An mechatronics coupling design approach for aerostatic bearing spindles

In this paper, a new design approach for aerostatic bearing spindles (ABS) is firstly proposed which takes into account of the interactions between the mechanical and the servo subsystems, including the integration of electromagnetic effects, static pressure characteristics, servo control and mechanical characteristics. According to the air bearing design principle, the geometry of the spindle rotor is designed. The fluid software is used to analyze the influence of the bearing capacity and stiffness on the stability of the spindle. The simulation shows when the air film thickness is 12 μm, the bearing has good load carrying capacity and rigidity. In addition, the influence of motor harmonics on the spindle shaft modes is considered to avoid the resonance of ABS, and to ensure ABS anti-interference capability, proper inertia of ABS is calculated and analyzed. Finally, ABS has a good follow-up effect on the servo control and machining performance through the experimental prototype. The electromechanical coupling design approach for ABS proposed in this paper, can achieve a peak value better than 0.8 μm (surface size: 9 mm × 9 mm) and a surface roughness better than 8 nm in end face turning experiments

Editorial: Targeting nucleotide metabolism for enhancing antitumor immunity.

Editorial

A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC

Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey

COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have adopted a number of extraordinary steps to restrict the spreading virus, where early COVID-19 diagnosis is essential. Medical images such as X-ray images and Computed Tomography scans are becoming one of the main diagnostic tools to combat COVID-19 with the aid of deep learning-based systems. In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis. We also provide a summary of the X-ray and CT image datasets used in various studies for COVID-19 detection. The key difficulties and potential applications of deep learning in fighting against COVID-19 are finally discussed. This work summarizes the latest methods of deep learning using medical images to diagnose COVID-19, highlighting the challenges and inspiring more studies to keep utilizing the advantages of deep learning to combat COVID-19