ABSTRACT
Recently, polyurethane elastomer (TPU) has attracted more and more attention depending on its excellent optical, mechanical, and retreatment properties. The high strength of polyurethane has always been pursued, which can enable its application in more fields. In this work, an aliphatic polyurethane elastomer membrane (HRPU6) was successfully synthesized, and its strength was obviously improved by solvent annealing technology. The tensile strength and adhesion strength can reach 64.56 and 2.58 MPa, but 36.55 and 1.57 MPa only before solvent annealing, respectively. The impact strength of laminated glass based on HRPU has also been significantly improved after solvent annealing, confirmed through drop ball impact testing. It has been confirmed that the increase in strength of HRPU6 is attributed to the enhancement of hydrogen bonding and the improvement of the phase separation degree.
ABSTRACT
Background: Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency. Methods: In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value. Results: We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system. Conclusions: Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.