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| 1 | World’s first spaceflight on-orbit demonstration of a flexible solar array system based on shape memory polymer composites显示文摘With a 10%reversible compressive strain in more than 10 deformation cycles,the shape memory polymer composites(SMPCs)could be used for deployable structure and releasing mechanism.In this paper,without traditional electro-explosive devices or motors/controllers,the deployable SMPC flexible solar array system(SMPC-FSAS)is studied,developed,ground-based tested,and finally on-orbit validated.The epoxy-based SMPC is used for the rolling-out variable-stiffness beams as a structural frame as well as an actuator for the flexible blanket solar array.The releasing mechanism is primarily made of the cyanate-based SMPC,which has a high locking stiffness to withstand 50 g gravitational acceleration and a large unlocking displacement of 10 mm.The systematical mechanical and thermal qualification tests of the SMPC-FSAS flight hardware were performed,including sinusoidal sweeping vibration,shocking,acceleration,thermal equilibrium,thermal vacuum cycling,and thermal cycling test.The locking function of the SMPC releasing mechanisms was in normal when launching aboard the SJ20 Geostationary Satellite on 27 Dec.,2019.The SMPC-FSAS flight hardware successfully unlocked and deployed on 5 Jan.,2020 on geostationary orbit.The triggering signal of limit switches returned to ground at the 139 s upon heating,which indicated the successful unlocking function of SMPC releasing mechanisms.A pair of epoxy-based SMPC rolled variable-stiffness tubes,which clapped the flexible blanket solar array,slowly deployed and finally approached an approximate 100%shape recovery ratio within 60 s upon heating.The study and on-orbit successful validation of the SMPC-FSAS flight hardware could accelerate the related study and associated productions to be used for the next-generation releasing mechanisms as well as space deployable structures,such as new releasing mechanisms with low-shocking,testability and reusability,and ultra-large space deployable solar arrays. | LAN Xin LIU LiWu ZHANG FengHua LIU ZhengXian WANG LinLin LI QiFeng PENG Fan HAO SiDa DAI WenXu WAN Xue TANG Yong WANG Mian HAO YanYan YANG Yang YANG Cheng LIU YanJu LENG JinSong | 2020 | Science China(Technological Sciences)2020，63，8： | 7 |
| 2 | Power management in co-phase traction power supply system with super capacitor energy storage for electrified railways显示文摘Increasing railway traffic and energy utilization issues prompt electrified railway systems to be more economical,efficient and sustainable.As regenerative braking energy in railway systems has huge potential for optimized utilization,a lot of research has been focusing on how to use the energy efficiently and gain sustainable benefits.The energy storage system is an alternative because it not only deals with regenerative braking energy but also smooths drastic fluctuation of load power profile and optimizes energy management.In this work,we propose a co-phase traction power supply system with super capacitor(CSS_SC)for the purpose of realizing the function of energy management and power quality management in electrified railways.Besides,the coordinated control strategy is presented to match four working modes,including traction,regenerative braking,peak shaving and valley filling.A corresponding simulation model is built in MATLAB/Simulink to verify the feasibility of the proposed system under dynamic working conditions.The results demonstrate that CSS_SC is flexible to deal with four different working conditions and can realize energy saving within the allowable voltage unbalance of 0.008%in simulation in contrast to 1.3%of the standard limit.With such a control strategy,the performance of super capacitor is controlled to comply with efficiency and safety constraints.Finally,a case study demonstrates the improvement in power fluctuation with the valley-to-peak ratio reduced by 20.3%and the daily load factor increased by 17.9%. | Xiaohong Huang Qinyu Liao Qunzhan Li Sida Tang Ke Sun | 2020 | Railway Engineering Science2020，28，1： | 4 |
| 3 | Induction of Pluripotency in Mouse Somatic Cells with Lineage Specifiers显示文摘 | Jian Shu Chen Wu Yetao Wu Zhiyuan Li Sida Shao Wenhui Zhao Xing Tang Huan Yang Lijun Shen Xiaohan Zuo Weifeng Yang Yan Shi Xiaochun Chi Hongquan Zhang Ge Gao Youmin Shu Kehu Yuan Weiwu He Chao Tang Yang Zhao Hongkui Deng | 2013 | Cell2013，，5： | 1 |
| 4 | Machine learning-based prognostic and metastasis models of kidney cancer显示文摘Background:Kidney cancer originates from the urinary tubule epithelial system of the renal parenchyma,accounting for 20% of all urinary system tumors.Approximately 70% of cases are localized at diagnosis,and 30%are metastatic.Most localized kidney cancers can be cured by surgery,but most metastatic patients relapse after surgery and eventually die of kidney cancer.Therefore,accurately predicting patient survival and identifying high-risk metastatic patients will effectively guide interventions and improve prognosis.Methods:This study used the data of 12,394 kidney cancer patients from the surveillance,epidemiology,and end results database to construct a research cohort related to kidney cancer survival and metastasis.Eight machine learning models(including support vector machines,logistic regression,decision tree,random forest,XGBoost,AdaBoost,K-nearest neighbors,and multilayer perceptron)were developed to predict the survival and metastasis of kidney cancer and six evaluation indicators(accuracy,precision,sensitivity,specificity,F1 score,and area under the receiver operating characteristic[AUROC])were used to verify,evaluate,and optimize the models.Results:Among the eight machine learning models,Logistic Regression has the highest AUROC in both prediction scenarios.For 3-year survival prediction,the Logistic Regression model had an accuracy of 0.684,a sensitivity of 0.702,a specificity of 0.670,a precision of 0.686,an F1 score of 0.683,and an AUROC of 0.741.For tumor metastasis prediction,the Logistic Regression model had an accuracy of 0.800,a sensitivity of 0.540,a specificity of 0.830,a precision of 0.769,an F1 score of 0.772,and an AUROC of 0.804.Conclusion:In this study,we selected appropriate variables from both statistical and clinical significance and developed and compared eight machine learning models for predicting 3-year survival and metastasis of kidney cancer.The prediction results and evaluation results demonstrated that our model could provide decision support for early intervention for kidney cancer patients. | Yuxiang Zhang Na Hong Sida Huang Jie Wu Jianwei Gao Zheng Xu Fubo Zhang Shaohui Ma Ye Liu Peiyuan Sun Yanping Tang Chun Liu Jianzhong Shou Meng Chen | 2022 | Cancer Innovation2022，1，2： | 0 |
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