Cross-lagged analysis of rumination and social anxiety among Chinese college students.

BACKGROUND: Social anxiety, which is widely prevalent among Chinese college students, poses a significant barrier to their holistic psychological and physiological development. Although numerous cross-sectional studies have examined the relationship between rumination and social anxiety, there is still a gap in understanding their interplay over time. This longitudinal study aimed to explore and analyze the intricate interrelations between these two factors, with the ultimate goal of informing the development of effective mental health education interventions for university students. METHODS: Using the Ruminative Responses Scale (RRS) and the Interaction Anxiousness Scale (IAS), a two-stage longitudinal follow-up study of 392 college students from three universities in Henan Province was conducted over a six-month period (October 2022 to March 2023) using a cross-lagged model to explore the correlation between rumination and social anxiety. The results of the correlation analysis showed that rumination was positively associated with social anxiety at both time points (r = 0.18,0.12, p < 0.01). RESULTS: Cross-lagged regression analyses revealed that the predictive effect of the first measure (T1) rumination on the second measure (T2) rumination was statistically significant (ß = 0.32, p < 0.001). The predictive effect of T1 social anxiety on T2 social anxiety was statistically significant (ß = 0.65, p < 0.001), the predictive effect of T1 rumination on T2 social anxiety was statistically significant (ß = 0.33, p < 0.001), and the prediction of T1 social anxiety on T2 rumination was statistically significant (ß = 0.28, p < 0.001). CONCLUSION: College students' rumination and social anxiety are mutually predictive of each other, and interventions by educators in either of these areas have the potential to interrupt the vicious cycle between ruminant thinking and social anxiety.

Human behavior recognition based on sparse transformer with channel attention mechanism.

Human activity recognition (HAR) has recently become a popular research field in the wearable sensor technology scene. By analyzing the human behavior data, some disease risks or potential health issues can be detected, and patients' rehabilitation progress can be evaluated. With the excellent performance of Transformer in natural language processing and visual tasks, researchers have begun to focus on its application in time series. The Transformer model models long-term dependencies between sequences through self-attention mechanisms, capturing contextual information over extended periods. In this paper, we propose a hybrid model based on the channel attention mechanism and Transformer model to improve the feature representation ability of sensor-based HAR tasks. Extensive experiments were conducted on three public HAR datasets, and the results show that our network achieved accuracies of 98.10%, 97.21%, and 98.82% on the HARTH, PAMAP2, and UCI-HAR datasets, respectively, The overall performance is at the level of the most advanced methods.

Effect of entrepreneurial education and creativity on entrepreneurial intention in college students: mediating entrepreneurial inspiration, mindset, and self-efficiency.

Entrepreneurship in higher education is increasingly valuing entrepreneurial creativity as a significant driver for improving university students' innovative abilities. The purpose of this study was to examine the direct influence of entrepreneurial education and creativity on entrepreneurial intention, as well as the indirect role of entrepreneurial inspiration, mindset, and self-efficiency. This study gathered survey responses from 448 university business students from three Chinese provinces of Shandong, Jiangsu and Zhejiang. The results indicated that entrepreneurial education and creativity have a positive and significant effect on entrepreneurial intent. In addition, the results demonstrated that the combination of entrepreneurial mindset, inspiration, and self-efficacy partially mediates the relationship between entrepreneurial education and entrepreneurial creativity. In addition, additional implications and restrictions are discussed in this article.

MSM-ViT: A multi-scale MobileViT for pulmonary nodule classification using CT images.

BACKGROUND: Accurate classification of benign and malignant pulmonary nodules using chest computed tomography (CT) images is important for early diagnosis and treatment of lung cancer. In terms of natural image classification, the ViT-based model has greater advantages in extracting global features than the traditional CNN model. However, due to the small image dataset and low image resolution, it is difficult to directly apply the ViT-based model to pulmonary nodule classification. OBJECTIVE: To propose and test a new ViT-based MSM-ViT model aiming to achieve good performance in classifying pulmonary nodules. METHODS: In this study, CNN structure was used in the task of classifying pulmonary nodules to compensate for the poor generalization of ViT structure and the difficulty in extracting multi-scale features. First, sub-pixel fusion was designed to improve the ability of the model to extract tiny features. Second, multi-scale local features were extracted by combining dilated convolution with ordinary convolution. Finally, MobileViT module was used to extract global features and predict them at the spatial level. RESULTS: CT images involving 442 benign nodules and 406 malignant nodules were extracted from LIDC-IDRI data set to verify model performance, which yielded the best accuracy of 94.04% and AUC value of 0.9636 after 10 cross-validations. CONCLUSION: The proposed new model can effectively extract multi-scale local and global features. The new model performance is also comparable to the most advanced models that use 3D volume data training, but its occupation of video memory (training resources) is less than 1/10 of the conventional 3D models.

The impact of value co-creation behavior on customers' experiences with and loyalty to P2P accommodations.

This study explores the impact of customers' value co-creation behavior on their experiences with and loyalty to P2P accommodations. We propose a theoretical model integrating two lines of tourism research: customer value co-creation and customer experience. To extract the dimensions of customer experience and test the proposed model, 34 in-depth interviews were conducted along with a survey of Chinese Airbnb users. Structural equation modeling and mediation analysis were implemented to assess relationships involving customers' value co-creation behavior, experience, and loyalty. Results indicate that customer citizenship behavior directly influences loyalty. In particular, relationships involving customers' participation behavior and citizenship behavior with loyalty are both mediated by customer experience. Relevant implications and future research opportunities are discussed.

An adaptive pulmonary nodule detection algorithm.

Recently, lung cancer has been paid more and more attention. People have reached a consensus that early detection and early treatment can improve the survival rate of patients. Among them, pulmonary nodules are the important reference for doctors to determine the lung health. With the continuous improvement of CT image resolution, more suspected pulmonary nodule information appears from the impact of chest CT. How to relatively and accurately locate the suspected nodule location from a large number of CT images has brought challenges to the doctor's daily diagnosis. To solve the problem that the original DBSCAN clustering algorithm needs manual setting of the threshold, this paper proposes a region growing algorithm and an adaptive DBSCAN clustering algorithm to improve the accuracy of pulmonary nodule detection. The image is roughly processed and ROI (Regions of Interest) region is roughly extracted by CLAHE transform. The region growing algorithm is used to roughly process the adjacent region's expansibility and the suspected region in ROI, and mark the center point in the region and the boundary point of its point set. The mean value of region range is taken as the threshold value of DBSCAN clustering algorithm. The center of the point domain is used as the starting point of clustering, and the rough set of points is used as the MinPts threshold. Finally, the clustering results are labeled in the initial CT image. Experiments show that the pulmonary nodule detection method proposed in this paper effectively improves the accuracy of the detection results.