Investigation of fracture behavior of cement-bonded corundum castables using wedge splitting test and digital image correlation method

To investigate the fracture behavior of cement-bonded corundum castables, various cement contents and pre-treating temperatures have been comparatively studied using the wedge splitting method and the digital image correlation technique. The results show that the microstructure enhances the mechanical properties, so the fracture energy and the maximum load as well as the fracture modes are affected correspondingly. The castables demonstrate the highest fracture energy and maximum load at 1600 °C with cement content of 10 wt% due to an appropriate amount of CA6. At the temperatures of 110 and 1100 °C, the crack propagation within the matrix and along the interface are dominated whereas within the aggregates significantly increased at 1600 °C, leading to the brittleness of materials. However, increasing the cement content can reduce their brittleness, caused by the maximum strain in thex-direction, largest length of the main crack, and high ratio of crack propagation in the matrix.     

Clustering adaptive canonical correlations for high-dimensional multi-modal data

Multi-modal canonical correlation analysis (MCCA) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data. MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data. However, the two-stage clustering is difficult to ensure the adaptability of dimension reduction and clustering, which will affect the final clustering performance. To solve the issue, we propose a novel clustering adaptive multi-modal canonical correlations (CAMCCs) method, which constructs a unified optimization model of multi-modal correlation learning and clustering. The method not only realizes discriminant learning of correlation projection directions under unsupervised cases, but also is able to directly obtain class labels of multi-modal data. Additionally, the method also realizes out-of-sample extension in class labels. Solutions of CAMCCs are optimized by an iterative way, and we analyze its convergence. Extensive experimental results on various datasets have demonstrated the effectiveness of the method.     

Inverse identification of constitutive parameters from heat source fields: A local approach applied to hyperelasticity

In this paper, a new inverse identification method of constitutive parameters is developed from full kinematic and thermal field measurements. It consists in reconstructing the heat source field from two different approaches by using the heat diffusion equation. The first one requires the temperature field measurement and the value of the thermophysical parameters. The second one is based on the kinematic field measurement and the choice of a thermo-hyperelastic model that contains the parameters to be identified. The identification is carried out at the local scale, ie, at any point of the heat source field, without using the boundary conditions. In the present work, the method is applied to the challenging case of hyperelasticity from a heterogeneous test. Due to large deformations undergone by the rubber specimen tested, a motion compensation technique is developed to plot the kinematic and the thermal fields at the same points before reconstructing the heterogeneous heat source field. In the present case, the constitutive parameter of the Neo-Hookean model has been identified, and its distribution has been characterized with respect to the strain state at the surface of a cross-shaped specimen.     

A review of topic modeling methods

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.     

Measurement of heat transfer coefficients for steam condensation on a vertical 21.5-mm-O.D. tube in the presence of air

ABSTRACT

Governing the rate of heat transport by condenser tubes in the passive containment cooling system (PCCS), the steam condensation over a vertical cylinder in the presence of air was investigated experimentally. The main objective of this study was to explore if the condensation heat transfer coefficient relies on the tube dimension, which has been a variable missed in most condensation models or has been embraced without experimental demonstration under phase change environments. The mean heat transfer coefficient was measured in the condensation test facility named JERICHO (JNU Experimental Rig for Investigation of Condensation Heat transfer On tube). The outer diameter of the condenser tube used in this study was set to 21.5 mm. The measured heat transfer coefficients were compared to those obtained from the 40-mm-O.D. tube, and a multiplier to correct the variation of the heat transfer coefficient with the tube diameter was proposed for its application to Lee correlation. The proposed correlation was further validated against another set of experimental data obtained from a separate test facility housing the 31.8-mm-O.D. tube.     

面部运动单元检测研究综述

面部运动单元检测旨在让计算机从给定的人脸图像或视频中自动检测需要关注的运动单元目标。经过二十多年的研究，尤其是近年来越来越多的面部运动单元数据库的建立和深度学习的兴起，面部运动单元检测技术发展迅速。首先，阐述了面部运动单元的基本概念，介绍了已有的常用面部运动单元检测数据库，概括了包括预处理、特征提取、分类器学习等步骤在内的传统检测方法；然后针对区域学习、面部运动单元关联学习、弱监督学习等几个关键研究方向进行了系统性的回顾梳理与分析；最后讨论了目前面部运动单元检测研究存在的不足以及未来潜在的发展方向。     

Mythical systems: mathematic and logical theory

The process of elaboration of the symbolic universe leads to exciting insights regarding the search for human emotional security. The symbols end up as explanatory axes of universal reality and on them are constructed myths that form a superstructure for belief systems. Human society is a multi-level system with a material structure (society), an ideological superstructure (belief systems, values, etc.) and a super superstructure with two parts: mythical (origin and justification) and utopic (final goal). All mythical belief systems have a numinous-religious nature.     

R-curves determination of ordinary refractory ceramics assisted by digital image correlation method

As a figure-of-merit, the rising ratio of crack propagation resistance to fracture initiation resistance indicates a reduction of the brittleness and enhances the thermal shock resistance of ordinary refractory ceramics. The significant nonlinear fracture behaviour is related to the development of a fracture process zone (FPZ). The universal dimensionless load–displacement diagram method is applied as a promising graphical method for the determination of R-curves for magnesia refractories showing different brittleness. By applying digital image correlation (DIC) together with the graphical method, the problems arisen with accurate determination of the fracture initiation resistance and the crack length are overcome. Meanwhile, the R-curve is subdivided with respect to the fracture processes, viz the fracture initiation, the development of FPZ and the onset of traction free macro-crack. With the simultaneous crack lengths evaluated from DIC, the contribution of each fracture process to the crack propagation resistance at certain loading stage is quantitatively presented.     

A Method for Systematic Assessment of Intrinsically Disordered Protein Regions by NMR

Intrinsically disordered proteins (IDPs) that lack stable conformations and are highly flexible have attracted the attention of biologists. Therefore, the development of a systematic method to identify polypeptide regions that are unstructured in solution is important. We have designed an “indirect/reflected” detection system for evaluating the physicochemical properties of IDPs using nuclear magnetic resonance (NMR). This approach employs a “chimeric membrane protein”-based method using the thermostable membrane protein PH0471. This protein contains two domains, a transmembrane helical region and a C-terminal OB (oligonucleotide/oligosaccharide binding)-fold domain (named NfeDC domain), connected by a flexible linker. NMR signals of the OB-fold domain of detergent-solubilized PH0471 are observed because of the flexibility of the linker region. In this study, the linker region was substituted with target IDPs. Fifty-three candidates were selected using the prediction tool POODLE and 35 expression vectors were constructed. Subsequently, we obtained ¹⁵N-labeled chimeric PH0471 proteins with 25 IDPs as linkers. The NMR spectra allowed us to classify IDPs into three categories: flexible, moderately flexible, and inflexible. The inflexible IDPs contain membrane-associating or aggregation-prone sequences. This is the first attempt to use an indirect/reflected NMR method to evaluate IDPs and can verify the predictions derived from our computational tools.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.