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Nuclear energy is used more and more widely as a clean energy source, but nuclear energy facilities are risky, and when a nuclear leak occurs, it is necessary to detect equipment in a nuclear radiation environment. In the nuclear radiation environment, due to the impact of high-energy particles on the camera sensor, the collected image contains a lot of radiation noise, which greatly reduces the visual perception of the image. Aiming at the problem that radiation noise reduces image quality, a radiation image compound filtering algorithm combining median filtering and multi-frame average filtering is proposed based on radiation noise characteristics. Compared with several common filtering algorithms, the radiation noise image is filtered under the same radiation dose and achieve the highest Peak Signal Noise Rate (PSNR) and Structural Similarity (SSIM), and compared with the multi-frame average filtering method, the number of image frames required by the algorithm in this paper is greatly reduced. Experimental results show that the algorithm can effectively eliminate radiation noise and is more suitable for image filtering in radiation environment.

Chronic gastritis mainly includes chronic non-atrophic gastritis (CNAG), autoimmune gastritis (AIG), and type B gastritis. Early detection of AIG and type B gastritis will help identify high-risk groups for gastric cancer and prevent the development of irreversible peripheral neuropathy. We aim to develop a computer-assisted diagnosis (CADx) system by presenting a novel Convolution and Relative Self-Attention Parallel Network (CRSAPNet). We collected 3576 endoscopic images of chronic gastritis from 205 patients. MBConv and Relative Self-Attention Parallel Block (CRSAPB) was proposed to concatenate local features (such as mucosal folds and mucosal vessels extracted by MBConv) and global features (such as atrophied area extracted by Relative Self-Attention) in parallel in the last two stages of CRSAPNet. The CADx system distinguished AIG from type B gastritis and CNAG. The CRSAPNet achieved the highest overall accuracy of 95.44% (94.65% precision, 93.51% recall, 94.08% F1-score for AIG) with the fewest parameters. We used Grad-CAM to visually analyze the heat maps. We only replaced the original blocks of the third stage of ResNet50 and ConvNeXt-T with CRSAPB, resulting in an overall accuracy improvement of 0.37%, and 4.19%, respectively. Furthermore, the CADx system classified the three types of chronic gastritis for the first time. The CRSAPNet achieved an overall accuracy of 91.62%, and the overall accuracies in the location of the gastric body and gastric fundus were 93.43% and 92.51%, respectively. A new state-of-the-art deep learning network is introduced to distinguish AIG from type B gastritis and CNAG, and a classification for three types of chronic gastritis is reported for the first time.

An underwater magnetic induction positioning and communication system and an energy-efficient distributed control algorithm for underwater base stations are proposed in this paper. The positioning and communication system consists of a number of base stations and an Autonomous Underwater Vehicle (AUV) equipped with three-axis source coils respectively. The AUV receives this signal and based on its amplitude and phase information is able to locate the AUV and communicate with the base station. Due to the short positioning distance of magnetic induction positioning technology, a large number of base stations need to be installed underwater, which puts high demands on the control of the base stations. In this paper, an energy-efficient distributed control algorithm for underwater base stations is proposed to enable the AUV to meet the operational requirements while minimizing the total energy consumption of the base station. According to the simulation results, the design solves the problem that traditional underwater positioning and communication equipment cannot work stably for long periods of time in a high radiated environment, with a positioning error of no more than 10 cm within a preset operating range, and the algorithm proposed in this paper is able to reduce energy wastage by about 20%.

Aiming at the force-tracking error phenomenon of impedance control in an unknown surface environment, an adaptive variable-damping impedance control algorithm is proposed, and the stability and convergence of the algorithm are deduced. An adaptive-law selection rule is proposed to aim at the phenomenon that the adaptive parameters are too large to cause the system oscillation and overshoot and too small to cause the adaptive line variation in the curved surface environment. Finally, experiments conclude that the impedance control based on the adaptive variable-damping algorithm has a better force-tracking effect than the ordinary impedance control in the curved surface environment where the contact surface between the end-effector of the manipulator and the atmosphere is unknown.

In previous studies, the keypoints and their feature descriptors of a hyperspectral image have not received enough attention. Compared with RGB images, the rich feature information of a hyperspectral image can not only constrain the spatial positions of the keypoints, but also provide rich spectral features for their descriptors. In this paper, we propose a deep neural network method called Hyperspectral SuperPoint Network (HSSPN) to detect the keypoints from a hyperspectral image and generate the corresponding feature descriptors. The HSSPN method contains a Shared Encoder (which is responsible for fully extracting the features of the spatial and spectral dimensions) and two decoders: the Keypoint Decoder accurately detects the keypoints according to the feature map outputted by the Shared Encoder, while the Descriptor Decoder is responsible for generating stable feature descriptors. We collected some hyperspectral images with an electronically controlled turntable, and then annotated them with a Hyperspectral Homographic Adaptation (HHA) network. The trained HSSPN gave an excellent keypoint matching performance on the test set. Hyperspectral 3D reconstruction can obtain information in a 3D space and a spectral space at the same time. Most of the previous reconstruction methods are separated for the treatments of 3D reconstruction and hyperspectral imaging, which require complicated registrations. In this study, we propose a SfM-based hyperspectral 3D reconstruction method, in which we first collect hyperspectral images from multiple viewing angles, and then apply the HSSPN method to detect the keypoints and calculate their feature descriptors. These keypoints and feature descriptors are then used to reconstruct each spectral image of the target with the SfM method. The 3D reconstruction models at these wavelengths are simply superimposed to obtain a hyperspectral 3D reconstruction model. Compared with conventional SIFT-based SfM methods, our method achieves better results in the reconstruction accuracy.

In this paper, a parallel Image-based visual servoing/force controller is developed in order to solve the interaction problem between the collaborative robot and the environment so that the robot can track the position trajectory and the desired force at the same time. This control methodology is based on the image-based visual servoing (IBVS) dynamic computed torque control and couples the force control feedback in parallel. Simulations are performed on a collaborative Delta robot and two types of image features are tested to determine which one is better for this parallel IBVS/force controller. The results show the efficiency of this controller.

An autonomous underwater vehicle (AUV) can only carry limited energy for improved flexibility, but this brings the problem of working endurance. The acquisition of environmental energy by an underwater robot is a positive way to supplement energy and increase endurance. However, the instability and difference in power output capacity of different environmental energies will lead to low utilization of environmental energy. In this study, a multi-energy acquisition model is established to manage the AUV’s energy, which includes a heart rate sampling system, a mixed-energy circuit topology, and a maximum power output algorithm of environmental energy based on power trajectory tracking. The simulating results show that the power output control strategy can effectively improve the multi-energy acquisition and utilization efficiency of underwater vehicles, which has a positive significance and could improve endurance time.

Osteoporosis is a major health problem in the elderly. Almost every bone can fracture due to the increased bone fragility in osteoporosis, posing a major challenge to public health. 12-Deoxyphorbol-13-hexadecanoate (DHD), one of the main bioactive components of Stellera chamaejasme L. (Lang Du), is considered to have antitumor, antibacterial, and antifungal properties. However, the role of DHD in osteoporosis is still elusive. In this study, we demonstrated for the first time that DHD inhibits the receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenesis and bone resorption in a dose- and time-dependent manner without exhibiting cytotoxicity in vitro. Mechanistically, we found that DHD not only represses the expression of osteoclasts marker genes by suppressing RANKL-induced mitogen-activated protein kinase (MAPK) and calcium signaling pathways but also scavenges reactive oxygen species (ROS) through enhancing cytoprotective enzymes expression. Furthermore, DHD inhibits the activation of nuclear factor of activated T cells 1 (NFATc1) during RANKL-induced osteoclasts formation. Preclinical studies revealed that DHD protects against bone loss in ovariectomy (OVX) mice. In sum, our data confirmed that DHD could potentially inhibit osteoclastogenesis by abrogating RANKL-induced MAPK, calcium, and NFATc1 signaling pathways and promoting the expression of ROS scavenging enzymes, thereby preventing OVX-induced bone loss. Thus, DHD may act as a novel therapeutic agent to manage osteoporosis.

This paper proposes a novel adaptive dynamic programming (ADP) approach to address the optimal consensus control problem for discrete-time multiagent systems (MASs). Compared with the traditional optimal control algorithms for MASs, the proposed algorithm is designed on the basis of the event-triggered scheme which can save the communication and computation resources. First, the consensus tracking problem is transferred into the input-state stable (ISS) problem. Based on this, the event-triggered condition for each agent is designed and the event-triggered ADP is presented. Second, neural networks are introduced to simplify the application of the proposed algorithm. Third, the stability analysis of the MASs under the event-triggered conditions is provided and the estimate errors of the neural networks’ weights are also proved to be ultimately uniformly bounded. Finally, the simulation results demonstrate the effectiveness of the event-triggered ADP consensus control method.

This approach has been derived mainly to improve quality and efficiency of global path planning for a mobile robot with unknown static obstacle avoidance features in grid-based environment. The quality of the global path in terms of smoothness, path consistency and safety can affect the autonomous behavior of a robot. In this paper, the efficiency of Ant Colony Optimization (ACO) algorithm has improved with additional assistance of A* Multi-Directional algorithm. In the first part, A* Multi-directional algorithm starts to search in map and stores the best nodes area between start and destination with optimal heuristic value and that area of nodes has been chosen for path search by ACO to avoid blind search at initial iterations. The path obtained in grid-based environment consist of points in Cartesian coordinates connected through line segments with sharp bends. Therefore, Markov Decision Process (MDP) trajectory evaluation model is introduced with a novel reward policy to filter and reduce the sharpness in global path generated in grid environment. With arc-length parameterization, a curvilinear smooth route has been generated among filtered waypoints and produces consistency and smoothness in the global path. To achieve a comfort drive and safety for robot, lateral and longitudinal control has been utilized to form a set of optimal trajectories along the reference route, as well as, minimizing total cost. The total cost includes curvature, lateral and longitudinal coordinates constraints. Additionally, for collision detection, at every step the set of optimal local trajectories have been checked for any unexpected obstacle. The results have been verified through simulations in MATLAB compared with previous global path planning algorithms to differentiate the efficiency and quality of derived approach in different constraint environments.

Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach to identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method.

The synchronous research for fuzzy coupled neural networks (FCNNs) is studied by a new strategy of column pinning controllers. In this paper, the Lyapunov Krasovskii functional (LKF) is taken as an important element for the pinning control laws. The networks are interconnected by coupling gains that define a physical interaction graph. Different from the preset technique in traditional intermittent control, a novel additional communication control graphs of pinning control law are introduced, which has not been investigated before. The proposed control laws can achieve the control objectives of being introduced as an array of vector with Kronecker produce operation. Under the proposed framework of intermittent control, numerical simulations via MATLAB are used to confirm the availability of the suggested control laws.

Herein, we report the γ-ray ionizing radiation response of a commercial monolithic active-pixel sensor (MAPS) camera under strong-dose-rate irradiation with an online detection and monitoring system for strong radiation conditions. We present the first results of the distribution of three types of MAPS camera and establish a linear relationship between the average response signal and radiation dose rate in the strong-dose-rate range. There is an obvious response signal in the video frames when the camera module parameters are set to automatic, but the linear response is very poor. However, the fixed image parameters are not good at adapting to the changes of the environment and affect the quality of the video frames. A dual module online radiation detection and monitoring probe was made to carry out effective video monitoring and radiation detection at the same time. The measurement results show that the dose rate detection error is less than 5% with a dose rate in the range of 60 to 425 Gy/h, and the visible light image does not have obvious distortion, deformation, or color shift due to the interference of the radiation response event and radiation damage. Hence, the system test results show that it can be used for online detection and monitoring in a strong radiation environment.

Designing a robot with the best accuracy is always an attractive research direction in the robotics community. In order to create a Gough–Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough–Stewart platform: leg-direction-based visual servoing, line-based visual servoing, and image moment visual servoing. Depending on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough–Stewart platform for each type of controller. Then, we perform co-simulations on the three optimized Gough–Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps get both the optimum design parameters of the robot and the performance of the controller {robot + dedicated controller}.

Background/Aims: Elabela (ELA) or Toddler is a recently identified hormone that plays a crucial role in embryonic development through the activation of the apelin receptor (APJ). Our previous study indicated that ELA is highly expressed in adult kidney and the ELA receptor signaling pathway is functional in mammalian systems. Whereas nothing is yet known regarding ELA and diabetic kidney disease (DKD). Here, we evaluated the relationship between serum ELA levels and albuminuria in patients with type 2 diabetes (T2D). Methods: An observational study involving 80 patients divided into groups according to their baseline urinary albumin/creatinine ratio (ACR): Group 1 (ACR ≤ 29 mg/g), Group 2 (ACR = 30–299 mg/g), Group 3 (ACR ≥ 300 mg/g with normal serum creatinine), and Group 4 (ACR ≥ 300 mg/g with increased serum creatinine). The demographic, clinical, and biochemical variables including serum ELA were obtained or measured through disease history, physical examination, or laboratory evidence. Results: The results showed that the serum ELA levels decreased gradually with the deterioration of DKD from the stages of normal albuminuria, microalbuminuria, macroalbuminuria, to macroalbuminuria and elevated serum creatinine. In addition, ELA had a significantly negative correlation with ACR (r = -0.561, P < 0.001), retinopathy (r = -0.424, P < 0.001), serum creatinine (r = -0.269, P = 0.016), SBP (r = -0.249, P = 0.026), DBP (r = -0.261, P = 0.020) and a positive correlation with eGFR (r = 0.318, P = 0.004). Furthermore, stepwise multiple linear regression analysis showed that ACR, retinopathy, and LDL-C were considered the most relevant variables to ELA, and ELA, retinopathy, eGFR, and age were important predictors for ACR (t = -4.546, P = 0.000). Conclusions: To our knowledge, this is the first study to explore the clinical relationship between ELA levels and CKD. Decreased serum ELA levels might be a significant clinical predictor in patients with DKD or even as a promising agent for treating CKD patients.

This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster.

DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks.

To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes a visual saliency detection method for hypertemperature in three-dimensional space through dual-source images. The key novelty of this method is that it can achieve accurate salient object detection without relying on high-performance hardware equipment. First, the redundant point clouds are removed through adaptive sampling to reduce the computational memory. Second, the original images are merged with infrared images and the dense point clouds are surface-mapped to visually display the temperature of the reconstructed surface and use infrared imaging characteristics to detect the plane coordinates of temperature anomalies. Finally, transformation mapping is coordinated according to the pose relationship to obtain the spatial position. Experimental results show that this method not only displays the temperature of the device directly but also accurately obtains the spatial coordinates of the heat source without relying on a high-performance computing platform.

The pinning synchronous problem for complex networks with interval delays is studied in this paper. First, by using an inequality which is introduced from Newton-Leibniz formula, a new synchronization criterion is derived. Second, combining Finsler’s Lemma with homogenous matrix, convergent linear matrix inequality (LMI) relaxations for synchronization analysis are proposed with matrix-valued coefficients. Third, a new variable subintervals method is applied to expand the obtained results. Different from previous results, the interval delays are divided into some subdelays, which can introduce more free weighting matrices. Fourth, the results are shown as LMI, which can be easily analyzed or tested. Finally, the stability of the networks is proved via Lyapunov’s stability theorem, and the simulation of the trajectory claims the practicality of the proposed pinning control.

A considerable number of studies have been conducted to study the microbial profiles in inflammatory conditions. A common phenomenon in inflammatory bowel disease (IBD) is the reduction of the diversity of microbiota, which demonstrates that microbial diversity negatively correlates with disease severity in IBD. Increased microbial diversity is known to occur in disease remission. Species diversity plays an important role in maintaining the stability of the intestinal ecosystem as well as normal ecological function. A reduction in microbial diversity corresponds to a decrease in the stability of the ecosystem and can impair ecological function. Fecal microbiota transplantation (FMT), probiotics, and prebiotics, which aim to modulate the microbiota and restore its normal diversity, have been shown to be clinically efficacious. In this study, we hypothesized that a reduction in microbial diversity could play a role in the development of IBD.

To improve the work efficiency of on-site inspection personnel in diagnosing faults of power transmission lines, in this paper we propose an infrared image segmentation method based on DeepLabV3+ for identifying key components of transmission line. We collected 556 infrared images of transmission lines in our own power supply system, and expanded the original data by data augmentation method. Based on the comparison of the DeepLabV3+ model with three different backbone networks, MobileNetV2 with the best performance is selected as the main backbone network. Compared with FCN, U-Net, and SegNet, the test results show that DeepLabV3+ using MobileNetV2 (compared with ResNet50 and Xception) can segment the five types of key components in power transmission lines from infrared images more accurately and faster. The MIoU on the test set is 0.8624, which is better than the performance of FCN, U-Net, and SegNet. This lays a foundation for improving the work efficiency of on-site inspection personnel and improving the continuous power supply capacity, stability, and safe operation level of the power grid. [ABSTRACT FROM AUTHOR]

Methods of manual analysis for infrared image and temperature detection of power transmission and transformation equipment typically have problems, such as low efficiency, strong subjectivity, easy to make mistakes and poor real-time feedback. In this paper, a high temperature anomaly detection method based on SegFormer in infrared image of power transmission and transformation equipment is proposed. Many infrared images of power transmission and transformation equipment are collected and preprocessed, and the temperature information of each infrared image is read out using the DJI sdk tool to construct the temperature data matrix. In the segmentation stage, the SegFormer network is used to segment the key parts of the power transmission and transformation equipment to obtain the mask for detection. The maximum values of the temperature data in the mask area are calculated, and the high temperature anomaly detection at the key parts of the power transmission and transformation equipment is realized. The test results on the test set show that the overall performance of the method is the highest as compared to other methods such as FCN, UNet, SegNet, DeepLabV3+, and an automatic temperature recognition can be realized, which has important practical value for the detection of high temperature anomaly at the key parts of power transmission and transformation equipment. [ABSTRACT FROM AUTHOR]