the intensity of the new vortex by changing the distance between the jet orifice and the piston surface. Furthermore, TB can change the combustion process by squeezing the in-cylinder unidirectional flow in the combustion stroke. Since the flame mainly propagates to the front of the cylinder (FOC) at the initial combustion stroke, as the TB layout moves from the BOC to the FOC, the squeezing effect of TB on the flame in the FOC becomes increasingly significant, resulting in increased combustion efficiency in the cylinder. [Display omitted] • Hydrogen/natural gas blends fuel was used to reduce carbon emissions. • A computational model of the turbulent jet ignition rotary engine was established. • The mixture formation, combustion, and emissions process were analyzed. • The optimal combination of cylinder structure for the rotary engine was given. • Compared with the original model, optimal cylinder structure PVIP increased by 12.6%. [ABSTRACT FROM AUTHOR]

This article establishes a novel electroencephalograph (EEG)-based brain–computer interface (BCI) system for ground vehicle control with potential application of mobility assistance to the disabled. To enable an intuitive motor imagery (MI) paradigm of “left,” “right,” “push,” and “pull,” a driving simulator based EEG data recording and automatic labeling platform is built for dataset making. In the preprocessing stage, a wavelet and canonical correlation analysis (CCA) combined method is used for artifact removal and improving signal-to-noise ratio. An ensemble learning based training and testing framework is proposed for MI EEG data classification. The average classification accuracy of proposed framework is about 91.75%. This approach essentially takes advantage of the common spatial pattern (CSP) with ability of extracting the feature of event-related potentials and the convolutional neural networks (CNNs) with powerful capacity of feature learning and classification. To convert the classification results of EEG data segments into motion control signals of ground vehicle, shared control strategy is used to realize the control command of “left-steering,” “right-steering,” “acceleration,” and “stop” considering collision avoidance with obstacles detected by a single-line LIDAR. The online experimental results on a model vehicle platform validate the significant performance of the established BCI system and reveal the application potential of BCI on the vehicle control and automation. [ABSTRACT FROM AUTHOR]

This paper proposes an actuator fault detection method for quadrotor UAV, in which sensitivity to fault and robustness to disturbance are simultaneously guaranteed. First, a quadrotor UAV system is converted to a linear-parameter-varying (LPV) form related to the altitude velocity and attitude angular velocity. Secondly, based on this LPV model, a fault detection method is designed, in which H − performance is applied to enhance fault sensitivity and L ∞ performance is applied for guarantee robustness of the residual against the disturbance. And then the L ∞ performance provides a detection threshold combined with initial flight condition for residual evaluation. Finally, based on the QDrone testbed, both numerical simulations and plentiful online experiments are implemented to validate the performance of the proposed fault detection scheme. • A fault detection method with mixed H − / L ∞ performance is designed for quadrotor UAV. • Time varying threshold is derived to avoid false alarms during quadrotor UAV take-off phase. • Both comparative simulation and experimental tests are conducted using QDrone platform. [ABSTRACT FROM AUTHOR]

Lightweight electric vehicles (LEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. However, due to the drastic reduction of vehicle weights and body size, the effects of payload parameter variation in LEV control and estimation system become much more pronounced and have to be systematically analysed. This paper proposes a novel payload parameter sensitivity analysis to provide quantitative insight into the sensitivity of payload parameter on the LEV system responses and state estimation. The analysis-oriented LEV dynamic model considering payload parameter variations is developed. Then, the trajectory sensitivity index of the influential parameters is defined and derived with the perturbation approach, the median method is used to improve the calculation accuracy for the trajectory sensitivity of payload parameter. Finally, the extended Kalman filter is designed to show the effect and importance of the sensitive payload parameters on the observation accuracy, the payload parameter variations along with fundamental state estimation such as vehicle sideslip angle, longitudinal velocity and vehicle roll angle are analysed. Simulation results with Matlab/Simulink-Carsim® show that the proposed method can accurately describe the relationship between the vehicle payload parameters and system state estimation, which is helpful to design and evaluate LEV controller and observer performances. [ABSTRACT FROM AUTHOR]

In this paper, a non-pneumatic tire (NPT) based on the bionic petal structure has been proposed to have high load carrying capacity in low stress level. Firstly, according to GB/T 2977-2008 truck tire specifications, the numerical models of bionic petal NPTs (BP-NPTs) for five different spokes are developed. A set of third-order Bezier curves is introduced to control the geometric configuration. Then, the static and dynamic characteristics of BP-NPTs are investigated numerically, and compared with typical NPTs like Tweel and Hexagonal Honeycomb non-pneumatic wheel (NPW). The results indicate that BP-NPTs have better performance by taking all of these factors into consideration. Also, the influence law of key parameters including the curvature and thickness distribution on the load carrying capacity, the maximum local stresses of spokes and tread under static or dynamic load are obtained. Finally, a multi-objective optimization design method by involving the Hammersley test design, moving least-square agent model and particle swarm optimization is proposed to optimize BP-NPTs. The results demonstrate that the performance of optimized structure is significantly improved. The mass of tire and the maximum stress of spokes under dynamic loads are reduced by 9.3% and 12.3%, respectively, and the load carrying capacity is improved by 10.1%. It is indicated that the BP-NPTs have a bright future in the automobile application. [ABSTRACT FROM AUTHOR]

At present, the impact of cuproptosis-related genes in the study of osteosarcoma is largely unknown. Genome-wide data of osteosarcoma and controls were downloaded from 3 different databases, and specific diagnostic models associated with cuproptosis in osteosarcoma were constructed by support vector machines with artificial intelligence, random forest trees and LASSO regression. Differential analysis of immune cell infiltration was examined using routine blood data from 25,665 cases. Differential expression was examined using immunohistochemistry and PCR. PDHA1 and CDKN2A were obtained as specific cuproptosis-related biomarkers for osteosarcoma after artificial intelligence analysis. PDHA1, CDKN2A and neutrophils were differentially expressed in OS and control groups. PDHA1 and CDKN2A are significantly dysregulated in OS and are able to serve as biomarkers of OS. [ABSTRACT FROM AUTHOR]

The accurate knowledge of tire–road friction coefficient (TRFC) contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles. The performance of the existing estimation methods would decline when a vehicle performs complex driving maneuvers. In addition, the mass parameter mismatch also deteriorates the estimation accuracy of TRFC. To address these problems, in this article, an integrated scheme for TRFC estimation is proposed by combining a strong tracking unscented Kalman filter and an interactive multiple model unscented Kalman filter. Real-time experiments are implemented on a mass-produced vehicle to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed approach has better estimation accuracy than the existing ones under various driving scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic stable control protocol for heterogeneous vehicle platoon subject to communication topologies change, external disturbance, and information delay. First, a random vehicle platoon system composed entirely of several pure electric vehicles is built. The random variation of data transmission link among the platoon in a natural traffic environment is considered and molded by the Markov chain combined with the directed graph method. The influence of delays and discrete data in wireless communication, road slope, and air resistance on the vehicle platoon is also considered by introducing the external interferences and equivalent information delays. Additionally, to ensure the vehicle platoon’s inner-vehicle stability, the variable-gain distributed controller is proposed based on the Markovian jumping system stability theory and $ H_\infty$ control. Finally, the $ \mathcal {L}_{2}$ stochastic string stability is defined to attenuate perturbations as they propagate through the platoon. Simulation studies about a vehicle platoon under four communication topologies random switching with two different control methods are provided to verify the theoretical result. It is shown that, compared to traditional platoon robust control, it is possible to achieve the vehicle platoon’s stability even if in the continuous mutation of unstable topologies by using the proposed control approach. [ABSTRACT FROM AUTHOR]

The accurate information of sideslip angle (SA) and tire cornering stiffness (TCS) is essential for advanced chassis control systems. However, SA and TCS cannot be directly measured by in-vehicle sensors. Thus, it is a hot topic to estimate SA and TCS with only in-vehicle sensors by an effective estimation method. In this article, we propose a novel fuzzy adaptive robust cubature Kalman filter (FARCKF) to accurately estimate SA and TCS. The model parameters of the FARCKF are dynamically updated using recursive least squares. A Takagi–Sugeno fuzzy system is developed to dynamically adjust the process noise parameter in the FARCKF. Finally, the performance of FARCKF is demonstrated via both simulation and experimental tests. The test results indicate that the estimation accuracy of SA and TCS is higher than that of the existing methods. Specifically, the estimation accuracy of SA is at least improved by more than 48%, while the estimators of TCS are closer to the reference values. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-objective eco-routing strategy aimed at optimizing driving routes by minimizing both energy consumption and travel time. First, the vehicle and powertrain dynamics are modelled. The energy consumption of three powertrain types, including hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV), are calculated based on a white-box model. Second, this paper models the traffic network by considering the intensity of each road. The energy consumption associated with different intensity levels is also modelled using a typical driving cycle. The dual-objective routing problem, considering both energy consumption and travel time, is solved by an Iterative Dijkstra's algorithm. Finally, several simulation results demonstrate that the proposed dual-objective eco-routing strategy achieves a favorable balance between energy consumption and travel time. Specifically, for HEV, the dual-objective eco-routing strategy resulted in a 5.3% reduction in travel time with a marginal increase of only 0.4% in fuel consumption compared to the single-objective eco-route. In addition, the eco-routing strategy exhibits distinct patterns across different vehicle powertrains. HEV and BEV tend to utilize heavy traffic conditions more frequently compared to the CV. • Build the energy consumption models of hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV). • Energy consumption associated with different intensity levels is modelled. • Formulate the dual-objective routing problem, considering both energy consumption and travel time. • Eco-routing strategy exhibits distinct patterns across different vehicle powertrains and traffic conditions. [ABSTRACT FROM AUTHOR]

The rapid development of science and technology has created favorable conditions for Connected and Automated Vehicles (CAVs). Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. However, complicated urban traffic environments, such as the flyover, significantly influence vehicular positioning accuracy. The inability of CAV to accurately perceive self-localization information has become an urgent issue to be addressed. This paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. First, the three-dimensional positioning error model of the host vehicle concerning adjacent vehicles in azimuth angle and pitch angle and intervehicle distances under the vehicle-to-vehicle communication was established. Second, two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. To verify the proposed algorithm's effect, the PreScan-Simulink joint simulation is carried out. The results show that the host vehicle's localization accuracy by the proposed method can be improved by 25% compared with direct linearization. Besides, by combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%. [ABSTRACT FROM AUTHOR]

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability. [ABSTRACT FROM AUTHOR]

Agricultural machine automatic navigation poses great challenge to the precise agricultural technology system nowadays. To this end, this paper proposes a novel steering assistance system (SAS) to assist drivers in the path-tracking. First, the driver steering model is investigated through the driver simulator tests. Combining the wheeled tractor kinematics model, a driver-vehicle model is developed. Then, a polytopic linear parameter-varying (LPV) system is adopted to describe the uncertainties, including time-varying driver model parameters and velocity, in the model, based on which an output-feedback robust controller is developed to ensure robust stability within the polytope space. Moreover, a regional pole placement method is adopted to improve the transient performance of the system. Finally, driver-in-the-loop and field tests conducted to value the controller. The results show the effectiveness of the proposed method to improve the path-tracking performance for the agricultural machine navigation, while reducing the physical and mental workload of drivers. This control method is expected to be a paradigm for the precise navigation system of the agricultural machinery. [ABSTRACT FROM AUTHOR]

Drought can weaken forest activity and even lead to forest mortality, and the response of different forest types to drought can be diverse. Deciduous broadleaf forest (DBF) and deciduous needleleaf forest (DNF) are two of the majority forest types in northern China. In this region, a severe drought event happened in 2002. However, due to the lack of data, the spatio-temporal characteristics of the ecosystem stability of different forest types here remain unclear. In this study, we used a machine learning approach (model tree ensemble, MTE) to drive fluxsite gross primary productivity (GPP), combined with remote sensing-based GPP and a vegetation index data (EVI), to analyze the spatial and temporal characteristics of resistance and resilience of DNF and DBF in northern China during and after the 2002 drought. The results showed that the DBFs were more acclimatized to the drought, while the resistance and resilience of DNF and DBF were diverse under different consecutive drought events. These results could be suggestive for forest conservation and vegetation modeling. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving, ensuring pedestrian safety has become a hot research topic. The traditional pedestrian crossing behavior research mostly track and predict pedestrian crossing from the view of vehicles, but do not have in-depth research on the impact of other elements in the traffic scene. This paper proposes a method for analyzing pedestrian crossing behavior based on roadside equipment for specific road sections. First, a pedestrian awareness-based social force model (PASFM) is proposed by introducing the factors that influence pedestrian crossing decisions in traffic scenarios into the social force model (SFM), including zebra crossings, fellow walkers and vehicles. To explore the psychological state of vehicles and pedestrians when they interact, evolutionary game theory is used to simulate the psychological characteristics of pedestrians when they encounter vehicles, and then reflect in the changes of the desired speed. Then Centralized Unscented Kalman Filter (CUKF) is proposed to complete the whole process of tracking and predicting the motion state of pedestrian crossing behavior, which uses PASFM as its prediction process. Finally, simulations and experiments are designed to show the effectiveness of our methods. Results show that the proposed method performs better during pedestrian tracking and can predict their crossing behavior to a certain extent. [ABSTRACT FROM AUTHOR]

This paper presents an end-to-end deep reinforcement learning (DRL) approach aimed at efficiently determining energy-optimal routes for a group of electric logistic vehicles, with the objective of minimizing operating costs. First, an Energy-Minimization Electric Vehicle Routing Problem (EM-EVRP) is formulated with an energy consumption model for electric vehicles, rather than Distance Minimization EVRP commonly favored in the literature. The energy consumption model incorporates several factors such as vehicle dynamics, road information, and charging losses. Then, the problem is reformulated based on the Markov decision process and solved using the transformer-based DRL method. The policy network is designed following the Transformer structure, including an encoder, a feature embedding module, and a decoder, where the feature embedding module is added to provide contextual information. Finally, extensive experiments demonstrate the superior of the proposed DRL method over existing learning-based methods and conventional methods, in solving both EM-EVRP and DM-EVRP. Notably, the formulated EM-EVRP achieves greater cost reduction than the traditional DM-EVRP. • Build an energy consumption model of electric vehicle for Electric Vehicle Routing Problem. • Formulate a novel energy-oriented Electric Vehicle Routing Problem. • Propose a deep reinforcement learning with transformer-based neural network to solve the Electric Vehicle Routing Problem. • Feature embedding module is added between the encoder and decoder of Transformer to provide contextual information. [ABSTRACT FROM AUTHOR]

• A rotating auxetic vibration energy harvester (RAVEH) for vehicle wheel is proposed. • A dynamic model for RAVEH is established based on Euler-Lagrange theory to predict the energy harvesting performances. • Compared to the experimental results, the dynamic models are accurate to predict the energy harvesting performances. In this paper, a rotating auxetic vibration energy harvester (RAVEH) is proposed to provide a new solution for battery-free technology for intelligent tires. This RAVEH is self-adjusting at different vehicle speeds through centrifugal force generated by rotational motion. A dynamic model for RAVEH is established based on Euler-Lagrange theory to predict the energy harvesting performances and experimentally verified. A comparison between the RAVEH and traditional rotating flat energy harvester is conducted and the effects of the geometric parameters are also discussed thoroughly. It is found that RAVEH can increase the peak voltage by 42.90%, the peak power by 139.14%, the bandwidth by 54.55%, and the power range by 80.95% compared with a flat energy harvester. The designed RAVEH is capable of powering the sensors corresponding to the intelligent tires and realizing the battery-free technology. [ABSTRACT FROM AUTHOR]

With the intensification of the greenhouse effect, carbon neutrality has become a global goal. The development of vehicle platoons provides a new idea for energy saving and emission reduction. To explore the practical application prospect of vehicle platoon energy-saving methods and reveal the limitations and improvement direction of current vehicle platoon research, this paper reviews the current research status of vehicle platoon energy-saving methods and analyzes the energy-saving method. The energy-saving methods are summarized from aerodynamic and vehicle speed optimization perspectives. Energy-saving methods based on aerodynamics include field tests, wind-tunnel tests, and CFD. Energy-saving methods based on vehicle speed optimization mainly focus on the acceleration and deceleration behavior optimization of vehicles in the platoon. The limitations and prospects of automotive and vehicle platoon development are also put forward. • Energy-saving methods of platoons based on aerodynamic are reviewed. • Energy-saving methods of platoons based on control and optimization are analyzed. • The prospects of the development of platoons at the national level are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes an offline sizing method and an online energy management strategy for the electric vehicle with semi-active hybrid battery system (HBS). The semi-active HBS is composed by Nickel Cobalt Rechargeable (NCR) and lithium titanate (LTO) batteries with a bi-directional DC/DC converter. First, the vehicle dynamics and the HBS are modelled. Second, a hierarchical optimal sizing method is proposed to minimize the distance-based cost (DBC) of electric taxi in a variety of driving cycles. The lower layer optimizes the energy management strategy (EMS) with dynamic programming (DP), while the upper layer optimizes the sizes of HBS for minimum DBC. Based on the sizing results, the DBC decreases firstly and then increases with the increasing LTO size. In addition, the results of DP indicate the SOC of the LTO batteries works between 50% and 80% for optimal NCR lifespan. Third, by using the rule extracted from DP, a learning-based EMS, i.e., deep deterministic policy gradient (DDPG), is proposed with excellent real-time control potential. Finally, the simulation results show that the proposed DDPG EMS achieves the improved performance than fuzzy logic control EMS and closed result with what can be achieved through DP, yet the computation time is much less. • NCR/LTO hybrid battery system is proposed. • A hierarchical framework is proposed to optimize the size of HBS. • Rule-based strategy is extracted from the optimal EMS. • DDPG is proposed to achieve the near-optimal real-time control. [ABSTRACT FROM AUTHOR]

[Display omitted] • This method provides a mild route for α,α-dithioketones by di-sulfenylation of methyl ketones. • The reaction shows good tolerance of functional groups and high selectivity. • Two C S bonds are simultaneously built on the same carbon atom in one-pot. A direct di-sulfenylation of methyl ketones with disulfides mediated by KOH-DMSO superbase system has been developed. This reaction provides an efficient route to α,α-dithioketones, which has the advantages of readily available non-prefunctionalized substrates, good functional group tolerance, excellent yields as well as high selectivity. In a single operation, two C S bonds are simultaneously built on the same carbon atom under mild reaction conditions. [ABSTRACT FROM AUTHOR]

• An eco-driving control strategy is proposed for vehicles approaching a signalized intersection. • The discharge of the vehicle queue is modeled and predicted. • The proposed strategy is based on a computationally efficient iterative dynamic programming algorithm. • Monte-Carlo simulation using real-world traffic data is performed for in-depth energy efficiency analysis. • Energy-saving potential enabled by the proposed strategy is investigated for both internal combustion engine and electric vehicles. This paper takes into consideration of vehicle queues at the intersection and proposes an energy-efficient driving strategy to improve vehicle energy efficiency and overall traffic throughput in an urban traffic environment. The proposed strategy is applicable for both electric vehicle and internal combustion engine vehicle, and the control framework is formed by three sections, a vehicle queue discharge predictor, a spatial-domain optimal control strategy for energy consumption minimization, and a speed tracker with consideration of collision avoidance constraints. The former is based on the intelligent driver model, which predicts an accurate vehicle queue discharge time. Then the iterative dynamic programming is utilized to find the optimal solutions with fast computational speed. Finally, the optimal speed profile is followed by a Proportion-Integration controller while keeping a safe inter-vehicular distance. A Monte-Carlo simulation is designed to evaluate the energy efficiency of the proposed strategy in the stochastic traffic environment. Compared to the regular eco-approach and departure and constant speed strategies that lack awareness of the queue, significant energy saving can be achieved of the proposed strategy. In addition, three typical cases are selected to study the energy efficiency when the proposed strategy is applied to internal combustion engine and electric vehicles, respectively. The results show the energy efficiency of electric vehicles is less sensitive to the queuing effect at the intersection because of regenerative braking and the overall higher efficiency of the electric motor in contrast to the internal combustion engine, especially in stop-and-go scenarios. [ABSTRACT FROM AUTHOR]

• A photothermal wave model for three-layer ceramic/metal composite solids is derived. • The spatially varied volumetric and interior interface heat sources are considered. • Two quadratic nonlinear frequency modulation (NLFM) excitation waveforms are explored. • The concave quadratic NLFM excitation waveform is superior to conventional waveforms. • 12 different window functions are explored for maximizing pulse compression quality. Pulse compression thermography has been recently developed to increase the signal-to-noise ratio (SNR) and enhance depth resolvability. However, a distinctively high SNR and depth resolvability is usually required for the accurate characterization of the (thin) topcoat thickness and defects of multilayer ceramic/metal composite solids (CMCSs). To this end, based on the Green function approach, a photothermal wave model for three-layer CMCSs consisting of a semitransparent ceramic coating and two-layer opaque metal solids is first derived, where a spatially varied volumetric heat source and an interior interface heat source described as the Dirac delta function are considered. The dependence of the thermal wave behaviors on both the thickness and photothermal properties of the semitransparent coating is investigated using the model. Inspired by high-precision radar target detection, two nonlinear frequency modulation (NLFM) waveforms, whose instantaneous frequency curves are respectively convex and concave quadratic function, are explored. Results indicate the NLFM waveform with the instantaneous frequency curve of concave quadratic function outperforms conventional LFM and Barker waveforms in terms of the SNR and depth resolvability. Finally, we reveal that a proper window function can further significantly improve the pulse compression quality. Our investigation can provide direct guidance for the accurate and reliable non-destructive testing and evaluation of multilayer CMCSs. [ABSTRACT FROM AUTHOR]

• Eco-compensation demand became aggregated in eastern and western of the YREB. • Climate change is the main environment metric to affect the eco-compensation demand. • The contribution of climate on eco-compensation demands increased in eastern YREB. Climate change may substantially affect the validity of identifying eco-compensation demand. Exploring the temporal-spatial variation in eco-compensation demands and assessing the contribution of climate change are essential for improving the justice and effectiveness. Since 2000, Chinese government implemented ambitious ecological and economic policies at Yangtze River Economic Belt (YREB) to balance the relationship between environment protection and socio-economic development. We selected YREB to explore the driving factors of temporal-spatial variation in eco-compensation demands for two major ecosystem services, i.e. carbon storage and oxygen release. The results (1) spatially, the eco-compensation demand became aggregated in the eastern and western areas of the Yangtze River Economic Belt (YREB) from 2003 to 2015; (2) the climate has become more suitable for vegetation growth in the changed eastern area (CE) from 2003 to 2015, while the changed central area (CC) presents a drier trend from 2003 to 2011; (3) during the study period, the climate change became the main factors among the environment metrics to affect identifying the eco-compensation demand, specifically, compared to the period 2003–2005, the contribution of climate change on eco-compensation demands in changed central area (CC) and changed eastern area (CE), during 2006–2015, increased from 16.90% to 18.43% and from 2.72% to 11.89%, respectively. This study could help policymakers addressing reliable compensation policies for regional sustainable development and may guide researchers in providing more practical and comprehensive eco-compensation approaches in the future. [ABSTRACT FROM AUTHOR]

l Spring photosynthetic phenology (SOP) was advanced in subtropical China. l Temperature and precipitation determined the SOP in subtropical forests. l The importance of precipitation in SOP increased with decreasing latitudes. l Interactive effect should be applied in subtropical phenology modeling. Climate warming has significantly advanced plant spring phenology in temperate and boreal biomes in the northern hemisphere. However, the response of subtropical forest phenology to climate change remains largely unclear. This study aimed to determine the spatiotemporal patterns of spring photosynthetic phenology in subtropical forests in China over the period 2002-2017 and explore its underlying mechanism in response to the changes of different climate variables. We applied four methods to extract the start of the photosynthetic period (SOP) from a solar–induced chlorophyll fluorescence (SIF) data set during the period 2002 to 2017, and determined correlations between SOP and environmental factors using partial correlation analyses. Overall, the SOP was advanced by 6.8 days. Furthermore, we found that the SIF-based SOP is highly correlated with the flux data–based photosynthetic onset dates, demonstrating that SIF can be a useful index in characterizing the photosynthetic phenology in subtropical forests. Interestingly, based on partial correlation analysation temperature dominated the SOP in the northern subtropical forest, but the importance of precipitation increased with decreasing latitudes, and the primary climatic control of SOP in southern monsoon evergreen forests is precipitation. These results suggested that the predicted increase in temperature and shift in precipitation regimes under ongoing climate change might potentially largely affect the photosynthetic phenology, and thus affect the carbon and water cycles in subtropical forests. [ABSTRACT FROM AUTHOR]