Desymmetrization-Addition Reaction of Cyclopropenes to Imines via Synergistic Photoredox and Cobalt Catalysis.

Herein, we designed a reaction for the desymmetrization-addition of cyclopropenes to imines by leveraging the synergy between photoredox and asymmetric cobalt catalysis. This protocol facilitated the synthesis of a series of chiral functionalized cyclopropanes with high yield, enantioselectivity, and diastereoselectivity (44 examples, up to 93% yield and >99% ee). A possible reaction mechanism involving cyclopropene desymmetrization by Co-H species and imine addition by Co-alkyl species was proposed. This study provides a novel route to important chiral cyclopropanes and extends the frontier of asymmetric metallaphotoredox catalysis.

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7.

Variety detection provides technical support for selecting XinHui citrus for use in the production of XinHui dried tangerine peel. Simultaneously, the mutual occlusion between tree leaves and fruits is one of the challenges in object detection. In order to improve screening efficiency, this paper introduces a YOLO(You Only Look Once)v7-BiGS(BiFormer&GSConv) citrus variety detection method capable of identifying different citrus varieties efficiently. In the YOLOv7-BiGS network model, initially, the BiFormer attention mechanism in the backbone of the YOLOv7-based network strengthens the model's ability to extract citrus' features. In addition, the introduction of the lightweight GSConv convolution in place of the original convolution within the ELAN of the head component effectively streamlines model complexity while maintaining performance integrity. To environment challenge validate the effectiveness of the method, the proposed YOLOv7-BiGS was compared with YOLOv5, YOLOv7, and YOLOv8. In the comparison of YOLOv7-BiGS with YOLOv5, YOLOv7, and YOLOv8, the experimental results show that the precision, mAP and recell of YOLOv7-BiGS are 91%, 93.7% and 87.3% respectively. Notably, compared to baseline methods, the proposed approach exhibited significant enhancements in precision, mAP, and recall by 5.8%, 4.8%, and 5.2%, respectively. To evaluate the efficacy of the YOLOv7-BiGS in addressing challenges posed by complex environmental conditions, we collected occluded images of Xinhui citrus fruits from the Xinhui orchard base for model detection. This research aims to fulfill performance criteria for citrus variety identification, offering vital technical backing for variety detection endeavors.

DG2GAN: improving defect recognition performance with generated defect image sample.

This article aims to improve the deep-learning-based surface defect recognition. In actual manufacturing processes, there are issues such as data imbalance, insufficient diversity, and poor quality of augmented data in the collected image data for product defect recognition. A novel defect generation method with multiple loss functions, DG2GAN is presented in this paper. This method employs cycle consistency loss to generate defect images from a large number of defect-free images, overcoming the issue of imbalanced original training data. DJS optimized discriminator loss is introduced in the added discriminator to encourage the generation of diverse defect images. Furthermore, to maintain diversity in generated images while improving image quality, a new DG2 adversarial loss is proposed with the aim of generating high-quality and diverse images. The experiments demonstrated that DG2GAN produces defect images of higher quality and greater diversity compared with other advanced generation methods. Using the DG2GAN method to augment defect data in the CrackForest and MVTec datasets, the defect recognition accuracy increased from 86.9 to 94.6%, and the precision improved from 59.8 to 80.2%. The experimental results show that using the proposed defect generation method can obtain sample images with high quality and diversity and employ this method for data augmentation significantly enhances surface defect recognition technology.

Optimization of surgical exposure for harvesting gracilis-semitendinosus tendons.

PURPOSE: This study was conducted to provide anatomical data and surface markers for the safe and efficient exposure of surgical incisions for harvesting gracilis tendons (GT) and semitendinosus tendons (STT) while avoiding technical pitfalls and nerve injury during harvest for ligament reconstruction. METHODS: Seventy-four Chinese cadaveric lower limbs were dissected to expose the infrapatellar branch of the saphenous nerve (IPBSN) and pes anserinus (PA). Measurements of the borders and accessory bands of the PA tendons were taken. The arrangement of PA tendons and distribution of the IPBSN were assessed. RESULTS: The PA was roughly shaped like a quadrangle, with its superior border at the horizontal plane of the tibial tuberosity (TT). The GT and STT bifurcation point was located on the medial border of the PA. From medial side to lateral side, the sartorius tendons (ST), GT, and STT fused gradually and formed the lateral border of the PA at the distal end. The tendon arrangement of the PA was primarily affected by ST, which commonly covered GT and STT completely. Variant tendons were found in 41.9% of specimens. The insertion of the accessory bands was distal but close to the inferior border of the PA. Accessory bands were observed only in STT and ST, and STT accounted for the most. The width of the first accessory band of STT was similar to the width of the STT. Additionally, most of the IPBSNs were proximal to the horizontal plane of the TT. CONCLUSION: For clearly exposing the GT and STT, it is crucial to expose the GT and STT bifurcation point on the medial border of the PA, whether directly or indirectly through the incision.The influence of ST insertion and the variability of tendons within the PA must be paid attention to during the operation. To protect IPBSNs highly, the incision should not be higher than the TT level.