Textile materials have been enriched in function at the composite level with continuous developments in the textile industry. Zinc oxide (ZnO) nanoparticles (ZnO-NPs) are strongly influenced by ultraviolet (UV) filter, antifungal, high catalysis, and semiconductor/piezoelectric coupling characteristics. Therefore, the antibacterial property and UV resistance of ZnO-NP materials are zcomprehensively analysed to provide a basis for applying ZnO-NP in the textile industry. In addition, the textile preparation and application of ZnO-NP in piezoelectric power generation is discussed. Based on relevant documents for ZnO-textile industry applications, scanning electron microscopy analysis, biological activity analysis, and UV transmittance analysis of textiles containing composite materials prove that textiles based on ZnO-based composite materials (ZnO-NP materials) have antibacterial properties and UV resistance. The antibacterial property and UV resistance of ZnO-NP materials are analysed comprehensively to provide a basis for applying ZnO-NP in the textile industry. After the photocatalytic reaction, its practical application as slurry type suspensions is limited because of the difficulty of separating the catalyst particles. In terms of its piezoelectric power generation characteristics, intensity of current voltage analysis and X-ray diffraction analysis reveal that textiles based on ZnO-NP materials have obvious semiconductor characteristic and obvious current enhancement signals locally, indicating that the textiles can achieve better piezoelectric properties.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The knowledge map and visualization on the technological hotspots and the developmental trends of China's textile manufacturing industry is investigated to understand the developmental frontiers of the textile manufacturing industry technology. This work contributes to the knowledge of research and development trends of the textile manufacturing and apparel industry in a macroscopic way. The Web of Science database and the core set of the Web of Science was explored and 2852 articles in the related fields are identified from 2010 to 2019. The scientific knowledge map of the textile manufacturing technology industry is explored using CiteSpace software. For the last decade, the developmental status, research hotspots and developmental trends of the textile manufacturing and apparel industry are analysed and summarised from the perspectives of key words, hot trends and core authors. The outcomes obtained reveal that in the past 10 years, through the analysis of the technical literature of the textile manufacturing industry, different perspectives were explored where the textile manufacturing industry develops from the initial textile manufacturing treatment. The decolourisation and removal of azo dyes and other traditional textile manufacturing to the composite materials, cotton fabrics leads to the improvement of textile manufacturing wastewater treatment. Currently, the textile manufacturing industry technology has gradually developed towards an intelligent knowledge visualization and decision support. Therefore, this work suggests the developmental directions of textile manufacturing from traditional to intelligent trends, further providing a reference for the later developmental trend and the dynamic planning of China's textile manufacturing industry technology.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using k -means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms’ capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Supervised learning needs a lot of data. Deep learning is vital in modern AI. Supervised learning requires a large labeled dataset. The selection of parameters prevents over- or underfitting. Unsupervised learning is used to overcome the challenges outlined above (performed by the clustering algorithm). To accomplish this, two processing processes were used: (1) utilizing nonlinear deep learning networks to turn data into a latent feature space ( Z ). The Kullback–Leibler divergence is used to test the objective function convergence. This article explores a novel research on hyperspectral microscopic picture using deep learning and effective unsupervised learning.

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