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Nanogenerator-Based Self-Charging Energy Storage Devices

Nano-Micro Letters 2019年第2期11卷 5-23页

作者： Kun Zhao Yuanhao Wang Lu Han Yongfei Wang Xudong Luo Zhiqiang Zhang Ya Yang CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-Nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 100083People’s Republic of China State Key Laboratory of Advanced Processing and Recycling of Nonferrous Metals School of Materials Science and EngineeringLanzhou University of TechnologyLanzhou 730050People’s Republic of China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciencesürümqi 830011XinjiangPeople’s Republic of China School of High Temperature Materials and Magnesite Resources Engineering University of Science and Technology Liaoning185 Qianshan Zhong RoadAnshan 114044People’s Republic of China School of Chemical Engineering University of Science and Technology Liaoning185 Qianshan Zhong RoadAnshan 114044People’s Republic of China School of Nanoscience and Technology University of Chinese Academy of SciencesBeijing 100049People’s Republic of China

One significant challenge for electronic devices is that the energy storage devices are unable to provide su cient energy for continuous and long-time operation,leading to frequent recharging or inconvenient battery *** satisfy the needs of next-generation electronic devices for sustainable working,conspicuous progress has been achieved regarding the development for nanogenerator-based self-charging energy storage ***,the development of the self-charging energy storage devices is *** will be on preparation of nanomaterials for Li-ion batteries and supercapacitors,structural design of the nanogenerator-based self-charging energy storage devices,performance testing,and potential ***,the challenges and perspectives regarding self-charging energy storage devices are also discussed.

关键词： Nanomaterial Nanogenerator Energy storage device Self-charging

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Fruit detection using faster R-CNN based on deep network 2nd

Fruit detection using faster R-CNN based on deep network

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Ma, Linjuan Zhang, Fuquan Xu, Lin School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783030045845

Fruit detection is of great significance in the agriculture. Recently, deep neural network has been widely studied in fruit detection. In our paper, we present a new approach of fruit detection, which uses Faster R-CNN based on deep network AlexNet. Our aim is to develop a fast, accurate and convenient model to detect and classify the fruit. Data augmentation is also used to increase the fruit dataset and avoid the overfitting in some degree. We use the fruits-360 dataset to perform the experiments. © Springer Nature Switzerland AG 2019.

关键词： Deep neural networks

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Assessment of the aroma profiles of 15 commercial Chinese jujubes using GC-IMS

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Journal of Future Foods 2025年

作者： Jianxin Song Junguo Wang Fangyuan Wei Tong Zhang Xin Li Xiaoya Pan Mengyuan Zhao Chao Zhang Xiaodong Wang Junran Chen School of Functional Food and Wine Shenyang Pharmaceutical University Shenyang 110016 China Institute of Agri-Food Processing and Nutrition Beijing Academy of Agriculture and Forestry Sciences Key Laboratory of Vegetable Postharvest Processing Ministry of Agriculture and Rural Affairs Beijing 100097 China School of Biological Science and Food Engineering Chuzhou University Chuzhou 239000 China College of Food Science and Engineering Stata Key Laboratory of Food Nutrition and Safety Tianjin University of Science and Technology Tianjin 300457 China

Jujube is the homology of medicine and food, which popular for its characteristic flavor. Aroma is one of the most key factors that determining the quality of commercial Chinese jujube. In this study, gas chromatography-ion mobility spectrometry (GC-IMS) was applied for aroma characterization of 15 varieties of commercial Chinese jujubes. Results showed a total of 29 aroma compositions were identified, and aldehydes (10.42%−22.87%), ketones (26.45%−42.06%), alcohols (2.42%−7.30%), and esters (16.41%−26.86%) were common in 15 varieties of commercial Chinese jujubes. 3-Hydroxy-2-butanone, methyl acetate, benzaldehyde, 2,3-butanedione and 2-methylbutyric acid contributed great to the aroma profiles of different commercial Chinese jujubes. Moreover, significant ( P <0.05) differences of aroma profiles were found among 15 varieties of commercial Chinese jujubes, and it can be well distinguished by 3D-topographic, characteristic fingerprint and principal component analysis (PCA) results. In conclusion, JSBZ (Ji Shan Ban Zao), HMDZ (Ha Mi Da Zao) and HPZ (Hu Ping Zao) respectively presented significantly different aroma profile characteristics. GC-IMS instrument and PCA statistical method were superior and effective in assessing the differences of aroma profiles of different commercial Chinese jujubes.

关键词： Commercial jujube Aroma profile Variety GC-IMS PCA

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Dissolution-regrowth of hierarchical Fe-Dy oxide modulates the electronic structure of nickel-organic frameworks as highly active and stable water splitting electrocatalysts

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Chinese Journal of Catalysis 2020年第11期41卷 1745-1753页

作者： Zixia Wan Qiuting He Jundan Chen Tayirjan Taylor Isimjan Bao Wang Xiulin Yang Guangxi Key Laboratory of Low Carbon Energy Materials School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004GuangxiChina State Key Laboratory of Biochemical Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Saudi Arabia Basic Industries Corporation(SABIC)at King Abdullah University of Science and Technology(KAUST) Thuwal 23955-6900Saudi Arabia

As the kinetically sluggish oxygen evolution reaction(OER)is considered to be a bottleneck in overall water splitting,it is necessary to develop a highly active and stable electrocatalyst to overcome this ***,we successfully fabricated a three-dimensional iron-dysprosium oxide co-regulated in-situ formed MOF-Ni arrays on carbon cloth(FeDy@MOF-Ni/CC)through a facile two-step hydrothermal *** studies demonstrate that the designed FeDy@MOF-Ni/CC catalyst requires an overpotential of only 251 mV to reach 10 mA cm-2 with a small Tafel slope of 52.1 mV ***,the stability declined by only 5.5%after 80 h of continuous testing in 1.0 M ***,a cell voltage of only 1.57 V in the overall water splitting system is sufficient to achieve 10 mA cm-2;this value is far better than that of most previously reported *** excellent catalytic performance originates from the unique 3D rhombus-like structure,as well as coupling synergies of Fe-Dy-Ni *** combination of lanthanide and transition metal species in the synthesis strategy may open entirely new possibilities with promising potential in the design of highly active OER electrocatalysts.

关键词： Metal-organic frameworks Dysprosium oxide Synergistic effect Oxygen evolution Water splitting

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Strengthening mechanisms and high-temperature oxidation behavior of extruded hollow WE43 magnesium profile with lamellar heterostructures

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Journal of Rare Earths 2025年

作者： Xuhao Hu Chaojie Che Xinlin Li Liren Cheng Hongjie Zhang State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun 130022 China Key Laboratory of Superlight Materials and Surface Technology Ministry of Education College of Materials Science and Chemical Engineering Harbin Engineering University Harbin 150001 China Department of Chemistry Tsinghua University Beijing 100084 China

A major challenge for the application of Mg alloy hollow profiles in railway vehicles is to simultaneously improve their mechanical properties and flame retardancy. This work fabricated a large-size asymmetric hollow profile of Mg-4.04Y-2.38Nd-1.02Gd-0.41Zr (WE43) alloy by porthole die extrusion. The influences of process parameters on the temperature and stress fields were investigated by finite element method (FEM). The microstructures were analyzed using a combination of optical microscope, scanning electron microscopy, transmission electron microscopy, spherical aberration transmission electron microscopy and X-ray electron diffraction. The mechanical properties of the alloy were evaluated using hardness and tensile testing methods. X-ray photoelectron spectroscopy was employed to conduct compositional analysis of the film following high-temperature oxidation. Lamellar heterostructures were formed in the extruded profile by high-temperature extrusion, with rapid cooling and a high extrusion ratio, followed by artificial aging. The average values of yield strength (YS), ultimate tensile strength (UTS) and elongation (EL) of the extruded profiles after T5 treatment were 291 MPa, 385 MPa, and 16.4%, respectively. The profiles demonstrate a good balance between strength and plasticity, attributed to the synergistic effect of weak basal texture, precipitation strengthening and heterogeneous deformation induced strengthening. Finally, the extruded hollow profile does not catch fire during high-temperature oxidation up to 1050 °C due to the protection of the dense Y 2 O 3 and MgO composite layer on the matrix. This work is of great significance in guiding the application and industrial production of large-sized hollow profiles of WE43 alloy in railway vehicles.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Extracting Photometric Redshift from Galaxy Flux and Image Data using Neural Networks in the CSST Survey

arXiv

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arXiv 2021年

作者： Zhou, Xingchen Gong, Yan Meng, Xian-Min Cao, Ye Chen, Xuelei Chen, Zhu Du, Wei Fu, Liping Luo, Zhijian Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Science Center for China Space Station Telescope National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Beijing100101 China Key Laboratory for Computational Astrophysics National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Beijing100101 China Centre for High Energy Physics Peking University Beijing100871 China Shanghai Key Lab for Astrophysics Shanghai Normal University Shanghai200234 China

The accuracy of galaxy photometric redshift (photo-z) can significantly affect the analysis of weak gravitational lensing measurements, especially for future high-precision surveys. In this work, we try to extract photo-z information from both galaxy flux and image data expected to be obtained by China Space Station Telescope (CSST) using neural networks. We generate mock galaxy images based on the observational images from the Advanced Camera for Surveys of Hubble Space Telescope (HST-ACS) and COSMOS catalogs, considering the CSST instrumental effects. Galaxy flux data are then measured directly from these images by aperture photometry. The Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) are constructed to predict photo-z from fluxes and images, respectively. We also propose to use an efficient hybrid network, which combines the MLP and CNN, by employing the transfer learning techniques to investigate the improvement of the result with both flux and image data included. We find that the photo-z accuracy and outlier fraction can achieve σNMAD = 0.023 and η = 1.43% for the MLP using flux data only, and σNMAD = 0.025 and η = 1.21% for the CNN using image data only. The result can be further improved in high efficiency as σNMAD = 0.020 and η = 0.90% for the hybrid transfer network. These approaches result in similar galaxy median and mean redshifts 0.8 and 0.9, respectively, for the redshift range from 0 to 4. This indicates that our networks can effectively and properly extract photo-z information from the CSST galaxy flux and image data. Copyright © 2021, The Authors. All rights reserved.

关键词： Image enhancement

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Transboundary cooperation in infrastructure operation generates economic and environmental co-benefits in the Lancang-Mekong River Basin (vol 2, pg 589, 2024)

NATURE WATER

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NATURE WATER 2024年第6期2卷 602-602页

作者： Yu, Yang Bo, Yan Castelletti, Andrea Dumas, Patrice Gao, Jinyu Cai, Ximing Liu, Junguo Kahil, Taher Wada, Yoshihide Hu, Shiruo Liu, Bo Zhou, Feng Zhao, Jianshi State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Tsinghua University Beijing China Rural Revitalization Finance Department China Construction Bank Beijing China Centralized Control Center Yalong River Hydropower Development Company Chengdu China Sino-France Institute of Earth Systems Science Laboratory for Earth Surface Processes College of Urban and Environmental Sciences Peking University Beijing China Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Institute of Environmental Engineering ETH Zurich Zurich Switzerland Centre International de Recherche sur l’Environnement et le Développement Nogent sur Marne France Department of Civil and Environmental Engineering University of Illinois at Urbana‐Champaign Champaign IL USA School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China International Institute for Applied Systems Analysis Laxenburg Austria National Key Laboratory of Water Disaster Prevention Jiangsu Province Engineering Research Center of Watershed Geospatial Intelligence College of Geography and Remote Sensing Hohai University Nanjing China Institute of Carbon Neutrality Laboratory for Earth Surface Processes College of Urban and Environmental Sciences Peking University Beijing China Southwest United Graduate School Kunming China

The Lancang-Mekong River Basin is facing booming water resources infrastructure development, long-term transboundary conflicts and trade-offs between economic goals and ecosystem services provision. Optimizing the pathway towards sustainable infrastructure operation has lacked multisector-coordinated and decision behaviour-based perspectives for transboundary water systems. In this study we quantified how, and to what extent, transboundary cooperation generates economic and environmental co-benefits by jointly using a coupled simulation–optimization approach and cooperative game theoretical analysis. We found that full cooperation outweighs partial or non-cooperation modes to promote economic benefits by 3 to 21% and to minimize the losses in fishery and sediment transport from 23% and 60% to 12% and 22%, respectively. Full cooperation becomes more beneficial and stable alongside infrastructure expansion, climate change and satisfying the hydrological needs of river ecosystems. These findings underscore the importance of full cooperation for sustaining socio-environmental systems and highlight the need for a benefit reallocation mechanism and designed flow management to stabilize basin-level full cooperation in the Lancang-Mekong River Basin.

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Single-cell transcriptome analysis of in vivo and in vitro human pancreas development

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Genes & Diseases 2025年

作者： Xiaofei Zhang Hongyan Yi Zhuo Ma Yinsuo Zhao Yanlin Ma Eli Song Tao Xu Key Laboratory of Molecular Biophysics of the Ministry of Education College of Life Science and Technology Huazhong University of Science and Technology Wuhan Hubei 430074 China National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research The First Affiliated Hospital of Hainan Medical University Hainan Medical University Haikou Hainan 571199 China Guangzhou Laboratory Guangzhou Guangdong 510005 China Shandong First Medical University & Shandong Academy of Medical Sciences Jinan Shandong 250117 China

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