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223 results on '"Lu, Lidan"'

25. Research progress on autologous fat transplantation in the treatment of velopharyngeal insufficiency

Author

LU Lidan and XU Hui

Subjects
autologous fat transplantation, velopharyngeal insufficiency, cleft palate, indications, donor site, injection dose, recipient site, complications, Medicine
Abstract

Autologous fat transplantation in the treatment of velopharyngeal insufficiency has the advantages of good histocompatibility, small local trauma, few complications, reversible operation and simple postoperative nursing, which can effectively increase the velopharyngeal closure area. If the clinical effect is poor, other surgical methods can be used at any time for replacement. Although there are many advantages in the treatment of velopharyngeal insufficiency with autologous fat transplantation, there are still some problems in the selection of indication, donor site, injection dose, recipient site, follow-up evaluation, complications and prevention and treatment. Current research shows that autologous fat transplantation is mainly used in patients with mild or moderate velopharyngeal insufficiency, but with the improvement of fat acquisition and treatment techniques, the indications for autologous fat transplantation continue to expand, and autologous fat transplantation combined with palatoplasty or pharyngoplasty has been proposed for the treatment of severe velopharyngeal insufficiency. However, there are complications, such as fat absorption and obstructive sleep apnea syndrome. In addition, the application of autologous fat transplantation in severe VPI patients and how to improve the long-term stability of autologous fat transplantation need further study.

Published
2021
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36. Interface Engineering and Electron-Hole Wave Function Overlap of InAs/AlSb Superlattice Infrared Detectors.

Author

Yu, Jing, Zhu, Lianqing, Lu, Lidan, Chen, Weiqiang, Zheng, Xiantong, Zhang, Dongliang, Fu, Yuegang, and Ou, Jianzhen

Subjects
INFRARED detectors, WAVE functions, BLOCH waves, ENGINEERING, QUANTUM efficiency
Abstract

InAs/AlSb is a material system that can be used as a low-noise avalanche detector and operates in the short-wave infrared band. The interface parameters determine the wave function overlap (WFO). Maximizing the WFO of InAs/AlSb superlattices improves the quantum efficiency (QE) of infrared avalanche photodetectors (APDs). However, this remains a huge challenge. Here, the 8-band k·p perturbation method based on Bloch wave envelope function approximation was used to calculate the energy level structure of InAs/AlSb superlattices. The results indicate that the WFO is enhanced with increasing InSb interface thickness or when the InSb (or AlAs) interface is far from the intersection of InAs and AlSb. As the AlAs interface thickness increases, the WFO enhances and then reduces. The maximum increase in WFO is 15.7%, 93%, and 156.8%, respectively, with three different models. Based on the stress equilibrium condition, we consider the interface engineering scheme proposed for enhancing WFO with an increase of 16%, 114%, and 159.5%, respectively. Moreover, the absorption wavelength shift is less than ±0.1 μm. Therefore, the interface layer thickness and position can be designed to enhance the WFO without sacrificing other properties, thereby improving the QE of the device. It provides a new idea for the material epitaxy of APDs. [ABSTRACT FROM AUTHOR]

Published
2023
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39. nBn结构InAs/GaSb超晶格中/长双波段探测器优化设计

Author

Liu Wenjing, 刘文婧, primary, Zhu Lianqing, 祝连庆, additional, Zhang Dongliang, 张东亮, additional, Zheng Xiantong, 郑显通, additional, Yang Yichen, 杨懿琛, additional, Wang Wenjie, 王文杰, additional, Liu Yuan, 柳渊, additional, Lu Lidan, 鹿利单, additional, and Liu Ming, 刘铭, additional

Published
2023
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41. A method to improve the computational performance of nonlinear all—optical diffractive deep neural network model

Author

Sun, Yichen, Dong, Mingli, Yu, Mingxin, Lu, Lidan, Liang, Shengjun, Xia, Jiabin, and Zhu, Lianqing

Abstract

To further improve the computational performance of the diffractive deep neural network (D2NN) model, we use the ReLU function to limit the phase parameters, which effectively solves the problem of vanishing gradient that occurs in the mitigation model. We add various commonly used nonlinear activation functions to the hidden layer of the model and establish the ReLU phase-limit nonlinear diffractive deep neural network (ReLU phase-limit N-D2NN) model. We evaluate the model by comparing the performance of various nonlinear activation functions, where confusion matrix and accuracy are used as evaluation methods. The numerical simulation results show that the model achieves better classification performance on the MNIST and Fashion-MNIST datasets, respectively. In particular, the highest classification performance is obtained by the ReLU phase-limit N-D2NN model, in which the hidden layer uses PReLU, with 98.38% and 90.14%, respectively. This paper provides a theoretical basis for applying the nonlinear D2NN systems in natural scenes.

Published
2023
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