Drought stress limits the growth and development of grapevines, thereby reducing productivity, but the mechanisms by which grapevines respond to drought stress remain largely uncharacterized. Here, we characterized a group A bZIP gene from “Kyoho” grapevine, VlbZIP30, which was shown to be induced by abscisic acid (ABA) and dehydration stress. Overexpression of VlbZIP30 in transgenic Arabidopsis thaliana enhanced dehydration tolerance. Transcriptome analysis revealed that a major proportion of ABA-responsive and/or drought-responsive genes are transcriptionally regulated by VlbZIP30 during ABA or mannitol treatment at the cotyledon greening stage. We identified an A. thaliana G-box motif (CACGTG) and a potential grapevine G-box motif (MCACGTGK) in the promoters of the 39 selected A. thaliana genes upregulated in the transgenic plants and in the 35 grapevine homologs, respectively. Subsequently, using two grapevine-related databases, we found that 74% (23/31) and 84% (21/25) of the detected grapevine genes were significantly upregulated by ABA and drought stress, respectively, suggesting that these genes are involved in ABA or dehydration stress and may be regulated by VlbZIP30 in grapevine. We propose that VlbZIP30 functions as a positive regulator of dehydration-responsive signaling in the ABA core signaling pathway.

Infrared small-target detection has widespread influences on anti-missile warning, precise weapon guidance, infrared stealth and anti-stealth, military reconnaissance, and other national defense fields. However, small targets are easily submerged in background clutter noise and have fewer pixels and shape features. Furthermore, random target positions and irregular motion can lead to target detection being carried out in the whole space–time domain. This could result in a large amount of calculation, and the accuracy and real-time performance are difficult to be guaranteed. Therefore, infrared small-target detection is still a challenging and far-reaching research hotspot. To solve the above problem, a novel multimodal feature fusion network (MFFN) is proposed, based on morphological characteristics, infrared radiation, and motion characteristics, which could compensate for the deficiency in the description of single modal characteristics of small targets and improve the recognition precision. Our innovations introduced in the paper are addressed in the following three aspects: Firstly, in the morphological domain, we propose a network with the skip-connected feature pyramid network (SCFPN) and dilated convolutional block attention module integrated with Resblock (DAMR) introduced to the backbone, which is designed to improve the feature extraction ability for infrared small targets. Secondly, in the radiation characteristic domain, we propose a prediction model of atmospheric transmittance based on deep neural networks (DNNs), which predicts the atmospheric transmittance effectively without being limited by the complex environment to improve the measurement accuracy of radiation characteristics. Finally, the dilated convolutional-network-based bidirectional encoder representation from a transformers (DC-BERT) structure combined with an attention mechanism is proposed for the feature extraction of radiation and motion characteristics. Finally, experiments on our self-established optoelectronic equipment detected dataset (OEDD) show that our method is superior to eight state-of-the-art algorithms in terms of the accuracy and robustness of infrared small-target detection. The comparative experimental results of four kinds of target sequences indicate that the average recognition rate P_avg is 92.64%, the mean average precision (mAP) is 92.01%, and the F1 score is 90.52%.

Actuator fault will lead to the damage of vehicle driving ability. How to analyze the residual driving ability of the vehicle and use the residual driving ability to carry out targeted fault tolerance control (FTC) is the difficulty of FTC for automatic driving vehicles. In this paper, an active FTC strategy for actuator fault of four wheels independent drive vehicles and a reference target reshaped scheme (RTRS) are proposed. The method consists of two parts. The first part calculates the limit of the vehicle's resultant longitudinal force, lateral force and yaw moment through the analysis of the friction limit between the tire and the ground and the vehicle's driving demand, which is called the driving capability feasible region (DCFR). Considering the impact of actuator fault on the tire force, the second analysis obtains the vehicle's residual driving limit, which is called the fault tolerant feasible region (FTFR). The upper Model Predictive Control (MPC) motion controller based on the FTFR and the lower allocation controller are designed to realize the FTC algorithm of the vehicle. The second part focuses on the fault scenes that can not be solved by a fault-tolerant controller alone. By weakening the driving task, priority is given to ensuring that the vehicle driving meets the driving capacity limit. The specific way is based on the optimization dynamic solution method, which comprehensively considers the FTC and the original reference target and uses the future output matrix equation of the upper MPC motion controller to design the optimization target to solve the feasible vehicle driving state, so as to achieve the RTRS. Carsim Simulink joint simulation is used to conduct FTC simulation of vehicle stability system actuator faults. The experimental results verify the effectiveness of the proposed FTC method and the RTRS to solve the problem of vehicle stability system actuator fault.

Tire force distribution with the consideration of practical constraints is a key part of automatic driving. This paper is intended to improve the operational capability of the vehicle under low friction coefficient. The speed and front wheel angle are restricted within a specific range caused by the limitation of executable drive space, which is defined as an input constrained problem of the nonlinear system. The hierarchical design concept is adopted in this paper, including algorithms of both control and allocation to determine the driving performance cooperatively. The methodology of multi-input and multi-output sliding mode control (MIMO SMC) is applied in the high-level controller to guarantee the vehicle system stable. Confronted with circumstances that the vehicle may be seriously unstable due to longitudinal forces and lateral forces beyond the feasible region in the friction circle, the scheme of dynamic adjustment of control target is provided owing to there is no reasonable solution for tire force distribution in the low-level controller. The coefficients of reconstruction and attenuation are introduced to reconstruct the optimized controller and weaken inputs of the closed loop system, respectively. The simulation results verify that the proposed design can redistribute generalized forces and moments between the wheels especially for the poor road condition.

Placement of anchor nodes is a main issue in ZigBee technology. Network lifetime and energy consumption are the two critical factors that need to be considered in underground mine localization system (UMLS). According to the complicated environment in coal tunnels, we proposed an optimal strategy based on two-tiered anchor nodes placement for UMLS, which minimizes the average energy consumption of anchor nodes and prolongs the ZigBee wireless networks lifetime (ZWNL). Simulation results show that the optimal anchor nodes placement strategy provide significant benefit over a commonly uniform placement scheme. And the numerical results prove that the two-tiered anchor nodes placement obviously outperform the one-tiered in terms of both network lifetime and average energy consumption.