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An adaptive neural network event-triggered consensus control method incorporating a state observer was proposed for a class of uncertain nonlinear multi-agent systems (MASs) with actuator failures. To begin, a state observer was constructed in an adaptive backstepping framework to estimate the MASs' unmeasurable states, and a radial basis function neural network (RBFNN) was employed to approximate the unknown nonlinear function of MASs. Meanwhile, to reduce the impact of actuator failure on the performance of MASs, the adaptive event-triggered mechanism (ETM) was designed to dynamically compensate for actuator failures, which alleviated the communication burden among individual agents by decreasing the update frequency of the control signals. Furthermore, all followers can track the leader's output signal with the synchronization errors converging to zero. Finally, simulation examples were used to verify the effectiveness of the proposed control strategy.

Abstract A well-designed educational programming dataset is a valuable asset for students and educators. Such a dataset enables students to improve their programming performances continuously, provides researchers with significant data sources to identify students’ learning behaviours and enhance the quality of programming education. Several existing datasets for programming education are either limited by a small number of participating students or a short span of learning records, bringing great challenges to investigate students’ learning patterns in programming. We present a graph-based large-scale dataset specialized in programming learning on Online Judge (OJ) platform. The dataset, named ACcoding, was built by a university teaching group. As of the submission date of the initial manuscript of this paper (May 6, 2022), the dataset contains 4,046,652 task-solving records submitted by 27,444 students on 4,559 programming tasks over a span of 6 years. The large size of the dataset, combined with rich functional features, empowers educators to trace students’ programming progress and choose appropriate programming tasks for specific training purposes. We also presents examples of applications used by the dataset.

AimsThis study evaluated the impact of wheat straw return and microbial agent application on rice field environments.MethodsUsing Rice variety Chuankangyou 2115 and a microbial mix of Bacillus subtilis and Trichoderma harzianum. Five treatments were tested: T1 (no straw return), T2 (straw return), T3, T4, and T5 (straw return with varying ratios of Bacillus subtilis and Trichoderma harzianum).ResultsResults indicated significant improvements in rice root length, surface area, dry weight, soil nutrients, and enzyme activity across T2-T5 compared to T1, enhancing yield by 3.81-26.63%. T3 (50:50 microbial ratio) was optimal, further increasing root dry weight, soil enzyme activity, effective panicle and spikelet numbers, and yield. Dominant bacteria in T3 included MBNT15, Defluviicoccus, Rokubacteriales, and Latescibacterota. Higher Trichoderma harzianum proportions (75% in T5) increased straw decomposition but slightly inhibited root growth. Correlation analysis revealed a significant positive relationship between yield and soil microorganisms like Gemmatimonadota and Firmicutes at the heading stage. Factors like dry root weight, straw decomposition rate post-jointing stage, and elevated soil enzyme activity and nutrient content from tiller to jointing stage contributed to increased panicle and spikelet numbers, boosting yield.ConclusionThe optimal Bacillus subtilis and Trichoderma harzianum ratio for straw return was 50:50, effectively improving soil health and synergizing high rice yield with efficient straw utilization.

Abstract This paper investigates a leader‐following consensus problem for nonlinear multi‐agent systems (MASs) with measurement noises under fixed and Markovian switching topologies, respectively. Noises are considered when each agent measures the states of its neighbours, where intensities of noises are vector functions of relative states. To alleviate the utilization of communication and computation resources, a dynamic event‐triggered consensus protocol is designed, where the coupling strength is restricted in a given interval. By using the stochastic stability theorem, it is shown that leader‐following consensus is achieved under fixed topology, where an estimation of the convergence rate is given. Moreover, the problem is studied under switching topologies subjecting to the Markovian process, which is applicable to practical situations with a time‐varying communication environment. Finally, simulation examples are given to show the correctness of the proposed results.

A fixed-time adaptive guaranteed performance tracking control is investigated for a category of nonholonomic mobile robots (NMRs) under asymmetric state constraints. For the sake of favorable transient and steady-state properties of the system, a prescribed performance function (PPF) is introduced and a transform function is further constructed. Based on the backstepping technique, an asymmetric barrier Lyapunov function is formulated to ensure the tracking errors converge within a human-specified time. On the foundation of this, the occupation of communication channel is effectively reduced by assigning an event-triggered mechanism (ETM) with relative threshold to the process of controller design. By utilizing the proposed control strategy, the NMR is capable of implementing the enemy dislodging mission while the enemy can always be caught by the NMR and the collision would never be presented. Finally, two simulation experiments are given to verify the effectiveness of the proposed scheme.

A guaranteed performance event-triggered adaptive consensus control is established for uncertain multiagent systems under time-varying actuator faults. To eliminate the impact caused by actuator faults, an adaptive neural network compensation strategy is developed. Simultaneously, by implementing the asymmetric barrier Lyapunov function and transform function, a prescribed time consensus control with guaranteed performance, is constructed. Furthermore, to reduce the frequency of information transmission, an adjustable switching event-triggered control (ASETC) is proposed by using a modified hyperbolic tangent function. It combines the advantage of the relative threshold strategies and the characteristics of the hyperbolic tangent function, giving better flexibility in saving network resources and guaranteeing system performance. By applying the constructed control method, systems with prescribed performance consensus in a prescribed time are achievable while limited network resources and unknown time-varying faults are present. Some simulation examples implemented in MATLAB (R2022a) are given to demonstrate the above results.

IntroductionNitrogen (N) fertilizer management, especially postponing N topdressing can affect rice eating quality by regulating starch quality of superior and inferior grains, but the details are unclear. This study aimed to evaluate the effects of N topdressing on starch structure and properties of superior and inferior grains in hybrid indica rice with different tastes and to clarify the relationship between starch structure, properties, and taste quality.MethodsTwo hybrid indica rice varieties, namely the low-taste Fyou 498 and high-taste Shuangyou 573, were used as experimental materials. Based on 150 kg·N hm-2, three N fertilizer treatments were established: zero N (N0), local farmer practice (basal fertilizer: tillering fertilizer: panicle fertilizer=7:3:0) (N1), postponing N topdressing (basal fertilizer: tillering fertilizer: panicle fertilizer=3:1:6) (N2).ResultsThe starch granules of superior grains were more complete, and the decrease in small granules content and the stability of starch crystals were a certain extent less than those of inferior grains. Compared with N1, under N2, low-taste and high-taste varieties large starch granules content were significantly reduced by 6.89%, 0.74% in superior grains and 4.26%, 2.71% in inferior grains, the (B2 + B3) chains was significantly reduced by 1.61%, 0.98% in superior grains, and 1.18%, 0.97% in inferior grains, both reduced the relative crystallinity and 1045/1022 cm-1, thereby decreasing the stability of the starch crystalline region and the orderliness of starch granules. N2 treatment reduced the ΔHgel of two varieties. These changes ultimately contributed to the enhancement of the taste values in superior and inferior grains in both varieties, especially the inferior grains. Correlation analysis showed that the average starch volume diameter (D[4,3]) and relative crystallinity were significantly positively correlated with the taste value of superior and inferior sgrains, suggesting their potential use as an evaluation index for the simultaneous enhancement of the taste value of rice with superior and inferior grains.DiscussionBased on 150 kg·N hm-2, postponing N topdressing (basal fertilizer: tillering fertilizer: panicle fertilizer=3:1:6) promotes the enhancement of the overall taste value and provides theoretical information for the production of rice with high quality

IntroductionControlled-release fertilizers effectively improve crop yield and nitrogen use efficiency (NUE). However, their use increases the cost of crop production. Optimal management modes involving urea replacement with controlled-release N fertilizers to increase rice yield through enhanced NUE are not widely explored.MethodsField experiments were conducted from 2017 to 2018 to determine the effects of different controlled-release N fertilizers combined with urea [urea-N (180 kg ha-1, N1)]. We used controlled-release N (150 kg ha-1, N2) as the base, and four controlled-release N and urea-N ratio treatments [(80%:0% (N3), 60%:20% (N4), 40%:40% (N5), or 20%:60% (N6) as the base with 20% urea-N as topdressing at the panicle initiation stage under 150 kg ha-1] to study their impact on the grain yield and NUE of machine-transplanted rice.Results and discussionGrain yield and NUE were positively correlated with increases in photosynthetic production, flag leaf net photosynthetic rate (Pn), root activity, N transport, and grain-filling characteristics. The photosynthetic potential and population growth rate from the jointing to the full-heading stage, highly effective leaf area index (LAI) rate and Pn at the full-heading stage, root activity at 15 d after the full-heading stage, and N transport in the leaves from the full-heading to mature stage were significantly increased by the N4 treatment, thereby increasing both grain yield and NUE. Furthermore, compared with the other N treatments, the N4 treatment promoted the mean filling rate of inferior grains, which is closely related to increased filled grains per spikelet and filled grains rate. These effects ultimately improved the grain yield (5.03-25.75%), N agronomic efficiency (NAE, 3.96-17.58%), and N partial factor productivity (NPP, 3.98-27.13%) under the N4 treatment. Thus, the N4 treatment with controlled-release N (60%) and urea-N (20%) as a base and urea-N (20%) as topdressing at the panicle-initiation stage proved effective in improving the grain yield and NUE of machine-transplanted hybrid indica rice. These findings offer a theoretical and practical basis for enhancing rice grain yield, NUE, and saving the cost of fertilizer.

A fast convergence adaptive neural network event-triggered control strategy is proposed for the trajectory tracking issue of uncertain robotic systems with output constraints. To cope with the constraints on the system output in the actual industrial field while reducing the burden on communication resources, an adaptive event-triggered mechanism is designed by using logarithm-type barrier Lyapunov functions and an event-triggered mechanism. Meanwhile, the combination of neural networks and fast finite-time stability theory can not only approximate the unknown nonlinear function of the system, but also construct the control law and adaptive law with a fractional exponential power to accelerate the system’s convergence speed. Furthermore, the tracking errors converge quickly to a bounded and adjustable compact set in finite time. Finally, the effectiveness of the strategy is verified by simulation examples.

Rice lacks sufficient amounts of zinc despite its vitality for human health. Leaf senescence enables redistribution of nutrients to other organs, yet Zn retransfer during deficiency is often overlooked. In this hydroponic experiment, we studied the effect of Zn deficiency on rice seedlings, focusing on the fourth leaf under control and deficient conditions. Growth phenotype analysis showed that the growth of rice nodal roots was inhibited in Zn deficiency, and the fourth leaf exhibited accelerated senescence and increased Zn ion transfer. Analyzing differentially expressed genes showed that Zn deficiency regulates more ZIP family genes involved in Zn ion retransfer. OsZIP3 upregulation under Zn-deficient conditions may not be induced by Zn deficiency, whereas OsZIP4 is only induced during Zn deficiency. Gene ontology enrichment analysis showed that Zn-deficient leaves mobilized more biological pathways (BPs) during aging, and the enrichment function differed from that of normal aging leaves. The most apparent “zinc ion transport” BP was stronger than that of normal senescence, possibly due to Zn-deficient leaves mobilizing large amounts of BP related to lipid metabolism during senescence. These results provide a basis for further functional analyses of genes and the study of trace element transfer during rice leaf senescence.

In this paper, a prescribed performance adaptive event-triggered consensus control method is developed for a class of multiagent systems with the consideration of input dead zone and saturation. In practical engineering applications, systems are inevitably suffered from input saturation. In addition, input dead zone is widely existing. As the larger signal is limited and the smaller signal is difficult to effectively operate, system efficacious input encounters unknown magnitude limitations, which seriously impact system control performance and even lead to system instability. Furthermore, when constrained multiagent systems are required to converge quickly, the followers would achieve it with drastic and quick variation of states, which may violate the constraints and even cause security problems. To address those problems, an adaptive event-triggered consensus control is proposed. By constructing the transform function and the barrier Lyapunov function, while state constrained is guaranteed, multiagent systems quickly converge with prescribed performance. Finally, some examples are adopted to confirm the effectiveness of the proposed control method.

The focus of this paper is on solving the state estimation problem for general continuous-time linear systems through the use of distributed networked observers. To better reflect the communication environment, stochastic noises are considered when observers exchange information. In the networked observers, each local observer measures only part of the system output, and the state estimation can not be accomplished within a single observer. Then, all observers communicate through a pre-specified graph to make up information in the remaining system output. By solving a parametric algebraic Riccati equation (ARE), a simple method to calculate parameters in the observers is proposed. Furthermore, using the stability theory of stochastic differential equations, state omniscience is discussed in almost sure sense and in the mean square sense for the cases of state-dependent noises and non-state-dependent noises, respectively. It is shown that, for observable linear systems, the resulting observers work in a coordinated mode to reach state omniscience under the connected graph. Illustrative examples are provided to show the effectiveness of the distributed observers.

Generating a distinguishable feature for local patch is a main task in computer vision which aims at matching local patches. Recently, local patch descriptors from deep convolutional neural network (CNN) with a triplet loss have achieved promising performance. In this paper, we design a quadruplet loss, which can achieve a better result than other pairwise loss and triplet loss methods. Our loss is inspired by the thoughts of uniform distribution. It separates non-matching examples by using the hard sampled non-matching pairs in a batch, and simultaneously uses the random sampled non-matching examples to keep non-matching pairs to obey uniform distribution. A compact descriptor named QuadrupletNet is generated by combining the proposed quadruplet loss and L2Net CNN architecture. From our experiment, QuadrupletNet shows better performance on the Brown dataset and Hpatches dataset than Triplet loss methods on the same training set. The pre-trained QuadrupletNet is publicly available.

This paper investigates the distributed consensus problem of general linear multiagent systems (MASs) with communication noises under fixed and Markovian switching topologies, respectively. Each agent can obtain full state of itself and receive its neighbors’ state with noises, where intensities of noises are vector functions of relative states of agents. Bearing in mind the above constrains, a consensus protocol is proposed, where the gain matrix is obtained by the algebraic Riccati equation and the coupling strength is restricted in a given interval. By using the stochastic stability theorem, we show that mean square consensus is achieved in fixed topology case and switching topologies case, respectively. Furthermore, an estimation of the exponential convergence rate of consensus is given explicitly. Finally, simulation examples are given to show the correctness of the proposed results.

In this paper, a quasi-quadratic online adaptive dynamic programming (QOADP) algorithm is proposed to realize optimal economic dispatch for smart buildings. Load demand of high volatility is considered, which is modeled by an uncontrollable state. To reduce residual errors of the approximation structure, a quasi-quadratic-form parametric structure was designed elaborately with a bias term to counteract effects of uncertainties. Based on action-dependent heuristic dynamic programming (ADHDP), an implementation of the QOADP algorithm is presented that involved obtaining optimal economic dispatch for smart buildings. Finally, hardware-in-loop (HIL) experiments were conducted, and the performance of the proposed QOADP algorithm is superior to that of two other typical algorithms.

Efficiently scavenging piezoelectric vibration energy is attracting a lot of interest. One important type is the frequency up-conversion (FUC) energy harvester, in which a low-frequency beam (LFB) impacts a high-frequency beam (HFB). In this paper, four interface circuits, standard energy harvesting (SEH), self-powered synchronous electric charge extraction (SP-SECE), self-powered synchronized switch harvesting on inductor (SP-SSHI) and self-powered optimized SECE (SP-OSECE), are compared while rectifying the generated piezoelectric voltage. The efficiencies of the four circuits are firstly tested at constant displacement and further analyzed. Furthermore, the harvested power under FUC is tested for different electromechanical couplings and different load values. The results show that SP-OSECE performs best in the case of a weak coupling or low-load resistance, for which the maximum power can be 43% higher than that of SEH. As the coupling level increases, SP-SSHI becomes the most efficient circuit with a 31% higher maximum power compared to that of SEH. The reasons for the variations in each circuit with different coupling coefficients are also analyzed.

Burn-in test is widely used to improve the product reliability from the customer's perspective by identifying and screening out defective individuals before they are marketed. For those high reliable products whose failures are caused by gradual degradation, burn-in test not only could pick out weak units, but also increases the degradation of normal units, and hence the test duration is regarded as one key factor in the test policy optimization. In this paper, a new burn-in framework is proposed, which combines a sliding window strategy with one-dimensional convolutional neural network, completes the off-line training for classification model, and then obtains the optimal burn-in time with a group-accuracy strategy. And an online optimization algorithm is constructed to reduce the burn-in time as much as possible without deteriorating the screening effect, thereby to reduce the unnecessary lifetime loss of normal units involved in the test. The effectiveness of the presented framework is validated by the experiment. Compared to conventional strategies based on degradation models, the proposed method has better performance and robustness.

Burn-in is an effective and widely used means to improve product reliability by eliminating weak units before they are distributed in the market. Traditional burn-in that distinguishes weak units by failure during testing is inefficient and incompetent for degradation-failed products in which weak units degrade faster than normal individuals. Hence, the manufacturers have to turn to the degradation-based method. The mean lifetime to failure (MTTF) of a burnt-in population is diminished because of this type of burn-in increases the degradation level of all tested units. Ignoring the impact of burn-in leads to inferior test decisions. This study develops a multi-objective burn-in method that can simultaneously minimize the burn-in cost and maximize the burnt-in population's MTTF. We employ the time-transformed Wiener process with random effects to model the nonlinear degradation path of products and develop a burn-in scheme with two decision variables, namely, test duration and screening cutoff level. Cost expression and lifetime-based optimal objective are analytically developed. The optimal test policy is determined using the multi-objective evolutionary algorithm based on decomposition. A simulation study is conducted to demonstrate the usage and effectiveness of the multi-objective burn-in method.

Descriptors with learned features, other than descriptors with handcrafted features, have been popular in the research community. In this paper, we propose a novel piecewise loss function to improve the performance of learned descriptor, which is inspired by the nearest neighbor distance ratio matching strategy (the matching criterion for SIFT). In our strategy, if the distance ratio between positive and closest negative example is larger than a threshold, the loss will be chosen as HardNet loss; If the ratio is smaller than the threshold, the loss will be confusion. Applying the proposed loss function to the L2Net and HardNet architecture, the proposed descriptor is named ConfusionNet. Specifically, we find that adding the confusion to train local descriptors can achieve better performance. Comparing with previous works, we show in the experiments that ConfusionNet achieves state-of-the-art results in patch retrieval, patch verification, and image matching.

The paper investigates the state estimation problem of general continuous-time linear systems with the consideration of time-varying communication delay. A solution is proposed in terms of the networked distributed observer, which consists of multiple local observers. Each local observer relies on only part of the system output and exchanges information with neighbors through undirected links modeled by a prespecified communication graph. A simple approach for computing observer parameters is presented by solving a parametric algebraic Riccati equation. Furthermore, by the Lyapunov–Krasovskii stability theorem, an upper bound of the delay could be calculated explicitly and together with the conditions of joint observability and connectivity of the communication graph; the resulting distributed observers work coordinately to achieve an asymptotic estimate of the full plant state. An illustrative example is provided to confirm the analytical results.

In this paper, we mainly solve the adaptive control problem of robot manipulators with uncertain kinematics, dynamics, and actuators parameters, which has been a long-standing, yet unsolved problem in the robotics field, because of the technical difficulties in handling highly coupled effect between control torque and the mentioned uncertainties. To overcome the difficulties, we propose a new Lyapunov-based adaptive control methodology, which effectively fuses the inverse Jacobian technique and the actuator adaptation law, with which the chattering in tracking errors caused by actuator parameter perturbation is well suppressed. It is demonstrated that the asymptotic convergence of all closed-loop signals is guaranteed. Moreover, the effectiveness of our control scheme is illustrated through simulation studies.

A new adaptive tracking control problem of a nonlinear teleoperation system is considered in this article, which differs from the previous ones in that both kinematics and dynamics of manipulators are uncertain and perturbation of the actuator parameters exists due to overheating of the actuator. To overcome the first which is also the key problem, we propose a new adaptive tracking control framework which additionally contains a neural network mechanism and a robust adaptive mechanism. Based on the framework, an adaptive controller is further established to compensate for the perturbation of the actuator parameters online. By employing the neural networks and adaption method in control design, an extended teleoperation control system is effectively accommodated. With the Lyapunov theory, the tracking error is shown to converge to a residual around zero as the time goes to infinity. The effectiveness of the proposed scheme has been verified in the physical platform.

This paper deals with the output consensus problem of heterogeneous networks in a leader-follower manner that are interconnected by a physical coupling graph. The network under consideration consists of linear agents with different dynamics/dimensions. Both the state-feedback and output-feedback control protocols based upon information flow prescribed by a separate communication graph are developed, using the internal model principle and relative outputs of neighboring agents. With the small-gain theorem, we convert the consensus problem into a H∞ control problem of decoupled linear systems having the same dimensions as a single agent, where the disturbance attenuation constraints depend on the largest singular value related to the global information of physical and communication graphs. Then, we provide local synthesis procedures for control gains in terms of feasible solutions of algebraic Riccati equations. Finally, simulation examples are presented to verify the performance of the theoretical results.

The relationship between control and communication constraints is becoming of central importance in the consensus problem of networked agents. In this paper, we investigate such a problem for nonlinear multiagent systems with Lipschitz dynamics. To reflect communication constraints, the topology is assumed to switch within a finite set of digraphs characterised by an average dwell time switching signal. By constructing a suitable multiple Lyapunov function, we show that consensus can be reached under the designed consensus protocol. A multistep algorithm for designing consensus protocol is then developed by solving the Lyapunov equation and algebraic Riccati equation. Finally, simulation examples are delineated to substantiate the effectiveness of the proposed algorithms.

This paper deals with the leader-following consensus of multi-agent systems with matched nonlinear dynamics. Compared with previous works, the major difficulty here is caused by the simultaneous existence of nonidentical agent dynamics and unknown system parameters, which are more practical in real-world applications. To tackle this difficulty, a distributed adaptive control law for each follower is proposed based on algebraic graph theory and algebraic Riccati equation. By a Lyapunov function method, we show that the designed control law guarantees that each follower asymptotically converges to the leader under connected communication graphs. A simulation example demonstrates the effectiveness of the proposed scheme.

This paper considers the consensus problem of nonlinear multi-agent systems under switching directed topologies. Specifically, the dynamics of each agent incorporates an intrinsic nonlinear term and the interaction topology may not contain a spanning tree at any time. By designing a state-controlled switching law, we show that the multi-agent system with the neighbor-based protocol can achieve consensus if the switching topologies jointly contain a spanning tree. Moreover, an easily manageable algebraic criterion is deduced to unravel the underlying mechanisms in reaching consensus. Finally, a numerical example is exploited to illustrate the effectiveness of the developed theoretical results.

The selection of water temperature regulation equipment plays a crucial role in the design of workshops. At present, the choice of water temperature control equipment is usually based on the volume of the fish pond and thermal parameter calculation, combined with aquaculture experience. Empirical formulas only work in specific conditions due to factors like the environment, climate, and fish types,resulting in inaccurate equipment selection outcomes. Recognizing this limitation, this paper proposes to apply CFD simulation of the temperature field to accurately calculate the heat exchange value between indoor air and water, thereby predicting the heat exchange values during aquaculture activities in the aquaculture workshop. providing a new approach for equipment selection. This paper selects a puffer fish breeding workshop in Dalian as the simulation object, establishing a 3D unsteady-state Computational Fluid Dynamics model. The model considers outdoor temperature, solar radiation, and phase-change heat transfer in water. Comparison with experimental data reveals a root mean square error of 0.46°C for the simulated results. During summer, the highest cooling load occurs at 16:00, reaching 94.6 kW. It is recommended to employ the Daikin GCHP-40MAH ground source heat pump as the water temperature control equipment. CFD simulation validates its effectiveness in shaping the indoor temperature field post-installation. the investment in water temperature control equipment can be reduced to a certain degree. This provides a reference value for the selection of water temperature equipment in aquaculture workshops. [ABSTRACT FROM AUTHOR]