Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation

An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection.Comment: 41 pages, 14 figure

The contribution and FIP bias of three types of materials inside ICMEs associated with different flare intensities

The studies on the origination and generation mechanisms of ICME materials are crucial for understanding the connection between CMEs and flares. The materials inside ICMEs can be classified into three types, coming from corona directly (corona-materials), heated by magnetic reconnection in corona (heated-corona-materials), and generated by chromospheric evaporation (chromospheric-evaporation-materials). Here, the contribution and First Ionization Potential (FIP) bias of three types of materials inside ICMEs associated with different flare intensities are analyzed and compared. We find that the speeds and scales of near-Earth ICMEs both increase with flare intensities. The proportions of heated-corona-materials are nearly constant with flare intensities. The contributions of corona-materials (chromospheric-evaporation-materials) are significantly decreased (increased) with flare intensities. More than two-thirds of materials are chromospheric-evaporation-materials for ICMEs associated with strong flares. The FIP bias of corona-materials and heated-corona-materials is almost the same. The FIP bias of chromospheric-evaporation-materials is significantly higher than that of corona-materials and heated-corona-materials, and it is increased with flare intensities. The above characteristics of FIP bias can be explained reasonably by the origination and generation mechanisms of three types of ICME materials. The present study demonstrates that the origination and generation mechanisms of ICME materials are significantly influenced by flare intensities. The reasons for the elevation of FIP bias, if ICMEs are regarded as a whole, are that the FIP bias of chromospheric-evaporation-materials is much higher, and the chromospheric-evaporation-materials contributed significantly to the ICMEs which associated with strong flares.Comment: 12 pages, 3 figures, accepted for publication in Ap

Two jasmonic acid carboxyl methyltransferases in Gossypium hirsutum involved in MeJA biosynthesis may contribute to plant defense

Jasmonic acid (JA) and methyl jasmonate (MeJA), the crucial plant hormones, can induce the emission of plant volatiles and regulate the behavioral responses of insect pests or their natural enemies. In this study, two jasmonic acid carboxyl methyltransferases (JMTs), GhJMT1 and GhJMT2, involved in MeJA biosynthesis in Gossypium. hirsutum were identified and further functionally confirmed. In vitro, recombinant GhJMT1 and GhJMT2 were both responsible for the conversion of JA to MeJA. Quantitative real-time PCR (qPCR) measurement indicated that GhJMT1 and GhJMT2 were obviously up-regulated in leaves and stems of G. hirsutum after being treated with MeJA. In gas chromatography-mass spectrometry (GC-MS) analysis, MeJA treatment significantly induced plant volatiles emission such as (E)-β-ocimene, (Z)-3-hexenyl acetate, linalool and (3E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), which play vital roles in direct and indirect plant defenses. Moreover, antennae of parasitoid wasps Microplitis mediator showed electrophysiological responses to MeJA, β-ocimene, (Z)-3-hexenyl acetate and linalool at a dose dependent manner, while our previous research revealed that DMNT excites electrophysiological responses and behavioral tendencies. These findings provide a better understanding of MeJA biosynthesis and defense regulation in upland cotton, which lay a foundation to JA and MeJA employment in agricultural pest control

Polynomial Smooth Twin Support Vector Machines

Smoothing functions can transform the unsmooth twin support vector machines (TWSVM) into smooth ones, and thus better classification results can be obtained. It has been one of the key problems to seek a better smoothing function in this field for a long time. In this paper, a novel version for smooth TWSVM, termed polynomial smooth twin support vector machines (PSTWSVM), is proposed. In PSTWSVM, using the series expansion, a new class of polynomial smoothing is proposed, and then their important properties are discussed. It is shown that the approximation accuracy and smoothness rank of polynomial functions can be as high as required. Subsequently, the polynomial functions are adopted to convert the original constrained quadratic programming problems of TWSVM into unconstrained minimization problems, and then are solved by the well-known Newton-Armijo algorithm. The effectiveness of the proposed method is demonstrated via experiments on synthetic and real-word benchmark datasets