Planar polishing process method based on real-time dressing of a polishing pad surface.

Planar polishing is an important manufacturing process for high-precision planar components. In this study, a real-time dresser and a planar polishing process based on real-time dressing for large-aperture optical plane components were developed. Efficient dressing of a polishing pad surface can be achieved with the real-time dresser. Compared with the conventional method, real-time correction for the surface shape of the polishing pad was realized via the temperature parameter t in the real-time dresser, and this parameter can be optimized through optimization experiments. Finally, a series of experiments was carried out to verify the effectiveness of the real-time dresser on surface dressing. Through the real-time dressing of the polishing pad surface, the flatness peak-valley deviation and the root-mean-square deviation of the flat optical element surface (430×430mm) can reach 3λ and 0.9λ, which is improved by 25% and 33%, respectively.

Combined processing strategy based on magnetorheological finishing for monocrystalline silicon x-ray mirrors.

Higher requirements for monocrystalline silicon x-ray mirrors have been put forward with the development of synchrotron radiation optics. The existing processing technologies limit their efficiency while achieving high-precision manufacturing of x-ray mirrors. Hence, this paper formulates a processing strategy of employing magnetorheological finishing (MRF) to make the precision of x-ray mirrors fully meet the standard. The combination of fine polishing and super-smooth processing can effectively improve the surface quality of mirrors. The residual error, wavefront gradient, and surface roughness of the mirror can reach 7.2 nm, 0.42 µrad, and 0.28 nm, respectively, after several iterations. The research not only indicates that MRF can replace the existing manufacturing method and greatly improve processing efficiency, but also provides technical support for optimizing the processing route of x-ray mirrors.

Analysis of the smoothing characteristics and shape-retaining ability of conformal vibration polishing and suppression strategy for full-spatial frequency errors of optics.

Conformal vibration polishing (CVP) employing flexible polishing tools is expected to be an efficient means of optical processing, and all current research on it is limited to planar components. Hence, the smoothing characteristics of the middle spatial frequency (MSF) errors and the ability to maintain the surface shape of different types of optics in CVP are analyzed. A combined processing method based on magnetorheological finishing and CVP for full-spatial frequency errors is proposed and verified by experiment. The peak-to-valley value, MSF errors, and surface roughness of the large-diameter component can reach 75 nm, 1.1 nm, and 0.37 nm after 9 h of processing. The research not only demonstrates the excellent removal characteristics of CVP and the effectiveness of the proposed method but also provides an additional choice for the high-precision manufacturing of optics.

Investigation of subsurface damage density and morphology impact on the laser-induced damage threshold of fused silica.

The laser-induced damage threshold (LIDT) of fused silica is affected by laser field intensity modulation and laser energy absorption. In this paper, the subsurface damage (SSD) density and morphology are detected by the small-angle taper polishing method. The modulation effect of SSD morphology on the incident laser/electric field is analyzed by the finite difference time domain (FDTD) simulation. Finally, the LIDT of the taper polished surface is tested to analyze the relationship among LIDT, SSD density, and SSD morphology, and the results show a high correlation. A reliable regression model is obtained based on the results, which shows that LIDT is inversely proportional to SSD density and the light intensity enhancement factor (LIEF).

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset.

BACKGROUND AND OBJECTIVE: Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. METHODS: NNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. RESULTS: The SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. CONCLUSIONS: The proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.