Too little or too much? Exploring the effectiveness of different policies in air pollution control from technical and non-technical pathways.

Most environmental policy studies focus on the technical pathway effect but ignore the non-technical pathway. This paper analyzes the synergistic governance effects of three types of environmental policies on the technical and non-technical pathways. The super-efficient slacks-based measure-data envelopment analysis (SBM-DEA) assesses the green total factor productivity, while the Malmquist index decomposes into pure technical efficiency. The findings indicate that: (1) command-and-control policy has the 'too-little-of-a-good-thing' effect, but the policy intensity in most Chinese provinces is strong enough to reduce air pollution, while market-based incentive policy may be 'too-much-of-a-good-thing', but Chinese provinces have not reached the inflection point; (2) there are considerable differences in the environmental effects of different policies through technical and non-technical pathways; (3) different policies have various focuses. Command-and-control policy focuses on the non-technical pathway, whereas market-based incentive policy can induce technological progress.

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.

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.

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 microcoil localization for pulmonary nodules before VATS: a retrospective evaluation of risk factors for pleural marking failure.

OBJECTIVES: To summarize the experiences of CT-guided microcoil localization before video-assisted thoracoscopic surgery (VATS) and to investigate the risk factors associated with pleural marking failure. METHODS: Totally, 249 consecutive patients with 279 pulmonary nodules who underwent CT-guided microcoil localization prior to VATS were enrolled in this study. According to intraoperative observation, all the nodules were divided into two groups. The clinical characteristics and microcoil localization procedure-related variables of the nodules were analyzed by univariate analysis and multivariate logistic regression analysis to screen the independent factors associated with procedure results. RESULTS: Among the 279 nodules, 28 failed to observe the proximal end of the microcoil deployed on visceral pleura during VATS. The logistic regression revealed that needle-pleura angle (≤ 30°: OR = 39.022, p = 0.003), pleura-microcoil distance (≤ 10 mm: OR = 87.054, p < 0.001; 10~20 mm: OR = 10.088, p = 0.010), and presence of pleural indentation (OR = 21.623, p < 0.001) were independent risk factors for pleural marking failure. CONCLUSIONS: CT-guided microcoil localization for pulmonary nodules is a safe and effective procedure. Small needle-pleura angle (≤ 30°), pleura-microcoil distance (≤ 20 mm), and the presence of pleural indentation during the procedure are significant risk factors contributing to microcoil pleura marking failure. KEY POINTS: • CT-guided microcoil localization for pulmonary nodules was a safe and effective procedure. • CT-guided microcoil localization for pulmonary nodules yielded low complication rates. • Small needle-pleura angle, short pleura-microcoil distance, and the presence of pleural indentation were contributing to pleura marking failure.

Real-time measurement of a probabilistic-shaped 200Gb/s DP-16QAM transceiver.

Continuous real-time measurements are demonstrated from a 200Gb/s format configurable CFP transceiver that uses dual-polarization probabilistic-shaped 16QAM (DP-PS16QAM) modulation. Placed in a 50GHz coherent DWDM transmission system, DP-PS16QAM achieves a back-to-back 1.8dB OSNR gain over uniform DP-16QAM. It also transports over 1940km with EDFA-only amplification, thus doubling propagation distance of uniform DP-16QAM. Furthermore, a 1Tb/s super-channel consisting of five 200Gb/s DP-PS16QAM sub-carriers is placed in a 200GHz grid, and it achieves over 1600km transmission and 5b/s/Hz SE with a raw SE at 6.86b/s/Hz.

Trellis-based feed-forward carrier recovery for coherent optical systems.

An efficient trellis-based phase noise mitigation algorithm is proposed to highly improve the performance of coherent transmission systems, especially in high order modulation formats. The proposed method targets the coherent optical systems where the performance is limited by various sources of phase noise including laser line-width, fiber non-linearity, and phase noise induced by phase-locked loop. Considering hardware limitations of ultra-high data rate processing in optical systems, a hardware-efficient parallelized and pipelined architecture is utilized. Experimental results in 200 Gb/s DP-16QAM co-propagated with 10-G channels demonstrate significant performance improvement over other existing methods.

Coherent transceiver operating at 61-Gbaud/s.

We use 85-Gs/s digital-to-analog convertor (DAC), 85Gs/s analog-to-digital convertor (ADC), commercial optoelectronic (OE) components with an overall electronic 3dB-bandwidth of less than 15GHz, and novel digital signal processing (DSP) algorithms implemented in CMOS to realize real time coherent transceiver operation at a record baud rate of 61-Gbaud/s. Novel DSP approaches for mitigating narrow filtering effect is critical to acquire data clock, and to improve modem performance. With transmitter pre-emphasis, novel timing recovery, and soft output maximum likelihood sequence estimation (MLSE), we are able to achieve error free operation of single carrier 200-Gbit/s polarization division multiplexed quadrature phase shift keying (PDM-QPSK) after forward error correction (FEC) at 15.2dB OSNR with pre-FEC error rate of 1.4E-2, and single carrier 400-Gbit/s PDM 16-ary quadrature amplitude modulation (16QAM) after FEC at 30.2dB OSNR with pre-FEC error rate of 9.5E-3. Error free transmission for 200-Gbit/s PDM-QPSK and 400-Gbit/s PDM-16QAM was achieved after 1200km propagation with 6dB link margin and 80km propagation respectively.

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.

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.

A Fixed-Time Proximal Gradient Neurodynamic Network With Time-Varying Coefficients for Composite Optimization Problems and Sparse Optimization Problems With Log-Sum Function.

This article presents a novel proximal gradient neurodynamic network (PGNN) for solving composite optimization problems (COPs). The proposed PGNN with time-varying coefficients can be flexibly chosen to accelerate the network convergence. Based on PGNN and sliding mode control technique, the proposed time-varying fixed-time proximal gradient neurodynamic network (TVFxPGNN) has fixed-time stability and a settling time independent of the initial value. It is further shown that fixed-time convergence can be achieved by relaxing the strict convexity condition via the Polyak-Lojasiewicz condition. In addition, the proposed TVFxPGNN is being applied to solve the sparse optimization problems with the log-sum function. Furthermore, the field-programmable gate array (FPGA) circuit framework for time-varying fixed-time PGNN is implemented, and the practicality of the proposed FPGA circuit is verified through an example simulation in Vivado 2019.1. Simulation and signal recovery experimental results demonstrate the effectiveness and superiority of the proposed PGNN.

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.

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.

Predicting chaos in memristive oscillator via harmonic balance method.

This paper studies the possible chaotic behaviors in a memristive oscillator with cubic nonlinearities via harmonic balance method which is also called the method of describing function. This method was proposed to detect chaos in classical Chua's circuit. We first transform the considered memristive oscillator system into Lur'e model and present the prediction of the existence of chaotic behaviors. To ensure the prediction result is correct, the distortion index is also measured. Numerical simulations are presented to show the effectiveness of theoretical results.

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.

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.

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.

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.