Search

Abstract Understanding the interplay between top–down and bottom–up attention in visual working memory (VWM) is crucial, although the specific challenges arising from this interaction remain ambiguous. In this study, we address this complexity by examining how cue informativeness and probe status of the salient items influence this interaction. Through three experiments, we manipulated top–down attention by varying probe frequencies using pre-cues and bottom–up attention by varying the visual salience of memory items. Experiment 1 explored cue informativeness at 100% and 50%, while Experiments 2 and 3 maintained cue informativeness at 80% and 50%. Additionally, Experiment 1 tested a few of the salient items, Experiment 2 excluded them, and Experiment 3 tested half of them in each cue condition. Across all experiments, we consistently observed cueing benefits for cue-directed items, albeit with costs to non-cued items. Furthermore, cue informativeness and the probe status of salient items emerged as critical factors influencing the interaction between top–down and bottom–up attention in VWM. These findings underscore the pivotal roles of cue informativeness and salient item relevance in shaping the dynamics of top–down and bottom–up attention within VWM.

Abstract As most practitioners (including teachers) do not know how AI functions and cannot make full use of AI in education, there is an urgent need to investigate teachers’ intentions to learn AI and related determinants so as to promote their AI learning. This study collected survey data from a total of 318 K-12 teachers from sixteen provinces or municipalities in China. A two-step structural equation modeling approach was performed to analyze the data. Our findings show that K-12 teachers’ perceptions of the use of AI for social good and self-efficacy in learning AI are two direct determinants of behavioral intentions to learn AI, while awareness of AI ethics and AI literacy are two indirect ones. AI literacy has a direct impact on perceptions of the use of AI for social good, self-efficacy in learning AI and awareness of AI ethics and has an indirect impact on behavioral intentions to learn AI. This study represents one of the earliest attempts to empirically examine the power of AI literacy and explore the determinants of behavioral intentions to learn AI among K-12 teachers. Our findings can theoretically and practically contribute to the virgin field of K-12 teachers’ AI learning.

Traditional robotic arms rely on complex programming and predefined trajectories to operate, which limits their applicability. To improve the flexibility and adaptability of the robot arm, the research focuses on improving the grasping performance of the robot arm based on vision technology. Kinect technology is used to capture human arm movements, and Kalman filter is introduced to smooth image data, so as to optimize the motion recognition process. In this study, the residual network model is further improved, and ELU activation function and pre-activation mechanism are introduced to enhance the classification accuracy of gesture images. The results showed that the improved ResNet50 model achieves 95% recognition accuracy after 25 iterations of training, while the original model is 80%. The application of Kalman filter makes the motion tracking curve smoother and shows the correction effect of this method. In simulation tests, the robotic arm is able to identify different elbow bending angles with 90–96 percent accuracy, while mimicking five specific hand gestures with 96–98 percent accuracy. These data support the practicability and effectiveness of the application of vision capture technology and deep learning model in the field of intelligent control of robotic arms.

ObjectiveThis paper proposes a fuzzy sliding mode controller based on T-S fuzzy logic for the vertical plane motion control of an autonomous underwater glider (AUG) with limited actuator capability. MethodsIn the fuzzy sliding mode controller, the fuzzy switching rate is used to replace the switching rate in the fixed time controller to effectively suppress buffeting. The fuzzy switching rate is obtained by fitting the switching rate of the fixed time controller with T-S fuzzy rules. Based on the limited capabilities of AUG actuators, a saturation auxiliary system is designed to improve the actuator saturation effect. Finally, the performance of the system is verified by Lyapunov stability analysis and numerical simulation. ResultsThe results show that the AUG under the fuzzy sliding mode controller and the saturation auxiliary system can converge in finite time. The effectiveness of the fuzzy sliding mode controller and the saturation auxiliary system are verified by numerical simulation. ConclusionsBy making comparisons with the fixed-time controller, it is verified that the two controllers have similar control performance, and the buffeting of the fuzzy sliding mode controller is lesser.

Salinity has become a crucial environmental factor seriously restricting maize (Zea mays L.) growth, development and productivity. However, how plants respond to salt stress is still poorly understood. In this study, we report that a maize brassinosteroid-signaling kinase gene ZmBSK1 plays a significant role in salt stress response. Expression pattern analysis revealed that the transcript level of ZmBSK1 was upregulated by NaCl treatment both in maize leaves, roots, and stems. Phenotypic and physiological analysis showed that overexpression of ZmBSK1 in maize improved salt tolerance by reducing the malondialdehyde (MDA) content, the percentage of electrolyte leakage, O2− and H2O2 accumulation under salt stress, relying on the increases of antioxidant defense enzyme activities and proline content. qRT-PCR analysis showed that overexpression of ZmBSK1 also positively modulated the expression levels of reactive oxygen species (ROS)-scavenging and proline biosynthesis-related genes under salt stress. Moreover, immunoprecipitation-mass spectrometry (IP-MS) assay and firefly luciferase complementation imaging (LCI) assay showed that ZmBSK1 could associate with heat shock protein ZmHSP8 and 14-3-3-like protein ZmGF14-6, and their gene expression levels could be significantly induced by NaCl treatment in different maize tissues. Our findings unravel the new function of ZmBSK1 in salt stress response, which provides the theoretical bases for the improvement of maize salt resistance.

The unmanned surface vessel (USV) plays an important role in smart ocean. This study proposes an adaptive fault‐tolerant tracking control for USVs in the presence of input saturations and error constraints. A tan‐type barrier Lyapunov function is utilised for the error constraints and the neural networks are employed to treat the model uncertainty. Moreover, the adaptive technique combined with the backstepping method not only enables the actuator fault‐tolerant controller to address the fault effects but also handles the external disturbances and input saturations. The proposed control approach can track the desired trajectory with error constraints and the system is guaranteed to be uniformly bounded under certain actuator failure. Numerical simulation is carried out to verify the effectiveness of this control strategy.

In this paper, under-directed topology, distributed coordinated tracking control schemes are proposed for multiple Euler-Lagrange systems with time-varying communication delays, nonlinear uncertainties, and external disturbances. Concerning with different leader velocities, both constant leader velocity case and time-varying leader velocity case are addressed by designing two different distributed observers. Combining with the proposed distributed leader velocity observers, two coordinated tracking control schemes are developed by the effort of neural network approximation and sliding mode technique, which can compensate the nonlinearities and uncertainties. For the first case, tracking errors are rigorously proved to be globally asymptotically converged by using Lyapunov-Krasovskii method. To further eliminate chattering caused by the discontinuous sign function, the saturation function is used for the second case, and the proposed control algorithm ensures the same convergence of tracking errors via Lyapunov analysis. Finally, the effectiveness of the proposed distributed tracking control schemes is verified by the numerical examples.

The development of marine petroleum exploitation becomes an important part of marine economy development gradually. Ocean bottom flying node (OBFN) is a kind of autonomous underwater vehicle which can explore marine petroleum and natural gas in large-scale deployment. This paper investigates the prescribed performance fault-tolerant trajectory tracking control method for an OBFN in presence of modeling uncertainties, ocean current disturbances, and thruster faults. By using a new performance function and an error transformation scheme, the proposed controller is able to achieve the desired tracking performance and the maximum convergence time is known as a priori. A disturbance observer is designed to deal with the influence of the modeling uncertainties, ocean disturbances, and thruster faults. The stability of the closed-loop OBFN system is proved by using Lyapunov theory. Finally, simulation results under different types of thruster faults illustrate the effectiveness of the proposed disturbance-observer-based prescribed performance fault-tolerant trajectory tracking controller.

This paper designs a prescribed performance fault-tolerant control strategy for the underwater acoustic sensor network nodes (UASNN) trajectory tracking control in the presence of ocean current disturbances, modeling uncertainties, and thruster faults. By using a general uncertainties observer, the influence of disturbances and uncertainties are estimated. Additionally, a novel performance function which determines explicitly the maximum convergence time is utilized. Based on the new performance function and corresponding error transformation, the 6-DOF tracking errors are restricted to prescribed bounds to ensure the desired transient and steady response. Furthermore, when considering thruster saturation, we introduce an auxiliary system to compensate for the saturation. The closed-loop system stability is proved by Lyapunov theory. The numerical simulations for three thruster faults are carried out to demonstrate that the proposed strategy is effective.

Spray application is considered to be the most common method of insecticide use in apple orchard, while trunk injection has often be used in labor-intensive areas. Here, a comparison of both methods in aphid control efficiency and non-target effects was conducted. We evaluated the effects of thiamethoxam by either spray or injection on apple aphid Aphis citricola, and examined the temporal dynamic of thiamethoxam in leaves by using residue analysis. Results showed that thiamethoxam had a remarkable suppression effect on Aphis citricola, and both application methods had obvious control efficiency with the highest value above 90%. The control effect of spray method on Aphis citricola reached the maximum at 7 days after application, while that of injection method reached the maximum at 14 days after application. Moreover, the control effect of injection after 14 days and that of spray after 7 days were not significant, suggesting the spray method had a higher quick-acting effect than the injection method, and the two methods had a similar persistence effect. The population dynamics of non-target insects (ladybugs, parasitoid wasps and predatory bugs) showed basically the same as that of blank controlled. The control effect evaluation of thiamethoxam on Aphis citricola suggest that injection treatment was more effective in protecting natural enemies than spray treatment, and thiamethoxam didn't interfere with natural enemies to control Aphis citricola with both two application methods.

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. Two distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws.

The ocean bottom flying node is a novel autonomous underwater vehicle that explores the oil and gas resources in deep water. Thousands of the ocean bottom flying nodes track different predefined trajectories arriving at target points in a small ocean area, respectively. A class of prescribed performance adaptive trajectory tracking control method is investigated for the ocean bottom flying node trajectory tracking problem with ocean current disturbances, model uncertainties as well as thruster faults. Based on a predefined performance function and an error transformation, the ocean bottom flying node trajectory tracking error is restricted to prespecified bounds to ensure a desired transient and steady response. Radial basis function neural network is used to approximate the general uncertainty caused by ocean current disturbances, model uncertainties, and thruster faults. Further, the upper bound of approximation error is estimated by an adaptive law. Using the adaptive laws, we propose a prescribed performance adaptive trajectory tracking controller. The simulation examples on an ocean bottom flying node system show that the proposed control scheme can compensate for the effect of the general uncertainty while obtaining the fast transient process and expected trajectory tracking accuracy.

This article investigates the trajectory tracking control problem for unmanned surface vessels with input saturation and full-state constraints. The barrier Lyapunov function is used to solve the problem of state constraints, and the adaptive method is employed to handle the unknown random disturbances and saturation problems. The proposed control approach can guarantee that the control law and signals of closed-loop system are uniformly bounded and achieve the asymptotic tracking. Finally, simulation studies are provided to show the effectiveness of the proposed method.