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Fatty acid composition and content in seed oil of plants in Chungbaishan region was studied by gas chromatography and reported.

This report includes the original manuscript and the supplementary material of "Realization of Biquadratic Impedance as Five-Element Bridge Networks".

Human emotion recognition could greatly contribute to human–computer interaction with promising applications in artificial intelligence. One of the challenges in recognition tasks is learning effective representations with stable performances from electroencephalogram (EEG) signals. In this article, we propose a novel deep-learning framework, named channel-fused dense convolutional network, for EEG-based emotion recognition. First, we use a 1-D convolution layer to receive weighted combinations of contextual features along the temporal dimension from EEG signals. Next, inspired by state-of-the-art object classification techniques, we employ 1-D dense structures to capture electrode correlations along the spatial dimension. The developed algorithm is capable of handling temporal dependencies and electrode correlations with the effective feature extraction from noisy EEG signals. Finally, we perform extensive experiments based on two popular EEG emotion datasets. Results indicate that our framework achieves prominent average accuracies of 90.63% and 92.58% on the SEED and DEAP datasets, respectively, which both receive better performances than most of the compared studies. The novel model provides an interpretable solution with excellent generalization capacity for broader EEG-based classification tasks.

This work investigates the fixed-time distributed coordination control for multiple Euler-Lagrange systems and, in particular, addresses containment control with stationary/dynamic leaders as well as leaderless synchronization control. For the containment control scenario with stationary leaders, the subgraph associated with followers is directed. When dynamic leaders are involved, the information transfer between neighboring followers is bidirectional, for which a novel distributed estimator is developed. For the leaderless synchronization control scenario, the communication network among agents is unidirectional. Three fixed-time distributed control schemes are designed for the aforementioned three cases by applying the fixed-time stability theory. The convergence of the coordination control objectives can be achieved in a fixed time that does not depend on any initial conditions of agents' states, and the settling times are also explicitly derived. Finally, numerical simulations are presented to demonstrate the feasibility of the developed control strategies.

In this paper, a distributed average tracking (DAT) problem is studied for Lipschitz-type of nonlinear dynamical systems. The objective is to design DAT algorithms for locally interactive agents to track the average of multiple reference signals. Here, in both dynamics of agents and reference signals, there is a nonlinear term satisfying a Lipschitz-type condition. Three types of DAT algorithms are designed. First, based on state-dependent-gain design principles, a robust DAT algorithm is developed for solving DAT problems without requiring the same initial condition. Second, by using a gain adaption scheme, an adaptive DAT algorithm is designed to remove the requirement that global information, such as the eigenvalue of the Laplacian and the Lipschitz constant, is known to all agents. Third, to reduce chattering and make the algorithms easier to implement, a couple of continuous DAT algorithms based on time-varying or time-invariant boundary layers are designed, respectively, as a continuous approximation of the aforementioned discontinuous DAT algorithms. Finally, some simulation examples are presented to verify the proposed DAT algorithms.

With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This paper investigates three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax. It is found that different attack methods have their specific attack preferences on changing the target network structures. Such attack pattern are further verified by experimental results on some real-world networks, revealing that generally the top four most important network attributes on detecting adversarial samples suffice to explain the preference of an attack method. Based on these findings, the network attributes are utilized to design machine learning models for adversarial sample detection and attack method recognition with outstanding performance.

Mechanisms of the Nickel-Catalysed Hydrogenolysis and Cross-Coupling of Aryl Ethers Ni OMe Nu Nucleophile L n Ni(II) Ar O Me LA L n Ni(0) Ar OMe Nu Ni(I) L n Nu Oxidative addition Lewis acid assisted Anionic nickelates Ni(I) intermediates

Locally active memristor (LAM), which has an ability to amplify fluctuations, is a natural component for constructing artificial neuron circuits. This paper proposes a novel third-order neuron circuit by paralleling two LAMs and a capacitor. Firstly, two parallel LAMs are equivalently modeled as a second-order LAM to facilitate theoretical analysis. Regarding the third-order neuron, the parameter design and operating condition are obtained by calculating its small signal impedance functions poles or Jacobin matrixs eigenvalues. It is demonstrated that the neuron exhibits various neuromorphic behaviors, including periodic spiking, chaos and burst-number adaptation. Due to the two different LAMs, the proposed thirdorder system has multiple equilibrium points, leading to the generation of coexisting attractors. Interestingly, the generated chaotic attractor does not revolve around a single unstable equilibrium point, but is located between two unstable equilibrium points. Furthermore, the emergence of oscillating behaviors is dependent on the distance between the two unstable equilibrium points. Detailed theoretical and simulation analysis are presented to investigate the neuron dynamics and provide an explanation for the observed neuromorphic behaviors. Finally, physical circuit implementation of the neuron is constructed based on the memristor emulator, which also demonstrates the practicability of the proposed neuron model and the correctness of the theoretical analysis.