An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles

The automatic classification of aluminum profile surface defects is of great significance in improving the surface quality of aluminum profiles in practical production. This classification is influenced by the small and unbalanced number of samples and lack of uniformity in the size and spatial distribution of aluminum profile surface defects. It is difficult to achieve high classification accuracy by directly using the current advanced classification algorithms. In this paper, digital image processing methods such as rotation, flipping, contrast, and luminance transformation were used to augment the number of samples and imitate the complex imaging environment in actual practice. A RepVGG with CBAM attention mechanism (RepVGG-CBAM) model was proposed and applied to classify ten types of aluminum profile surface defects. The classification accuracy reached 99.41%, in particular, the proposed method can perfectly classify six types of defects: concave line (cl), exposed bottom (eb), exposed corner bottom (ecb), mixed color (mc), non-conductivity (nc) and orange peel (op), with 100% precision, recall, and F1. Compared with the existing advanced classification algorithms VGG16, VGG19, ResNet34, ResNet50, ShuffleNet_v2, and basic RepVGG, our model is the best in terms of accuracy, macro precision, macro recall and macro F1, and the accuracy was improved by 4.85% over basic RepVGG. Finally, an ablation experiment proved that the classification ability was strongest when the CBAM attention mechanism was added following Stage 1 to Stage 4 of RepVGG. Overall, the method we proposed in this paper has a significant reference value for classifying aluminum profile surface defects

Design on the Winter Jujubes Harvesting and Sorting Device

According to the existing problems of winter jujube harvesting, such as the intensive labor of manual picking, damage to the surface of winter jujubes, a winter jujube harvesting and sorting device was developed. This device consisted of vibration mechanism, collection mechanism, and sorting mechanism. The eccentric vibration mechanism made the winter jujubes fall, and the umbrella collecting mechanism can collect winter jujube and avoid the impact of winter jujube on the ground, and the sorting mechanism removed jujube leaves and divided the jujube into two types, and the automatic leveling mechanism made the device run smoothly in the field. Through finite element analysis and BP (Back Propagation) neural network analysis, the results show that: The vibration displacement of jujube tree is related to the trunk diameter and vibration position; the impact force of winter jujubes falling is related to the elastic modulus of umbrella material; the collecting area can be increased four times for each additional step of the collection mechanism; jujube leaves can be effectively removed when blower wind speed reaches 45.64 m/s. According to the evaluation standard grades of the jujubes harvesting and sorting, the device has good effects and the excellent rate up to 90%, which has good practicability and economy

Gender-Specific Risk of Central Compartment Lymph Node Metastasis in Papillary Thyroid Carcinoma

Our aim was to evaluate the impact of gender on the predictive factors of central compartment lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC). A retrospective study of 590 patients treated for PTC was performed. Univariate and multivariate analyses showed that gender (female; P=0.001), age (≥45 y; P<0.001), tumor size (>1 cm; P<0.001), and multifocality (P=0.004) were independent predictive factors of CLNM in PTC patients. Patients were divided into male group (n=152) and female group (n=438). Age (≥45 y; P=0.001), T4 (P=0.006) and multifocality (P=0.024) were independent predictive risk factors of CLNM in male patients. As for female patients, age (≥45 y; P<0.001), tumor size (>1 cm; P<0.001), multifocality (P=0.002), and microcalcification (P=0.027) were independently correlated with CLNM. The sensitivity of the multivariate model for predicting CLNM in male patients was 64.9%, specificity was 82.9%, and area under the ROC curve (AUC) was 0.764. As for female patients, the sensitivity was 55.7%, specificity was 77.9%, and AUC was 0.73. This study showed that the predictive factors of CLNM indeed varied according to gender. To have a more accurate evaluation of CLNM, different predictive systems should be used for male and female patients

Strategies in product engineering of mesenchymal stem cell-derived exosomes: unveiling the mechanisms underpinning the promotive effects of mesenchymal stem cell-derived exosomes

Articular cartilage injuries present a significant global challenge, particularly in the aging population. These injuries not only restrict movement due to primary damage but also exacerbate elderly degenerative lesions, leading to secondary cartilage injury and osteoarthritis. Addressing osteoarthritis and cartilage damage involves overcoming several technical challenges in biological treatment. The use of induced mesenchymal stem cells (iMSCs) with functional gene modifications emerges as a solution, providing a more stable and controllable source of Mesenchymal Stem Cells (MSCs) with reduced heterogeneity. Furthermore, In addition, this review encompasses strategies aimed at enhancing exosome efficacy, comprising the cultivation of MSCs in three-dimensional matrices, augmentation of functional constituents within MSC-derived exosomes, and modification of their surface characteristics. Finally, we delve into the mechanisms through which MSC-exosomes, sourced from diverse tissues, thwart osteoarthritis (OA) progression and facilitate cartilage repair. This review lays a foundational framework for engineering iMSC-exosomes treatment of patients suffering from osteoarthritis and articular cartilage injuries, highlighting cutting-edge research and potential therapeutic pathways