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EVOH in situ fibrillation and its effect of strengthening, toughening and hydrophilic modification on PVDF hollow fiber microfiltration membrane via TIPS process

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Journal of Materials Science 2019年第7期54卷 5971-5987页

作者： Cui, Zhenyu Tang, Xiuxiu Li, Wei Liu, Haonan Zhang, Jing Wang, Hong Li, Jianxin State Key Laboratory of Separation Membranes and Membrane Processes National Center for International Joint Research on Separation Membranes School of Material Science and Engineering Tianjin Polytechnic University Tianjin 300387 China School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin 300387 China

In order to enhance the strength and overcome the poor antifouling capacity of poly(vinylidene fluoride) (PVDF) membrane used in water treatment, herein, poly(ethylene-co-vinyl alcohol) (EVOH) was selected and the PVDF/EVOH blend hollow fiber microfiltration membrane was prepared via the thermally induced phase separation (TIPS) technique. The morphology for the pristine and blend membrane was compared, and the distribution of EVOH on outer surface and within matrix was explored. The fibrous-shaped EVOH enhanced the breaking strength of the membrane markedly (up to 13.63 MPa) due to the in situ fibrillation;especially, when the blend membrane was immersed into water, the internal plasticization of water molecular enhanced the toughness of the membrane and the elongation at break increased up to 86.39% compared with 27.63% for the corresponding dry membrane and 36.62% for the pristine membrane, respectively. The addition of EVOH introduced hydroxyl group into the bulk and thus endowed the membrane with a better hydrophilicity (the contact angle is as low as 43°) and higher pure water flux (up to 449.11 L m⁻² h⁻¹) compared with pristine PVDF membrane. Moreover, the blend membrane showed a better rejection of carbonic particle (nearly 100%) and higher flux recovery rate (up to 87.30%). The present investigation offers an effective and simple pattern to regulate microstructure and enhance mechanical strength, flux and hydrophilicity of the polymeric microfiltration membrane via the TIPS process for water treatment. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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Interpretable Spatiotemporal Deep Learning Model for Traffic Flow Prediction Based on Potential Energy Fields

Interpretable Spatiotemporal Deep Learning Model for Traffic...

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IEEE International Conference on Data Mining (ICDM)

作者： Jiahao Ji Jingyuan Wang Zhe Jiang Jingtian Ma Hu Zhang School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China MOE Engineering Research Center of ACAT Beihang University Beijing China Department of Computer Science The University of Alabama Tuscaloosa Alabama USA

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Traffic flow prediction is of great importance in traffic management and public safety, but is challenging due to the complex spatial-temporal dependencies as well as temporal dynamics. Existing work either focuses on traditional statistical models, which have limited prediction accuracy, or relies on black-box deep learning models, which have superior prediction accuracy but are hard to interpret. In contrast, we propose a novel interpretable spatiotemporal deep learning model for traffic flow prediction. Our main idea is to model the physics of traffic flow through a number of latent Spatio-Temporal Potential Energy Fields (ST-PEFs), similar to water flow driven by the gravity field. We develop a Wind field Decomposition (WD) algorithm to decompose traffic flow into poly-tree components so that ST-PEFs can be established. We then design a spatiotemporal deep learning model for the ST-PEFs, which consists of a temporal component (modeling the temporal correlation) and a spatial component (modeling the spatial dependencies). To the best of our knowledge, this is the first work that make traffic flow prediction based on ST-PEFs. Experimental results on real-world traffic datasets show the effectiveness of our model compared to the existing methods. A case study confirms our model interpretability.

关键词： Deep learning Potential energy Predictive models Prediction algorithms Data models Spatiotemporal phenomena Safety

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Coalition game based user association for mmWave mobile relay systems in rail traffic scenarios

arXiv

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arXiv 2021年

作者： Chen, Chen Niu, Yong Mao, Shiwen Zhang, Xiaodan Han, Zhu Ai, Bo Gao, Meilin Xiao, Huahua Wang, Ning The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Management Shenzhen Institute of Information Technology Shenzhen518172 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The Tsinghua Space Center Tsinghua University Beijing100091 China The ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China

Rail transportation, especially, high-speed rails (HSR), is an important infrastructure for the development of national economy and the promotion of passenger experience. Due to the large bandwidth, millimeter wave (mmWave) communication is regarded as a promising technology to meet the demand of high data rates. However, since mmWave communication has the characteristic of high attenuation, mobile relay (MR) is considered in this paper. Also, full-duplex (FD) communications have been proposed to improve the spectral efficiency. However, because of the high speed, as well as the problem of penetration loss, passengers on the train have a poor quality of service. Consequently, an effective user association scheme for HSR in mmWave band is necessary. In this paper, we investigate the user association optimization problem in mmWave mobile relay systems where the MRs operate in the FD mode. To maximize the system capacity, we propose a cooperative user association approach based on coalition formation game, and develop a coalition formation algorithm to solve the challenging NP-hard problem. We also prove the convergence and Nash-stable property of the proposed algorithm. Extensive simulations are done to show the system performance of the proposed scheme under various network settings. It is demonstrated that the proposed distributed low complexity scheme achieves a near-optimal performance and outperforms two baseline schemes in terms of average system throughput. Copyright © 2021, The Authors. All rights reserved.

关键词： Game theory

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A Model Based Heterogeneous Data Collaboration Method

A Model Based Heterogeneous Data Collaboration Method

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IEEE International Conference on Electronics and Communication engineering (ICECE)

作者： Junjie Xue Junhua Zhou Guoqiang Shi Yanhong Jiang Bowen Wei Hongyan Quan Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Beijing Simulation Center Science and Technology on Space System Simulation Laboratory Beijing China School of Software Engineering East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9781728192581

ISBN: (纸本)9781728192598

Model based heterogeneous data collaboration is important in the integrated design and manufacturing technology. In this work, we propose a systematic method of collaborative design method for heterogeneous data sources. In our method, we use the Open Model Based engineering Environment (OpenMBEE) to study. In our work, we setup environment and construct the integrated multi-tools achieving consistency design, and then we successfully design and implement a reasonable scheme to parameterize the model in requirement analysis and store the extracted parameters in Model Management System (MMS) for transporting to the visualization tool for further simulation. We integrate the mathematical analysis tools of MATLAB, and successfully design the interface in the tool integration, and realize the model consistency in the problem of integrating multiple tools in the design based on model center. The effectiveness of the proposed integration strategies is verified by some practical instances. That is verified that our provided study can an effective design idea for complex production.

关键词： Analytical models Collaboration Production Tools Data models Mathematical model Matlab

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Leveraged Matrix Completion with Noise

arXiv

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arXiv 2020年

作者： Huang, Xinjian Liu, Weiwei Du, Bo Tao, Dacheng The School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Computer Science Wuhan University Wuhan China The School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China The School of Computer Science The Faculty of Engineering The University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

Completing low-rank matrices from subsampled measurements has received much attention in the past decade. Existing works indicate that O(nr log2(n)) datums are required to theoretically secure the completion of an n × n noisy matrix of rank r with high probability, under some quite restrictive assumptions: (1) the underlying matrix must be incoherent;(2) observations follow the uniform distribution. The restrictiveness is partially due to ignoring the roles of the leverage score and the oracle information of each element. In this paper, we employ the leverage scores to characterize the importance of each element and significantly relax assumptions to: (1) not any other structure assumptions are imposed on the underlying low-rank matrix;(2) elements being observed are appropriately dependent on their importance via the leverage score. Under these assumptions, instead of uniform sampling, we devise an ununiform/biased sampling procedure that can reveal the "importance" of each observed element. Our proofs are supported by a novel approach that phrases sufficient optimality conditions based on the Golfing Scheme, which would be of independent interest to the wider areas. Theoretical findings show that we can provably recover an unknown n × n matrix of rank r from just about O(nr log2(n)) entries, even when the observed entries are corrupted with a small amount of noisy information. The empirical results align precisely with our theories. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Fabricating PVDF hollow fiber microfiltration membrane with a tenon-connection structure via the thermally induced phase separation process to enhance strength and permeability

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European Polymer Journal 2019年 111卷 49-62页

作者： Cui, Zhenyu Li, Wei Zeng, Haiyi Tang, Xiuxiu Zhang, Jing Qin, Shuhao Han, Na Li, Jianxin State Key Laboratory of Separation Membranes and Membrane Processes/National Center for International Joint Research on Separation Membranes School of Material Science and Engineering Tianjin Polytechnic University Tianjin 300387 China School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin 300387 China National Engineering Research Canter for Compounding and Modification of Polymer Materials Guiyang 550014 China

In this work, thermally induced phase separation (TIPS) method was adopted to manufacture polyvinylidene fluoride (PVDF) blend hollow fiber microfiltration membrane with tenon-connection structure, which is used to fasten in Chinese traditional building by the stronger friction between rabbet and mortise, for water treatment by blending polyvinyl butyral (PVB). In this microstructure formation process, the distribution of PVB within the matrix and its regulatory role were investigated. Especially, the comprehensive effect of rabbeting and PVB adhesion on strength (The maximum breaking strength reached 17.08 MPa) was discussed. The results showed that a suitable PVB will markedly reduce the thickness of skin-layer close to the outside surface even for the solid-liquid (S-L) phase separation while the excessive PVB result in a thick skin-layer. Besides the strength, PVB markedly improved the hydrophilicity and permeability of the membrane. The breaking strength is more than 8 MPa, the maximum pure water flux is as high as 929.23 L m⁻² h⁻¹ and the rejection rate of carbon particles is nearly 100% even the polymer content is only 22%. In addition, the membrane showed a β-phase and an improved anti-fouling capacity. The investigation presented a simple way to regulate microstructure, enhance mechanical strength, flux and anti-fouling capacity of the membrane via the TIPS process for water treatment. © 2018

关键词： Adhesion effect Permeability Strengthening Tenon-connection Thermally induced phase separation

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Transferring knowledge distillation for multilingual social event detection

arXiv

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

arXiv

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arXiv 2022年

作者： Han, Chu Pan, Xipeng Yan, Lixu Lin, Huan Li, Bingbing Yao, Su Lv, Shanshan Shi, Zhenwei Mai, Jinhai Lin, Jiatai Zhao, Bingchao Xu, Zeyan Wang, Zhizhen Wang, Yumeng Zhang, Yuan Wang, Huihui Zhu, Chao Lin, Chunhui Mao, Lijian Wu, Min Duan, Luwen Zhu, Jingsong Hu, Dong Fang, Zijie Chen, Yang Zhang, Yongbing Li, Yi Zou, Yiwen Yu, Yiduo Li, Xiaomeng Li, Haiming Cui, Yanfen Han, Guoqiang Xu, Yan Xu, Jun Yang, Huihua Li, Chunming Liu, Zhenbing Lu, Cheng Chen, Xin Liang, Changhong Zhang, Qingling Liu, Zaiyi The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi541004 China The Department of Pathology Guangdong Provicial People's Hospital Ganzhou Hospital Ganzhou China The College of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science and Engineering South China University of Technology Guangzhou510640 China The Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Radiology Guangzhou First People's Hospital Hospital of South China University of Technology Guangzhou510180 China The Zhejiang Dahua Technology Co. Ltd Zhejiang310053 China The School of Biomedical Engineering University of Science and Technology of China Hefei230022 China The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Electronic and Computer Engineering & Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The School of Automation Science and Engineering Xi'An Jiaotong University Xian710049 Chi

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques. © 2022, CC BY.

关键词： Semantic Segmentation

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224-Gb/s 16QAM SV-DD Transmission Using Pilot-Assisted Polarization Recovery with Integrated Receiver

224-Gb/s 16QAM SV-DD Transmission Using Pilot-Assisted Polar...

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Optical Fiber Communication (OFC) Conference

作者： Sen Zhang Liwang Lu Linsheng Fan Bin Chen Tingting Zhang Tianjian Zuo Lei Liu Jin Tang Li Zeng Pengxin Chen Jiahao Huo Xian Zhou Alan Pak Tao Lau Chao Lu Liu Liu Changjian Guo Network Technology Laboratory Huawei Technologies Co. Ltd. Shenzhen China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology South China Academy of Advanced Optoelectronics South China Normal University Guangzhou China State Key Laboratory for Modern Optical Instrumentation College of Optical Science and Engineering Centre for Optical & Electromagnetic Research International Research Center for Advanced Photonics Zhejiang University Hangzhou China B&P Laboratory Huawei Technologies Co. Ltd. Shenzhen China School of Computer & Communication Engineering Beijing University of Science and Technology Beijing China The Hong Kong Polytechnic University Hong Kong China The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen

We propose and experimentally demonstrate a low-complexity pilot-assisted analytical method for polarization demultiplexing in SV-DD systems, achieving a 224-Gb/s 16QAM transmission over 40-km SSMF and can tolerate up... 详细信息

ISBN: (纸本)9781943580866

关键词： Demultiplexing Receivers Optical fiber communication

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