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| 1 | Defect passivation by nontoxic biomaterial yields 21% efficiency perovskite solar cells显示文摘Defect passivation is one of the most important strategies to boost both the efficiency and stability of perovskite solar cells(PSCs).Here,nontoxic and sustainable forest-based biomaterial,betulin,is first introduced into perovskites.The experiments and calculations reveal that betulin can effectively passivate the uncoordinated lead ions in perovskites via sharing the lone pair electrons of hydroxyl group,promoting charge transport.As a result,the power conversion efficiencies of the p-i-n planar PSCs remarkably increase from 19.14%to 21.15%,with the improvement of other parameters.The hydrogen bonds of betulin lock methylamine and halogen ions along the grain boundaries and on the film surface and thus suppress ion migration,further stabilizing perovskite crystal structures.These positive effects enable the PSCs to maintain 90%of the initial efficiency after 30 days in ambient air with 60%±5%relative humidity,75%after 300 h aging at 85℃,and 55%after 250 h light soaking,respectively.This work opens a new pathway for using nontoxic and low-cost biomaterials from forest to make highly efficient and stable PSCs. | Shaobing Xiong Tianyu Hao Yuyun Sun Jianming Yang Ruru Ma Jiulong Wang Shijing Gong Xianjie Liu Liming Ding Mats Fahlman Qinye Bao | 2021 | Journal of Energy Chemistry2021，30，4： | 2 |
| 2 | 近年中国北方草地变绿受降水增加的驱动显示文摘中国北方的暖湿化是近期的热点话题,然而其对植被生长的影响仍不清楚。本研究基于长时间序列(1982–2018年)数据,研究了归一化植被指数(NDVI)的时间动态及其气候驱动因子之间的关系,以探索近年来气候的暖湿化是否会导致该区植被变绿。我们采用分段回归探测了NDVI的变化趋势是否存在转变点,用Pearson相关分析描述了植被指数与气候因子的关系。最后,采用逐步多元回归方法研究了气候因子对NDVI时间变化的贡献率。研究结果表明,NDVI时间变化趋势的转变点出现在2008年,GIMMS NDVI在1982–2008年略有增加,上升速率为0.00022 yr?1,在2008–2015年上升速率达到0.002 yr?1,MODIS NDVI在2008–2018年上升速率为0.0018 yr?1。降水是NDVI变化的主要驱动因子,气温和饱和水汽压差(VPD)对NDVI的变化影响较小。总体看来,NDVI时间序列变化趋势存在转变点,并且近期气候的暖湿化主导了中国北方草地植被变绿,这为今后更好地预测该地区气候变化下的植被覆盖变化提供了依据。 | Kai Di Zhongmin Hu Mei Wang Ruochen Cao Minqi Liang Genan Wu Ruru Chen Guangcun Hao Yaolong Zhao | 2021 | Journal of Plant Ecology2021，14，5： | 2 |
| 3 | Non-metallic gold nanoclusters for oxygen activation and aerobic oxidation显示文摘In recent decade, Au nanoclusters of atomic precision(Au_nL_m, where L = organic ligand: thiolate and phosphine) have been shown as a new promising nanogold catalyst. The well-de fined Au_nL_m catalysts possess unique electronic properties and frameworks, providing an excellent opportunity to correlate the intrinsic catalytic behavior with the cluster's framework as well as to study the catalytic mechanisms over gold nanoclusters. In this review, we only demonstrate the important roles of the gold nanoclusters in the oxygen activation(e.g.,~3O_2 to ~1O_2) and their selective oxidations in the presence of oxygen(e.g., CO to CO_2, sul fides to sulfoxides, alcohol to aldehyde, styrene to styrene epoxide, amines to imines, and glucose to gluconic acid). The size-speci ficity(Au_(25)(1.3 nm), Au_(38)(1.5 nm), Au_(144)(1.9 nm), etc.), ligand engineering(e.g., aromatic vs aliphatic), and doping effects(e.g., copper, silver, palladium, and platinum)are discussed in details. Finally, the proposed reactions' mechanism and the relationships of clusters' structure and activity at the atomic level also are presented. | Guomei Zhang Ruru Wang Gao Li | 2018 | Chinese Chemical Letters2018，29，5： | 2 |
| 4 | Automatic rec- ognition of woven fabric pattern baaed on image processing and BP neural network 显示文摘 | Ruru Pan Weidong Gao Jihong Liu Hongbo Wang | 2011 | Journal of the Textile Institute2011，102，1： | 1 |
| 5 | Automatic recognition of woven fabric based on pattern database 显示文摘 | PAN Ruru GAO Weidong LIU Jihong | 2010 | Fibers and Polymers2010，11，2： | 1 |
| 6 | Applied research of route similarity analysis based on association rules显示文摘 | XIANG Zhe LIU Ruru HU Qinyou | 2012 | TransNav the Internation- al Journal on Marine Navigation & Safety of Sea Transportation2012，6，2： | 1 |
| 7 | Deep Neural Network with Strip Pooling for Image Classification of Yarn-Dyed Plaid Fabrics显示文摘Historically,yarn-dyed plaid fabrics(YDPFs)have enjoyed enduring popularity with many rich plaid patterns,but production data are still classified and searched only according to production parameters.The process does not satisfy the visual needs of sample order production,fabric design,and stock management.This study produced an image dataset for YDPFs,collected from 10,661 fabric samples.The authors believe that the dataset will have significant utility in further research into YDPFs.Convolutional neural networks,such as VGG,ResNet,and DenseNet,with different hyperparameter groups,seemed themost promising tools for the study.This paper reports on the authors’exhaustive evaluation of the YDPF dataset.With an overall accuracy of 88.78%,CNNs proved to be effective in YDPF image classification.This was true even for the low accuracy of Windowpane fabrics,which often mistakenly includes the Prince ofWales pattern.Image classification of traditional patterns is also improved by utilizing the strip pooling model to extract local detail features and horizontal and vertical directions.The strip pooling model characterizes the horizontal and vertical crisscross patterns of YDPFs with considerable success.The proposed method using the strip pooling model(SPM)improves the classification performance on the YDPF dataset by 2.64%for ResNet18,by 3.66%for VGG16,and by 3.54%for DenseNet121.The results reveal that the SPM significantly improves YDPF classification accuracy and reduces the error rate of Windowpane patterns as well. | Xiaoting Zhang Weidong Gao Ruru Pan | 2022 | Computer Modeling in Engineering & Sciences2022，，3： | 1 |
| 8 | Automatic recognition of woven fabric patterns based on pattern database显示文摘 | Ruru Pan Weidong Gao Jihong Liu Hongbo Wang | 2010 | Fibers and Polymers2010，，2： | 1 |
| 9 | Distributed implantation of a flexible microelectrode array for neural recording显示文摘Flexible multichannel electrode arrays(fMEAs)with multiple flaments can be flexibly implanted in various patterns.It is necessary to develop a method for implanting the fMEA in different locations and at various depths based on the recording demands.This study proposed a strategy for reducing the microelectrode volume with integrated packaging.An implantation system was developed specifically for semiautomatic distributed implantation.The feasibility and convenience of the fMEA and implantation platform were verified in rodents.The acute and chronic recording results provied the effectiveness of the packaging and implantation methods.These methods could provide a novel strategy for developing fMEAs with more flaments and recording sites to measure functional interactions across multiple brain regions. | Chunrong Wei Yang Wang Weihua Pei Xinyong Han Longnian Lin Zhiduo Liu Gege Ming Ruru Chen Pingping Wu Xiaowei Yang Li Zheng Yijun Wang | 2022 | Microsystems & Nanoengineering2022，8，3： | 1 |
| 10 | The Coupled Model of Atmosphere and Ground for Air Pollution Remote Sensing and Its Application on Guangdong Provinee, China显示文摘 | Deng Ruru Xiong Shouping | 2005 | IGARSS2005，7，： | 1 |
| 11 | Dynamic measurement of fabric wrinkle recovery angle by video sequence pro- cessing显示文摘 | Lei Wang Jianli Liu Ruru Pan | 2014 | Textile Research Journal2014，84，7： | 1 |
| 12 | Automatic recognition of woven fabric pattern based on image processing and BP neural network 显示文摘 | PAN Ruru GAO Weidong LIU Jihong | 2011 | The Journal of the Textile Institute2011，102，1： | 1 |
| 13 | Matching design and mismatching analysis towards radar absorbing coatings based on conducting plate显示文摘 | Cao Maosheng Qin Ruru Qiu Chengjun Zhu Jin | 2003 | Materials and Design2003，24，： | 1 |
| 14 | Augmented Lagrangian Alternating Direction Method for Tensor RPCA显示文摘Tensor robust principal component analysis(TRPCA) problem aims to separate a low-rank tensor and a sparse tensor from their sum. This problem has recently attracted considerable research attention due to its wide range of potential applications in computer vision and pattern recognition. In this paper, we propose a new model to deal with the TRPCA problem by an alternation minimization algorithm along with two adaptive rankadjusting strategies. For the underlying low-rank tensor, we simultaneously perform low-rank matrix factorizations to its all-mode matricizations; while for the underlying sparse tensor,a soft-threshold shrinkage scheme is applied. Our method can be used to deal with the separation between either an exact or an approximate low-rank tensor and a sparse one. We established the subsequence convergence of our algorithm in the sense that any limit point of the iterates satisfies the KKT conditions. When the iteration stops, the output will be modified by applying a high-order SVD approach to achieve an exactly low-rank final result as the accurate rank has been calculated. The numerical experiments demonstrate that our method could achieve better results than the compared methods. | Ruru HAO Zhixun SU | 2017 | Journal of Mathematical Research with Applications2017，37，3： | 1 |
| 15 | Automatic recognition of the color effect of yarn-dyed fabric by the smallest repeat unit recognition algori- thm显示文摘 | ZHANG Jie PAN Ruru GAO Weidong | 2015 | Textile Research Journal2015，85，： | 1 |
| 16 | Automatic recogni- tion of woven fabric pattern based on image processing and BP neural network显示文摘 | Pan Ruru Gao Weidong Liu Jibong | 2011 | Joumal of the Textile Institute2011，102，： | 1 |
| 17 | Automatic recognition of woven fabric pattern based on pattern database显示文摘 | PAN Ruru GAO Weidong LIU Jihong | 2010 | Fibers and Polymers2010，11，2： | 1 |
| 18 | Matching design and mismatching analysis towards radar absorbing coatings based on conducting plate显示文摘 | Maosheng Cao Ruru Qin Chengjun Qiu Jing Zhu | 2002 | Materials and Design2002，，5： | 1 |
| 19 | M^(2)LC-Net: A Multi-Modal Multi-Disease Long-Tailed Classification Network for Real Clinical Scenes显示文摘Leveraging deep learning-based techniques to classify diseases has attracted extensive research interest in recent years.Nevertheless,most of the current studies only consider single-modal medical images,and the number of ophthalmic diseases that can be classified is relatively small.Moreover,imbalanced data distribution of different ophthalmic diseases is not taken into consideration,which limits the application of deep learning techniques in realistic clinical scenes.In this paper,we propose a Multimodal Multi-disease Long-tailed Classification Network(M^(2)LC-Net)in response to the challenges mentioned above.M^(2)LC-Net leverages ResNet18-CBAM to extract features from fundus images and Optical Coherence Tomography(OCT)images,respectively,and conduct feature fusion to classify 11 common ophthalmic diseases.Moreover,Class Activation Mapping(CAM)is employed to visualize each mode to improve interpretability of M^(2)LC-Net.We conduct comprehensive experiments on realistic dataset collected from a Grade III Level A ophthalmology hospital in China,including 34,396 images of 11 disease labels.Experimental results demonstrate effectiveness of our proposed model M^(2)LC-Net.Compared with the stateof-the-art,various performance metrics have been improved significantly.Specifically,Cohen’s kappa coefficient κ has been improved by 3.21%,which is a remarkable improvement. | Zhonghong Ou Wenjun Chai Lifei Wang Ruru Zhang Jiawen He Meina Song Lifei Yuan Shengjuan Zhang Yanhui Wang Huan Li Xin Jia Rujian Huang | 2021 | China Communications2021，18，9： | 0 |
| 20 | Earth gravity field solution with combining CHAMP and GRACE data显示文摘Satellite gravity data fusion with multi-type and huge-amount is one of the hot topics in physical geodesy.After a brief review of dynamic approach,the CHAMP-only and GRACE-only gravity fields by using HL-SST and LL-SST data from 2003 to 2009 are recovered respectively.An combination strategy of CHAMP and GRACE data by using Helmert variance component estimation(VCE) is proposed based on normal equation level fusion.Three gravity field models with 150° and order by CHAMP-only data,GRACE-only data and combining CHAMP and GRACE data from 2003 to 2009 are recovered.The comparisons between our recovered models and those latest released models were performed.The external accuracy validations using marine gravity anomalies from DTU13 products and height anomalies from GPS/leveling data are also conducted in this paper.The results show that long-term CHAMP data do contribute to the accuracy improvement of gravity field solution.The accuracy of the combined model using CHAMP and GRACE data is better than those of the individuals and comparative to the models published by international groups. | Tianhe Xu Lei Ren Ruru Gao | 2017 | Geodesy and Geodynamics2017，8，4： | 0 |
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