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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]

Accurate knowledge of the vehicle states is the foundation of vehicle motion control. However, in real implementations, sensory signals are always corrupted by delays and noises. Network induced time-varying delays and measurement noises can be a hazard in the active safety of over-actuated electric vehicles (EVs). In this paper, a brain-inspired proprioceptive system based on state-of-the-art deep learning and data fusion technique is proposed to solve this problem in autonomous four-wheel actuated EVs. A deep recurrent neural network (RNN) is trained by the noisy and delayed measurement signals to make accurate predictions of the vehicle motion states. Then unscented Kalman predictor, which is the adaption of unscented Kalman filter in time-varying-delay situations, combines the predictions of the RNN and corrupted sensory signals to provide better perceptions of the locomotion. Simulations with a high-fidelity, CarSim, full-vehicle model are carried out to show the effectiveness of our RNN framework and the entire proprioceptive system. [ABSTRACT FROM AUTHOR]

A three-component reaction for the synthesis of new coumarin-fused tetracyclic system from 4-hydroxycoumarin, aldehydes, and 5-aminopyrazoles/5-aminoisoxazole is described. In the presence of , 4,7-dihydro-1H-pyrazolo[3,4- b]pyridines ( 4) and pyrazolo[3,4- b]pyridines ( 5) were obtained in acetonitrile and dimethylsulfoxide medium, respectively. The reaction gave rise to 4,5-dihydro-1H-pyrazolo[3,4- b]pyridin-6(7 H)-ones ( 6) in acetic acid-ethanol combination system, which involved the C-O . 4-Hydroxy-6-methyl-2H-pyran-2-one and acenaphthylene-1,2-dione were also examined, affording the corresponding C-O products. Mechanism indicates that the reaction is reversible in acetic acid-ethanol combination system. [ABSTRACT FROM AUTHOR]

This paper introduces the development of an autonomous driving system in autonomous electric vehicles, which consists of a simplified motion-planning program and a Model-Predictive-Control-Based (MPC-based) control system. The motion-planning system is based on polynomial parameterization, which computes a path toward the expected longitudinal and lateral positions within required time interval in real scenarios. Then the MPC-based control system cooperates the front steering and individual wheel torques to track the planned trajectories, while fulfilling the physical constraints of actuators. The proposed system is evaluated through simulation, using a seven-degrees-of-freedom vehicle model with a ‘magic formula’ tire model. The simulations and validation throughCarSimshow that the proposed planner algorithm and controller are feasible and can achieve requirements of autonomous driving in normal scenarios. [ABSTRACT FROM PUBLISHER]

This paper proposes a robust gain-scheduled H ∞ controller for lateral stability control of four-wheel-independent-drive electric vehicles via linear parameter-varying technique. The controller aims at tracking the desired yaw rate and vehicle sideslip angle by controlling the external yaw moment. In the design of controller, uncertain factors such as vehicle mass and tire cornering stiffness in vehicle lateral dynamics are represented via the norm-bounded uncertainty. To address the importance of time-varying longitudinal velocity for vehicle lateral stability control, a linear parameter-varying polytopic vehicle model is built, and the built vehicle model depends affinely on the time-varying longitudinal speed that is described by a polytope with finite vertices. In order to reduce conservative, the hyper-rectangular polytope is replaced by a hyper-trapezoidal polytope. Simultaneously, the quadratic D -stability is also applied to improve the transient response of the closed-loop system. The resulting gain-scheduling state-feedback controller is finally designed, and solved utilizing a set of linear matrix inequalities derived from quadratic H ∞ performance and D -stability. Simulations using Matlab/Simulink-Carsim® are carried out to verify the effectiveness of the proposed controller with a high-fidelity, CarSim®, full-vehicle model. It is found from the results that the robust gain-scheduled H ∞ controller suggested in this paper provides improved vehicle lateral stability, safety and handling performance. [ABSTRACT FROM AUTHOR]

Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha−1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y−1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y−1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y−1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests. [ABSTRACT FROM AUTHOR]

In the presence of a catalytic amount of zirconium(IV) chloride, an efficient synthesis of new 4-(2-hydroxyphenyl)pyrazolo[3,4-b]pyridines has been developed by the reaction 2-hydroxychalcones with 3-methyl-1-phenyl-1H-pyrazol-5-amine in refluxing ethanol. All these novel compounds have been characterised by 1H NMR, 13C NMR, HRMS, IR spectra and X-ray crystallographic analysis. [ABSTRACT FROM AUTHOR]

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index ( LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend ( LAIGS) estimated by satellite datasets (average trend of 0.0070 yr−1, ranging from 0.0035 yr−1 to 0.0127 yr−1), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS ( P < 0.05), and marginally significantly ( P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. [ABSTRACT FROM AUTHOR]

Conversion of lignin into value-addedchemicals is attracting growingattention due to the depletion of fossil fuels and the abundant resourceof lignin. In this study, hydrothermal conversion of a model compoundof lignin, catechol, into value-added four-carbon dicarboxylic acids(C4-DCAs), such as tartaric (HOOC–CH(OH)–CH(OH)–COOH),malic (HOOC–CH2–CH(OH)–COOH), andfumaric (HOOCCHCHCOOH) acids was investigated.The yield of total C4-DCAs can reach as high as 41.0%, and alkaliplayed a key role in not only promoting the production but also avoidingthe decomposition of C4-DCAs. The reaction mechanism of hydrothermalconversion catechol into C4-DCAs showed that catechol is first oxidizedto o-quinone, which is then attacked by the hydroxylradical (OH•) or the hydroperoxyl anion (HO2–) via conjugate addition to decompose intoC4-DCAs. This result is helpful to facilitate studies for developinga new, green, and sustainable process to produce value-added C4-DCAsfrom lignin. [ABSTRACT FROM AUTHOR]

Thereduction of CuO with a lignin model compound, guaiacol, isinvestigated, to develop a new and green method for both Cu smeltingand the utilization of lignin. The results showed that CuO can becompletely reduced into Cu and guaiacol selectively converted to carboxylicacids such as fumaric, maleic, acetic, and formic acids at a mildtemperature of 250 °C. The presence of NaOH has a significanteffect on enhancing the reduction of CuO and the selective yield ofcarboxylic acids. Two possible reaction pathways were proposed forthe reduction of CuO with guaiacol. One pathway involves the oxidativedecomposition of guaiacol into carboxylic acids. The other reactionpathway proceeds with the polymerization of guaiacol into oligomers,which can be reused for CuO reduction due to their easy hydrolyzationto monomers under hydrothermal conditions. [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]

A new application of NBS, which results in the oxidative coupling of β-keto esters to selectively form C-C single and C=C double bonds, can be controlled by the amount of NBS and t-BuOK employed. This methodology adds a new entry to C-C single and C=C double-bond formation between active methylene groups under mild conditions with high selectivity. [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]

Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red–green–blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 × 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture. [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]

A facile solvent- and catalyst-free method for the synthesis of a series of new hydroxylated 2,4,6-trisubstituted pyridines under microwave irradiation was developed. The expected products were obtained through a simplified purification process without protection of the hydroxyl group. UV-Vis and fluorescence spectra of these conjugated multi-hydroxyl compounds were also investigated. [ABSTRACT FROM AUTHOR]

Star-shaped conjugated oligomers have many applications in materials science. Two new pseudo-C3-symmetric star-shaped conjugated molecules 2,4,6-tris(4-styrylphenyl)pyridine and 2,4,6-tris(4-((E)-2-(thiophen-2-yl)vinyl)phenyl) pyridine have been synthesised and their structures characterised by 1H NMR, 13C NMR, mass spectrometry and elemental analysis. UV-Vis and fluorescence spectrum show that the maximum absorption wavelengths are at 336 nm for 2,4,6-tris(4-styrylphenyl)pyridine and 356 nm for 2,4,6-tris(4-((E)-2-(thiophen-2-yl)vinyl)phenyl)pyridine, and the maximum emission wavelengths were at 398 nm and 408 nm respectively. [ABSTRACT FROM AUTHOR]

Abstract: A concise and efficient approach to polyenic compounds containing important diketone structural fragments is developed from readily available α,β-unsaturated methyl ketones in the presence of copper(II) oxide, iodine, and dimethyl sulfoxide. This is a new carbon–carbon double bond-forming reaction from two methyl sp3 C–H bonds and an attractive route to introduce the SMe group into the molecule from inexpensive dimethyl sulfoxide. The hypothetic self-sorting tandem reaction mechanism is also proposed in this paper. [Copyright &y& Elsevier]

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided. [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]

Reconstitution of control architecture creates a great challenge for distributed drive electric vehicles (DDEV), due to the emergence of a new distributed driving strategy. To this end, a novel distributed control architecture is proposed in this paper for integrated control of active front steering (AFS) system and direct yaw moment (DYC) system. First, a multi-agent system (MAS) is employed to construct a general framework, where AFS and DYC act as agents that work together to improve vehicle lateral stability and simultaneously reduce workloads of drivers during path tracking. The cooperative control strategies of two agents are obtained through Pareto-optimality theory to ensure optimal control performance of AFS and DYC. Then, on the basis of dynamic interaction between agents, terminal constraints, including terminal cost function and terminal input with local static feedback, are designed to guarantee the asymptotic stability of the close-loop system. Finally, virtual simulations are conducted to evaluate the proposed controller. The results indicate that the proposed control architecture can effectively preserve vehicle stability and reduce workloads of drivers, especially for the inexperienced driver. Furthermore, the hardware-in-loop (HIL) test results also demonstrate the feasibility of the proposed controller. [ABSTRACT FROM AUTHOR]

To enhance traffic efficiency, in this paper, a novel virtual belt algorithm is proposed for the management of an isolated autonomous intersection. The proposed virtual belt algorithm consists of an offline algorithm and an online algorithm. Using the offline algorithm, the considered intersection can be modeled as several virtual belts. The online algorithm is designed for the real-time application of the virtual belt algorithm. Compared with the related algorithms, the main advantage of the proposed algorithm is that, there are several candidate trajectories for each approaching vehicle. Thus, there are more opportunities for an approaching vehicle to obtain a permission to pass an intersection, which is effective to improve traffic efficiency. The proposed algorithm is validated using numerical simulations conducted by Matlab and VISSIM. The simulation results show that the proposed algorithm is effective for autonomous intersection management. [ABSTRACT FROM AUTHOR]

This paper presents an integrated platoon control framework for heterogeneous vehicles on curved roads with varying slopes, aerodynamic drag, and wireless communication delays. First, a linear heterogeneous vehicle platoon model with decoupled longitudinal and lateral dynamics is built based on nonlinear feedback linearization and arc-length parametric transformation of an arbitrary curved road. The external disturbances are also considered, including the up-down road slopes, wind and equivalent time-delays caused by the wireless communication delay and discrete data in onboard sensors. Second, to achieve stable platoon control on a curved road, the platoon uses the state-feedback control method in the longitudinal direction while each vehicle tracks the centerline of the curved road to realize lateral control. Longitudinally, an $ H_\infty$ platoon controller is designed based on the Lyapunov-Krasovskii functional to ensure the inner-vehicle and string stability of the platoon, which suffers from parameter uncertainty, and external disturbances. Additionally, the $ \mathcal {L}_2$ string stability is defined to guarantee that the perturbations do not grow unbounded as they propagate through the platoon. Simulation results indicate that the proposed robust controller achieves proper platoon intervehicle spacing in both the longitudinal and lateral directions. [ABSTRACT FROM AUTHOR]

Vehicle dynamics plays a significant role on the formation of a vehicle platoon, especially at high speed condition. This paper proposes a distributed platoon formation framework for homogeneous autonomous vehicles which takes the vehicle dynamics into consideration. The boundaries of vehicle stability are firstly calculated by considering both lateral dynamics and actuator saturation. By combining the stability boundaries with multi-objective flocking (MOF) algorithm, a distributed formation controller is designed, which can form the vehicle platoon with high cruising speed steadily and rapidly. In addition, a low-level tracking controller is proposed to constitute a hierarchical control architecture which can achieve stable and accurate formation of platoon. Simulation results show the proposed platoon formation controller achieves safe, stable and rapid platoon formation than the traditional MOF-based formation controller which does not consider the vehicle dynamics. [ABSTRACT FROM AUTHOR]

• A new time-domain-based energy-oriented spacing policy is proposed. • Discrete-time vehicle platoon system integrating communication delay and disturbance is provided. • Less conservative discrete robust platoon control methodology is developed. • L 2 -based string stability and its criterion of the discrete platoon are given. This paper presents a discrete control framework for a homogeneous vehicle platoon, which can minimize the energy consumption of each vehicle while ensuring the platoon string stability under external interference and communication delay. First, a novel variable spacing strategy named as the energy-oriented spacing policy is proposed by comparing the geometry relationship of each vehicle which drives at energy-optimal speed and the ideal speed under the constant spacing policy. Then, based on the energy-oriented spacing policy, the dynamics of a homogeneous discrete vehicle platoon composed of several pure electric vehicles are built, including the communication delay and external disturbance. Second, a discrete robust platoon controller that can reduce the conservatism of the control system is designed based on the Lyapunov-Krasovskii theorem and H ∞ method, which drives each vehicle at their energy-optimal speed while ensuring the string stability of platoon under communication delays and interferences. Third, the L 2 -based string stability of the discrete platoon is defined, and its string-stable criterion is given under communication delays and external disturbances. Simulation results indicate that the proposed energy-oriented spacing policy and the discrete controller can enable efficient driving of each vehicle while ensuring the platoon string stability. The energy consumption of the whole platoon is, respectively, reduced by 7.3% and 5.7% compared to the vehicle platoon controllers with constant spacing policy and constant time headway policy. [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]

Depending on the reaction conditions, the three-component reaction of (I), (III), and an aldehyde affords three different compounds, i.e. [ABSTRACT FROM AUTHOR]

Depending on the reaction conditions, the three-component reaction of (I), (III), and an aldehyde affords three different compounds, i.e. [ABSTRACT FROM AUTHOR]

A diagram is presented of ZrCl4-catalyzed synthesis of the derivatives of 4-(2-hydroxyphenyl)pyrazolo[3,4-b]pyridine.

Equipping a hydraulic hub-motor auxiliary system (HHMAS), which mainly consists of a hydraulic variable pump, a hydraulic hub-motor, a hydraulic valve block and hydraulic accumulators, with part-time all-wheel-drive functions improves the power performance and fuel economy of heavy commercial vehicles. The coordinated control problem that occurs when HHMAS operates in the auxiliary drive mode is addressed in this paper; the solution to this problem is the key to the maximization of HHMAS. To achieve a reasonable distribution of the engine power between mechanical and hydraulic paths, a nonlinear control scheme based on model predictive control (MPC) is investigated. First, a nonlinear model of HHMAS with vehicle dynamics and tire slip characteristics is built, and a controller-design-oriented model is simplified. Then, a steady-state feedforward + dynamic MPC feedback controller (FMPC) is designed to calculate the control input sequence of engine torque and hydraulic variable pump displacement. Finally, the controller is tested in the MATLAB/Simulink and AMESim co-simulation platform and the hardware-in-the-loop experiment platform, and its performance is compared with that of the existing proportional-integral-derivative controller and the feedforward controller under the same conditions. Simulation results show that the designed FMPC has the best performance, and control performance can be guaranteed in a real-time environment. Compared with the tracking control error of the feedforward controller, that of the designed FMPC is decreased by 85% and the traction efficiency performance is improved by 23% under a low-friction-surface condition. Moreover, under common road conditions for heavy commercial vehicles, the traction force can increase up to 13.4–15.6%. [ABSTRACT FROM AUTHOR]

The article presents chemical equations related to synthesis of benzopyran derivatives which includes di-aromatic fused [3.3.1] bicyclic compounds' facile synthesis and phase-transfer caytalysis, published in the volume 11 of 2013 issue of the journal "Organic & Biomolecular Chemistry."

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]

Depending on the conditions, the reactions provide the title systems selectively. [ABSTRACT FROM AUTHOR]

α-Bromination of aromatic methyl ketones (I) followed by cyclization with thiourea derivatives (II) affords aminothiazole derivatives (III) in moderate to good yields. [ABSTRACT FROM AUTHOR]

The expected products are obtained through a simplified purification procedure without protection of the hydroxyl group. [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]