Numerical Study of Hypersonic Boundary Layer Receptivity Characteristics Due to Freestream Pulse Waves

A finite difference method is used to do direct numerical simulation (DNS) of hypersonic unsteady flowfield under the action of freestream pulse wave. The response of the hypersonic flowfield to freestream pulse wave is studied, and the generation and evolution characteristics of the boundary layer disturbance waves are discussed. The effects of the pulse wave types on the disturbance mode in the boundary layer are investigated. Results show that the freestream disturbance waves significantly change the shock standoff distance, the distribution of flowfield parameters and the thermodynamic state of boundary layer. In the nose area, the main disturbance modes in the boundary layer are distributed near the fundamental mode. With the evolution of disturbance along with streamwise, the main disturbance modes are transformed from the dominant state of the fundamental mode to the collective leadership state of the second order and the third order harmonic frequency. The intensity of bow shock has significant effects on both the fundamental mode and the harmonic modes in each order. The strong shear structure of boundary layer under different types of freestream pulse waves reveals different stability characteristics. The effects of different types of freestream pulse waves are significant on the distribution and evolution of disturbance modes. The narrowing of frequency band and the decreasing of main disturbance mode clusters exist in the boundary layer both for fast acoustic wave, slow acoustic wave and entropy wave

The Roles of Platelet GPIIb/IIIa and αvβ3 Integrins during HeLa Cells Adhesion, Migration, and Invasion to Monolayer Endothelium under Static and Dynamic Shear Flow

During their passage through the circulatory system, tumor cells undergo extensive interactions with various host cells including endothelial cells and platelets. Mechanisms mediating tumor cell adhesion, migration, and metastasis to vessel wall under flow condition are largely unknown. The aim of this study was to investigate the potential roles of GPIIb/IIIa and αvβ3 integrins underlying the HeLa-endothelium interaction in static and dynamic flow conditions. HeLa cell migration and invasion were studied by using Millicell cell culture insert system. The numbers of transmigrated or invaded HeLa cells significantly increased by thrombin-activated platelets and reduced by eptifibatide, a platelet inhibitor. Meanwhile, RGDWE peptides, a specific inhibitor of αvβ3 integrin, also inhibited HeLa cell transmigration. Interestingly, the presence of endothelial cells had significant effect on HeLa cell migration regardless of static or cocultured flow condition. The adhesion capability of HeLa cells to endothelial monolayer was also significantly affected by GPIIb/IIIa and αvβ3 integrins. The arrested HeLa cells increased nearly 5-fold in the presence of thrombin-activated platelets at shear stress condition (1.84 dyn/cm2 exposure for 1 hour) than the control (static). Our findings showed that GPIIb/IIIa and αvβ3 integrins are important mediators in the pathology of cervical cancer and provide a molecular basis for the future therapy, and the efficient antitumor benefit should target multiple receptors on tumor cells and platelets

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner

Weak Supervision for Fake News Detection via Reinforcement Learning

Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.Comment: AAAI 202

Multi-Grained Named Entity Recognition

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.Comment: In ACL 2019 as a long pape

Research on high-precision digital image correlation measurement techniques for highly stable structures

This study proposes a novel digital image processing system that combines a diffraction-limited resolution (DLRF)-based measurement technique with a windowed form-center tracking algorithm. To evaluate the accuracy of this system, this paper compares and analyzes the effectiveness of conventional digital image techniques and DLRF-based methods for deformation displacement measurements. In addition, the study includes thermal stability tests under ambient noise and uniform high temperature conditions to evaluate the stability performance of the system in a complex environment. The experimental results show that the DLRF-based digital image correlation method proposed in this study performs well in reducing the mean deviation (from a maximum of 5.17 × 10-3 to 1.73 × 10-3) and root-mean-square error (from a maximum of 5.14 × 10-3 to 0.75 × 10-3). It is worth noting that the DLRF method is faster in processing when using the single-precision format than the double-precision format, with a speedup of up to 1.05 times. In addition, the multiple displacement averaging processing method can effectively filter the noise in the test, and the noise effect is only in the range of 0 to 2 μm in most areas. In the analysis of test points 10-34 and 57-80, the displacement error is controlled within 5 μm, indicating that the modified structural analysis model can be used for on-orbit micrometer-scale thermal deformation analysis. The study proves the high accuracy and stability of the digital image system proposed in this paper in the measurement of deformation displacement, which provides adequate technical support for accurate measurement in related fields

A Novel Measurement Method for Linear Thermal Expansion Coefficient of Laminated Composite Material Tubular Specimen

Materials of satellite integration truss frame are required to withstand temperature that range from about – 250 °C ~ + 150 °C. In order to reduce structural components deformation caused by such temperature change, material of truss frame mostly adopts laminated composite material tubes, whose linear thermal expansion coefficient (LTEC) is very small. Therefore, accurate measurement of LTEC of truss frame materials over a broad temperature range is essential for successful mission. To address this issue, this paper proposes a general experiment platform for measuring LTEC of laminated composite material specimen reaching length up to one meter in the temperature range from – 100 °C to +100 °C. The platform uses light-density optical fiber probe to measure length variation and thermocouple to record temperature variation. Thereafter, the thermal expansion coefficient and its measurement uncertainty can be obtained by establishing and solving mathematical model. Finally, LTEC measurement of a tubular composite materials specimen is conducted. The experiment result demonstrates the validity and practicality of the experiment platform and the measurement accuracy of LTEC which can reach up to 10-7/°C.DOI: http://dx.doi.org/10.5755/j01.ms.21.4.9708</p

Two new species of Helochares, with additional faunistic records from China (Coleoptera, Hydrophilidae, Acidocerinae)

Two new species, Helochares guoi Yang & Jia, sp. nov. and Helochares distinctus Jia & Tang, sp. nov., are described. Two species are recorded for the first time from China: Helochares negatus Hebauer, 1995 from Yunnan, and Helochares minusculus d’Orchymont, 1943 from Guangdong. Additional faunistic data from China are provided for the following species: Helochares hainanensis Dong & Bian, 2021, Helochares nipponicus Hebauer, 1995, Helochares sauteri d’Orchymont, 1943, Helochares densus Sharp, 1890, Helochares lentus Sharp, 1890, Helochares neglectus (Hope, 1854) and Helochares anchoralis Sharp, 1890. The Chinese fauna of Helochares comprises 16 species, 11 of which are illustrated in this contribution. Helochares crenatus Régimbart, 1921 is removed from the Chinese fauna