Co‐segmentation of multiple similar images using saliency detection and region merging

The aim of co‐segmentation is to simultaneously segment multiple images depicting an identical or similar object. In this study, a co‐segmentation method using saliency detection and region merging is proposed. The saliency detection results using different detection methods on different types of colour space are combined to produce seed regions for each image in the image group. The initial seed regions of all the images are refined by eliminating the dissimilar ones to ensure accurate seed regions for each images as possible. Region merging is performed on each image individually in order to allow our method to be applied to large image groups. The maximal similarity measurement and nearest similarity measurement are defined as merging rules. The deliberately designed merging strategy aims to merge two regions using the maximal similarity rule and label two regions as the same class but not merge them using the nearest similarity rule. The proposed method has been compared with some state‐of‐the‐art methods on three datasets, and the experimental results show its effectiveness

Membership-Degree Preserving Discriminant Analysis with Applications to Face Recognition

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm

Progressive Damage Behaviour Analysis and Comparison with 2D/3D Hashin Failure Models on Carbon Fibre–Reinforced Aluminium Laminates

It is known that carbon fibre–reinforced aluminium laminate is the third generation of fibre metal materials. This study investigates the response of carbon fibre–reinforced aluminium laminates (CARALL) under tensile loading and three-point bending tests, which evaluate the damage initiation and propagation mechanism. The 2D Hashin and 3D Hashin VUMAT models are used to analyse and compare each composite layer for finite element modelling. A bilinear cohesive contact model is modelled for the interface failure, and the Johnson cook model describes the aluminium layer. The mechanical response and failure analysis of CARALL were evaluated using load versus deflection curves, and the scanning electron microscope was adopted. The results revealed that the failure modes of CARALL were mainly observed in the aluminium layer fracture, fibre pull-out, fracture, and matrix tensile fracture under tensile and flexural loading conditions. The 2D Hashin and 3D Hashin models were similar in predicting tensile properties, flexural properties, mechanical response before peak load points, and final failure modes. It is highlighted that the 3D Hashin model can accurately reveal the failure mechanism and failure propagation mechanism of CARALL

Temporal segmentation of EEG based on functional connectivity network structure

Abstract In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences

Screening, simulation, and optimization design of small molecule inhibitors of the SARS-CoV-2 spike glycoprotein.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency of international concern. The spike glycoprotein (S protein) of SARS-CoV-2 is a key target of antiviral drugs. Focusing on the existing S protein structure, molecular docking was used in this study to calculate the binding energy and interaction sites between 14 antiviral molecules with different structures and the SARS-CoV-2 S protein, and the potential drug candidates targeting the SARS-CoV-2 S protein were analyzed. Tizoxanide, dolutegravir, bictegravir, and arbidol were found to have high binding energies, and they effectively bind key sites of the S1 and S2 subunits, inhibiting the virus by causing conformational changes in S1 and S2 during the fusion of the S protein with host cells. Based on the interactions among the drug molecules, the S protein and the amino acid environment around the binding sites, rational structure-based optimization was performed using the molecular connection method and bioisosterism strategy to obtain Ti-2, BD-2, and Ar-3, which have much stronger binding ability to the S protein than the original molecules. This study provides valuable clues for identifying S protein inhibitor binding sites and the mechanism of the anti-SARS-CoV-2 effect as well as useful inspiration and help for the discovery and optimization of small molecule S protein inhibitors