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Background: Nail pigmentation can be a clinical manifestation of malignant melanoma and a variety of benign diseases. Nail matrix biopsy for pathologic examination, the gold standard for diagnosis of subungual melanoma, is a painful procedure and may result in nail damage. Therefore, there is a great need for non-invasive methods and long-term follow-up for nail pigmentation. The objective of this study is to establish an intelligent precursor system to provide convenient monitoring for nail pigmentation, early warning subungual melanoma, and reduce nail biopsies to the minimum necessary. Methods: Dermoscopic images of nail lesions were obtained from outpatients between 2019 and 2020. The images were divided into the training set and the test set using k-fold cross validation at a ratio of 10:1. The deep learning model is developed based on the Pytorch framework. The model structure is optimized using the image segmentation model U-Net. An image segmentation module analyzed the contours of the whole nail plate and pigmented area according to the boundary features of the input images and a rule calculation module used the output information of the segmentation model to automatically analyze specific indicators referring to the "ABCDEF" rule. The model's results were compared with those of clinical experts. Results: From 550 dermoscopic images of nail lesions obtained, 500 were selected randomly as the training set, and the remaining 50 as the test set. Our image segmentation module realized automatic segmentation of the pigmented area and the whole nail plate with dice coefficient to be 0.8711 and 0.9652, respectively. Five qualitative indicators were selected in the interpretability analysis system and the models showed a certain degree of consistency with the evaluation by clinical experts, i.e., R2 for area ratio vs. breadth score was 0.8179 (P<0.001), for mean pixel value vs. pigment score was 0.7149 (P<0.001), for evenness vs. pigment score was 0.5247 (P<0.001). Conclusions: The proposed system made accurate segmentation of the nail plate and pigmented area and achieved medically interpretable index analysis. It is potentially a primer of an intelligent follow-up system that will enable convenient and spatially unaffected management and monitoring of nail pigmentation. It may provide clinicians with understandable auxiliary information for diagnosis.
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Nowadays, tool condition monitoring (TCM), which can prevent the waste of resources and improve efficiency in the process of machining parts, has developed many mature methods. However, TCM during the production of cutting tools is less studied and has different properties. The scale of the defects in the tool production process is tiny, generally between 10 µm and 100 µm for diamond tools. There are also very few samples with defects produced by the diamond tool grinding process, with only about 600 pictures. Among the many TCM methods, the direct inspection method using machine vision has the advantage of obtaining diamond tool information on-machine at a low cost and with high efficiency, and the method is accurate enough to meet the requirements of this task. Considering the specific, above problems, to analyze the images acquired by the vision system, a neural network model that is suitable for defect detection in diamond tool grinding is proposed, which is named DToolnet. DToolnet is developed by extracting and learning from the small-sample diamond tool features to intuitively and quickly detect defects in their production. The improvement of the feature extraction network, the optimization of the target recognition network, and the adjustment of the parameters during the network training process are performed in DToolnet. The imaging system and related mechanical structures for TCM are also constructed. A series of validation experiments is carried out and the experiment results show that DToolnet can achieve an 89.3 average precision (AP) for the detection of diamond tool defects, which significantly outperforms other classical network models. Lastly, the DToolnet parameters are optimized, improving the accuracy by 4.7%. This research work offers a very feasible and valuable way to achieve TCM in the manufacturing process.
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Electric-field (E-field) control of magnetic switching provides an energy-efficient means to toggle the magnetic states in spintronic devices. The angular tunneling magnetoresistance (TMR) of an magnetic tunnel junction (MTJ)/PMN-PT magnetoelectronic hybrid indicates that the angle-dependent switching fields of the free layer can decrease significantly subject to the application of an E-field. In particular, the switching field along the major axis is reduced by 59% from 28.0 to 11.5 Oe as the E-field increases from 0 to 6 kV/cm, while the TMR ratio remains intact. The switching boundary angle decreases (increases) for the parallel (antiparallel) to antiparallel (parallel) state switch, resulting in a shrunk switching window size. The non-volatile and reversible 180° magnetization switching is demonstrated by using E-fields with a smaller magnetic field bias as low as 11.5 Oe. The angular magnetic switching originates from competition among the E-field-induced magnetoelastic anisotropy, magnetic shape anisotropy, and Zeeman energy, which is confirmed by micromagnetic simulations.
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In the field of positioning measurement, a combination of complex components, a stringent environment, and time-consuming calibration are the main limitations. To address these issues, this paper presents a deep learning-based positioning methodology, which integrates image processing with nanomanufacturing technology. Non-periodic microstructure with nanoscale resolution is fabricated to provide the surface pattern. The main advantage of the proposed microstructure is its unlimited measurement range. A residual neural network is used for surface pattern recognition to reduce the search area, a survival probability mechanism is proposed to improve the transmission efficiency of the network layers, and template matching and sub-pixel interpolation algorithms are combined for pattern matching. The proposed methodology defines a comprehensive framework for the development of precision positioning measurement, the effectiveness of which was collectively validated by pattern recognition accuracy and positioning measurement performance. The trained network exhibits a recognition accuracy of 97.6%, and the measurement speed is close to real time. Experimental results also demonstrate the advantages and competitiveness of the proposed approach compared to the laser interferometer method.
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The VO2 thin films with sharp metalâ»insulator transition (MIT) were epitaxially grown on (001)-oriented Yttria-stabilized zirconia substrates (YSZ) using radio-frequency (RF) magnetron sputtering techniques. The MIT and structural phase transition (SPT) were comprehensively investigated under in situ temperature conditions. The amplitude of MIT is in the order of magnitude of 104, and critical temperature is 342 K during the heating cycle. It is interesting that both electron concentration and mobility are changed by two orders of magnitude across the MIT. This research is distinctively different from previous studies, which found that the electron concentration solely contributes to the amplitude of the MIT, although the electron mobility does not. Analysis of the SPT showed that the (010)-VO2/(001)-YSZ epitaxial thin film presents a special multi-domain structure, which is probably due to the symmetry matching and lattice mismatch between the VO2 and YSZ substrate. The VO2 film experiences the SPT from the M1 phase at low temperature to a rutile phase at a high temperature. Moreover, the SPT occurs at the same critical temperature as that of the MIT. This work may shed light on a new MIT behavior and may potentially pave the way for preparing high-quality VO2 thin films on cost-effective YSZ substrates for photoelectronic applications.
