Formation law and criterion of nebulous macroscopic segregation in ZL205A alloy castings

The appearance of macroscopic segregation in ZL205A alloy castings bears a super resemblance to the appearance of shrinkage porosity, and the chemical composition of the segregation is Al2Cu whose microstructure is in the form of dentrite or skeleton crystal. According to the characteristic of nebulous segregation, the formation process could be divided into two steps by the eutectic temperature of Al2Cu. Then a criterion for each of the two steps is brought forward on the basis of the shrinkage porosity criterion of low pressure casting

Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition

Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed. They are very important nonverbal communication clues, but are transient and of low intensity thus difficult to recognize. Recently deep learning based methods have been developed for micro-expression (ME) recognition using feature extraction and fusion techniques, however, targeted feature learning and efficient feature fusion still lack further study according to the ME characteristics. To address these issues, we propose a novel framework Feature Representation Learning with adaptive Displacement Generation and Transformer fusion (FRL-DGT), in which a convolutional Displacement Generation Module (DGM) with self-supervised learning is used to extract dynamic features from onset/apex frames targeted to the subsequent ME recognition task, and a well-designed Transformer Fusion mechanism composed of three Transformer-based fusion modules (local, global fusions based on AU regions and full-face fusion) is applied to extract the multi-level informative features after DGM for the final ME prediction. The extensive experiments with solid leave-one-subject-out (LOSO) evaluation results have demonstrated the superiority of our proposed FRL-DGT to state-of-the-art methods

Preparation and Characterization of Nanofibrillated Cellulose from Bamboo Fiber via Ultrasonication Assisted by Repulsive Effect

Nanofibrillated celluloses (NFCs) have recently drawn much attention because of their exceptional physicochemical properties. However, the existing preparation procedures either produce low yields or severely degrade the cellulose and, moreover, are not energy efficient. The purpose of this study was to develop a novel process using ultrasonic homogenization to isolate fibrils from bamboo fiber (BF) with the assistance of negatively charged entities. The obtained samples were characterized by the degree of substitution (DS) of carboxymethyl, Fourier-transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis, and transmission electron microscopy (TEM). The results showed that an NFC yield could be obtained above 70% through this route. The enzyme hydrolysis could enhance the surface charge of the fiber, and mechanical activation facilitates an increase in the DS. The disintegrating efficiency of the cellulose fibrils significantly depended on the input power of ultrasonication and the DS. FT-IR spectra confirmed the occurrence of the carboxymethylation reaction based on the appearance of the characteristic signal for the carboxyl group. From XRD analysis, it was observed that the presence of the carboxyl groups makes the isolation more efficient attributed to the ionic repulsion between the carboxylate groups of the cellulose chains

Cross-Media Semantic Matching based on Sparse Representation

With the rapid growth of multi-modal data, cross-media retrieval has aroused many research interests. In this paper, the cross-media retrieval includes two tasks: query image retrieves relevant text and query text retrieves relevant images. With the development of sparse representation, two independent sparse representation classifiers are used to map the heterogeneous features of images and texts into their common semantic space before implementing similarity comparison. The proposed method makes full use of semantic information, and it is effective in the retrieving task. The performance of this method was evaluated on Wiki dataset, NUS-WIDE dataset, Wiki dataset with CNN features and Pascal dataset with CNN features. The experimental results validate its effectiveness compared with several state-of-the-art algorithms on the Mean Average Precision and other performance indexes

Toll-like receptor-mediated innate immunity against herpesviridae infection: a current perspective on viral infection signaling pathways

BACKGROUND: In the past decades, researchers have demonstrated the critical role of Toll-like receptors (TLRs) in the innate immune system. They recognize viral components and trigger immune signal cascades to subsequently promote the activation of the immune system. MAIN BODY: Herpesviridae family members trigger TLRs to elicit cytokines in the process of infection to activate antiviral innate immune responses in host cells. This review aims to clarify the role of TLRs in the innate immunity defense against herpesviridae, and systematically describes the processes of TLR actions and herpesviridae recognition as well as the signal transduction pathways involved. CONCLUSIONS: Future studies of the interactions between TLRs and herpesviridae infections, especially the subsequent signaling pathways, will not only contribute to the planning of effective antiviral therapies but also provide new molecular targets for the development of antiviral drugs

Modeling and Analysis of ETC Control System with Colored Petri Net and Dynamic Slicing

Nowadays, an Electronic Toll Collection (ETC) control system in highways has been widely adopted to smoothen traffic flow. However, as it is a complex business interaction system, there are inevitably flaws in its control logic process, such as the problem of vehicle fee evasion. Even we find that there are more than one way for vehicles to evade fees. This shows that it is difficult to ensure the completeness of its design. Therefore, it is necessary to adopt a novel formal method to model and analyze its design, detect flaws and modify it. In this paper, a Colored Petri net (CPN) is introduced to establish its model. To analyze and modify the system model more efficiently, a dynamic slicing method of CPN is proposed. First, a static slice is obtained from the static slicing criterion by backtracking. Second, considering all binding elements that can be enabled under the initial marking, a forward slice is obtained from the dynamic slicing criterion by traversing. Third, the dynamic slicing of CPN is obtained by taking the intersection of both slices. The proposed dynamic slicing method of CPN can be used to formalize and verify the behavior properties of an ETC control system, and the flaws can be detected effectively. As a case study, the flaw about a vehicle that has not completed the payment following the previous vehicle to pass the railing is detected by the proposed method

Model simulations of the annual cycle of the landfast ice thickness in the East Siberian Sea

The annual cycle of the thickness and temperature of landfast sea ice in the East Siberian Sea has been examined using a one-dimensional thermodynamic model. The model was calibrated for the year August 2012–July 2013, forced using the data of the Russian weather station Kotel’ny Island and ECMWF reanalyses. Thermal growth and decay of ice were reproduced well, and the maximum annual ice thickness and breakup day became 1.64 m and the end of July. Oceanic heat flux was 2 W.m–2 in winter and raised to 25 W.m–2 in summer, albedo was 0.3–0.8 depending on the surface type (snow/ice and wet/dry). The model outcome showed sensitivity to the albedo, air temperature and oceanic heat flux. The modelled snow cover was less than 10 cm having a small influence on the ice thickness. In situ sea ice thickness in the East Siberian Sea is rarely available in publications. This study provides a method for quantitative ice thickness estimation by modelling. The result can be used as a proxy to understand the sea ice conditions on the Eurasian Arctic coast, which is important for shipping and high-resolution Arctic climate modelling