In recent years, many experiments have proved that microRNAs (miRNAs) play a variety of important regulatory roles in cells, and their abnormal expression can lead to the emergence of specific diseases. Therefore, it is greatly valuable to do research on the association between miRNAs and diseases, which can effectively help prevent and treat miRNA-related diseases. At present, effective computational methods still need to be developed to better identify potential miRNA–disease associations. Inspired by graph convolutional networks, in this study, we propose a new method based on Attention aware Multi-view similarity networks and Hypergraph learning for MiRNA-Disease Associations identification (AMHMDA). First, we construct multiple similarity networks for miRNAs and diseases, and exploit the graph convolutional networks fusion attention mechanism to obtain the important information from different views. Then, in order to obtain high-quality links and richer nodes information, we introduce a kind of virtual nodes called hypernodes to construct heterogeneous hypergraph of miRNAs and diseases. Finally, we employ the attention mechanism to fuse the outputs of graph convolutional networks, predicting miRNA–disease associations. To verify the effectiveness of this method, we carry out a series of experiments on the Human MicroRNA Disease Database (HMDD v3.2). The experimental results show that AMHMDA has good performance compared with other methods. In addition, the case study results also fully demonstrate the reliable predictive performance of AMHMDA.

Carbon fiber reinforced plastics (CFRP)/titanium alloy (Ti) stacks have been widely used in aviation field due to the superior mechanical properties. During integrated drilling of CFRP/Ti stacks, serious damage occurs in the CFRP layer because of the disparate properties of two stack components. Heat accumulation and thermal induced damage are typical and critical issue during drilling stacks, especially in the interface region. In this study, in order to deeply analyze the thermal influence of the interface region, a numerical model based on the finite difference method is developed to predict the three-dimensional drilling temperature field. Experiments with accurate measurement point are conducted to valid the rational of temperature prediction model. The results confirm that the temperature distributions predicted by numerical study have good agreements with the experimental results and the maximum error is about 10.3%. Furtherly, based on the drilling experiments, it can be found that thermal damage induced by cutting heat occurs as discoloration rings around the hole which could cause the elastic modulus of resin matrix decrease. An empirical model of thermal damage with maximum drilling temperature of the interface region are developed with the correlation of R² = 0.97. The findings point out that as the maximum drilling temperature exceeds 410 °C, serious thermal damage could occur in the resin matrix of CFRP layer.

A progressive damage model for aramid honeycomb cutting was proposed to reveal its cutting damage mechanism. It established the relationship between the mesoscale failure modes and the macroscale cutting damage types of the aramid honeycomb. The proposed model addressed the material assignment problem of impregnated honeycomb by developing a material calculation method that simulates the real manufacturing process of the aramid honeycomb. Cutting experiment of aramid honeycomb specimen was conducted concerning on the cutting forces response and cutting damages, which validated that the proposed method was effective for investigating the cutting process and mechanism for the aramid honeycomb. Predicted cutting mechanism results show that: (a) cutting process of the aramid honeycomb can be divided into three stages with four characteristic states—initial state, cut-in state, cut-out state and final state; (b) cell wall bending in the cutting direction relieves the cutting force, and strong plasticity of the aramid fiber makes it hard to break, which lead to uncut fiber and burr damages; (c) using sharp tip cutting tool to reduce cutting force and bonding both top and bottom of the honeycomb to make it stiffer are beneficial to obtain good cutting quality with less damages.

Glutarylation is a post-translational modification which plays an irreplaceable role in various functions of the cell. Therefore, it is very important to accurately identify the glutarylation substrates and its corresponding glutarylation sites. In recent years, many computational methods of glutarylation sites have emerged one after another, but there are still many limitations, among which noisy data and the class imbalance problem caused by the uncertainty of non-glutarylation sites are great challenges. In this study, we propose a new semi-supervised learning algorithm, named FCCCSR, to identify reliable non-glutarylation lysine sites from unlabeled samples as negative samples. FCCCSR first finds core objects from positive samples according to reverse nearest neighbor information, and then clusters core objects based on natural neighbor structure. Finally, reliable negative samples are selected according to clustering result. With FCCCSR algorithm, we propose a new method named FCCCSR_Glu for glutarylation sites identification. In this study, multi-view features are extracted and fused to describe peptides, including amino acid composition, BLOSUM62, amino acid factors and composition of k-spaced amino acid pairs. Then, reliable negative samples selected by FCCCSR and positive samples are combined to establish models and XGBoost optimized by differential evolution algorithm is used as the classifier. On the independent testing dataset, FCCCSR_Glu achieves 85.18%, 98.36%, 94.31% and 0.8651 in sensitivity, specificity, accuracy and Matthew’s Correlation Coefficient, respectively, which is superior to state-of-the-art methods in predicting glutarylation sites. Therefore, FCCCSR_Glu can be a useful tool for glutarylation sites prediction and FCCCSR algorithm can effectively select reliable negative samples from unlabeled samples. The data and code are available on https://github.com/xbbxhbc/FCCCSR_Glu.git

The tool structure is an important factor affecting the damage of CFRP/Ti stacks machining. However, the impact of tool structure on the formation process of stacks hole damage cannot be fully revealed through experimental methods alone. In contrast, finite element simulation can effectively overcome the limitations of experiments. In this study, a numerical simulation model is established to investigate the relationship between step drill structure and formation process of CFRP/Ti stacks hole damage. Based on this, the research discusses the effect of step drill structure on the burr height of Ti layer, delamination of CFRP, aperture deviation, defects in hole surface. The results show that when the stacking sequence is CFRP to Ti, the burr height of Ti at hole exit decreases first and then increases with the rising of the ratio of primary drill bit diameter to secondary drill bit diameter (k_d). When k_d is 0.6, the burr height of Ti at hole exit is the lower. As k_d increasing from 0.4 to 1.0, delamination factor of CFRP increases by 2.57%, which are affected little by the step drill structure due to the support of Ti. Besides, the aperture size deviation decreases first then increases with the rising of k_d, and the minimum aperture size deviation is 2.09 μm when k_d is 0.6. In addition, as k_d is 0.6, the hole wall defect is fewer. In conclusion, step drill with k_d of 0.6 is suitable for drilling of CFRP/Ti stacks.