Search

Abstract Boric acid and other impurities on the surface of boron (B) particles can interact with hydroxyl-terminated polybutadiene (HTPB), weakening the mechanical properties and energy release efficiency of boron-based solid rocket propellants. SA@B composite particles were created by coating stearic acid (SA) on the surface of B particles through solvent evaporation-induced self-assembly. The study investigated the impact of SA coating on the combustion performance of B particles and the mechanical properties of HTPB matrix composites. The results showed that the SA coating enhanced the oxidation efficiency of B particles in air. The combustion heat of SA@B composite particles is 30.29 MJ/g, about 50% higher than that of B particles. During the combustion of SA@B composite particles, fewer molten solid particles surround the flame, which enhances the stability of the combustion process of the B particles. Furthermore, the SA coating effectively enhanced the dispersion of B particles in HTPB. At a stretching speed of 100 mm/min, the tensile strength of the SA@B/HTPB composite materials is higher than that of the B/HTPB composite materials. Moreover, when the mass loading of the SA@B composite particles reaches 50 wt%, the tensile strength of SA@B/HTPB composite materials is 2.46 MPa. Activating the surface of boron particles with SA can significantly improve their compatibility with HTPB, which is crucial for the stable storage of boron-based solid rocket propellants.

Complement C3 (C3) is usually deposited spontaneously on the surfaces of invading bacteria prior to internalization, but the impact of C3 coating on cellular responses is largely unknown. Staphylococcus aureus (S. aureus) is a facultative intracellular pathogen that subverts autophagy and replicates in both phagocytic and nonphagocytic cells. In the present study, we deposited C3 components on the surface of S. aureus by complement opsonization before cell infection and confirmed that C3-coatings remained on the surface of the bacteria after they have invaded the cells, suggesting S. aureus cannot escape or degrade C3 labeling. We found that the C3 deposition on S. aureus notably enhanced cellular autophagic responses, and distinguished these responses as xenophagy, in contrast to LC3-associated phagocytosis (LAP). Furthermore, this upregulation was due to the recruitment of and direct interaction with autophagy-related 16-like 1 (ATG16L1), thereby resulting in autophagy-dependent resistance to bacterial growth within cells. Interestingly, this autophagic effect occurred only after C3 activation by enzymatic cleavage because full-length C3 without cleavage of the complement cascade reaction, although capable of binding to ATG16L1, failed to promote autophagy. These findings demonstrate the biological function of intracellular C3 upon bacterial infection in enhancing autophagy against internalized S. aureus.

Abstract Lumpy skin disease virus (LSDV) is a poxvirus that mainly affects cattle and can lead to symptoms such as severe reduction in milk production as well as infertility and mortality, which has resulted in dramatic economic loss in affected countries in Africa, Europe, and Asia. In this study, we successfully isolated two strains of LSDV from different geographical regions in China. Comparative genomic analyses were performed by incorporating additional LSDV whole genome sequences reported in other areas of Asia. Our analyses revealed that LSDV exhibited an ‘open’ pan-genome. Phylogenetic analysis unveiled distinct branches of LSDV evolution, signifying the prevalence of multiple lineages of LSDV across various regions in Asia. In addition, a reporter LSDV expressing eGFP directed by a synthetic poxvirus promoter was generated and used to evaluate the cell tropism of LSDV in various mammalian and avian cell lines. Our results demonstrated that LSDV replicated efficiently in several mammalian cell lines, including human A549 cells. In conclusion, our results underscore the necessity for strengthening LSD outbreak control measures and continuous epidemiological surveillance.

The root-cutting device is a key device in the mechanical harvesting equipment of Chinese cabbage. Aiming at the problems of high energy consumption of Chinese cabbage harvest cutting and low qualified rate of root cutting, a root cutting device was developed based on the physical and mechanical properties of Chinese cabbage. Plackett-Burman design, steepest ascent, and Box Behnken design were used to screen and evaluate the influencing factors of the cutting system. Analysis of variance was used to determine the degree of influence of each structural parameter on the maximum root-cutting reaction force. The optimal parameter combination of the root-cutting device is determined by integrating the Gray Wolf Optimization algorithm and radial basis function neural network model. Finally, the model performance was evaluated and compared with the original algorithm and other mainstream algorithms, and the optimized root-cutting device was installed on a test prototype for field trials. The experimental results showed that the key factors affecting the maximum root cutting reaction force were cutter speed, walking speed, and cutting pitch angle, and the optimal parameter combinations had values of 202 r/min, 0.28 m/s, and 12°. Compared with other mainstream models, the RBFNN-GWO model outperforms other models in terms of prediction accuracy, convergence speed, and loss error value. The average productivity of the harvester under the optimal parameter combination was 0.13 hm2/h, and the qualified rate of root cutting was 93.16%, which meets the requirements of mechanized harvesting of Chinese cabbage, and provides a theoretical basis for the realization of high-efficiency and low-loss harvesting of Chinese cabbage.

Abstract The differential games have been widely used to analyze the conflicts between intelligent agents. Motivated by the fact that the agents always have finite energy or time requirements, a novel reach‐avoid game with time limits is investigated in this work. The attacker aims to reach the target region without being captured or reaching its time limit, while the defender strives to intercept the attacker or delay it. This game is beyond the scope of the classical Hamilton‐Jacobi‐Isaacs (HJI) approach. To make the problem possible to solve, we introduce the concept of reaching region and provide the optimal strategies of the players based on it. Using these strategies, we construct a hypersurface, called the barrier, in the game state space which partitions it into two parts that lead to different outcomes of the game. In this work, the complete analytical expressions of the barrier in all possible situations are provided. The game results can be obtained by substituting the initial states into the related expression and there is barely any computational burden. Compare to the existing works, the game with time limits is more practical. Also, this work provides the foundation for analyzing general multiple‐attacker‐multiple‐defender games.

The lightweight, small diameter, and irregular shape of small vegetable seeds create difficulties for online monitoring of sowing quality. We propose a machine vision-based online monitoring method with a sowing test bench designed to address the challenges. Vision devices and image processing systems are employed to detect the quality of seed sowing. Firstly, the seed segmentation image is obtained by completing the steps of median filtering, graying and image segmentation. We then implement the Circumscribed circle method to detect the position of the seed. Afterward, the coordinate system is converted using calibrated results to eliminate non-seed impurities. Finally, we count the number of identified seeds to evaluate the recognition accuracy. The trial compared three algorithms: the image segmentation algorithm OTSU, the critical point localization algorithm SIFT, and the algorithm designed in the experiment. The algorithm we designed outperformed the others regarding recognition accuracy and processing time. The experimental method employed in the study encompasses various functionalities, including seeding counting, understanding detection, replaying, and monitoring deviations from seed bands during sowing. Cabbage seeds (1.50 mm–2.00 mm), tomato seeds (1.00 mm–1.50 mm), and radish seeds (0.50 mm–1.00 mm) were selected as the experimental subjects due to the uniform particle size distribution. The results demonstrate that the relative error between the online image recognition algorithm and the system’s seeding rate monitoring is below 3.0%. Moreover, the accuracy of missed seeding monitoring is 92.5%, while the accuracy of deviation monitoring during seeding is 92.0%. We observed that the image recognition algorithm employed in the system achieved a processing time of 0.29 seconds, with a seed band recognition rate of 96.8%, fulfilling the monitoring requirements for small seed sowing experiments. The processed images and collected data are presented in real-time on the upper computer terminal. This study significantly contributes to the advancement of small-grain vegetable seed sowing monitoring technology.

Regulated cell death (RCD) is a strategy employed by host cells to defend invasions of pathogens, such as viruses and bacteria. Ferroptosis is a type of RCD characterized by excessive accumulation of iron and lipid peroxidation. While ferroptosis is primarily considered as a mechanism associated with tumorigenesis, emerging evidence begin to suggest that it may play essential role during virus infections. Recent studies illustrated that activation of ferroptosis could either induce or prohibit various types of RCDs to facilitate virus replication or evade host surveillance. More experimental evidence has demonstrated how viruses regulate ferroptosis to influence replication, transmission, and pathogenesis. This review summarizes ferroptosis-related metabolism, including iron metabolism, lipid peroxidation, and antioxidant metabolism. Furthermore, we discuss the interplay between viral infections and host ferroptosis process, with a focus on the mechanism of how viruses exploit ferroptosis for its own replication. Understanding how ferroptosis impacts virus infection can offer valuable insights into the development of effective therapeutic strategies to combat virus infections.

In view of existing problems, such as the seed and fertilizer supply link for wheat seeders still relying on manual installation and the lack of practical application equipment, a seed–fertilizer replenishment device based on the three-degree-of-freedom mechanical arm and screw conveying principle is designed using the seed box installation and supply as the operation scenario to replace the manual installation process. Combined with the requirements of the seed box replenishment operation, the key parameters of the replenishment robot arm and the screw conveyor auger are determined. Then, the kinematic model of the replenishment robot arm is established based on the modified D-H method, forward and inverse kinematics calculations are performed, and the workspace is analyzed using the Monte Carlo method. Based on this, the robotic arm task path is designed, the fifth-degree polynomial interpolation method is used to complete the trajectory planning, and MATLAB R2016a software is used to simulate the motion trajectories of each joint, verifying the feasibility of the trajectory planning solution. Finally, a prototype is trial-produced and quadratic regression orthogonal testing and response surface analyses are conducted to obtain the optimal working parameters of the replenishment device. The verification test shows that when the angular velocity of the lumbar joint of the replenishment device is 4°/s, the speed of screw conveyor is 90 r/min, and the angle of the big arm is 12°, the conveying loss rate is 3.98%, and the conveying efficiency is 0.833 kg/s. The relative errors with the theoretical optimal values are 4.2% and 2.4%, respectively, both less than 5%. The supply trajectory is reasonable, and the robot arm runs smoothly. This study can provide reference for the design of seed–fertilizer replenishment device for wheat seeders.

Modal parameter analysis is a common method for structural characteristic analysis. To accurately identify the modal parameters that describe the vibration characteristics of the structure, the chassis frame of the rice transplanter is taken as the research object. The least squares complex frequency domain method, the admittance circle method, and the modal peak picking method are used to process the measured vibration signal. The modal parameters, such as the natural frequency, damping ratio, and modal shape of the chassis frame, are obtained. Except for the eight-order frequency error greater than 10%, the errors of all other orders are all less than 10%. In the modal test of the frame structure of the rice transplanter, the identification results of the modal parameters are generally reliable. The recognition accuracy of the PolyLeast-Squares Complex Frequency (PolyLSCF) algorithm is lower than the recognition accuracy of the admittance circle method and modal peak picking. The values of some off-diagonal elements in the modal mass matrix calculated by the PolyLSCF algorithm and the admittance circle method are greater than 0.2. The diagonal elements of the modal mass matrix calculated by the modal peak picking method are all 1.

Abstract To solve the inspection problems in cotton storage, as well as the need for environmental monitoring in the process of modern cotton bale storage, an intelligent inspection and temperature and humidity intelligent monitoring system based on RFID cotton bale was developed by adopting RFID (Radio Frequency Identification) technology, wireless temperature and humidity real-time monitoring technology and handheld terminal intelligent inspection technology. The system was composed of RFID positioning inspection module and temperature and humidity real-time monitoring and transmission module. The artificial neural network (ANN) based on the particle swarm optimization (PSO) algorithm was used to process the monitoring data of the system by Gaussian filtering, and an accurate classification model of RSSI and label position was established. The test results showed that: Through the comparative analysis of the RFID indoor positioning algorithm, the positioning error of the PSO-ANN algorithm was small. In the actual cotton bale warehouse test, the relative error of positioning and monitoring for RFID cotton bale intelligent inspection and monitoring system was less than 6.7%, which effectively improved the working efficiency of inspection personnel and the security of cotton bale storage. The relative error of temperature and humidity was less than 8% and less than 7%, which could display the temperature and humidity information in real time and meet the real-time demand. This study improved the management personnel's effective positioning and inspection of the cotton bale, prevented the loss of cotton bale, reduced the deterioration probability of cotton bale, and effectively improved the storage management level of the cotton bale. It was of great practical significance to realize the networking, automation, and intelligence of cotton bale storage management.

In response to the problem of bowl seedling detachment caused by the shaking of the transplanting machine in plant factories, this paper proposes a physical entity monitoring method for the digital twin (DT) plant factory transplanting system. The method is used to analyze the vibration signals of the transplanting machine under different operating conditions and explore the optimal working conditions. Firstly, a demand analysis for the physical entity of the DT plant factory transplanting system is conducted, focusing on practical applications. Then, an optimal deployment plan is designed based on the axiomatic design (AD) theory. Subsequently, a comparative analysis of the operating conditions of the plant factory transplanting equipment is carried out using data-driven approaches. Finally, the optimal working condition parameters are determined by comparing the modal vibration power spectral density of the transplanting equipment under different operating conditions. The results show that the maximum amplitude occurs in the Z-axis, with a magnitude of 2.1 m/s2. By comparing the cloud maps, it is evident that the vibration trends in the Z-axis and X-axis above the transplanting robotic arm are more pronounced compared to the Y-axis. This indicates that under the operating condition of transplanting 3000 plants per hour, a high transplanting efficiency can be maintained, and the vibration signals in the XYZ-axis above the transplanting robotic arm are relatively smooth, making them suitable for transplanting operations. This study combines digital twin technology to analyze the vibration signals of the plant factory transplanting machine under different operating conditions and explore the optimal working conditions. Compared to traditional monitoring platforms, this method facilitates the real-time visualization of different operating conditions of the transplanting machine in a virtual mapping, providing a more intuitive reflection of the equipment operation status.

In the model (m:n), to improve the autonomous collaborative interception capability for air vehicle, a new autonomous cross-collaborative interception algorithm based on GTSMC (Global Terminal Sliding Mode Control) and real-time virtual geometry is proposed in this paper. Firstly, the conception of an autonomous cross-collaboration is defined and the multi-air vehicle for the multi- object interception problem is formulated. Then, this paper presents the dynamic situation assessment function, which considers the real-time flight status and cooperative status of the air vehicle during the interception of the object. At the same time, this paper states the condition of whether the air vehicle is in a cooperative state and proves it. After completing the dynamic situation assessment, and considering the dynamic of the air vehicles, a new controller is designed by using GTSMC and the idea of backstepping method. Simultaneously, this paper gives a stability analysis of the closed-loop system by using Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed algorithm, several simulation cases which consider different interception scenarios are given. The simulation results show that the new collaborative interception algorithm can provide better autonomous cross-collaborative interception capability and higher accuracy.

In response to the problem of imprecise maintenance of plant spacing and row spacing during corn sowing operations in the middle of existing corn farms, this study designed a dynamic compensation system for corn seed dropping position. The test system includes a corn seeding machine operating unit, a seed dropping variable measurement and control system unit, a terminal control system, and a dynamic compensation system. The dynamic performance of the control system was tested experimentally. When undisturbed signals were loaded, regardless of whether the machine used the position compensation function or not, the coefficient of variation of longitudinal grain spacing was 2.6% ± 0.2%, and the longitudinal grain spacing was basically consistent. After adding disturbance signals, compared with not using the dynamic compensation function, the variation coefficient of seed spacing decreased by an average of 6.94% when using the dynamic compensation function. When using the dynamic compensation function, compared with not using dynamic compensation function, the variation coefficient of lateral grain spacing decreased by an average of 9.16%. Stability analysis of longitudinal and lateral grain spacing was conducted through bench tests, verifying the stability of the proposed dynamic compensation system for seed dropping position.

To address challenges such as the complex correlations among multiple parameters during the modeling process of plant factory transplanters, the large differences between simulations and actual models, and the difficulties in data acquisition and processing, this paper proposes the concept of a Plant Factory Transplanter (PFT) digital twin five-dimensional model based on research of plant factory transplanters. The PFT digital twin five-dimensional model builds on traditional 3D modeling and includes physical entities, virtual models, services, twin data, and connecting interactions. This study delves deeply into the connotations and construction methods of the PFT five-dimensional model from the five aspects of PFT physical entity, virtual entity, services, twin data, and connections, and illustrates the implementation steps and effects of each link. Finally, practical examples of the application of the PFT digital twin five-dimensional model are presented in actual scenarios. The five-dimensional modeling approach for plant factory transplanters based on digital twins can monitor the working status of transplanters online and evaluate the effectiveness of transplantation. This method overcomes problems such as poor adaptability and difficulty in updating physical models, thus improving the efficiency of monitoring and optimizing configuration parameters. Moreover, the generated virtual entities are more intuitively reflected in the control interface, significantly reducing the reliance of equipment operators on relevant professional skills. In the future, the proposed digital twin five-dimensional model is expected to be further refined and optimized, with creation tools and application scenarios studied. Application research will also be conducted to meet different application requirements.

An electro-hydraulic profiling mechanism has been gradually applied to provide suitable downforce for a no-till row unit and to ensure the consistency of the seed sowing depth. In order to improve the control effect of the sowing depth system and solve the problems of a complex structure, scattered valve sets and equipment suitability of an existing electro-hydraulic row unit, this paper takes the 2BJ-470B no-till row unit as the carrier and innovatively designs an integrated pressure-reducing cylinder (IPRC) based on electro-hydraulic technology by analyzing the sowing depth control process of an electro-hydraulic seeder. In addition, we develop the integrated electro-hydraulic-driven sowing depth measurement and control system. Combined with the feedforward compensation PID control algorithm, the dynamic regulation of the downforce is realized with the IPRC. The field test shows that under the setting of a sowing depth of 50 mm and a vehicle speed of 9~10 km·h−1, the qualified rate of the sowing depth under the three adjustment methods of self-weight adjustment, spring adjustment and electro-hydraulic adjustment is 89.2%, 96.7% and 98.6%, respectively, and the corresponding maximum coefficient of variation of the sowing depth is 16.7%, 12.9% and 6.4%, respectively. The seed groove environmental qualification rate (SFEQ) is further analyzed in combination with the soil compaction, and the mean values of the qualification rates of the three control methods at different vehicle speeds are 88.3%, 91.6% and 98.6%, respectively. The integrated electro-hydraulic-driven row unit has significant advantages over mechanical and self-weight regulation, and its whole machine integration degree and high adaptability realize the comprehensive control of the sowing depth and soil compaction strength.

The plant factory transplanter is a key component of the plant factory system. Its operation status directly affects the quality and survival rate of planted seedlings, which in turn affects the overall yield and economic efficiency. To monitor the operation status and transplanting quality of a transplanting machine in a timely manner, the primary task is to use a computerized and easy-to-use method to monitor the transplanting units. Inspired by the latest developments in augmented reality and robotics, a digital twin model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed. First, a data-driven and virtual model approach is combined to construct a multi-domain digital twin of the transplanting equipment. Then, taking the vibration frequency domain signal above the transplanting manipulator and the image features of the transplanting seedling tray as input variables, the evaluation method and configuration method of the plant factory transplanter digital twin system are proposed. Finally, the effect of the transplanter is evaluated, and the cycle can be repeated to optimize the transplanter to achieve optimal operation parameters. The results show that the digital twin model can effectively use the sensor data to identify the mechanical vibration characteristics and avoid affecting transplanting quality due to mechanical resonance. At a transplanting rate of 3000 plants/h, the transplanting efficiency can be maintained at a high level and the vibration signal of the X, Y, and Z-axis above the transplanting manipulator is relatively calm. In this case, Combined the optimal threshold method with the traditional Wiener algorithm, the identification rate of healthy potted seedlings can reach 94.3%. Through comprehensively using the optimal threshold method and 3D block matching filtering algorithm for image threshold segmentation and denoising, the recognition rate of healthy seedlings has reached over 96.10%. In addition, the developed digital twin can predict the operational efficiency and optimal timing of the detected transplanter, even if the environmental and sensor data are not included in the training. The proposed digital twin model can be used for damage detection and operational effectiveness assessment of other plant factory equipment structures.

To improve the performance of intercepting a target with different maneuvering modes and changing the mode suddenly during the interception, a new adaptive control algorithm for the IGC (Integrated Guidance and Control) system is proposed, using the global terminal sliding mode control method and a DNN (Deep Neural Network). Firstly, the missile-target problem is formulated and a new strict-feedback nonlinear IGC model with mismatched uncertainties is established. Secondly, the paper divides the IGC system into four subsystems, including a guidance subsystem, overload subsystem, attitude subsystem and the deep neural network subsystem. To transform the control signal between each subsystem and avoid the “differential explosion” problem, the paper defines the SOF (Second Order Filter). Thirdly, in combination with a deep neural network, a new modified global terminal sliding mode surface and the adaptive control law are designed. At last, using the Lyapunov theory, the stability of the IGC system is analyzed. Finally, to illustrate the effectiveness of the proposed algorithm, several simulation cases are given. The simulation results show the superiority of the proposed algorithm in adapting different maneuvering modes during the whole interception, improving the control performance and having a high interception accuracy.

The reach-avoid game theory is an ideal tool to handle the conflicts among intelligent agents and has been previously studied assuming full state information and no time limits on the players in the past decades. In this article, we extend the problem by requiring the defender to detect the attacker and adding maximum operation time constraints to the attacker. The attacker aims to reach the target region without being captured or reaching its time limit. The defender can employ strategies to intercept the attacker only when the attacker is detected. A geometric method is proposed to solve this game qualitatively. By analyzing the geometric property of the Apollonian circle and the detection range, we give the barrier under the condition that the attacker is initially detected and the attacker’s shortest route which guarantees its arrival at the target region when it is initially outside the detection range. Then, a barrier that separates the game space into two respective winning regions of the players is constructed based on the shortest route and the time limit of the attacker. The main contributions of this work are that this paper provides the first attempt to introduce the abovementioned two concepts simultaneously, which makes the game more practical, and we provide the complete solution of the game in all possible situations.

To improve the trajectory tracking accuracy, the anti-jamming performance, and the environment adaptability of a quadrotor, the paper proposes a new adaptive trajectory tracking algorithm with multilayer neural network and sliding mode control method. The major difference between other related approaches is that the paper uses the multilayer neural network in the system and the neural network is online computing in the whole process. Firstly, the paper establishes the quadrotor dynamic model and introduces the conception of Sigma-Pi neural network. Then, the paper adds the neural network to the attitude and trajectory tracking control loop. Moreover, the paper designs the adaptive neural network control law. At last, to illustrate the stability of the adaptive control law, the paper gives the Lyapunov stability analysis. Finally, to demonstrate the effectiveness of the method, the paper gives different types of simulation. Comparing with different cases, when increasing the layer of the neural network, the trajectory tracking performance becomes better. In addition, introducing multilayer neural network into the system could improve the anti-interference ability of the system and has a high-precision in tracking the desire trajectory.

A novel finite-time tracking control algorithm with disturbances observer is investigated for agile missiles in the presence of mismatched and matched disturbances. A finite-time disturbance observer with the continuous super-twisting algorithm is designed to quickly estimate the mismatched and matched disturbances. An adaptive law on the basis of immersion and invariance theory (I&I) is proposed to estimate the uncertainty of aerodynamics and compensate for the inaccuracy of modeling. An adaptive dynamic scaling factor supervised by a designed supervision factor is implemented. Moreover, a novel sliding mode controller is designed, and a barrier Lyapunov function (CTV-BLF) containing the system state constraints is constructed, which can guarantee that the violation of the constraint will not appear. The finite-time stability of the new proposition is proven via Lyapunov-based analysis. Comparative simulation results illustrate the effectiveness of the proposed scheme.

To intercept the great maneuvering target, combining with the sliding mode and the extended disturbance observer, a new control algorithm for integrated guidance and control (IGC) system is proposed in this paper. Firstly, the paper formulates the Missile–Target problem. Then the paper establishes an uncertain IGC dynamic model where the nonlinearities, the perturbations and the maneuvering of the target are regarded as disturbance. Secondly, a second-order disturbance observer is designed to estimate the disturbance and their derivatives.. After this, combining with the second-order disturbance observer, a modified sliding surface and the corresponding reaching law are designed to obtain the rudder deflection command directly. Thus, the real sense of IGC system is achieved. Next, the paper uses the Lyapunov stability theory to prove the stability of the system. Finally, the paper provides different simulation cases, which have different maneuver modes of the target, to demonstrate the superiority of the proposed method in reducing the response time, increasing the rudder response, and having a high interception probability.

The combine harvester is the main machine for fieldwork during the harvest season. When the harvester fails and cannot continue to work, this indirectly affects the harvest time and the yield in the field. The emergency maintenance service of agricultural machinery can be optimized through the dynamic planning of harvester maintenance tasks, using the operation and maintenance platform. According to the scene, a priority scheme for the operation and maintenance tasks, based on the improved Q-learning algorithm, was proposed. The continuous approximation capability of the model was improved by using the BP neural network algorithm and the Q function value, in iterations, was updated continuously. At the same time, the improved TOPSIS method, based on Mahalanobis distance, was used to calculate the closeness of each harvester maintenance task, so as to determine the priority of the equipment maintenance tasks. An operation and maintenance service platform for combine harvesters was developed based on the B/S architecture, with the goal of minimizing the operation and maintenance costs and improving the tasks’ complete efficiency. In this research process, dynamic scheduling rules were formulated. Operation and maintenance resources were optimized and rationally allocated through dynamic optimization scheduling methods, and feasible solution information was generated from the operation and maintenance service platform. Finally, the actual data from the enterprise were used for verification and analysis. The verification showed the following: through a comparison of algorithm performance, it was seen that the improved BP-Q-Learning algorithm can quickly find the operation and maintenance scheduling scheme in the maintenance scheduling; the priority rules can improve the efficiency of task execution, to a certain extent; the cost of the tasks’ execution can be significantly reduced; and the maintenance distance can be shortened. This research has reference significance for the formulation and optimization of agricultural machinery maintenance for cross-regional operations.

In view of the centralized operation, high failure rate and large number of harvesters involved in the cross-regional operation of combine harvesters, which has led to a surge in maintenance service demand and a lack of effective maintenance service systems, in order to be able to quickly solve problems arising from failures during the process of cross-regional operation, an operation and maintenance (O&M) service platform for the cross-regional operation of combine harvesters was designed in this research on the basis of data resources, supported by the computing power of a big data platform and centered on an artificial intelligence algorithm. Meeting the demand for maintenance service during cross-regional operation, we built a system platform integrating service order management, maintenance service activity management, and maintenance service resource management, and a technical algorithm for operation and maintenance service resource allocation and service path optimization was developed in order to achieve service function modularization and intelligent monitoring, while early warning and display were realized using multi-dimensional platforms such as a PC, a control screen, and a mobile App. This platform was able to solve problems arising when harvesters break down, maintenance service can be carried out quickly when traditional resource information is blocked and the demand for the service is difficult to meet. The reduction in cost and the increased efficiency for agricultural machinery enterprises was also achieved, while the problem of ensuring continued service was systematically solved during the process of cross-regional operation. Finally, the performance of the software architecture and the effect of path optimization were verified. The results showed that the platform system developed using the three-layer C/S architecture offered more stable characteristics, and the path optimization in the platform system was better able to reduce the maintenance time and distance, thus making it possible to realize the dynamic on-demand configuration and scheduling management of cross-region job service resources.

To realize the real-time monitoring of the cotton precision seeding operation process and improve the intelligence level of cotton precision planters, based on automatic color matching detection technology and visualization technology, this study designs a monitoring system for the sowing quality of cotton precision planters. The monitoring system is based on the double-silo turntable type cotton vertical disc hole seed metering device as the research carrier, and is composed of a missed seeding monitoring module and a visualization module. Among them, the missed seeding monitoring module includes an incremental rotary encoder, color code electric eye color fiber optic sensor, color code sensor amplifier, etc.; the visualization module includes data acquisition module, industrial computer, and so on. The missing seeding monitoring module is installed on the seed spacer of the cotton precision seed metering device. It uses Labview software for graphical programming and is equipped with a multi-functional industrial computer. It realizes the monitoring of parameters such as the number of sowings, the number of missed sowings, the speed of the hole seeder, the forward speed of the machine, and the sowing area. The results of the bench test and field test of the sowing monitoring system showed that the accuracy rate of the system’s broadcast monitoring was over 93%, and the accuracy rate of missed broadcast monitoring was over 91%. The system solved the technical problem that cotton film-laying and sowing were not easy to detect. It could accurately detect the quality of cotton sowing in real time and meet the actual requirements of sowing monitoring.

To meet the needs of remotely operating and maintaining combine harvesters in view of the problems of extensive operation, inefficient operation and maintenance, and backward management of combine harvesters, a remote operation and maintenance platform for the combine harvester has been designed. The platform mainly includes modules, such as data monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance. Among them, the data-monitoring module obtains the monitoring data of the vehicle terminal. It actively transmits to the remote operation and maintenance platform to remotely monitor the operation data or status of the combine harvester system. The fault prediction module builds a fault prediction model based on the fault features and data provided by all previous stages. It predicts and evaluates the actual operation of each component of the combine harvester in the foreseeable future, Based on the fault diagnosis model, the fault diagnosis module evaluates and classifies the current abnormal state of the combine harvester. The comprehensive operation and maintenance module formulates the corresponding maintenance and repair strategies according to the output results of the fault diagnosis and prediction. Through functional field testing of the platform, the results showed that the platform could perform real-time monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance for the operation information of the combine harvester. It helped to improve the intelligent operation level, management quality and operation, and maintenance efficiency of the combine harvester.

A new process of agile turn with engine reignition is proposed in this paper. Compared with the traditional process, this process includes deceleration phase, larger angle manoeuvre phase, and engine reignition phase. Firstly, the paper describes the new process of agile turn. Then, several constraints in this process are described. Considering all these constraints and assumptions, a new dynamic model including two-stage engine and deceleration parachute is established. Then, the optimal control laws are designed and the timing point determination of the secondary engine ignition is discussed. By using Pontryagin principle, the optimal control laws for each actuator are derived. In terms of determining the optimal timing point of the secondary engine ignition, the paper gives the process of proof. Finally, several numerical simulations are given to demonstrate the effectiveness of the method proposed in this paper. According to all these numerical simulations, it is obvious that the new process of agile turn proposed in this paper is better than traditional process especially in having a smaller turning radius, a shorter turning time, and a high terminal velocity.

To improve the accuracy of the parameters used in the discrete element simulation test, this study calibrated the simulation parameters of cotton seeds by combining a physical test and simulation test. Based on the intrinsic parameters used for the physical test of cotton seed, according to the freefall collision method, inclined plane sliding method, and inclined plane rolling method, the contact parameters of cotton seeds and cotton seeds, stainless steel, and nylon were measured, respectively. The physical test of the accumulation angle and angle of repose of the cotton seeds was conducted. It was obtained to process the image of the seed pile with Matrix Laboratory software. The Plackett–Burman test was used to screen the significance of the simulation parameters. The optimal value range of the significant parameters was determined according to the steepest climbing test. The second-order regression model of the significant parameters, the stacking-angle error, and the angle-of-repose error were obtained according to the Box–Behnken design test. Taking the minimum stacking-angle error and angle-of-repose error as the optimization target values, the following optimal parameter combination was obtained: the interspecies collision recovery coefficient was 0.413, the interspecies static friction coefficient was 0.695, and the interspecies rolling friction coefficient was 0.214. Three repetitive simulation experiments were conducted to prove the reliability of the calibration results. The research results can be used for discrete element simulation experiments for cotton precision seed metering.

In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this study which realized the functions of data monitoring and the full operation and maintenance of a combine harvester. At the same time, through the comprehensive operation and maintenance platform, the harvester information was obtained in real-time, the diagnosis results were obtained, and the maintenance service was effectively carried out through the platform. The IPSO-SVM fault diagnosis algorithm was proposed, and the performance of the fault diagnosis of the combine harvester was verified by the simulation test. The experimental verification showed that the system met the requirements of remote monitoring of combine harvesters, and the prediction accuracy of this method was 97.96%. Compared with SVM (87.51%), GA-SVM (89.44%), and PSO-SVM (92.56%), this system had better generalization ability and effectively improved the management level of the comprehensive operation and maintenance of the combine harvester. A theoretical basis and technical reference will be provided for the follow-up research for the comprehensive operation and maintenance platform of the combine harvester in this paper.

In order to solve the problems such as the inability to automatically mix a variety of solid fertilizers and the unreasonable fertilizer amount, improve fertilizer utilization, and reduce production costs, this study designs a variable formula fertilization control system based on a prescription diagram, including pressure sensor, speed sensor, servo motor, fertilizer discharge actuator, Programmable Logic Controller (PLC controller), vehicle control terminal, etc. Based on pre-loaded soil prescription diagram and combining fertilizer pressure and ground wheel speed detection information, the system obtained a formula fertilization control strategy through calculation to realize the function of fast and automatic formula of nitrogen, phosphorus, and potassium fertilizers and precise variable fertilization. The experimental study on the performance of the variable formula fertilization control system showed the following: the measurement error range of the pressure sensor was 0.005~0.03%; the relationship between the motor speed and the amount of nitrogen, phosphorus, and potassium fertilizer discharged was calibrated. Three gears were established for the motor speed: low (10 r/min), medium (30 r/min), and high (50 r/min); the measurement accuracy of the speed sensor was above 98%. The test verified that the control accuracy of the variable formula fertilization system reached more than 95%, which met the requirements of fast automatic formula and precise variable fertilization and had good practicability and economy.

In this paper, adaptive tracking control is applied to improve performances of an underactuated quadrotor helicopter with respect to attitude and position control. Firstly, the dynamic model is presented. Then a new trajectory tracking algorithm is designed by using the sigma-pi neural network and backstepping. The paper designs the sigma-pi neural network compensation control law and gives the Lyapunov-type stability analysis. Then the corresponding numerical simulations are performed by using MATLAB. Simulation results are shown to demonstrate the effectiveness of the proposed control strategy, which could reduce tracking error, decrease tracking time, and improve the anti-interference ability of the system.

The study focuses on the self-propelled forage harvester and analyzes various parameters such as the working speed and torque of the cutting table, chopped roll, throwing fan, walking parts, and the output flow rate and pressure of the hydraulic pump of the feeding section. Field experiments show that the fan, walking, chopping roller, and grain crushing roller drive powers account for 7-8%, 7-10%, 24-28%, and 13-21% of the engine power, respectively. The driving power of the inner and outer sides of the cutting table, fan, and walking power account for a relatively stable proportion of the engine output power. The study collects and analyzes operation parameters simultaneously to provide a reference for evaluating the performance and optimizing the design of corn forage harvesters. [ABSTRACT FROM AUTHOR]

To achieve the process of automatic film cutting for cotton and improve the efficiency of cotton precision film hole seeding machine operation, this paper proposes a wireless handle button control method and designs an automatic film and tape cutting system for cotton precision film-laying hole seeder. The system includes electromagnetic integrated valve group, hole opener lifting hydraulic cylinder, membrane pressing hydraulic cylinder, multifunctional hydraulic cylinder for cutting film, strapping support hydraulic cylinder, hydraulic limit sensor, arc-shaped film cutting shovel reset sensor, wireless control handle for film cutting and film cutting controller. The system is based on the VisualTFT platform, equipped with a multi-functional touch screen. By integrating functions such as obtaining the forward distance of the machine, hydraulic control principles, and wireless control models for film cutting and burying, it achieves automatic cutting and burying of ground film and drip tape processes. The experiment verified the working performance of the automatic film cutting and feeding system of the cotton seed precision film mulching hole seeder and the results showed that the average monitoring accuracy of the machine's forward distance was 98.16%; the response time for controlling the film cutting belt and burying film belt was <0.78 s, and the execution time was <2.68 s; the success rate of cutting film strips was 100.00% and the success rate of burying film strips was above 98.33%, which met the operational requirements of automatic film strip cutting for precise cotton seeding film hole planting machines. [ABSTRACT FROM AUTHOR]

Copyright of INMATEH - Agricultural Engineering is the property of INMATEH - Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Zoonotic poxviruses such as mpox virus (MPXV) continue to threaten public health safety since the eradication of smallpox. Vaccinia virus (VACV), the prototypic poxvirus used as the vaccine strain for smallpox eradication, is the best-characterized member of the poxvirus family. VACV encodes a serine protease inhibitor 1 (SPI-1) conserved in all orthopoxviruses, which has been recognized as a host range factor for modified VACV Ankara (MVA), an approved smallpox vaccine and a promising vaccine vector. FAM111A (family with sequence similarity 111 member A), a nuclear protein that regulates host DNA replication, was shown to restrict the replication of a VACV SPI-1 deletion mutant (VACV-ΔSPI-1) in human cells. Nevertheless, the detailed antiviral mechanisms of FAM111A were unresolved. Here, we show that FAM111A is a potent restriction factor for VACV-ΔSPI-1 and MVA. Deletion of FAM111A rescued the replication of MVA and VACV-ΔSPI-1 and overexpression of FAM111A significantly reduced viral DNA replication and virus titers but did not affect viral early gene expression. The antiviral effect of FAM111A necessitated its trypsin-like protease domain and DNA-binding domain but not the PCNA-interacting motif. We further identified that FAM111A translocated into the cytoplasm upon VACV infection by degrading the nuclear pore complex via its protease activity, interacted with VACV DNA-binding protein I3, and promoted I3 degradation through autophagy. Moreover, SPI-1 from VACV, MPXV, or lumpy skin disease virus was able to antagonize FAM111A by prohibiting its nuclear export. Our findings reveal the detailed mechanism by which FAM111A inhibits VACV and provide explanations for the immune evasive function of VACV SPI-1. [ABSTRACT FROM AUTHOR]