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694 results on '"Yao, Xincheng"'

104. Supplementary document for Synergetic effects of a front ITO nanocylinder array and a back square Al array to enhance light absorption for organic solar cells - 5664703.pdf

Author

Liu, Weining, Zhang, Li, Wang, Yanzhou, Yao, Xincheng, Xie, Caidong, liu, qiming, Li, Junshuai, Li, Yali, and He, Deyan

Subjects
ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, InformationSystems_MISCELLANEOUS
Abstract

Supplemental Document (ID:451603))

Published
2022
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128. Vascular complexity analysis in OCT angiography of diabetic retinopathy

Author

Alam, Minhaj, Le, David, Lim, Jennifer I., and Yao, Xincheng

Subjects
Adult, Male, Cross-Sectional Studies, Diabetic Retinopathy, Fundus Oculi, Humans, Retinal Vessels, Female, Fluorescein Angiography, Middle Aged, eye diseases, Article, Tomography, Optical Coherence
Abstract

This study aimed to verify the feasibility of using vascular complexity features for objective differentiation of controls and nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) patients.This was a cross-sectional study conducted in a tertiary, subspecialty, academic practice. The cohort included 20 control subjects, 60 NPDR patients, and 56 PDR patients. Three vascular complexity features, including the vessel complexity index, fractal dimension, and blood vessel tortuosity, were derived from each optical coherence tomography angiography image. A shifting-window measurement was further implemented to identify local feature distortions due to localized neovascularization and mesh structures in PDR.With mean value analysis of the whole-image, only the vessel complexity index and blood vessel tortuosity were able to classify NPDR versus PDR patients. Comparative shifting-window measurement revealed increased sensitivity of complexity feature analysis, particularly for NPDR versus PDR classification. A multivariate regression model indicated that the combination of all three vascular complexity features with shifting-window measurement provided the best classification accuracy for controls versus NPDR versus PDR.Vessel complexity index and blood vessel tortuosity were the most sensitive in differentiating NPDR and PDR patients. A shifting-window measurement increased the sensitivity significantly for objective optical coherence tomography angiography classification of diabetic retinopathy.

Published
2021

134. SnCl4‑Treated Ti3C2Tx MXene Nanosheets for Schottky Junction Solar Cells with Improved Performance.

Author

Yao, Xincheng, Li, Yali, Wang, Yanzhou, Gao, Zhe, Liu, Qiming, Fu, Yujun, He, Deyan, and Li, Junshuai

Abstract

Ti3C2Tx MXene has recently attracted increasing attention in optoelectronics because of its good optical and electrical properties. Herein, we report a facile scheme of adjusting its electrical property by solution-processed SnCl4 treatment to realize the significantly improved performance of an n-type silicon (n-Si)/MXene Schottky junction solar cell prepared just by drop-casting the ethanol suspension of Ti3C2Tx MXene nanosheets onto the n-Si surface and subsequent natural drying. It is found that the optimal treatment by SnCl4 increases the electrical conductivity of the MXene layer and reduces the defects at the interface of Si and MXene. Thanks to its good compatibility with the micrometer-scale texture, the demonstration device of the pyramid-textured n-Si/SnCl4-treated MXene nanosheets delivers a notable power conversion efficiency (PCE) of 9.3%, which is much higher than 4.7 and 4.8% for the pyramid-textured n-Si/MXene nanosheet device and the planar Si/MXene nanosheet device, respectively, both without SnCl4 treatment. Moreover, the SnCl4-treated pyramid-textured n-Si/MXene nanosheet device exhibits significantly improved operation stability with ∼88% of its initial PCE, much higher than ∼67% for the control device without SnCl4 treatment, both after storage in air for 30 days. [ABSTRACT FROM AUTHOR]

Published
2022
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144. Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.

Author

Ebrahimi, Behrouz, Le, David, Abtahi, Mansour, Dadzie, Albert K., Rossi, Alfa, Rahimi, Mojtaba, Son, Taeyoon, Ostmo, Susan, Campbell, J. Peter, Paul Chan, R. V., and Yao, Xincheng

Subjects
CONVOLUTIONAL neural networks, RETROLENTAL fibroplasia, COLOR photography, DEEP learning, CLASSIFICATION, WAVELENGTHS
Abstract

Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision, necessitating effective screening strategies. This study addresses the impact of color channels in fundus imaging on ROP diagnosis, emphasizing the efficacy and safety of utilizing longer wavelengths, such as red or green for enhanced depth information and improved diagnostic capabilities. This study aims to assess the spectral effectiveness in color fundus photography for the deep learning classification of ROP. A convolutional neural network end-to-end classifier was utilized for deep learning classification of normal, stage 1, stage 2, and stage 3 ROP fundus images. The classification performances with individual-color-channel inputs, i.e., red, green, and blue, and multi-color-channel fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared. For individual-color-channel inputs, similar performance was observed for green channel (88.00% accuracy, 76.00% sensitivity, and 92.00% specificity) and red channel (87.25% accuracy, 74.50% sensitivity, and 91.50% specificity), which is substantially outperforming the blue channel (78.25% accuracy, 56.50% sensitivity, and 85.50% specificity). For multi-color-channel fusion options, the early-fusion and intermediate-fusion architecture showed almost the same performance when compared to the green/red channel input, and they outperformed the late-fusion architecture. This study reveals that the classification of ROP stages can be effectively achieved using either the green or red image alone. This finding enables the exclusion of blue images, acknowledged for their increased susceptibility to light toxicity. [ABSTRACT FROM AUTHOR]

Published
2024
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