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[Objective] The study aims to explore the growth and physiological response of Canavalia maritima seedlings to water stress, and provide a reference for water management during the seedling stage and for the cultivation of high-quality plants in coastal sandy lands. [Methods] One-year-old C. maritima seedlings were subjected to simulated drought stress (soil relative water content at 20%, 40%, and 60%), suitable water condition (80%, control), and waterlogging (100%) with water control experiment in pots. Their growth and physiological indices were analyzed. [Results] Under both drought and waterlogging stress, leaf dry weight was decreased, while leaf relative water content was increased. Seedling height and leaf area were increased under waterlogging but decreased under severe drought stress. Total root length, total root surface area, total root volume, and mean root diameter were increased under drought stress and decreased under waterlogging. The content of malondialdehyde and the activities of catalase (CAT) and peroxidase (POD) were increased, while the activity of superoxide dismutase (SOD) was relatively stable. Soluble protein content was decreased, whereas soluble sugar and proline contents were increased under water stress. The chlorophyll content was decreased under severe drought stress. Initial fluorescence was increased, while maximum fluorescence, maximum photochemical efficiency, and maximum quantum yield were decreased under water stress. [Conclusion] C. maritima seedlings demonstrate strong tolerance to drought and waterlogging stress by altering water accumulation and distribution patterns, increasing the activities of CAT and POD, and enhancing soluble sugar and proline contents.

The purpose of the study was to characterize the response, as well as the regularitity of soil microorganisms in Podocarpus macrophyllus to different levels of nitrogen (N), phosphorus (P), and potassium (K). Using two-year old P. macrophyllus seedlings as the test species, the researchers controlled the gradients of N, P and K nutrient levels using the L9 orthogonal test in the potted soil. The dilution plate coating method and Biolog-ECO microplate method were used to explore the effects of different soil nutrient levels on the amount and community diversity of P. macrophyllus soil microorganisms and their utilization characteristics of six carbon sources. The results were as follows: (1) The number of soil bacteria (P

ObjectiveThe traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of changes in curvature and road bends. This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigating curves and paths. Although extending the time horizon of the standard fixed time horizon model predictive control (MPC) can improve curve path tracking accuracy, it comes with high computational costs, making it impractical in situations with restricted computing resources. Consequently, an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing.MethodsInitially, it was crucial to establish a kinematic model for the mowing robot, which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking. The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing. Second, the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot. The region located a certain distance ahead of the robot's current position, was identified to as the preview region, enabling a more accurate prediction of the robot's future traveling conditions. Calculations for both the curve factor and curve change factor were carried out within this preview region. The curvature factor represented the initial curvature of the path, while the curvature change factor indicated the extent of curvature variation in this region. These two variables were then fed into a fuzzy controller, which adjusted the prediction time horizon of the MPC. The integration enabled the mowing robot to promptly adjust to changes in the path's curvature, thereby improving its accuracy in tracking the desired trajectory. Additionally, a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance. This approach ensured that MPC activation occurred only when needed, rather than at every time step, resulting in reduced computational expenses especially during periods of smooth robot motion where unnecessary computation overhead could be minimized. By meeting kinematic and dynamic constraints, the optimization algorithm successfully identified an optimal control sequence, ultimately enhancing stability and reliability of the control system. Consequently, these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex environments.Results and DiscussionThe adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maximum absolute lateral error to within 0.13 rad and 11 cm, respectively, surpassing the performance of the MPC controller in the control group. Moreover, compared to both the first and fourth groups, the adaptive time-horizon MPC controller achieved a remarkable reduction of 75.39% and 57.83% in mean values for lateral error and heading error, respectively (38.38% and 31.84%, respectively). Additionally, it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral error (0.025 6 m) and course error (0.025 5 rad), outperforming all four fixed time-horizon MPC controllers tested in the study. Furthermore, this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.004 9 s, notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC.ConclusionsThe experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC. By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events, this approach can more effectively handle scenarios with restricted computational resources, ensuring superior control precision and stability. Furthermore, it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots, offering a more practical and reliable solution for their practical application.

ObjectiveHigh-precision localization technology serves as the crucial foundation in enabling the autonomous navigation operations of intelligent agricultural robots. However, the traditional global navigation satellite system (GNSS) localization method faces numerous limitations, such as tree shadow, electromagnetic interference, and other factors in the agricultural environment brings challenges to the accuracy and reliability of localization technology. To address the deficiencies and achieve precise localization of agricultural robots independent of GNSS, a localization method was proposed based on the fusion of three-dimensional light detection and ranging (LiDAR) data and inertial measurement unit (IMU) information to enhance localization accuracy and reliability.MethodsLiDAR was used to obtain point cloud data in the agricultural environment and realize self-localization via point cloud matching. By integrating real-time motion parameter measurements from the IMU with LiDAR data, a high-precision localization solution for agricultural robots was achieved through a specific fusion algorithm. Firstly, the LiDAR-obtained point cloud data was preprocessed and the depth map was used to save the data. This approach could reduce the dimensionality of the original LiDAR point cloud, and eliminate the disorder of the original LiDAR point cloud arrangement, facilitating traversal and clustering through graph search. Given the presence of numerous distinct crops like trees in the agricultural environment, an angle-based clustering method was adopted. Specific angle-based clustering criteria were set to group the point cloud data, leading to the segmentation of different clusters of points, and obvious crops in the agricultural environment was effectively perceived. Furthermore, to improve the accuracy and stability of positioning, an improved three-dimensional normal distribution transform (3D-NDT) localization algorithm was proposed. This algorithm operated by matching the LiDAR-scanned point cloud data in real time with the pre-existing down sampled point cloud map to achieve real-time localization. Considering that direct down sampling of LiDAR point clouds in the agricultural environment could result in the loss of crucial environmental data, a point cloud clustering operation was used in place of down sampling operation, thereby improving matching accuracy and positioning precision. Secondly, to address potential constraints and shortcomings of using a single sensor for robot localization, a multi-sensor information fusion strategy was deployed to improve the localization accuracy. Specifically, the extended Kalman filter algorithm (EKF) was chosen to fuse the localization data from LiDAR point cloud and the IMU odometer information. The IMU provided essential motion parameters such as acceleration and angular velocity of the agricultural robot, and by combining with the LiDAR-derived localization information, the localization of the agricultural robot could be more accurately estimated. This fusion approach maximized the advantages of different sensors, compensated for their individual limitations, and improved the overall localization accuracy of the agricultural robot.Results and DiscussionsA series of experimental results in the Gazebo simulation environment of the robot operating system (ROS) and real operation scenarios showed that the fusion localization method proposed had significant advantages. In the simulation environment, the average localization errors of the proposed multi-sensor data fusion localization method were 1.7 and 1.8 cm, respectively, while in the experimental scenario, these errors were 3.3 and 3.3 cm, respectively, which were significantly better than the traditional 3D-NDT localization algorithm. These findings showed that the localization method proposed in this study could achieve high-precision localization in the complex agricultural environment, and provide reliable localization assistance for the autonomous functioning of agricultural robots.ConclusionsThe proposed localization method based on the fusion of LiDAR data and IMU information provided a novel localization solution for the autonomous operation of agricultural robots in areas with limited GNSS reception. Through the comprehensive utilization of multi-sensor information and adopting advanced data processing and fusion algorithms, the localization accuracy of agricultural robots could be significantly improved, which could provide a new reference for the intelligence and automation of agricultural production.

Neuroblastoma (NB) is a common childhood cancer. Circular RNA RAN binding protein 17 (circRANBP17) has been identified to participate in diverse tumor progression. This study aims to explore the function and mechanism of circRANBP17 in NB. The levels of circRANBP17, miR-27b-3p and KDM1A in NB tissues and cells were measured by qRT-PCR. Mouse model assay was performed to investigate the effect of circRANBP17 knockdown on tumor formation in vivo. The levels of circRANBP17 and KDM1A were significantly up-regulated, and the level of miR-27b-3p was strikingly down-regulated in NB tissues and cells (SK-N-SH and SK-N-AS). Functional studies indicated that miR-27b-3p inhibitor mitigated the inhibitory effects on cell proliferation, migration, invasion and the promoting effect on cell apoptosis in SK-N-SH and SK-N-AS cells induced by circRANBP17 knockdown. Also, miR-27b-3p regulated NB cell malignancy by targeting KDM1A. Further studies revealed that miR-27b-3p inhibitor reversed the low expression of KDM1A induced by circRANBP17 knockdown. In support, circRANBP17 knockdown led to inhibition of tumor formation in vivo. In conclusion, circRANBP17 modulated KDM1A to promote cell proliferation, migration, invasion and restrain cell apoptosis in NB by sponging miR-27b-3p, and the new regulatory network may provide a theoretical basis for the further study of NB.

The microstructure and texture in pure nickel were investigated during multi-step cross cold rolling (CCR) and subsequent annealing. It was found that the deformation texture in the CCR nickel was dominated by Brass and rotated Brass about normal direction (ND) (BsND) texture components, along with marginal cube textures. The resulted deformation textures had a significant effect on the recrystallization behavior. Annealing of the CCR nickel at 550°C for 1 h led to the formation of dominant // ND fiber accompanied by minor rotated cube textures, rather than strong cube texture. Increasing the annealing temperature up to 800°C resulted in further enhancement of // ND fiber textures. The possible reasons for recrystallization behavior in annealed CCR sample were discussed based on in-situ annealing experiments from two aspects of oriented nucleation and oriented growth theories.

High growth of the population and developed industries, on the one hand, increased the demanded energy considerably. Growth of the fossil fuel-based power generation, on the other hand, has high environmental effects. Accordingly, renewable energy sources are mainly interested in the recent years as clean resources. These sources also can be economical cases in long term. Nevertheless, their intermittent feature is a main drawback. Integration of these resources with the rest of the grid can increase their advantages and tackle their main drawbacks. In the present work, a novel programming method is proposed for energy management and optimization of operational expenses over a grid-connected microgrid (MG). The considered MG contains various renewable resources. Some uncertainties should be considered in the integration of renewable resources with the grid, which can affect the system performance. Main uncertain parameters are studied in this paper such as predicted values for green power sources, maximum capacity for fuel cells (FCs), energy storage sources (ESSs), maximum capacity for transmission line, and also hourly loads, achieving a higher efficiency. This paper utilized fuzzy idea for presentation of mentioned uncertain parameters in the MG. Moreover, this work employed a 3-stage optimization algorithm for obtaining the best programming for the MG by considering the uncertain parameters. This paper also used a practical test system to validate the proposed method through minimizing the total cost. Additionally, a sensitivity analysis is performed to study the effects of oscillations of all components in the MG and their associated uncertainties over the obtained results.

The hybrid electrical city bus has two power sources to drive vehicle,so the control of vehicle driving mode transformation is become the core of the vehicle control strategy.It not only influences the vehicle fuel economy and emissions,but also influences the driving comfort of the vehicle.An HEV simulation model is built for performance analysis by interlinking of advanced software AVL CRUISE and Matlab/Simulink.By adjusting threshold of the driving mode transformation,the mode can be transformed smoothly.For achieving minimum equivalent fuel consumption,the genetic algorithm（GA）optimization method is applied to get the optima of control parameters which affect the vehicle driving mode transformation in different city bus cycles including Europe and China.The vehicle fuel economy is further improved.

Colleges and universities welcome new students every September. After the intense study in high school, the new university life will make the new students feel excited and embarrassed. They are about to face and meet new life, new ways of learning, new classmates and teachers, and so on. Since the new things and environment are very different from those for the new students before they entered the school, we need to pay attention to their psychological changes and promptly channel their psychological problems, otherwise it will cause a lot of psychological discomfort, and maybe it will have some negative impact on the development of various aspects of the follow-up of the new students. It can be seen that it is especially important to grasp the psychological characteristics of students who have just entered the school and to help them develop positive psychological quality, because it not only lays a solid foundation for the mental health education work of colleges and universities, but also promotes the all-round development of students.