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| 1 | Conversion of surgically verified unresectable to resectable hepatocellular carcinoma显示文摘 | Zhaoyou Tang Yeqin Yu Zengchen Ma Rong Yang Xinda Zhou Kangda Liu Jizhen Lu Yanming Bao Zhiying Lin Binghui Yang | 1989 | Chinese Journal of Cancer Research1989，，3： | 1 |
| 2 | Multi-Disease Prediction Based on Deep Learning: A Survey显示文摘In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in themedical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Amongthem, the hot deep learning field has shown greater potential in applications such as disease prediction and drugresponse prediction. From the initial logistic regression model to the machine learning model, and then to thedeep learning model today, the accuracy of medical disease prediction has been continuously improved, and theperformance in all aspects has also been significantly improved. This article introduces some basic deep learningframeworks and some common diseases, and summarizes the deep learning prediction methods correspondingto different diseases. Point out a series of problems in the current disease prediction, and make a prospect for thefuture development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates thehigh correlation between deep learning and the medical field in future development. The unique feature extractionmethods of deep learning methods can still play an important role in future medical research. | Shuxuan Xie Zengchen Yu Zhihan Lv | 2021 | Computer Modeling in Engineering & Sciences2021，，8： | 1 |
| 3 | Prototypical Network Based on Manhattan Distance显示文摘Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data,such as medical images,terrorist surveillance,and so on.The Metric Learning in the Few-shot Learning algorithmis classified by measuring the similarity between the classified samples and the unclassified samples.This paper improves the Prototypical Network in the Metric Learning,and changes its core metric function to Manhattan distance.The Convolutional Neural Network of the embedded module is changed,and mechanisms such as average pooling and Dropout are added.Through comparative experiments,it is found that thismodel can converge in a small number of iterations(below 15,000 episodes),and its performance exceeds algorithms such asMAML.Research shows that replacingManhattan distance with Euclidean distance can effectively improve the classification effect of the Prototypical Network,and mechanisms such as average pooling and Dropout can also effectively improve the model. | Zengchen Yu Ke Wang Shuxuan Xie Yuanfeng Zhong Zhihan Lv | 2022 | Computer Modeling in Engineering & Sciences2022，，5： | 0 |
| 4 | Recent advances in high-β_(N) experiments and magnetohydrodynamic instabilities with hybrid scenarios in the HL-2A Tokamak显示文摘Over the past several years,high-β_(N) experiments have been carried out on HL-2A.The high-β_(N) is realized using double transport barriers(DTBs)with hybrid scenarios.A stationary high-β_(N) (>2)scenario was obtained by pure neutral-beam injection(NBI)heating.Transient high performance was also achieved,corresponding to β_(N)≥3,ne/ne_(G)∼0.6,H_(98)∼1.5,f_(bs)∼30%,q_(95)∼4.0,and��∼0.4.The high-β_(N) scenario was successfully modeled using integrated simulation codes,that is,the one modeling framework for integrated tasks(OMFIT).In high-����plasmas,magnetohydrodynamic(MHD)instabilities are abundant,including low-frequency global MHD oscilla-tion with n=1,high-frequency coherent mode(HCM)at the edge,and neoclassical tearing mode(NTM)and Alfvénic modes in the core.In some high-β_(N) discharges,it is observed that the NTMs with m/n=3/2 limit the growth of the plasma energy and decrease β_(N).The low-n global MHD oscillation is consistent with the coupling of destabilized internal(m/n=1/1)and external(m/n=3/1 or 4/1)modes,and plays a crucial role in triggering the onset of ELMs.Achieving high-β_(N) on HL-2A suggests that core-edge interplay is key to the plasma confinement enhancement mechanism.Experiments to enhance β_(N) will contribute to future plasma operation,such as international thermonuclear experimental reactor. | Wei Chen Liming Yu Min Xu Xiaoquan Ji Zhongbing Shi Xiaoxue He Zhengji Li Yonggao Li Tianbo Wang Min Jiang Shaobo Gong Jie Wen Peiwan Shi Zengchen Yang Kairui Fang Jia Li Lai Wei Wulv Zhong Aiping Sun Jianyong Cao Xingyu Bai Jiquan Li Xuantong Ding Jiaqi Dong Qingwei Yang Yi Liu Longwen Yan Zhengxiong Wang Xuanru Duan | 2022 | Fundamental Research2022，2，5： | 0 |
| 5 | Enhanced mixing efficiency for a novel 3D Tesla micromixer for Newtonian and non-Newtonian fluids显示文摘The fabrication of constructs with gradients for chemical,mechanical,or electrical composition is becoming critical to achieving more complex structures,particularly in 3D printing and biofabrication.This need is underscored by the complexity of in vivo tissues,which exhibit heterogeneous structures comprised of diverse cells and matrices.Drawing inspiration from the classical Tesla valve,our study introduces a new concept of micromixers to address this complexity.The innovative micromixer design is tailored to enhance the re-creation of in vivo tissue structures and demonstrates an advanced capability to efficiently mix both Newtonian and non-Newtonian fluids.Notably,our 3D Tesla valve micromixer achieves higher mixing efficiency with fewer cycles,which represents a significant improvement over the traditional mixing method.This advance is pivotal for the field of 3D printing and bioprinting,and offers a robust tool that could facilitate the development of gradient hydrogel-based constructs that could also accurately mimic the intricate heterogeneity of natural tissues. | Abdellah AAZMI Zixian GUO Haoran YU Weikang LV Zengchen JI Huayong YANG Liang MA | 2023 | Journal of Zhejiang University-Science A(Applied Physics & Engineering)2023，24，12： | 0 |
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