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Background: Bacteria play a crucial role in maintaining the health of human skin. Research has demonstrated that probiotics present notable benefits for extraintestinal organs. Despite the extensive research on the impact of probiotics on skin health, there is a notable absence of regulatory frameworks governing their external application, with no approval from the FDA for any probiotic products for external use. The aim of this study is to offer a thorough summary of the research status in the field since 2000 and project future trends. Method: The Web of Science Core Collection and SCI-Expanded index were selected for an extensive search of studies concerning the role of probiotics in skin health since 2000. A total of 1,306 publications were identified. Employing a double-blind method, two subsets of literature were scrutinized and subsequently combined for analysis. Using CiteSpace, this research explored key aspects such as primary countries, institutions, authors, journals, trending topics, research frontiers, and emerging patterns in research related to application of probiotic for skin health. Result: This article included 709 research papers. The number of published papers has shown a rapid increase. The United States had the highest number of research papers (128), and Canada had the highest intermediate centrality (0.23). The University of California System emerged as the most prolific institution. Huang, Chun-Ming has published the most articles, and his research is at the forefront among those prolific authors. Twelve clusters were identified, with cluster #0 skin microbiota, #3 mechanisms, and #8 antimicrobial being the most recent. As for the hot topic, "diversity," "health," "skin microbiome," "oxidative stress," "microbiota," and "antioxidants" have been at the forefront of the current field. The overall research trend has shifted from clinical trials to mechanistic exploration and from oral treatments to external applications, with the research level moving from general categories to specific strains. Conclusion: This paper summarized and visualized academic achievements in the field of probiotic application for skin health using CiteSpace and VOSviewer, offering a systematic and comprehensive perspective, along with a longitudinal overview of this research field. [ABSTRACT FROM AUTHOR]

This article adopts a literature analysis method, taking the popular phenomenon of MBTI（Myers Briggs Type Indicator） in the current era of new media as the background, aiming to study the emergence and development process of MBTI from the perspective of audience psychology. The author conducts analytical research on communication psychology-related theories such as usage and satisfaction, selective contact hypothesis, and interest groups, deeply explores the application of MBTI in audience psychology research, analyzes its value in understanding audience behavior and psychological characteristics, and explores its challenges and prospects in practical applications. While MBTI has become popular, it has also brought some negative orientations due to its labeling, such as stereotypes and group anxiety. The fanaticism and initiative exhibited by individuals in the congenial group trigger group effects through communication. In the era of new media, with the rapid development of various media matrices, how to rationally view the emotional interactions of individuals and even groups constructed by MBTI, how to use the power of new media to correctly meet audience needs, and how to make the emergence and dissemination of MBTI conform to the benign trend of social development are all issues worthy of in-depth research.

Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping. [ABSTRACT FROM AUTHOR]

For different broccoli materials, it used to be necessary to manually plant in a large area for the investigation of flower ball information, and this method is susceptible to subjective influence, which is not only time-consuming and laborious but may also cause some damage to the broccoli in the process of investigation. Therefore, the rapid and nondestructive monitoring of flower heads is key to acquiring high-throughput phenotypic information on broccoli crops. In this study, we used an unmanned aerial vehicle (UAV) to acquire hundreds of images of field-grown broccoli to evaluate their flower head development rate and sizes during growth. First, YOLOv5 and YOLOv8 were used to complete the position detection and counting statistics at the seedling and heading stages. Then, UNet, PSPNet, DeepLabv3+, and SC-DeepLabv3+ were used to segment the flower heads in the images. The improved SC-DeepLabv3+ model excelled in segmenting flower heads, showing Precision, reconciled mean F1-score, mean intersection over union, and mean pixel accuracy values of 93.66%, 95.24%, 91.47%, and 97.24%, respectively, which were 0.57, 1.12, 1.16, and 1.70 percentage points higher than the respective values achieved with the DeepLabv3+ model. Flower head sizes were predicted on the basis of the pixel value of individual flower heads and ground sampling distance, yielding predictions with an R2 value of 0.67 and root-mean-squared error of 1.81 cm. Therefore, the development rate and sizes of broccoli flower heads during growth were successively estimated and calculated. Compared with the existing technology, it greatly improves work efficiency and can help to obtain timely information on crop growth in the field. Our methodology provides a convenient, fast, and reliable way for investigating field traits in broccoli breeding. [ABSTRACT FROM AUTHOR]

Traditional deep learning methods employing 2D images can only classify healthy and unhealthy seedlings; consequently, this study proposes a method by which to further classify healthy seedlings into primary seedlings and secondary seedlings and finally to differentiate three classes of seedling through a 3D point cloud for the detection of useful eggplant seedling transplants. Initially, RGB images of three types of substrate-cultivated eggplant seedlings (primary, secondary, and unhealthy) were collected, and healthy and unhealthy seedlings were classified using ResNet50, VGG16, and MobilNetV2. Subsequently, a 3D point cloud was generated for the three seedling types, and a series of filtering processes (fast Euclidean clustering, point cloud filtering, and voxel filtering) were employed to remove noise. Parameters (number of leaves, plant height, and stem diameter) extracted from the point cloud were found to be highly correlated with the manually measured values. The box plot shows that the primary and secondary seedlings were clearly differentiated for the extracted parameters. The point clouds of the three seedling types were ultimately classified directly using the 3D classification models PointNet++, dynamic graph convolutional neural network (DGCNN), and PointConv, in addition to the point cloud complementary operation for plants with missing leaves. The PointConv model demonstrated the best performance, with an average accuracy, precision, and recall of 95.83, 95.83, and 95.88%, respectively, and a model loss of 0.01. This method employs spatial feature information to analyse different seedling categories more effectively than two-dimensional (2D) image classification and three-dimensional (3D) feature extraction methods. However, there is a paucity of studies applying 3D classification methods to predict useful eggplant seedling transplants. Consequently, this method has the potential to identify different eggplant seedling types with high accuracy. Furthermore, it enables the quality inspection of seedlings during agricultural production. [ABSTRACT FROM AUTHOR]

To address the current issues of low intelligence and accuracy in seed-sorting devices, an intelligent seed sorter was developed in this study using machine-vision technology and the lightweight YOLOv5n. The machine consisted of a transmission system, feeding system, image acquisition system, and seed screening system. A lightweight YOLOv5n model, FS-YOLOv5n, was trained using 4756 images, incorporating FasterNet, Local Convolution (PConv), and a squeeze-and-excitation (SE) attention mechanism to improve feature extraction efficiency, detection accuracy, and reduce redundancy. Taking 'Zhengdan 958' corn seeds as the research object, a quality identification and seed sorting test was conducted on six test groups (each consisting of 1000 seeds) using the FS-YOLOv5n model. Following lightweight improvements, the machine showed an 81% reduction in parameters and floating-point operations compared to baseline models. The intelligent seed sorter achieved an average sorting rate of 90.76%, effectively satisfying the seed-sorting requirements. [ABSTRACT FROM AUTHOR]