Monte Carlo Simulation for the Morphology and Kinetics of Spherulites and Shish-Kebabs in Isothermal Polymer Crystallization

Monte Carlo method is used to capture the evolution of spherulites and shish-kebabs and to predict the crystallization kinetics in isothermal polymer crystallization. Effects of nucleation density and growth rate of spherulites, nucleation density, and length growth rate of shish-kebabs, respectively, on crystallization are investigated. Results show that nucleation densities of both spherulites and shish-kebabs strongly affect crystallization rate as well as morphology. An increase in nucleation density of either spherulites or shish-kebabs leads to a quicker crystallization rate and a smaller average spherulite size. It is also shown that nucleation density of shish-kebabs has a stronger impact on crystallization rate. Growth rate of spherulites and length growth rate of shish-kebabs also have significant effect on crystallization rate and morphology. An increase in growth rate of spherulites or length growth rate of shish-kebabs also speeds up the crystallization rate; additionally, a decrease in growth rate of spherulites or an increase in length growth rate of shish-kebabs results in a more highly anisotropic shish-kebab structure and a smaller average size of spherulites. Results also show that the effect of growth rate of spherulites is more important than the effect of length growth rate of shish-kebabs on crystallization

Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network

Network traffic classification is the basis of many network security applications and has attracted enough attention in the field of cyberspace security. Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. In this paper, we propose a MLP-Mixer based multi-view multi-label neural network for network traffic classification. Compared with the existing CNN-based methods, our method adopts the MLP-Mixer structure, which is more in line with the structure of the packet than the conventional convolution operation. In our method, the packet is divided into the packet header and the packet body, together with the flow features of the packet as input from different views. We utilize a multi-label setting to learn different scenarios simultaneously to improve the classification performance by exploiting the correlations between different scenarios. Taking advantage of the above characteristics, we propose an end-to-end network traffic classification method. We conduct experiments on three public datasets, and the experimental results show that our method can achieve superior performance.Comment: 15 pages,6 figure

FedForgery: Generalized Face Forgery Detection with Residual Federated Learning

With the continuous development of deep learning in the field of image generation models, a large number of vivid forged faces have been generated and spread on the Internet. These high-authenticity artifacts could grow into a threat to society security. Existing face forgery detection methods directly utilize the obtained public shared or centralized data for training but ignore the personal privacy and security issues when personal data couldn't be centralizedly shared in real-world scenarios. Additionally, different distributions caused by diverse artifact types would further bring adverse influences on the forgery detection task. To solve the mentioned problems, the paper proposes a novel generalized residual Federated learning for face Forgery detection (FedForgery). The designed variational autoencoder aims to learn robust discriminative residual feature maps to detect forgery faces (with diverse or even unknown artifact types). Furthermore, the general federated learning strategy is introduced to construct distributed detection model trained collaboratively with multiple local decentralized devices, which could further boost the representation generalization. Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery. The designed novel generalized face forgery detection protocols and source code would be publicly available.Comment: The code is available at https://github.com/GANG370/FedForgery. The paper has been accepted in the IEEE Transactions on Information Forensics & Securit

The north–south shift of the ridge location of the western Pacific subtropical high and its influence on the July precipitation in the Jianghuai region from 1978 to 2021

The Jianghuai region is the area between the Yangtze River and the Huai River in China and is a densely populated agriculture region therefore, the economics and human activity there are significantly affected by the precipitation changes, particularly during the summer when extreme storms and droughts normally occur. It will be helpful if the summer precipitation changes can be predicted. The monthly ERA5 atmospheric reanalysis data from 1978 to 2021 are used in this study to investigate the relationship between the ridge latitudinal location of the western Pacific subtropical high (WPSH) and the precipitation in July over the Jianghuai region. The results show that the WPSH ridge location has an important impact on the amount and spatial distribution of the precipitation in this region. When the ridge was northward, an anomalous anticyclonic circulation will appear over the western Pacific, leading to the weakening of the summer monsoon and the reduction of moisture transport from the Indian Ocean, therefore decreasing precipitation in the Jianghuai region, while the situation is opposite when the ridge was southward. The Niño 3.4 index in March and the India–Burma trough intensity index in June have significant correlations with the July WPSH ridge location, and both can be used as precursors to predict the WPSH ridge location and, therefore, the precipitation in this region

Long-range prediction of the tropical cyclone frequency landfalling in China using thermocline temperature anomalies at different longitudes

The landfalls of the tropical cyclone (TC) along the coast of China have caused huge economic damages. There are approximately nine TC landfalls in China every year. It will be beneficial if the landfall frequency can be predicted in advance. Inspired by the study of Sparks and Toumi (Commun Earth Environ, 30-1-2020), six datasets, including four ocean reanalyses and two object analyses from 1993 to 2019, are employed to study the consistency in the relationship between the thermocline temperature anomalies at different longitudes and the frequency of TC landfalls along the coastal areas of China (South China, East China, and the whole of China). The thermocline temperature anomalies at different longitudes are tested in order to confirm our hypothesis that the eastward and westward transports of ocean heat from the warm pool are the causes of the significant correlations. The results show some significant correlations at various longitudes, and the temperature anomalies can predict the TC landfall frequency for several months or longer. Further study also shows the close relationship between the ocean heat transport and the sea surface temperature anomalies at the genesis locations of TC landfalls. The locations of the western Pacific subtropical high (WPSH) during high-frequency TC landfall years also show favorable spatial patterns to the TC landfall in South China and East China, respectively. In years with a high TC frequency in South China, the westward displacement of the WPSH ridge steers TC toward South China, while during high-frequency TC landfall years in East China, WPSH is located further north, and the westward extension of the ridge is in close proximity to the East China Sea

Kaiso (ZBTB33) Downregulation by Mirna-181a Inhibits Cell Proliferation, Invasion, and the Epithelial–Mesenchymal Transition in Glioma Cells

Background/Aims: Kaiso (ZBTB33) expression is closely associated with the progression of many cancers and microRNA (miRNA) processing. MiR-181a plays critical roles in multiple cancers; however, its precise mechanisms in glioma have not been well clarified. The goal of this study was to evaluate the interaction between Kaiso and miR-181a in glioma. Methods: Quantitative real-time PCR (qRT-PCR) was performed to detect the levels of Kaiso and miR-181a in glioma tissues and cell lines. Cell proliferation, invasion, and the epithelial–mesenchymal transition (EMT) were evaluated to analyze the biological functions of miR-181a and Kaiso in glioma cells. The mRNA and protein levels of Kaiso were measured by qRT-PCR and western blotting, respectively. Meanwhile, luciferase assays were performed to validate Kaiso as a miR-181a target in glioma cells. Results: We found that the level of miR-181a was the lowest among miR-181a–d in glioma tissues and cell lines, and the low level of miR-181a was closely associated with the increased expression of Kaiso in glioma tissues. Moreover, transfection of miR-181a significantly inhibited the proliferation, invasion, and EMT of glioma cells, whereas knockdown of miR-181a had the opposite effect. Bioinformatics analysis predicted that Kaiso was a potential target gene of miR-181a, and the luciferase reporter assay demonstrated that miR-181a could directly target Kaiso. In addition, Kaiso silencing had similar effects as miR-181a overexpression in glioma cells, whereas overexpression of Kaiso in glioma cells partially reversed the inhibitory effects of the miR-181a mimic. Conclusionss: miR-181a inhibited the proliferation, invasion, and EMT of glioma cells by directly targeting and downregulating Kaiso expression