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New types of aerial robots (NTARs) have found extensive applications in the military, civilian contexts, scientific research, disaster management, and various other domains. Compared with traditional aerial robots, NTARs exhibit a broader range of morphological diversity, locomotion capabilities, and enhanced operational capacities. Therefore, this study defines aerial robots with the four characteristics of morphability, biomimicry, multi-modal locomotion, and manipulator attachment as NTARs. Subsequently, this paper discusses the latest research progress in the materials and manufacturing technology, actuation technology, and perception and control technology of NTARs. Thereafter, the research status of NTAR systems is summarized, focusing on the frontier development and application cases of flapping-wing micro-air vehicles, perching aerial robots, amphibious robots, and operational aerial robots. Finally, the main challenges presented by NTARs in terms of energy, materials, and perception are analyzed, and the future development trends of NTARs are summarized in terms of size and endurance, mechatronics, and complex scenarios, providing a reference direction for the follow-up exploration of NTARs.

Recently, testing and using micro-unmanned vehicles, such as unmanned aerial vehicles (UAVs), in scenarios such as supply transportation, agricultural management, and military operations have become more common. It is no longer sufficient to control a single UAV to accomplish all missions. With the increasing complexities associated with operating and task requirements, an unmanned swarm requires a series of algorithms with higher efficiency, greater generalization ability, and better adaptability than the earlier algorithms. A combination of unmanned swarms with artificial intelligence is becoming a common solution to manage the above requirements. Deep reinforcement learning (DRL) is a machine learning method that combines deep learning (DL) and reinforcement learning (RL); therefore, this method has the advantages of DL and RL. Using an RL method, an agent can learn from the environment by trial and error and make decisions that autonomously obtain high scores. However, when the given environment is complex, the decision function of the agent may be too difficult to implement and then the agent cannot make the correct decision. The DL method has strong fitting ability. A suitable deep neural network can simulate any linear or nonlinear function. If the DL method is used to simulate the decision function in RL, the hybrid method can solve the problem that an agent cannot solve and make a correct decision in a complex environment. The combination of an unmanned swarm and a DRL method has been widely studied. This paper introduces the concept of DRL from the perspective of principles and characteristics. This paper analyzes several typical DRL algorithms, discusses the various control requirements of a UAV swarm, and then focuses on the achievements of combining DRL and a UAV swarm control. Finally, this paper presents viewpoints on the application prospects and challenges related to landing and transformation in the combination field. The concept of an unmanned swarm originated from the study of the behavior of biological groups. Several species of bees, ants, birds, fish, and other creatures exhibit complex group behaviors. These clusters comprise many independent individuals in accordance with certain aggregation rules to form a coordinated, orderly group movement mechanism. Similar to biological clusters, in the field of robotics or UAVs, unmanned swarm systems are crowded intelligent systems. These systems consist of multiple homogeneous or heterogeneous unmanned equipment to achieve mutual behavior coordination and jointly complete specific tasks through interactive feedback and incentive response of information. In practical applications, an unmanned swarm system needs to meet the requirements of an open environment, a changeable situation, limited resources, and real-time responses. This system needs to have multicore collaborative capabilities such as distributed collaborative perception, intelligent collaborative decision-making, and robust collaborative control. The distributed intelligent collaborative control method based on DRL can fully meet the control requirements of high intelligence and robustness of unmanned cluster systems.

Abstract At present, the multi‐object tracking method based on transformer generally uses its powerful self‐attention mechanism and global modelling ability to improve the accuracy of object tracking. However, most existing methods excessively rely on hardware devices, leading to an inconsistency between accuracy and speed in practical applications. Therefore, a lightweight transformer joint position awareness algorithm is proposed to solve the above problems. Firstly, a joint attention module to enhance the ShuffleNet V2 network is proposed. This module comprises the spatio‐temporal pyramid module and the convolutional block attention module. The spatio‐temporal pyramid module fuses multi‐scale features to capture information on different spatial and temporal scales. The convolutional block attention module aggregates channel and spatial dimension information to enhance the representation ability of the model. Then, a position encoding generator module and a dynamic template update strategy are proposed to solve the occlusion. Group convolution is adopted in the input sequence through position encoding generator module, with each convolution group responsible for handling the relative positional relationships of a specific range. In order to improve the reliability of the template, dynamic template update strategy is used to update the template at the appropriate time. The effectiveness of the approach is validated on the MOT16, MOT17, and MOT20 datasets.

Paclitaxel, a potent anti-tumor drug widely recognized for its therapeutic efficacy, has faced limitations in clinical application due to its poor solubility. The use of Cremophor EL (CrEL) as a cosolvent in paclitaxel injections has been associated with hypersensitivity reactions in some patients. To overcome these challenges, we have developed a novel conjugate by linking a neuropilin-1 targeting peptide, RPPR, to paclitaxel, resulting in PTX-RPPR. This innovative approach has significantly enhanced the solubility of paclitaxel, achieving a 3.8 mg/mL concentration, a remarkable 90-fold increase over the native drug. PTX-RPPR has shown potent anti-tumor activity, inhibiting tumor cell proliferation with an IC50 ranging from 0.26 to 1.64 μM and effectively suppressing migration, invasion, and angiogenesis at a concentration of 75 nM. Notably, in a 4T1 mammary carcinoma model, PTX-RPPR administered at a dose of 0.7 μmol/kg exhibited tumor growth inhibition comparable to that of paclitaxel at a higher dose of 3.5 μmol/kg, with superior efficacy in preventing lung metastasis. Furthermore, PTX-RPPR effectively reduced NRP-1 expression in both tumors and lungs post-treatment. In contrast to paclitaxel formulated with CrEL, PTX-RPPR did not induce IL-6 expression, suggesting a safer profile in terms of immunological response. Characterized by a particle size of 200 nm and a zeta potential of +30 mV, the nano-formulation of PTX-RPPR demonstrated remarkable stability over seven days. This study introduced PTX-RPPR as a promising peptide-drug conjugate that addresses the solubility and hypersensitivity issues associated with paclitaxel, offering a safer therapeutic strategy for cancer treatment.

This study proposes an image-based visual servoing (IBVS) method based on a velocity observer for an unmanned aerial vehicle (UAV) for tracking a dynamic target in Global Positioning System (GPS)-denied environments. The proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target. A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is proposed. The depth model ensures image feature accuracy and image trajectory smoothness in rotating target tracking. The relative velocities of the UAV and the dynamic target are estimated using the proposed velocity observer. Thanks to the velocity observer, translational velocity measurements are not required, and the control chatter caused by noise-containing measurements is mitigated. An integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the anti-disturbance ability. The stability of the velocity observer and IBVS controller is analyzed using the Lyapunov method. Comparative simulations and multistage experiments are conducted to illustrate the tracking stability, anti-disturbance ability, and tracking robustness of the proposed method with a dynamic rotating target.

Existing routing protocols without awareness of the topology causes excessive retransmissions, energy holes, and long delay, data routing performance was seriously deteriorated.Considering the relation of topology and routing, an intelligent route planning with jointing topology control (IRPJTC) method was proposed.IRPJTC consisted of two part, the virtual force-based adaptive topology control (VFATC), and the PPO-based geographic routing protocol (PPO-GRP).Based on neighbor’s mobility information, the distance between UAVs was adaptively adjusted by VFATC to provide stable links between UAVs.Combined with link stability metric in VFATC, end-to-end delay and energy consumption, a multi-objective reward function was designed by PPO-GRP to train optimal routing strategy.According to the performance study, the proposed IRPJTC reduces existing routing protocols by 12.11% of end-to-end delay, and 4.56% of energy consumption, and has a better energy balance ability.

In order to meet the demand of the intelligent and efficient picking of fresh citrus fruit in a natural environment, a flexible and independent picking method of fresh citrus fruit based on picking pattern recognition was proposed. The convolutional attention (CA) mechanism was added in the YOLOv7 network model. This makes the model pay more attention to the citrus fruit region, reduces the interference of some redundant information in the background and feature maps, effectively improves the recognition accuracy of the YOLOv7 network model, and reduces the detection error of the hand region. According to the physical parameters of the citrus fruit and stem, an end-effector suitable for picking citrus fruit was designed, which effectively reduced the damage during the picking of citrus fruit. According to the actual distribution of citrus fruits in the natural environment, a citrus fruit-picking task planning model was established, so that the adaptability of the flexible handle can make up for the inaccuracy of the deep learning method to a certain extent when the end-effector picks fruits independently. Finally, on the basis of integrating the key components of the picking robot, a production test was carried out in a standard citrus orchard. The experimental results show that the success rate of the citrus-picking robot arm is 87.15%, and the success rate of picking in the natural field environment is 82.4%, which is better than the success rate of 80% of the market picking robot. In the picking experiment, the main reason for the unsuccessful positioning of citrus fruits is that the position of citrus fruits is beyond the picking range of the end-effector, and the motion parameters of the robot arm joint will produce errors, affecting the motion accuracy of the robot arm, leading to the failure of picking. This study can provide technical support for the exploration and application of the intelligent fruit-picking mode.

The aim of individuals consuming health supplements is to attain a robust state through nutritional regulation. However, some unscrupulous manufacturers, motivated by profit, fraudulently incorporate drugs or unauthorized components with therapeutic effects into the product for instant product performance enhancement. The long-term use of these products may inadvertently inflict harm on human health and fail to promote nutritive healthcare. The illegal inclusion of these substances is prevalent in kidney-tonifying and sexuality-enhancing products. Developing effective analytical methods to identify these products and screen for illegal added ingredients can effectively prevent such products from reaching and remaining on the market. A target screening method for the detection and quantification of 90 phosphodiesterase type 5 inhibitors (PDE-5is) in 5 kinds of health products was developed and validated. The type of dietary supplements varied from tablets, capsules, and protein powder to wine and beverages. Sample preparation was completed with a one-step liquid phase extraction. The screening process of 90 PDE-5is was done efficiently within 25 min by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) using the dynamic multiple reaction monitoring (dMRM) technique. The LODs of 90 PDE-5is were detected at levels ranging from 25 to 85 ng/g or ng/mL. This novel targeting methodology was effective and can be applied to routine market supervision. Among 286 batches of samples, 8 batches were found to be positive. Three kinds of PDE-5is were first detected in healthy products. The screening method demonstrated herein will be a promising and powerful tool for rapid screening of PDE-5is.

The lack of spatial pose information and the low positioning accuracy of the picking target are the key factors affecting the picking function of citrus-picking robots. In this paper, a new method for automatic citrus fruit harvest is proposed, which uses semantic segmentation and rotating target detection to estimate the pose of a single culture. First, Faster R-CNN is used for grab detection to identify candidate grab frames. At the same time, the semantic segmentation network extracts the contour information of the citrus fruit to be harvested. Then, the capture frame with the highest confidence is selected for each target fruit using the semantic segmentation results, and the rough angle is estimated. The network uses image-processing technology and a camera-imaging model to further segment the mask image of the fruit and its epiphyllous branches and realize the fitting of contour, fruit centroid, and fruit minimum outer rectangular frame and three-dimensional boundary frame. The positional relationship of the citrus fruit to its epiphytic branches was used to estimate the three-dimensional pose of the citrus fruit. The effectiveness of the method was verified through citrus-planting experiments, and then field picking experiments were carried out in the natural environment of orchards. The results showed that the success rate of citrus fruit recognition and positioning was 93.6%, the average attitude estimation angle error was 7.9°, and the success rate of picking was 85.1%. The average picking time is 5.6 s, indicating that the robot can effectively perform intelligent picking operations.

Naturally occurring substances and their derivatives function as vital resources for pesticides that can be used in fields, such as insecticide production and fungicide development. As a botanical entity displaying multifaceted biological functions, wormwood has received thorough scrutiny across multiple sectors. The insect repellency potency combined with antibacterial and antifungal activities of wormwood position it as a potential candidate for prospective development into eco-friendly chemical pesticides. In this research, Wormwood essential oil was procured via ethanol water under ultrasonic scenarios and subsequently diluted with PEG 400 to formulate green chemical pesticides. The defensive efficacy of this green pesticide on plants was validated through 2 weeks of clustered plant growth experiments. Active constituents that exerted their effects were scrutinized by GC-MS. Furthermore, this green pesticide also displays efficacious effects on the prevention and management of aphids, exhibiting a dose-dependent relationship. 4-terpenol, eucalyptol, carvacrol, and L-borneol were identified by GC-MS as the predominant active constituents in this green chemical pesticide. Wormwood can be leveraged to develop green chemical pesticides, which can protect plants without contaminating the environment.

Abstract Based on a linear copositive Lyapunov function (LF), a combined cyclic stabilizing switching law with one stage being classified into four cases is proposed for continuous time positive switched systems composed of two unstable subsystems. Under the combined cyclic stabilizing switching law, the positive switched system is asymptotically stable and robust against certain class of perturbations. Finally, simulation verifies the effectiveness of the proposed switching law.

Abstract Constrained multi-objective optimization problems (CMOPs) exist widely in the real world, which simultaneously contain multiple constraints to be satisfied and multiple conflicting objectives to be optimized. Therefore, the challage in addressing CMOPs is how to better balance constraints and objectives. To remedy this issue, this paper proposes a novel dual-population based constrained multi-objective evolutionary algorithm to solve CMOPs, in which two populations with different functions are employed. Specifically, the main population considers both objectives and constraints for solving the original CMOPs, while the auxiliary population is used only for optimization of objectives without considering constraints. In addition, a dynamic population size reducing mechanism is proposed, which is used to adjust the size of the auxiliary population, so as to reduce the consumption of computing resoruces in the later stage. Moreover, an independent external archive is set to store feasible solutions found by the auxiliary population, so as to provide high-quality feasible solutions for the main population. The experimental results on 55 benchmark functions show that the proposed algorithm exhibits superior or at least competitive performance compared to other state-of-the-art algorithms.

This paper investigates the H∞ consensus problem of discrete-time Markov jump fractional-order multiagent systems (DTMJFOMASs) under denial-of-service (DoS) attacks. By applying the short-memory principle, we can obtain discrete-time Markov jump multiagent systems with partially unknown probabilities. A novel quantized event-triggering mechanism (QETM), based on a mode-dependent logarithmic quantizer, is proposed to enhance transmission efficiency among multiagents. A distributed controller with quantized output is developed. Sufficient conditions are provided to ensure the system achieves H∞ consensus through Lyapunov stability theory. Finally, two examples are given to verify the effectiveness of the proposed model.

ABSTRACT Salmonella are intracellular bacterial pathogens for which, as with many of the other Enterobacteriaceae, antibiotic resistance is becoming an increasing problem. New antibiotics are being sought as recommended by the World Health Organization and other international institutions. These must be able to penetrate macrophages, and infect the major host cells and the Salmonella-containing vacuole. This study reports screening a small library of Food and Drug Administration (FDA)-approved drugs for their antibacterial effect in macrophages infected with a rapid-multiplying mutant of Salmonella Enteritidis. The most effective drug that was least toxic for macrophages was Nifuratel, a nitrofuran antibiotic already in use for parasitic infections. In mice, it provided 60% protection after oral infection with a lethal S. Enteritidis dose with reduced bacterial numbers in the tissues. It was effective against different serovars, including multidrug-resistant strains of Salmonella Typhimurium, and in macrophages from different host species and against Listeria monocytogenes and Shigella flexneri. It reduced IL-10 and STAT3 production in infected macrophages which should increase the inflammatory response against Salmonella. IMPORTANCE Salmonella can keep long-term persistence in host's macrophages to evade cellular immune defense and antibiotic attack and exit in some condition and reinfect to cause salmonellosis again. In addition to multidrug resistance, this infection circle causes Salmonella clearance difficult in the host, and so there is a great need for new antibacterial agents that reduce intramacrophage Salmonella survival to block endogenous Salmonella reinfection.

Intelligent unmanned system combat is moving from concepts to actual combat applications. The military applications of intelligent unmanned systems are emphasized by focusing on the needs of the combat missions that they will undertake on future battlefields and summarizing the development statuses of land, air and maritime unmanned systems. Aiming at the key problems faced by intelligent unmanned systems and the practical difficulties that will be faced on the battlefield in the future, we analyze the key technologies required for intelligent unmanned systems, including autonomous perception and understanding in complex environments, behavioral decision-making and trajectory planning, autonomous navigation and positioning, multi-scenario autonomous skill learning and intelligent control, unmanned cluster cooperative control, natural human-computer interaction, etc. At the same time, aiming at the problems that will be encountered in the further development of intelligent unmanned systems on the battlefield in the future, we start with individual enhancement and cluster enhancement, and conduct a detailed analysis of the development trends of intelligent unmanned systems.

Introduction: Protein p97 is an extensively investigated AAA ATPase with various cellular activities, including cell cycle control, ubiquitin–proteasome system, autophagy, and NF-κB activation.Method: In this study, we designed, synthesized and evaluated eight novel DBeQanalogs as potential p97 inhibitors in vivo and in vitro.Results: In the p97 ATPase inhibition assay, compounds 6 and 7 showed higher potency than the known p97 inhibitors, DBeQ and CB-5083. Compounds 4-6 dramatically induced G0/G1 phase arrest in the HCT116 cells, and compound 7 arrested the cells in both G0/G1 and S phases. Western blots showed elevated levels of SQSTM/p62, ATF-4, and NF-κB in HCT116 cells with the treatment of compounds 4–7, confirming their role in inhibiting the p97 signaling pathway in cells. In addition, the IC50 of compounds 4–6 against HCT116, RPMI-8226, and s180 proliferation were 0.24–6.9 µM with comparable potency as DBeQ. However, compounds 4–6 displayed low toxicity against the normal human colon cell line. Thus, compounds 6 and 7 were proved to be potential p97 inhibitors with less cytotoxicity. In vivo studies using the s180 xenograft model have demonstrated that compound 6 inhibited tumor growth, led to a significant reduction of p97 concentration in the serum and tumor, and indicated non-toxicity on the body weight and organ-to-brain weight ratios except for the spleen at the dose of 90 μmol/kg/day for 10 days. Furthermore, the present study indicated that compound 6 may not induce s180 mice myelosuppression often observed in the p97 inhibitors.Conclusion: Compound 6 displayed high binding affinity to p97, great p97 ATPase inhibition, selective cytotoxicity, remarkable anti-tumor effect, and upregulated safety, which improved the clinical potential of p97 inhibitors.

The autonomous shape decision-making problem of a morphing aircraft (MA) with a variable wingspan and sweep angle is studied in this paper. Considering the continuity of state space and action space, a more practical autonomous decision-making algorithm framework of MA is designed based on the deep deterministic policy gradient (DDPG) algorithm. Furthermore, the DDPG with a task classifier (DDPGwTC) algorithm is proposed in combination with the long short-term memory (LSTM) network to improve the convergence speed of the algorithm. The simulation results show that the shape decision-making algorithm based on the DDPGwTC enables MA to adopt the optimal morphing strategy in different task environments with higher autonomy and environmental adaptability, which verifies the effectiveness of the proposed algorithm.

Abstract This paper investigates the exponential synchronization for T‐S fuzzy complex networks (TSFCNs) with discontinuous activations and mixed time‐varying delays. Based on the IF‐THEN rules, the T‐S fuzzy model of complex networks has been obtained to approximate non‐linear dynamic systems via interpolating certain local linear system. A fuzzy sampled‐data control strategy is designed to achieve exponential stability for the TSFCN by constructing the bilateral time‐dependent Lyapunov–Krasovskii functional. Furthermore, according to the Filippov discontinuity theory, the exponential synchronization criteria of TSFCN with discontinuous activations and mixed time‐varying delays under the linear matrix inequality constraints are obtained. Finally, two numerical simulations are provided to illustrate the effectiveness and feasibility of the proposed method.

This paper proposes a visual-inertial navigation system (VINS) combined with a dynamic object detection (DOD) algorithm, to improve the localization and state estimate accuracy of autonomous driving vehicles (ADV) in dynamic environments. Firstly, based on the YOLOv5 network, we train the proposed DOD model to detect dynamic objects in the road environment. Secondly, by removing the feature points in the dynamic object regions, we track the remaining feature points to eliminate the influence of the dynamic object. Furthermore, we model the global positioning system (GPS) measurement as a general factor and introduce its residual factor into the cost function to eliminate the cumulative error. Finally, we validate the performance of the proposed method on public datasets and real-world experiments. The results show that the proposed method can effectively eliminate the influence of the dynamic object and eliminate the cumulative error. It provides theoretical guidance for ADV navigation in dynamic or large-scale outdoor environments.

Short-term load (STL) forecasting plays a significant role in modern power system management. Improving the accuracy of STL forecasting is helpful for power enterprises to design reasonable operation planning, thus improving the economic and social benefits of the system. Hybrid methods consisted of single multiplicative neuron (SMN) model, various particle swarm optimization (PSO) algorithms and nonlinear filters are developed in this study. For this purpose, a SMN model based nonlinear state–space model is established using the weights and biases of SMN model and the output of SMN model to present the state vector and the measurement equation at first. Then PSO variants are used to optimize the weights and biases of SMN model with known STL training data. Finally, the nonlinear filters are employed to perform dynamic state estimation for STL forecasting by taking the optimal weights and biases of SMN model during training phase as the initialized value, and the STL forecasting results are represented by the predicted measurement value of nonlinear filters. The effectiveness of the suggested approaches is tested by using the STL datasets from Australian energy market operator, and the experimental results have demonstrated their attractiveness of the proposed methods by compared with other methods.

Computer vision technologies have been widely implemented in the defect detection. However, most of the existing detection methods generally require images with high quality, and they can only process code characters on simple backgrounds with high contrast. In this paper, a defect detection approach based on deep learning has been proposed to efficiently perform defect detection of code characters on complex backgrounds with a high accuracy. Specifically, image processing algorithms and data enhancement techniques were utilized to generate a large number of defect samples to construct a large data set featuring a balanced positive and negative sample ratio. The object detection network called BBE was build based on the core module of EfficientNet. Experimental results show that the mAP of the model and the accuracy reach 0.9961 and 0.9985, respectively. Individual character detection results were screened by setting relevant quality inspection standards to evaluate the overall quality of the code characters, the results of which have verified the effectiveness of the proposed method for industrial production. Its accuracy and speed are high with high robustness and transferability to other similar defect detection tasks. To the best of our knowledge, this report describes the first time that the BBE has been applied to defect inspections for real plastic container industry.

Abstract Accurate defocus blur detection has instigated wide research interest for the last few years. However, it is still a meaningful yet challenging machine vision task, and most methods rely on prior knowledge. Convolutional neural networks have proved the huge success for different tasks within the computer vision, and machine learning flew. A simple yet effective method of defocus blur detection was proposed in this paper, which by applying the deep residual convolutional encoder‐decoder network. The aims of DRDN is to automatically generate pixel‐level predictions for defocus blur images, and reconstruct output detection results of the same size as the input, which by performing several deconvolution operations at multiple scales through the transposed convolution, and skip connection. Afterwards, we used the slide window detection strategy and traversed the input image with a certain stride. Experiments on challenging benchmarks of defocus blur detection show that our algorithm achieved state‐of‐the‐art performance, and powerfully balanced the detection accuracy, and detection time.

This article investigates the adaptive and exponential synchronization problems of fractional-order Takagi-Sugeno (T-S) fuzzy complex networks (FOTSFCN) with coupling time-varying delays and uncertainty. Firstly, the FOTSFCN model is established by embedding T-S fuzzy logic to reduce the complexity and robustness of the FOTSFCN. Secondly, based on the adaptive pinning control strategy, we obtain the asymptotic synchronization and exponential synchronization criteria to achieve synchronous control of FOTSFCN. Furthermore, according to the Lyapunov stability theory, we prove that the FOTSFCN controlled by the proposed controller is robust stable and the effect of coupling time-varying delays and uncertain disturbances on the tracking error can be subsided in view of the linear matrix inequalities (LMIs). Finally, the effectiveness and applicability of the theoretical results are presented through two illustrative examples.

Tracking algorithm based on correlation filter have been extensively investigated due to their powerful performance in benchmark datasets and competitions. However, the periodic assumption has contributed boundary effects and the complex scenarios will give rise to model drift, which have an extremely negative effect on both tracking precision and success rate. To mitigate these challenges, a novel multi-model and multi-expert correlation filter (MMCF) approach is proposed in this paper. The key innovation of the proposed method is to employ multiple models and experts for tracking. Multiple models can excavate diverse a large quantity of feature information, and the target information between different models can complement each other. Multiple experts provide several possible predictions, while an evaluation mechanism selects the most reliable prediction utilizing their assessment scores. To further improve the performance, an adaption strategy is utilized to update the multiple models, which can reduce the weight of the bad samples to prevent model drift. Experiments performed on three recent benchmark datasets OTB50, OTB2013, OTB100, TC-128 and UAV123@10fps, have demonstrated the superiority of our approach in comparison to the state-of-the-art trackers. Our MMCF tracker operates a speed of about 58 frames per second (FPS) running on a single central processing unit(CPU).

The recognition and analysis of tables on printed document images is a popular research field of the pattern recognition and image processing. Existing table recognition methods usually require high degree of regularity, and the robustness still needs significant improvement. This paper focuses on a robust table recognition system that mainly consists of three parts: Image preprocessing, cell location based on contour mutual exclusion, and recognition of printed Chinese characters based on deep learning network. A table recognition app has been developed based on these proposed algorithms, which can transform the captured images to editable text in real time. The effectiveness of the table recognition app has been verified by testing a dataset of 105 images. The corresponding test results show that it could well identify high-quality tables, and the recognition rate of low-quality tables with distortion and blur reaches 81%, which is considerably higher than those of the existing methods. The work in this paper could give insights into the application of the table recognition and analysis algorithms.

The scientific and effective control of steam wetness in steam turbines is of great significance for improving power generation efficiency. Based on the research status of wet steam, the influence of different surface heating intensity in a stator cascade was studied. The distribution of condensation flow parameters in White cascade under 0–700 kW/m2 surface heating intensity is calculated. On this basis, the positive heating intensity region was determined and refined to obtain the best heating condition with higher accuracy. The results show that the condensation is restrained and the outlet wetness is decreased as the blade surface heating intensity increases. The steam wetness and droplet diameter in the flow field can be controlled by adding heating intensity. Additionally, at the initial stage of applying heat to the blade, an increasing enthalpy drop occurs, and the entropy increase experiences a decline, while negative effects rapidly emerge if the heating intensity is too high. The optimum heating intensity is 120 kW/m2. Compared with the 0 kW/m2, the average outlet wetness, total pressure loss coefficient and entropy increase of 120 kW/m2 surface heating intensity can be reduced by 1.1266%, 15.5% and 1.7%, respectively, and the enthalpy drop can be increased by 1.7%.

In this work, a novel robust natural text recognition network (RNTR-Net) is proposed based on a combination of convolutional neural network (CNN) (for feature extraction) and a recurrent neural network (RNN) (for sequence recognition). The pipeline design comprises an improved block of residual learning combined with a general residual block to extract feature maps. Two bidirectional Long Short Term Memory (LSTM) networks are used for sequence recognition, and a transcription layer is used for decoding. The proposed network can handle text images suffering from distortion or other degradations. Compared with previous algorithms, we achieve superior results in general datasets, including the IIIT-5K, Street View Text and ICDAR datasets. Moreover, the performance of the presented network is either highly competitive or even state-of-the-art regarding the highly challenging SVT-Perspective and CUTE80 datasets. We obtain considerable performance of 84.7% and 62.6% on lexicon-free IIIT-5K and CUTE80 datasets, respectively. The experimental results demonstrate the effectiveness of our network.

Unmanned aerial manipulator (UAM) is usually a combination of a quadrotor and a robotic arm that can exert active influences on the environments. The control problems of the UAM system include model uncertainty caused by its center of gravity shift and external disturbances from the environments. To handle these two disturbances, a tracking control strategy is proposed for position and attitude control of the UAM in this paper. In particular, the model of the UAM is established considering with center of gravity shift and disturbances from environments. In the position control, both internal disturbances and external disturbances are compensated by using a sliding mode controller. In the attitude control, an adaptive law is designed to estimate internal disturbances, and a disturbance observer is designed to estimate external disturbances. The stability analysis of the proposed controller is provided and the effectiveness of the proposed method is verified in simulation.

Intelligent connected electric vehicles (EVs) are widely considered as a trend in the global automotive industry to make transportation safer, cleaner and more comfortable. As an important component of intelligent connected EVs, the brake-by-wire (BBW) system is the key to determining the performance of the vehicle and the efficiency of braking energy recovery. BBW systems are showing great promise in the area of vehicle braking due to the increasing requirements on safety and energy efficiency of vehicles. In recent years, BBW systems have undergone great changes in terms of structure and control methods, not only increasing safety and energy recovery efficiency, but also integrating more functions. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys and summarizes the research in literature to bring topical information to the field of intelligent transportation systems. The objective of this study is to present an overview of both the development trend and key technologies of the BBW system for intelligent connected EVs. Firstly, the development, structure and core components of BBW system are described. Secondly, the control method and control strategy of BBW system are analyzed. As much as the system shows great promise, the problems existing on its current technology should be addressed as well. Finally, some important countermeasures are proposed to solve the existing technical problems of the BBW system.

Target tracking plays an important role in the construction, operation, and maintenance of the space station by the robot, which puts forward high requirements on the accuracy of target tracking. However, the special space environment may cause complex non-Gaussian noise in target tracking data. And the performance of traditional Kalman Filter will deteriorate seriously when the error signals are non-Gaussian, which may lead to mission failure. In the paper, a novel Kalman Filter algorithm with Generalized Maximum Correntropy Criterion (GMCKF) is proposed to improve the tracking accuracy with non-Gaussian noise. The GMCKF algorithm, which replaces the default Gaussian kernel with the generalized Gaussian density function as kernel, can adapt to multi-type non-Gaussian noises and evaluate the noise accurately. A parameter automatic selection algorithm is proposed to determine the shape parameter of GMCKF algorithm, which helps the GMCKF algorithm achieve better performance for complex non-Gaussian noise. The performance of the proposed algorithm has been evaluated by simulations and the ground experiments. Then, the algorithm has been applied in the maintenance experiments in TianGong-2 space laboratory of China. The results validated the feasibility of the proposed method with the target tracking precision improved significantly in complex non-Gaussian environment.

Under some unexpected conditions, drive rods and control-rod assemblies may not be disconnected. If this situation is not detected, the control rod will be lifted out of the reactor core together with the upper reactor internals. This situation will seriously affect the follow-up work and reduce the economy and safety protection of the nuclear power plant. To ensure safety, the tripping status must be checked after tripping. Follow-up work can be carried out after checking and confirming that all drive rods are in the tripping status. There are many problems for traditional inspection methods, such as misjudgment, low accuracy, and labor consumption. This paper proposes a visual inspection system for the uncoupling state of the control-rod drive rod of the nuclear reactor. The proposed method is based on the fitting model of the ellipse parameter of the drive-rod head and the height of the drive rod. The ellipse of the drive-rod head is firstly accurately detected. Then, a mathematical model between the ellipse parameter and the height of the drive rod is established. The measurement error caused by the swing of the head of the drive rod is eliminated. The accurate measurement of the height difference before and after the tripping of the drive rod is computed. Finally, the status of the uncoupling of the drive rod is judged according to the difference. Many experiments are carried out with our developed system. The experimental results show that the proposed system realizes remote operation, ensures the quality of trip-status inspection, improves work efficiency, and reduces the workload of staff.

Citrus harvesting is time-intensive and labor-intensive, relying mainly on manual harvesting. The automatic harvesting of fruit and vegetable crops can not only reduce the physical labor of fruit farmers in the harsh field environment but also greatly improve the harvesting efficiency. Based on the principle of manual citrus picking, an end-effector with three-finger grasping is designed in this study. First, the structure of the end-effector was designed to achieve the function of stable grasping and effective cutting of citrus fruits, and then the working process and key parameters of the end-effector were explained in detail. Finally, a picking test was conducted without considering robot vision. The test results show that the end-effector has a picking success rate of 95.23% for citrus with a diameter of 30–100 mm and an average picking time of 4.65 s for a single fruit. This end-effector can realize the picking function for citrus of different sizes and shapes and has the advantages of high adaptability, stable gripping and no damage to the fruit.

Salmonella Enteritidis (SE) can spread from the intestines to cause systemic infection, mainly involving macrophages. Intramacrophage Salmonella exits and reinfects neighboring cells, leading to severe disease. Salmonella genes involved in exiting from macrophages are not well understood or fully identified. A focA::Tn5 mutant was identified by an in vitro assay, with increased ability to exit from macrophages. A defined SEΔfocA mutant and its complemented derivative strain, SEΔfocA::focA, were constructed to confirm this phenotype. Although the lethal ability of focA mutants was similar to that of the parental SE in mice, it was isolated earlier from the liver and spleen than the parental SE. focA mutants induced higher levels of proinflammatory IL-12 and TNF-α compared with the parental SE and SEΔfocA::focA. focA mutants showed higher cytotoxicity and lower formate concentrations than SE and SEΔfocA::focA, whereas there was no change in pyroptosis, apoptosis and flagella formation ability. These current data suggest that the focA gene plays an important role in regulating intramacrophage Salmonella exiting and extraintestinal spread in mice, although the specific mechanism requires further in-depth studies.

Parcel detection is crucial to achieving automatic sorting in intelligent logistics systems. Most parcels in logistics centers are currently detected manually, imposing low efficiency and high error rate, severely limiting logistics transportation efficiency. Therefore, there is an urgent need for automated parcel detection. However, parcels in logistics centers have challenges such as dense stacking, occlusion and background interference, making it difficult for existing methods to detect parcels accurately. To address the above problem, we developed an improved Faster R-CNN-based parcel detection model spurred by current deep-learning-based object detection trends. The proposed method first solves the false detection problem due to parcel mutual occlusion by augmenting Faster R-CNN with an edge detection branch and adding object edge loss to the loss function. Furthermore, the self-attention ROI Align module is proposed to address the problem of feature misalignment caused by the quantization rounding operation in the ROI Pooling module. The module uses an attention mechanism to filter and enhance the features and uses bilinear interpolation to calculate the feature pixel values, improving detection accuracy. The implementation of the proposed method was validated using parcel images collected in the field and the public dataset SKU110K and compared with four existing parcel detection methods. The results show that our method’s Recall, Precision, map@0.5 and Fps are 96.89%, 98.76%, 98.42% and 22.83%, respectively, which significantly improves the parcel detection accuracy.

This paper investigates the substation inspection problems of multimobile robots for large power stations in smart microgrids. Most multirobot inspection robots generally face the challenge of path planning, while the current widely used biological excitation neural network (BENN) methods often have the defect of the neuronal active field near boundaries and obstacles. To end this, we propose an improved biological excitation neural network (IBENN) method for path planning based on a detailed architecture and ontology framework, through which the single-point inspection, multipoint inspection, and full-area inspection tasks of substations in smart microgrids can be well completed. Simulation results show that the designed IBENN-based multirobot collaboration inspection (MRCI) system can effectively shorten the path as well as the number of turns and then show better performance than most existing results when implementing various substation inspection tasks.

This paper is concerned with the adaptive event-triggered finite-time pinning synchronization control problem for T-S fuzzy discrete complex networks (TSFDCNs) with time-varying delays. In order to accurately describe discrete dynamical behaviors, we build a general model of discrete complex networks via T-S fuzzy rules, which extends a continuous-time model in existing results. Based on an adaptive threshold and measurement errors, a discrete adaptive event-triggered approach (AETA) is introduced to govern signal transmission. With the hope of improving the resource utilization and reducing the update frequency, an event-based fuzzy pinning feedback control strategy is designed to control a small fraction of network nodes. Furthermore, by new Lyapunov–Krasovskii functionals and the finite-time analysis method, sufficient criteria are provided to guarantee the finite-time bounded stability of the closed-loop error system. Under an optimization condition and linear matrix inequality (LMI) constraints, the desired controller parameters with respect to minimum finite time are derived. Finally, several numerical examples are conducted to show the effectiveness of obtained theoretical results. For the same system, the average triggering rate of AETA is significantly lower than existing event-triggered mechanisms and the convergence rate of synchronization errors is also superior to other control strategies.

Salmonella enteritidis and Salmonella pullorum belonging to Group O9Salmonella are major causative agents of infectious diseases in chicken. O9 antigen as a part of lipopolysaccharide (LPS) is a predominant detected target for Salmonella infection. To identify the infection, an anti-O9 monoclonal antibody (McAb)-based direct competitive enzyme-linked assay (O9 Dc-ELISA) was developed after constraints were optimized; the establishment and application of O9 Dc-ELISA, compared to two commercial kits and plate agglutination test (PAT), showed that O9 Dc-ELISA could screen out more positive samples than the PAT method could and produce the same agreement rates with commercial kits in terms of sensitivity in addition to strong specificity to clinical serum samples.

Food safety remains a matter of great concern in most countries and the composition in food is crucial to food safety. It is very important to make sense of the quality and change of food ingredients. In this research, the change of γ-butyrolactone (GBL), one kind of food additive in beverage, had been evaluated by nuclear magnetic resonance (NMR) technique. The 1H-NMR results of seven beverages covering various kinds with spiked GBL indicated that GBL was transformed into dangerous γ-hydroxybutyric acid (GHB) in six popular beverages under certain conditions which could happen during transportation and storage. Further results of quantitative 13C-NMR showed that pH and temperature were two key factors affecting the transforming degree of GBL to GHB. Lower pH and higher temperature will increase the degree of transformation. GHB was a neurotransmitter on the chemical control list, which was absolutely forbidden to be added to food. This nondestructive NMR detecting technology which did not need the complex pretreatment method to directly determine food ingredients can be useful for identifying the risk of food safety from the changes of food composition during transport and storage.

γ-Hydroxybutyrate (GHB) is a neurotransmitter, which exhibits a strong central nervous system depressant effect. The abuse of GHB or its precursor substances (γ-butyrolactone (GBL) and 1,4-butanediol (1,4-BD)) may cause serious problems. This study developed a fast and effective UHPLC-MS/MS method for the simultaneous quantification of GHB, GBL, and 1,4-BD in four popular beverages, including carbonated drinks, tea, apple cider vinegar, and coffee. The established method overcomes the influence of the in-source collision-induced dissociation of unstable compounds during quantification. The limits of detection were 0.2 μg/mL for GBL and 0.5 μg/mL for GHB and 1,4-BD with excellent linearity in the range of 0.2–50 μg/mL. The recoveries of the three compounds at three spiked levels (2.5, 5.0, and 10.0 μg/mL) in the four kinds of beverages studied were between 90 and 110%, while the relative standard deviations (RSDs) were all

This paper proposes an idea for modeling and control of a V2G charging station (CS) for electric vehicles (EVs) by using synchronverter technology. First, the architecture of the CS is introduced. Then, a T-S fuzzy controller is designed to decide the reference real power of the synchronverter by considering the grid frequency. Due to the inner frequency- and voltage-drooping mechanisms of the synchronverter, the input and output real and reactive power of the CS will be automatically adjusted on the basis of the reference value according to the degree of deviation from the nominal value of the grid frequency and voltage. To ensure the safety of this operation, an adaptive frequency droop coefficient mechanism is designed to adapt the change of the total energy storage of a CS unit by changing the slope of the P-f control characteristic of the synchronverter. The performance of the CS with the proposed control strategy is investigated with EVs of different battery states, different users' sets and under different grid status. Simulation results demonstrate that the proposed strategy can not only effectively perform controlled charging/discharging of each single electric vehicle inside the CS, but also improve the performance of the electricity grid in terms of efficiency, stability and reliability.

Here, a robust adaptive trajectory tracking algorithm is proposed for free-form surface grinding robot (FSGR) in metal surface production line. Machine-learning method is used for robot dynamic approximation which is hard to obtain directly. Adaptive law is proposed to adjust the neural network parameters. Sliding-mode control is employed to deal with the disturbance, joint friction, and approximation error of the adaptive machine learning. The scheme based on machine-learning feedforward compensation can significantly reduce the chattering of sliding mode. The performance of the proposed control scheme is illustrated through simulations.

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l(1) in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness.

Salmonella is an important pathogenic microorganism that can infect humans and animals and has been studied globally as a model microorganism for its pathogenesis. The SpiC protein of T3SS2 is a significant factor that has been studied for almost 20 years, but to date, the function/effect of SpiC in the pathogenesis of Salmonella has not been completely understood. There is controversy over the functions of SpiC protein in the literature. Thus, an overview of the literature on SpiC protein is provided here which highlights expression features of SpiC protein and its various functions and effect.

In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly.

Inspired by the human 3D visual perception system, we present an obstacle detection and classification method based on the use of Time-of-Flight (ToF) cameras for robotic navigation in unstructured environments. The ToF camera provides 3D sensing by capturing an image along with per-pixel 3D space information. Based on this valuable feature and human knowledge of navigation, the proposed method first removes irrelevant regions which do not affect robot’s movement from the scene. In the second step, regions of interest are detected and clustered as possible obstacles using both 3D information and intensity image obtained by the ToF camera. Consequently, a multiple relevance vector machine (RVM) classifier is designed to classify obstacles into four possible classes based on the terrain traversability and geometrical features of the obstacles. Finally, experimental results in various unstructured environments are presented to verify the robustness and performance of the proposed approach. We have found that, compared with the existing obstacle recognition methods, the new approach is more accurate and efficient.

The actuator dead zone of free-form surface grinding robots (FFSGRs) is very common in the grinding process and has a great impact on the grinding quality of a workpiece. In this paper, an improved trajectory tracking algorithm for an FFSGR with an asymmetric actuator dead zone was proposed with consideration of friction forces, model uncertainties, and external disturbances. The presented control algorithm was based on the machine learning and sliding mode control (SMC) methods. The control compensator used neural networks to estimate the actuator’s dead zone and eliminate its effects. The robust SMC compensator acted as an auxiliary controller to guarantee the system’s stability and robustness under circumstances with model uncertainties, approximation errors, and friction forces. The stability of the closed-loop system and the asymptotic convergence of tracking errors were evaluated using Lyapunov theory. The simulation results showed that the dead zone’s non-linearity can be estimated correctly, and satisfactory trajectory tracking performance can be obtained in this way, since the influences of the actuator’s dead zone were eliminated. The convergence time of the system was reduced from 1.1 to 0.8 s, and the maximum steady-state error was reduced from 0.06 to 0.015 rad. In the grinding experiment, the joint steady-state error decreased by 21%, which proves the feasibility and effectiveness of the proposed control method.