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Synergistic Enhancement with Mesoporous Silica Colloidal Electrolyte for Lithium-Oxygen Batteries

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SSRN

SSRN 2022年

作者： Mu, Hailiang Zhuge, Xiangqun Liu, Tong Ding, Zhengping Ren, Guogang Liu, Terence Xiaoteng Xu, Ben Bin Luo, Zhihong Luo, Kun Changzhou Key Laboratory of Intelligent Manufacturing and Advanced Technology for Power Battery Changzhou University Changzhou213164 China Guangxi Key Laboratory of Optical and Electronic Materials and Devices Guilin University of Technology Guilin541004 China Department of Mechanical and Construction Engineering Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of Engineering and Technology School of Physics Engineering and Computer Science University of Hertfordshire Hatfield Hertfordshire AL10 9AB United Kingdom

Protecting lithium anode and accelerating cathode reaction kinetics are indispensable to developing reliable lithium-oxygen batteries (LOBs). Herein, we demonstrate a mesoporous silica (mSiO 2 ) colloidal electrolyte that synergistically enhances both the anode and cathode processes in LOBs. The cycle life was significantly elongated for 6 ~7 times at different rates of 1 A g -1 , 3 A g -1 and 5 A g -1 , respectively, and the full discharge capacity was also enlarged for 10 times with addition of mSiO 2 in the aprotic electrolyte. Morphological and electrochemical analyses indicate that the large surface area and mesoporous structure of mSiO 2 serve to provide uniform Li + flux at the anode, absorb the superoxide intermediates by oxygen reduction reaction in the electrolyte, and improve the mass transfer in the decomposition of discharge product at the cathode, which resulted in the apparent protection of lithium metal and electrolyte, as well as the improved cyclability of the LOBs. The design principle can be expanded to other porous materials at low cost, and offers a new route toward the development of applicable lithium-air batteries. © 2022, The Authors. All rights reserved.

关键词： Electrolytes

Efficient complete denture metal base design via a dental feature-driven segmentation network

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computers in Biology and Medicine 2024年 175卷 108550-108550页

作者： Li, Cheng Jin, Yaming Du, Yunhan Luo, Kaiyuan Fiorenza, Luca Chen, Hu Tian, Sukun Sun, Yuchun Center of Digital Dentistry Faculty of Prosthodontics Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials No.22 Zhongguancun South Avenue Haidian District Beijing100081 China Nanjing Profeta Intelligent Technology Co. Ltd No. 12 Mozhou East Road Jiangning District Jiangsu Province Nanjing City211111 China Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL61820 United States Biomedicine Discovery Institute Monash University MelbourneVIC3800 Australia

Background and objective: Complete denture is a common restorative treatment in dental patients and the design of the core components (major connector and retentive mesh) of complete denture metal base (CDMB) is the basis of successful restoration. However, the automated design process of CDMB has become a challenging task primarily due to the complexity of manual interaction, low personalization, and low design accuracy. Methods: To solve the existing problems, we develop a computer-aided Segmentation Network-driven CDMB design framework, called CDMB-SegNet, to automatically generate personalized digital design boundaries for complete dentures of edentulous patients. Specifically, CDMB-SegNet consists of a novel upright-orientation adjustment module (UO-AM), a dental feature-driven segmentation network, and a specific boundary-optimization design module (BO-DM). UO-AM automatically identifies key points for locating spatial attitude of the three-dimensional dental model with arbitrary posture, while BO-DM can result in smoother and more personalized designs for complete denture. In addition, to achieve efficient and accurate feature extraction and segmentation of 3D edentulous models with irregular gingival tissues, the light-weight backbone network is also incorporated into CDMB-SegNet. Results: Experimental results on a large clinical dataset showed that CDMB-SegNet can achieve superior performance over the state-of-the-art methods. Quantitative evaluation (major connector/retentive mesh) showed improved Accuracy (98.54 ± 0.58 %/97.73 ± 0.92 %) and IoU (87.42 ± 5.48 %/70.42 ± 7.95 %), and reduced Maximum Symmetric Surface Distance (4.54 ± 2.06 mm/4.62 ± 1.68 mm), Average Symmetric Surface Distance (1.45 ± 0.63mm/1.28 ± 0.54 mm), Roughness Rate (6.17 ± 1.40 %/6.80 ± 1.23 %) and Vertices Number (23.22 ± 1.85/43.15 ± 2.72). Moreover, CDMB-SegNet shortened the overall design time to around 4 min, which is one tenth of the comparison methods. Conclusions: CDMB-SegNet

关键词： computer aided design

A Novel K-Repetition Design for SCMA

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arXiv 2022年

作者： Lai, Ke Liu, Zilong Lei, Jing Wen, Lei Chen, Gaojie Xiao, Pei Department of Communication Engineering College of Electronic Science and Engineering National University of Defence Technology China School of Computer Science and Electronics Engineering University of Essex United Kingdom The Department of Engineering University of Leicester LeicesterLE1 7RH United Kingdom State Key Laboratory of ISN Xidian University Xi’an710071 China

This work presents a novel K-Repetition based HARQ scheme for LDPC coded uplink SCMA by employing a network coding (NC) principle to encode different packets, where K-Repetition is an emerging technique (recommended in 3GPP Release 15) for enhanced reliability and reduced latency in future massive machine-type communication. Such a scheme is referred to as the NC aided K-repetition SCMA (NCK-SCMA). We introduce a joint iterative detection algorithm for improved detection of the data from the proposed LDPC coded NCK-SCMA systems. Simulation results demonstrate the benefits of NCK-SCMA with higher throughput and improved reliability over the conventional K-Repetition SCMA. © 2022, CC0.

关键词： Network coding

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

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arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement

ACL-Net: Adaptive and Collaborative Learning Network for Multi-Site Prostate MRI Segmentation

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ACL-Net: Adaptive and Collaborative Learning Network for Mul...

IEEE International Conference on Big Data

作者： Zibo Ma Bo Zhang Zheng Zhang Wendong Wang Yue Mi Haiwen Huang Jingyun Wu Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications School of Modern Post Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Peking University First Hospital Beijing China Department of Urology Peking University First Hospital Department of Urology Peking University First Hospital Beijing China Department of Radiology Peking University First Hospital

ISBN: (纸本)9781665480468

High-performance deep learning models require large amounts of data with high quality annotations for model training, while the labeling work usually takes a lot of time for the experts. Meanwhile, the inter-observer variability al-ways exist between annotations from different experts and the distribution shift between the data acquired from different medical institutions. To address these challenges, we propose an end-to-end domain adaptive collaborative learning network for multi-institutional prostate MRI segmentation. Specifically, we introduce an unpaired image translation module to match the image domains between different institutions, which can alleviate the heterogeneity between 1.5T and 3T prostate MR images during model training. Moreover, we design a self-taught strategy to transfer domain-aware knowledge to jointly learn generic and unique representations. Furthermore, we evaluate our approach in scenarios with limited or without annotations, experimental results show that our approach has better adaptation performance than traditional supervised learning approaches, and has the potential to extend to unsupervised domain adaptation scenario. We also evaluate our approach with prostate MRI segmentation benchmark datasets, experimental results show that our approach outperforms several state-of-the-art methods.

关键词： Training Deep learning Image segmentation Adaptation models Adaptive systems Annotations Federated learning

Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics

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arXiv 2021年

作者： Wang, Ruigang Koelwijn, Patrick J.W. Manchester, Ian R. Tóth, Roland Australian Centre for Field Robotics Sydney Institute for Robotics and Intelligent Systems University of Sydney SydneyNSW2006 Australia Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and L2-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods. © 2021, CC BY.

关键词： state feedback

A survey of multicast communication in optical network-on-chip (Onoc) 10th

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A survey of multicast communication in optical network-on-ch...

10th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2019

作者： Yang, Wen Chen, Yawen Huang, Zhiyi Zhang, Haibo Gu, Huaxi Yu, Cui Department of Computer Science University of Otago Dunedin9016 New Zealand State Key Laboratory of ISN Xidian University Xi’an710071 China Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province China Jiliang University Hangzhou310018 China

Optical Network-on-Chip (ONoC) is an emerging chip-level optical interconnection technology to realise high-performance and power-efficient inter-core communication for many-core processors. For on-chip networks, multicast communication is an important communication pattern, which is not only widely used in parallel computing applications in Chip Multi-Processors (CMPs), but also commonly adopted in emerging areas such as neuromorphic computing. However, the opti-misation of multicast communication in an ONoC is not well studied and there is a significant room to improve the performance. In this paper, we present a comprehensive survey of the multicast communication in an ONoC, covering the development of both architecture design and networking design as well as our recent research outcomes. Moreover, we propose the design challenges and future research directions for optimis-ing multicast in an ONoC, which can provide guidance and insight for the future researchers and chip developers. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Surveys

Machine Learning Meets Big Data: An Overview of Diagnostic and Prognostic Prediction for Cancer 15th

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Machine Learning Meets Big Data: An Overview of Diagnostic a...

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Ma, Baoshan Chai, Bingjie Fang, Mingkun Qi, Jishuang Hou, Xiaoyu Zhang, Xinyu Gao, Zongjiang Meng, Fanyu Song, Fengju College of Information Science and Technology Dalian Maritime University Dalian116026 China Institute of Environmental Systems Biology College of Environmental Science and Engineering Dalian Maritime University Dalian116026 China Maritime Intelligent Transportation Research Group Dalian Maritime University Dalian116026 China Department of Epidemiology and Biostatistics Key Laboratory of Cancer Prevention and Therapy Tianjin National Clinical Research Center of Cancer Tianjin Medical University Cancer Institute and Hospital Tianjin300000 China

ISBN: (纸本)9789811613531

The past decades have witnessed the rapid development on biological technology and artificial intelligence (AI). Large amounts of omics data have been produced and accumulated by high-throughput sequencing technology. The accumulation of multi-omics data and the innovation of machine-learning provide a variety of resources for diagnostic and prognostic prediction of cancer, which efficiently enhance clinical decision making, improve prognosis, and accelerate the progress of precision medicine. In this review, first we reviewed the common omics data. Second, we briefly introduced several popular machine learning methods. Thirdly, we systematically summarized the substantial achievements obtained in a number of prediction studies of cancer, including breast cancer (BRCA), kidney renal clear cell carcinoma (KIRC), glioblastoma multiforme (GBM). Lastly, it is promising that the advanced AI technologies such as deep learning will be widely applied to facilitate diagnosis and treatment for cancer patients as massive omics data are efficiently and accurately measured. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images

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arXiv 2022年

作者： Hou, Junlin Xu, Jilan Xiao, Fan Zhao, Rui-Wei Zhang, Yuejie Zou, Haidong Lu, Lina Xue, Wenwen Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University China Shanghai Eye Diseases Prevention & Treatment Center Shanghai Eye Hospital Shanghai China

Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally focus on single-field fundus images, which have limited field of view for precise eye examinations. In clinical applications, ophthalmologists adopt two-field fundus photography as the dominating tool, where the information from each field (i.e., macula-centric and optic disc-centric) is highly correlated and complementary, and benefits comprehensive decisions. However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies. In this work, we first construct a new benchmark dataset (DRTiD) for DR grading, consisting of 3,100 two-field fundus images. To the best of our knowledge, it is the largest public DR dataset with diverse and high-quality two-field images. Then, we propose a novel DR grading approach, namely Cross-Field Transformer (CrossFiT), to capture the correspondence between two fields as well as the long-range spatial correlations within each field. Considering the inherent two-field geometric constraints, we particularly define aligned position embeddings to preserve relative consistent position in fundus. Besides, we perform masked cross-field attention during interaction to filter the noisy relations between fields. Extensive experiments on our DRTiD dataset and a public DeepDRiD dataset demonstrate the effectiveness of our CrossFiT network. The new dataset and the source code of CrossFiT will be publicly available at https://***/FDU-VTS/DRTiD. © 2022, CC BY.

关键词： Grading