Consensus of a novel heuristic nonlinear multi-agent system in DOS attack network environment via saturation impulse control mechanism.

This paper mainly studies the consensus control strategy for a novel heuristic nonlinear multi-agent system. Compared with most existing related researches, firstly, the novel heuristic nonlinear multi-agent system has the ability to construct its communication network topology heuristically, and can withstand long-term DOS(Denial of Service) attacks, with the advantages of high practicality and security. Secondly, in order to control the multi-agent system, a control protocol based on both saturation effect and impulse control mechanism is studied, which has the advantages of high efficiency, low cost and wide applicability. Thirdly, for the multi-agent system, its dynamic model is constructed and analyzed by Lyapunov stability theory and matrix measure theory, and some sufficient conditions for achieving consensus are obtained. Finally, through two simulation experiments and some corresponding comparative analysis, the correctness, efficiency, and superiority of the theories proposed in this paper were verified.

Mean square exponential stabilization analysis of stochastic neural networks with saturated impulsive input.

The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid and the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results.

Air embolism after CT-guided localization of pulmonary ground-glass nodules.

OBJECTIVE: To investigate the incidence of air embolism (AE) related to CT-guided localization of pulmonary ground-glass nodules (GGNs) prior to video-assisted thoracoscopic surgery (VATS). METHODS: The data of all patients who received CT-guided localization of GGNs before VATS from May 2020 to October 2021 were retrospectively analyzed. RESULTS: A total of 1395 consecutive patients with 1553 GGNs were enrolled. AEs occurred in seven patients (0.5%). In four of the seven patients with AE, the embolism was detected before the patients left the CT table and emergency treatments were carried out. Among them, one patient had chest tightness and unilateral limb dyskinesia, one patient had convulsions and transient loss of consciousness, and two patients had no definite clinical symptoms. After a short-term high-flow oxygen inhalation, the clinical symptoms of two patients with symptomatic AE disappeared and two patients with asymptomatic AE did not show any symptoms. In the remaining three patients with AE, the embolism were detected retrospectively when evaluating the images in the PACS for this study. Fortunately, these three patients never developed clinical symptoms related to AE. All seven patients with AE underwent VATS on the day of localization and all GGNs were successfully removed under the guidance of markers. CONCLUSION: The incidence of AE related to CT-guided localization of GGNs was 0.5%, which was significantly higher than expected. Post-localization whole thoracic CT should be performed and observed carefully so as to avoid missed AE and delayed treatment. ADVANCES IN KNOWLEDGE: The incidence of AE related to CT-guided localization of GGNs was 0.5%. In order to timely detect AE, whole thoracic CT scan rather than local CT in the lesion area should be performed after localization. A small amount of AE may be missed if the post- localization CT images are not carefully observed.

Nonconvex low-rank tensor approximation with graph and consistent regularizations for multi-view subspace learning.

Multi-view clustering is widely used to improve clustering performance. Recently, the subspace clustering tensor learning method based on Markov chain is a crucial branch of multi-view clustering. Tensor learning is commonly used to apply tensor low-rank approximation to represent the relationships between data samples. However, most of the current tensor learning methods have the following shortcomings: the information of the local graph is not taken into account, the relationships between different views are not shown, and the existing tensor low-rank representation takes a biased tensor rank function for estimation. Therefore, a nonconvex low-rank tensor approximation with graph and consistent regularizations (NLRTGC) model is proposed for multi-view subspace learning. NLRTGC retains the local manifold information through graph regularization, and adopts a consistent regularization between multi-views to keep the diagonal block structure of representation matrices. Furthermore, a nonnegative nonconvex low-rank tensor kernel function is used to replace the existing classical tensor nuclear norm via tensor-singular value decomposition (t-SVD), so as to reduce the deviation from rank. Then, an alternating direction method of multipliers (ADMM) which makes the objective function monotonically non-increasing is proposed to solve NLRTGC. Finally, the effectiveness and superiority of the NLRTGC are shown through abundant comparative experiments with various state-of-the-art algorithms on noisy datasets and real world datasets.

Synchronization of Uncertain Coupled Neural Networks With Time-Varying Delay of Unknown Bound via Distributed Delayed Impulsive Control.

This article investigates the issue of synchronization for a type of uncertain coupled neural networks (CNNs) involving time-varying delay with unmeasured or unknown bound by delayed impulsive control with distributed delay. A new Halanay-like delayed differential inequality is presented, and both cases of impulsive control and impulsive perturbation are well-considered. Stemmed from this new inequality and techniques of linear matrix inequalities (LMIs), some sufficient criteria are obtained to achieve both dynamically and statically global µ -synchronization of the delayed CNNs, and a distributed-delay-dependent impulsive controller is designed. A numerical simulation is provided to demonstrate the validity of the obtained theoretical results.

A Proximal Neurodynamic Network With Fixed-Time Convergence for Equilibrium Problems and Its Applications.

This article proposes a novel fixed-time converging proximal neurodynamic network (FXPNN) via a proximal operator to deal with equilibrium problems (EPs). A distinctive feature of the proposed FXPNN is its better transient performance in comparison to most existing proximal neurodynamic networks. It is shown that the FXPNN converges to the solution of the corresponding EP in fixed-time under some mild conditions. It is also shown that the settling time of the FXPNN is independent of initial conditions and the fixed-time interval can be prescribed, unlike existing results with asymptotical or exponential convergence. Moreover, the proposed FXPNN is applied to solve composition optimization problems (COPs), l1 -regularized least-squares problems, mixed variational inequalities (MVIs), and variational inequalities (VIs). It is further shown, in the case of solving COPs, that the fixed-time convergence can be established via the Polyak-Lojasiewicz condition, which is a relaxation of the more demanding convexity condition. Finally, numerical examples are presented to validate the effectiveness and advantages of the proposed neurodynamic network.

Output Feedback-Based Consensus for Nonlinear Multiagent Systems: The Event-Triggered Communication Strategy.

The current investigation explores the leader-following consensus problem for nonlinear multiagent systems under the output feedback control mechanism and the event-triggered communication mechanism. Owing to the physical instrument constraints, a significant portion of the state variables is not readily available. Therefore, this article put forward a distributed event-based leader-following consensus protocol only using agents' relative output measurements and underlying neighbors. Furthermore, this article develops two event-triggered mechanisms simultaneously, one is the event-triggered communication mechanism in the sensor-to-controller channel, and another is the event-triggered controller update in the controller-to-actuator track. Besides that, it is proven that the developed event-triggered control protocol can settle the leader-following consensus problem of the nonlinear multiagent systems, and the Zeno behavior is excluded in both the channels. Finally, we perform two simulation examples to illustrate the efficacy of the obtained results.

Constrained hybrid control for parametric uncertainty systems via step-function method.

In this paper, considering that sometimes signal transmission may be interrupted, or signal input errors may occur, we establish a novel class of parametric uncertainty hybrid control system models including the impulsive control signals under saturated inputs, which can reflect the signal transmission process more realistically. Based on the step-function method, improved polytopic representation approach and Schur complement, we find the stability conditions, which are less conservative than those with the traditional Lyapunov method, of the considered control system. In addition, we investigate the design of the control gains and the auxiliary control gains for easily finding the suitable control signals, the auxiliary signals and the estimation of the attraction domain. Moreover, our proposed methods are applied to the fixed time impulse problems of uncertain systems with or without Zeno behavior. Simulation results for the uncertain neural network systems are presented to show the feasibility and effectiveness of our stabilization methods using the step-function.

Mean-square stabilization of impulsive neural networks with mixed delays by non-fragile feedback involving random uncertainties.

In this paper, we consider a class of neural networks with mixed delays and impulsive interferences. Firstly, a sufficient condition is given to ensure the existence and uniqueness of the equilibrium point of the proposed neural networks by employing the contraction mapping theorem. Secondly, we discuss the issue of the exponential stability in mean-square of the equilibrium point by a non-fragilely delayed output coupling feedback which involves stochastically occurring gain oscillations. The designed feedback input can be tolerant of limited stochastic fluctuations of control gains and be robust against potential errors caused by factors like round-off. By combining methods of Lyapunov-Krasovskii functional and free-weighting matrix, a delay-dependent output coupling feedback with stochastically occurring uncertainties is designed and linear-matrix-inequalities(LMIs)-based sufficient conditions for the exponential stabilization in mean square are derived. Finally, three numerical examples are presented to illustrate the feasibility of theoretical results with a benchmark real-world problem.

Stabilization of nonlinear dynamical systems via intermittent control with non-instantaneous impulses and actuator saturation.

In many actual discontinuous control systems, the state might undergo some abrupt changes when the impulsive effects appear and the impulsive effect will stay active for a period of time which cannot be ignored. Obviously, the traditional instantaneous impulsive system models cannot describe the above process. Then in this paper, considering the actuator saturation and non-instantaneous impulse existing in the real environment, we formulate the new models to describe the nonlinear intermittent control system subject to actuator saturation with non-instantaneous impulsive effects occurring at the moments of system switching. Some exponential stability criteria of this considered intermittent control systems in the form of matrix inequalities are first put forward. Meanwhile, some LMI optimization problems are also proposed to design the control gain for estimating the attraction domain with a fixed decay rate. Finally, simulation results demonstrate the feasibility of the obtained theoretical results.

Characteristics and influencing factors of energy consumption in Chinese rural households.

In the 14th Five-Year Plan, China clearly proposed to achieve carbon peak by 2030 and carbon neutralization by 2060, which will be incorporated into the Ecological Civilization Construction. Therefore, it is particularly important to control the consumption of fossil energy in rural areas. Under this background, the paper is based on the filed survey data of rural households and uses the method of CLAD (the censored least absolute deviations) estimation of Tobit model to study the influencing factors and structural characteristics of rural households' energy consumption. The results show that the consumption of traditional energy with low-quality takes a main proportion of total energy consumption in rural households, which is unreasonable and needs transforming urgently. Also, there is heterogeneity among regions. Family characteristics, family wealth, and energy conservation initiative have an impact on total energy consumption and different types of energy. Especially, households with high frequency of energy conservation behavior and strong policy perception will reduce the consumption of high-polluting firewood and increase the use of coal and electricity.

Neural network-based adaptive synchronization for second-order nonlinear multiagent systems with unknown disturbance.

This paper handles the distributed adaptive synchronization problem for a class of unknown second-order nonlinear multiagent systems subject to external disturbance. It is supposed to be an unknown one for the underlying external disorder. First, the neural network-based disturbance observer is developed to deal with the impact induced by the strange disturbance. Then, a new distributed adaptive synchronization criterion is put forward based on the approximation capability of the neural networks. Next, we propose the necessary and sufficient condition on the directed graph to ensure the synchronization error of all followers can be reduced small enough. Then, the distributed adaptive synchronization criterion is further explored because it is difficult to obtain the relative velocity measurements of the agents. The distributed adaptive synchronization criterion without the velocity measurement feedback is also designed to fulfill the current investigation. Finally, the simulation example is performed to verify the correctness and effectiveness of the proposed theoretical results.

Marking ground glass nodules with pulmonary nodules localization needle prior to video-assisted thoracoscopic surgery.

OBJECTIVES: To evaluate the efficacy and safety of marking ground glass nodules (GGNs) with pulmonary nodules localization needle (PNLN) prior to video-assisted thoracoscopic surgery (VATS). MATERIALS AND METHODS: From June 2020 to February 2021, all patients with GGNs who received CT-guided localization using PNLN before VATS were enrolled. Clinical and imaging data were retrospectively analyzed. RESULTS: A total of 352 consecutive patients with 395 GGNs were included in the study. The mean diameter of GGNs was 0.95 ± 0.48 cm, and the shortest distance from nodules to the pleura was 1.73 ± 0.96 cm. All 395 GGNs were marked using PNLNs. The time required for marking was 7.8 ± 2.2 min. The marking success rate was 99.0% (391/395). The marking failure of four nodules was all due to the unsatisfactory position of PNLNs. No marker dislocation occurred. Marking-related complications included pneumothorax in 63 cases (17.9%), hemorrhage in 34 cases (9.7%), and hemoptysis in 6 cases (1.7%). All the complications were minor and did not need special treatment. Localization and VATS were performed on the same day in 95 cases and on different days in 257 cases. All GGNs were successfully removed by VATS. No patient converted to thoracotomy. Histopathological examination revealed 74 (18.7%) benign nodules and 321 (81.3%) malignant nodules. CONCLUSIONS: It is safe and reliable to perform preoperative localization of GGNs using PNLNs, which can effectively guide VATS to remove GGNs. KEY POINTS: • Preoperative localization of GGNs could effectively guide VATS to remove GGNs. • PNLN was based on the marking principle of hook-wire, through the improvement of its material, specially designed to mark pulmonary nodules. • The application of PNLN to mark GGNs had high success rate, good patient tolerance, and no dislocation. Meanwhile, VATS could be performed 2 to 3 days after marking GGNs with PNLN.

Synchronization of Neural Networks Involving Distributed-Delay Coupling: A Distributed-Delay Differential Inequalities Approach.

In this article, we address the synchronization issue for coupled neural networks (CNNs) with mixed couplings by way of the delayed impulsive control, where the delay is distributed. Particularly, mixed couplings comprise the current-state coupling and the distributed-delay coupling, where influences on network connections caused by the past information of CNNs over a certain period are considered. First, we propose a novel array of delayed impulsive differential inequalities involving distributed-delay-dependent impulses, where distributed delays can be relatively larger. Second, we apply such delayed inequalities to analyze the problem of synchronization for CNNs with two different topologies. Sufficient criteria and distributed-delay-dependent impulsive controller are derived thereby. Furthermore, using techniques of matrix decomposition, several low-dimensional criteria are set out, which are appropriate for applications of large scale CNNs. Finally, a numerical example of CNNs with both the current-state coupling and the distributed-delay coupling involving three cases, are exhibited to exemplify the validity and the efficiency of the obtained theoretical results.

CT-guided microcoil localization of pulmonary nodules: the effect of the position of microcoil proximal end on the incidence of microcoil dislocation.

OBJECTIVES: To evaluate the effect of the position of microcoil proximal end on the incidence of microcoil dislocation during CT-guided microcoil localization of pulmonary nodules (PNs). METHODS: This retrospective study included all patients with PNs who received CT-guided microcoil localization before video-assisted thoracoscopic urgery (VATS) resection from June 2016 to December 2019 in our institution. The microcoil distal end was less than 1 cm away from the nodule, and the microcoil proximal end was in the pleural cavity (the pleural cavity group) or chest wall (the chest wall group). The length of microcoil outside the pleura was measured and divided into less than 0.5 cm (group A), 0.5 to 2 cm (group B) and more than 2 cm (group C). Microcoil dislocation was defined as complete retraction into the lung (type I) or complete withdrawal from the lung (type II). The rate of microcoil dislocation between different groups was compared. RESULTS: A total of 519 consecutive patients with 571 PNs were included in this study. According to the position of microcoils proximal end on post-marking CT, there were 95 microcoils in the pleural cavity group and 476 in the chest wall group. The number of microcoils in group A, B, and C were 67, 448 and 56, respectively. VATS showed dislocation of 42 microcoils, of which 30 were type II and 12 were type I. There was no statistical difference in the rate of microcoil dislocation between the pleural cavity group and the chest wall group (6.3% vs 7.6%, x2 = 0.18, p = 0.433). The difference in the rate of microcoil dislocation among group A, B, and C was statistically significant (11.9%, 5.8%, and 14.3% for group A, B, and C, respectively, x2 = 7.60, p = 0.008). In group A, 75% (6/8) of dislocations were type I, while all eight dislocations were type II in group C. CONCLUSIONS: During CT-guided microcoil localization of PNs, placing the microcoil proximal end in the pleura cavity or chest wall had no significant effect on the incidence of microcoil dislocation. The length of microcoil outside the pleura should be 0.5 to 2 cm to reduce the rate of microcoil dislocation. ADVANCES IN KNOWLEDGE:: CT-guided microcoil localization can effectively guide VATS to resect invisible and impalpable PNs. Microcoil dislocation is the main cause of localization failure. The length of microcoil outside the pleura is significantly correlated with the rate and type of microcoil dislocation. Placing the microcoil proximal end in the pleura cavity or chest wall has no significant effect on the rate of microcoil dislocation.

A Novel Fixed-Time Converging Neurodynamic Approach to Mixed Variational Inequalities and Applications.

This article proposes a novel fixed-time converging forward-backward-forward neurodynamic network (FXFNN) to deal with mixed variational inequalities (MVIs). A distinctive feature of the FXFNN is its fast and fixed-time convergence, in contrast to conventional forward-backward-forward neurodynamic network and projected neurodynamic network. It is shown that the solution of the proposed FXFNN exists uniquely and converges to the unique solution of the corresponding MVIs in fixed time under some mild conditions. It is also shown that the fixed-time convergence result obtained for the FXFNN is independent of initial conditions, unlike most of the existing asymptotical and exponential convergence results. Furthermore, the proposed FXFNN is applied in solving sparse recovery problems, variational inequalities, nonlinear complementarity problems, and min-max problems. Finally, numerical and experimental examples are presented to validate the effectiveness of the proposed neurodynamic network.

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.

CT-guided preoperative localization of ground glass nodule: comparison between the application of embolization microcoil and the locating needle designed for pulmonary nodules.

OBJECTIVES: To compare the efficacy and safety of pre-operative localization of ground glass nodule (GGN) using embolization microcoils and the locating needles designed for pulmonary nodules. METHODS: From June 2019 to December 2020, 429 patients who received CT-guided localization of single GGN before video-assisted thoracoscopic surgery (VATS) were enrolled. The diameter and depth of GGNs were 0.84 ± 0.39 cm and 1.66 ± 1.37 cm. Among 429 cases, the first 221 GGNs were marked with microcoils (the microcoil group), and the remaining 208 GGNs were marked with the locating needles designed for pulmonary nodules (the locating needle group). SPSS 17.0 statistical software was used to compare the marking success rate, marking time, marking-related complications between two groups. p values < 0.05 were considered statistically significant. RESULTS: The marking time in the microcoil group was longer than that in the locating needle group (11.1 ± 3.9 vs 8.2 ± 2.0 min, t = -7.87, p = 0.000). The marking success rate in the microcoil group was lower than that in the locating needle group (91.4% vs 98.6%, χ2 = 11.27, p = 0.001). In the microcoil group, marking failures included 16 cases of microcoil dislocation and 3 cases of unsatisfactory microcoil position, while all 3 cases of marking failure in the locating needle group were due to unsatisfactory anchor position. No significant differences in the incidence of total complications (23.1% vs 22.1%), pneumothorax (18.1% vs 19.2%), hemorrhage (9.5% vs 9.1%), and hemoptysis (1.8% vs 1.4%) were observed between the two groups. All the complications were minor and did not need special treatment. Except for one case in the microcoil group, which was converted to thoracotomy, the remaining 428 GGNs were successfully resected by VATS. CONCLUSIONS: It is safe and effective to perform pre-operative localization of GGN using either embolization microcoil or the locating needle designed for pulmonary nodules. The locating needle is superior to microcoil for marking GGN in terms of procedure time and the success rate. The complication rate of both methods is similar. ADVANCES IN KNOWLEDGE: The locating needle designed for pulmonary nodules has recently been used to mark pulmonary nodule. Its structure can effectively avoid dislocation after localization, and the marking process is simple and quick. Compared with localization using microcoil, it takes less time and has higher success rate to mark GGNs using the locating needle. The complication rate of both methods is similar.

Cluster synchronization of delayed coupled neural networks: Delay-dependent distributed impulsive control.

This paper investigates the issue of cluster synchronization (CS) for the coupled neural networks (CNNs) with time-varying delays via the delay-dependent distributed impulsive control. A new Halanay-like inequality, where delayed impulses are taken into consideration, is proposed. Based on the Lyapunov theory and the new differential inequality, sufficient conditions of CS for delayed CNNs with fixed and switching coupling topology are obtained, respectively. Moreover, delay-dependent distributed impulsive controllers with fixed or switching topology are designed thereby. Finally, we present a numerical example of CNNs with fixed or switching coupling to verify the effectiveness of our results, respectively.

Energy choice in rural household cooking and heating: influencing factors and transformation patterns.

In China, the traditional biomass, coal, and other highly polluting energy sources used by rural households have had many negative effects on the environment and the health of residents. To explore the influencing factors of energy consumption choices in rural household cooking and heating in China and to promote more modern, efficient, and clean energy consumption modes for rural household cooking and heating, this article builds a multinomial logit model for theoretical analysis based on interview and survey data in rural areas of central and eastern China from January to February 2017. The results show that (1) biomass energy still occupies an important position in the life of Chinese rural households; (2) the driving factors for the cooking and heating energy choices of rural residents in central and eastern China include the number of appliances, family type, family members' leisure, heating demand, and natural endowment; and (3) because of the above main influencing factors, some policies that conform to the background of the times, such as household appliances upgrade policies and centralized housing policies, are worthy of government consideration.