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This paper aims to improve college students’ entrepreneurial abilities as its main goal. A platform for college students’ entrepreneurship development is constructed using cloud technology, and the entrepreneurship data on the platform is classified and divided by combining it with the KNN algorithm. The algorithm is optimized for distance calculation and distance sorting stages to reduce inter-program dependencies and shorten the program execution time. The distance between matrices is calculated by calling the functions in the cublas function library. The experimental data are used to analyze the feasibility of entrepreneurship training for college students. The results show that the KNN algorithm can be applied to data processing in cloud computing. When the data dimension is 128 and the number of sample points is 215, the algorithm can get a maximum speedup of 1.52 times. The server configuration of the platform needs to be in a 4-way dual-core or 4-core to ensure the platform’s normal operation. This study is beneficial in promoting the improvement and development of entrepreneurial skills for college students.

Micro-vibrations during the operation of space remote sensing equipment can significantly affect optical imaging quality. To address this issue, a bellows-type viscous damper serves as an effective passive damping and vibration isolation solution. This paper introduces a bellows-type viscous damper with adjustable damping capabilities, designed for mid- to high-frequency applications. We developed a system damping model based on hydraulic fluid dynamics to examine how different factors—such as viscous coefficients, damping hole lengths, hole diameters, chamber pressures, and volumes—influence the damping characteristics. To validate the theoretical model, we constructed an experimental platform. The experimental results show that the theoretical damping curves closely match the measured data. Moreover, increasing the chamber pressure effectively enhances the damper's damping coefficient, with the deviation from theoretical predictions being approximately 4%. [ABSTRACT FROM AUTHOR]

Background: Lumbar disc herniation may result in excruciating pain due to it being one of the most common diseases related to changes of intervertebral disc (IVD). In order to find a better clinical treatment and prevention scheme for relieving the pain caused by spine degeneration, the mechanical behavior analysis of IVD must be studied.Objective: A finite element method (FEM) is used in this study to analyze the mechanical behaviors of healthy and herniated IVD.Method: In order to compare the responses of IVD under different loading conditions for the annulus fibrosus of IVD, the hyperelastic and elastic constitutive FE models were used in the FEM.Results: The comparison shows that hyperelastic FE models have a much better capability to describe the mechanical behaviors of the IVD than elastic FE models. It can be found from FE simulation that there was a higher stress concentration at the annulus fibrosus of the herniated disc than the healthy disc.Conclusions: Higher stress concentration resulted in more damage and ease of bringing out lumbar disc herniation. Numerical examples of FE simulation indicate that the FEM with hyperelastic constitutive model has very good capability for analyzing the mechanical behaviors of IVD. [ABSTRACT FROM AUTHOR]

Objective: Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy., Methods: scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features., Results: By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients., Conclusion: Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ding, Xu, Hong, Zhou, He, Niu, Tian, Li, Zeng and Liu.)