Reactive air wetting and brazing of Al2O3 ceramics using Ag–Nb2O5 filler: Performance and interfacial behavior

We firstly performed the reactive air wetting and brazing of Al2O3 ceramics using Ag–(0.5‒12)Nb2O5 fillers, where Nb2O5 can react with liquid Ag and O2 from air to generate AgNbO3. The contact angle of the Ag–Nb2O5/Al2O3 system almost linearly decreases from ~71.6° to 32.5° with the Nb2O5 content increasing, and the joint shear strength reaches the maximum of ~65.1 MPa while employing the Ag–4Nb2O5 filler, which are mainly related to the formation and distribution of the AgNbO3 phase at the interface. Moreover, the interfacial bonding and electronic properties of related interfaces were investigated by first-principles calculations. The calculated works of adhesion (Wa) of Ag(111)/Ag–O–AgNbO3(001) and AgNbO3(001)/Al2O3(100) interfaces are higher than that of the Ag(111)/Al2O3(110) interface, indicating good reliability of the Ag/AgNbO3/Al2O3 structure. The relatively large interfacial charge transfer indicates the formation of Ag–Ag, Al–O, and Ag–O bonds in the Ag/AgNbO3/Al2O3 structure, which can contribute to the interfacial bonding

Expression and Molecular Modification of Chitin Deacetylase from Streptomyces bacillaris

Chitin deacetylase can be used in the green and efficient preparation of chitosan from chitin. Herein, a novel chitin deacetylase SbCDA from Streptomyces bacillaris was heterologously expressed and comprehensively characterized. SbDNA exhibits its highest deacetylation activity at 35 °C and pH 8.0. The enzyme activity is enhanced by Mn2+ and prominently inhibited by Zn2+, SDS, and EDTA. SbCDA showed better deacetylation activity on colloidal chitin, (GlcNAc)5, and (GlcNAc)6 than other forms of the substrate. Molecular modification of SbCDA was conducted based on sequence alignment and homology modeling. A mutant SbCDA63G with higher activity and better temperature stability was obtained. The deacetylation activity of SbCDA63G was increased by 133% compared with the original enzyme, and the optimal reaction temperature increased from 35 to 40 °C. The half-life of SbCDA63G at 40 °C is 15 h, which was 5 h longer than that of the original enzyme. The improved characteristics of the chitin deacetylase SbCDA63G make it a potential candidate to industrially produce chitosan from chitin

TranSegNet: Hybrid CNN-Vision Transformers Encoder for Retina Segmentation of Optical Coherence Tomography

Optical coherence tomography (OCT) provides unique advantages in ophthalmic examinations owing to its noncontact, high-resolution, and noninvasive features, which have evolved into one of the most crucial modalities for identifying and evaluating retinal abnormalities. Segmentation of laminar structures and lesion tissues in retinal OCT images can provide quantitative information on retinal morphology and reliable guidance for clinical diagnosis and treatment. Convolutional neural networks (CNNs) have achieved success in various medical image segmentation tasks. However, the receptive field of convolution has inherent locality constraints, resulting in limitations of mainstream frameworks based on CNNs, which is still evident in recognizing the morphological changes of retina OCT. In this study, we proposed an end-to-end network, TranSegNet, which incorporates a hybrid encoder that combines the advantages of a lightweight vision transformer (ViT) and the U-shaped network. The CNN features under multiscale resolution are extracted based on the improved U-net backbone, and a ViT with the multi-head convolutional attention is introduced to capture the feature information in a global view, realizing accurate localization and segmentation of retinal layers and lesion tissues. The experimental results illustrate that hybrid CNN-ViT is a strong encoder for retinal OCT image segmentation tasks and the lightweight design reduces its parameter size and computational complexity while maintaining its outstanding performance. By applying TranSegNet to healthy and diseased retinal OCT datasets separately, TranSegNet demonstrated superior efficiency, accuracy, and robustness in the segmentation results of retinal layers and accumulated fluid than the four advanced segmentation methods, such as FCN, SegNet, Unet and TransUnet

Lithography Hotspot Detection Method Based on Transfer Learning Using Pre-Trained Deep Convolutional Neural Network

As the designed feature size of integrated circuits (ICs) continues to shrink, the lithographic printability of the design has become one of the important issues in IC design and manufacturing. There are patterns that cause lithography hotspots in the IC layout. Hotspot detection affects the turn-around time and the yield of IC manufacturing. The precision and F1 score of available machine-learning-based hotspot-detection methods are still insufficient. In this paper, a lithography hotspot detection method based on transfer learning using pre-trained deep convolutional neural network is proposed. The proposed method uses the VGG13 network trained with the ImageNet dataset as the pre-trained model. In order to obtain a model suitable for hotspot detection, the pre-trained model is trained with some down-sampled layout pattern data and takes cross entropy as the loss function. ICCAD 2012 benchmark suite is used for model training and model verification. The proposed method performs well in accuracy, recall, precision, and F1 score. There is significant improvement in the precision and F1 score. The results show that updating the weights of partial convolutional layers has little effect on the results of this method