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

引用

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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Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation

引用

IEEE Journal on Selected Areas in Communications 2025年

作者： Wu, Shangrui Li, Yupeng Wang, Wenhua Guo, Jianxiong Fan, Wentao Liu, Qin Jia, Weijia Yu, Shui Cao, Jiannong Wang, Tian Beijing Normal-Hong Kong Baptist University Guangdong Provincial/Zhuhai Key Laboratory of IRADS Department of Computer Science Zhuhai China Hong Kong Baptist University Hong Kong Hong Kong Hong Kong Baptist University Department of Interactive Media Hong Kong Hong Kong Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an China Hunan University College of Computer Science and Electronic Engineering Changsha China University of Technology Sydney School of Computer Science Sydney Australia The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

The integration of edge computing with deep neural networks (DNNs) is crucial for intelligent industrial cyber-physical systems. Typically, deploying DNNs on heterogeneous edge devices relies on methods like model compression and partitioning. However, these approaches often result in homogeneous models across devices. This homogeneity limits the collective capability of edge computing systems, particularly in terms of generalization to diverse data distributions and adaptation to dynamic industrial environments. In this work, we propose to treat each DNN on an edge device as an independent model, aggregating their capabilities via ensemble learning to enhance generalization and dynamic adaptability. To realize this, we introduce the Adaptive Ensemble Knowledge Distillation Framework (AEKDF), combining cloud-based model training with edge computing based collaborative inference. In the cloud, AEKDF develops an enhanced Born Again Network that generates diverse, lightweight models tailored to specific edge devices through knowledge distillation. This process ensures model diversity which is critical to effective ensemble learning. On the edge, AEKDF employs an adaptive ensemble technique that aggregates prediction logits across devices, enabling rapid adaptation to changing environments and maintaining inference efficiency. Our extensive evaluations conducted on a realistic prototype demonstrate the substantial boost in predictive performance achieved by our AEKDF, showcasing a 4% to 10% accuracy improvement on the CIFAR-100 compared to conventional single-model approaches, while maintaining low latency. © 1983-2012 IEEE.

关键词： Deep neural networks

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Target-based Resource Allocation for Deep Learning Applications in a Multi-tenancy System

Target-based Resource Allocation for Deep Learning Applicati...

引用

IEEE Conference on high performance Extreme computing (HPEC)

作者： Wenjia Zheng Yun Song Zihao Guo Yongchen Cui Suwen Gu Ying Mao Long Cheng Computer & Information Science Department Fordham University Performance Engineering Laboratory University College Dublin

The neural-network based deep learning is the key technology that enables many powerful applications, which include self-driving vehicles, computer vision, and natural language processing. Although various algorithms focus on different directions, generally, they mainly employ an iteration by iteration training and evaluating the process. Each iteration aims to find a parameter set, which minimizes a loss function defined by the learning model. When completing the training process, the global minimum is achieved with a set of optimized parameters. At this stage, deep learning applications can be shipped with a trained model to provide services. While deep learning applications are reshaping our daily life, obtaining a good learning model is an expensive task. Training deep learning models is, usually, time-consuming and requires lots of resources, e.g. CPU and GPU. In a multi-tenancy system, however, limited resources are shared by multiple clients that lead to severe resource contention. Therefore, a carefully designed resource management scheme is required to improve the overall performance. In this project, we propose a target based scheduling scheme named TRADL. In TRADL, developers have options to specify a two-tier target. If the accuracy of the model reaches a target, it can be delivered to clients while the training is still going on to continue improving the quality. The experiments show that TRADL is able to significantly reduce the time cost, as much as 48.2%, for reaching the target.

关键词： Training Machine learning Resource management Task analysis Neural networks Target tracking Computational modeling

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Exploiting deep learning for secure transmission in an underlay cognitive radio network

arXiv

引用

arXiv 2021年

作者： Zhang, Miao Cumanan, Kanapathippillai Thiyagalingam, Jeyarajan Tang, Yanqun Wang, Wei Ding, Zhiguo Dobre, Octavia A. School of Information Science and Engineering Chongqing Jiaotong University Chongqing China Department of Electronic Engineering University of York YorkYO10 5DD United Kingdom Scientific Computing Department of Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus Ditcot United Kingdom School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen510006 China School of Information Science and Technology Nantong University Nantong China Nantong Research Institute for Advanced Communication Technologies Nantong China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom Department of Electrical and Computer Engineering Memorial University St. John’sNLA1B 3X5 Canada

This paper investigates a machine learning-based power allocation design for secure transmission in a cognitive radio (CR) network. In particular, a neural network (NN)based approach is proposed to maximize the secrecy rate of the secondary receiver under the constraints of total transmit power of secondary transmitter, and the interference leakage to the primary receiver, within which three different regularization schemes are developed. The key advantage of the proposed algorithm over conventional approaches is the capability to solve the power allocation problem with both perfect and imperfect channel state information. In a conventional setting, two completely different optimization frameworks have to be designed, namely the robust and non-robust designs. Furthermore, conventional algorithms are often based on iterative techniques, and hence, they require a considerable number of iterations, rendering them less suitable in future wireless networks where there are very stringent delay constraints. To meet the unprecedented requirements of future ultra-reliable low-latency networks, we propose an NN-based approach that can determine the power allocation in a CR network with significantly reduced computational time and complexity. As this trained NN only requires a small number of linear operations to yield the required power allocations, the approach can also be extended to different delay sensitive applications and services in future wireless networks. When evaluate the proposed method versus conventional approaches, using a suitable test set, the proposed approach can achieve more than 94% of the secrecy rate performance with less than 1% computation time and more than 93% satisfaction of interference leakage constraints. These results are obtained with significant reduction in computational time, which we believe that it is suitable for future real-time wireless applications. © 2021, CC0.

关键词： Cognitive radio

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Ultrahigh-energy γ-ray emission associated with the tail of a bow-shock pulsar wind nebula

arXiv

引用

arXiv 2025年

作者： Cao, Zhen Aharonian, F. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cai, C.M. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.B. Chen, X.J. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Diao, Y.X. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, X.Y. He, Y. Hernández-Cadena, S. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, S.C. Huang, C. Huang, D.H. Huang, J.J. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kaci, S. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, L. Li, R.L. Li, S.D. Li, T.Y. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.R. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, X. Liu, Y. Liu, Y. Liu, Y.N. Lou, Y.Q. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mou, G.B. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ni, M.Y. Nie, L. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China University of Science and Technology of China Anhui Hefei230026 China Yerevan State University 1 Alek Manukyan Street Yerevan0025 Armenia Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China China School of Physics Guangzhou China Sun Yat-sen University Zhuhai519000 China Guangdo

In this study, We present a comprehensive analysis of an unidentified point-like ultra-high-energy (UHE) gamma-ray source, designated as 1LHAASO J1740+0948u, situated in the vicinity of the middle-aged pulsar PSR J1740+1000. The detection significance reaches 17.1σ (9.4σ) above 25 TeV (100 TeV).The energy spectrum of the source extends up to 300 TeV and is well-described by a log-parabola function with N0 = (1.93 ±0.23) ×10−16 TeV−1 cm−2 s−1, α = 2.14±0.27, and β = 1.20±0.41 at E0 = 30 TeV. The associated pulsar, PSR J1740+1000, resides at a high Galactic latitude and powers a bow-shock pulsar wind nebula (BSPWN) with an extended X-ray tail. The best-fit position of the gamma-ray source appears to be shifted by 0.2◦ with respect to the pulsar position. As the (i) currently identified pulsar halos do not demonstrate such offsets, and (ii) centroid of the gamma-ray emission is approximately located at the extension of the X-ray tail, we speculate that the UHE γ-ray emission may originate from re-accelerated electron/positron pairs that are advected away in the bow-shock tail. © 2025, CC BY-NC-ND.

关键词： Pulsars

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Detection of very high-energy gamma-ray emission from the radio galaxy M87 with LHAASO

arXiv

引用

arXiv 2024年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Key Laboratory of Particle Astrophysics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Sun Y

The nearby radio galaxy M87 is a very-high-energy (VHE) gamma-ray emitter established by observations with ground-based gamma-ray detectors. Here we report the long-term monitoring of M87 from 2021 to 2024 with Large high Altitude Air Shower Observatory (LHAASO). M87 has been detected by LHAASO with a statistical significance ∼ 9σ. The observed energy spectrum extends to 20 TeV, with a possible hardening at ∼ 20 TeV and then a clear softening at higher energies. Assuming that the intrinsic spectrum is described by a single power law up to 20 TeV, a tight upper bound on the extragalactic background light (EBL) intensity is obtained. A strong VHE flare lasting eight days, with the rise time of τrrise = 1.05 ± 0.49 days and decay time of τddecay = 2.17 ± 0.58 days, was found in early 2022. A possible GeV flare is seen also in the Fermi-LAT data during the VHE flare period. The variability time as short as one day seen in the LHAASO data suggests a compact emission region with a size of ∼ 3 × 1015δ cm (δ being the Doppler factor of the emitting region), corresponding to a few Schwarzschild radii of the central supermassive black hole in M87. The continuous monitoring of the source reveals a duty cycle of ∼ 1% for VHE flares with a flux above 10−11 erg cm−2 s−1 Copyright © 2024, The Authors. All rights reserved.

关键词： Active Galactic Nuclei

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Broadband γ-ray spectrum of supernova remnant Cassiopeia A

arXiv

引用

arXiv 2025年

作者： Cao, Zhen Aharonian, F. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cai, C.M. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.B. Chen, X.J. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Diao, Y.X. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, X.Y. He, Y. Hernández-Cadena, S. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, S.C. Huang, C. Huang, D.H. Huang, J.J. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kaci, S. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, L. Li, R.L. Li, S.D. Li, T.Y. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.R. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, X. Liu, Y. Liu, Y. Liu, Y.N. Lou, Y.Q. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mou, G.B. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ni, M.Y. Nie, L. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China University of Science and Technology of China Anhui Hefei230026 China Yerevan State University 1 Alek Manukyan Street Yerevan0025 Armenia Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China Institute of Frontier

The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of ∼ 2.5′. Although no extension of this source has been detected in the γ-ray band, using more than 1000 days of LHAASO data above ∼ 0.8 TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telescopes (IACTs) and its flux near ∼ 1 TeV is about two times higher. In combination with analyses of more than 16 years of Fermi-LAT data covering 0.1 GeV − 1 TeV, we find that the spectrum above 30 GeV deviates significantly from a single power-law, and is best described by a smoothly broken power-law with a spectral index of 1.90 ± 0.15stat (3.41 ± 0.19stat) below (above) a break energy of 0.63 ± 0.21stat TeV. Given differences in the angular resolution of LHAASO-WCDA and IACTs, TeV γray emission detected with LHAASO may have a significant contribution from regions surrounding the SNR illuminated by particles accelerated earlier, which, however, are treated as background by IACTs. Detailed modelling can be used to constrain acceleration processes of TeV particles in the early stage of SNR evolution. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic rays

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A New Superhard Carbon Allotrope: T5-Carbon

SSRN

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SSRN 2020年

作者： Ding, Xian-Yong Zhang, Chao Wang, Dong-Qi Li, Bing-Sheng Wang, Qingping Yu, Zhi Gen Ang, Kah-Wee Zhang, Yong-Wei School of Materials Science and Engineering Anhui University of Science and Technology Huainan232001 China Multidisciplinary Initiative Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Environment-friendly Energy Materials Southwest University of Science and Technology MianyangSichuan621010 China Institute of High Performance Computing A*STAR Singapore138632 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

A novel carbon allotrope is predicted by first-principles calculations. This allotrope is obtained by replacing one of the two atoms in the primitive cell of diamond with a carbon tetrahedron, thus it contains five atoms in one primitive cell, termed T5-carbon. The stabilities of T5-carbon are checked in structural, thermal, vibrational and energy calculations. T5-carbon is a semiconductor with an indirect band gap of 3.18 eV, and has a lattice thermal conductivity of 409 W/mK. The Vickers hardness of T5-carbon is 76.5 GPa which is lower than that of diamond but larger than those of T-carbon and cubic boron nitride. © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

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Enhancement of optomechanically induced sum sidebands using parametric interactions

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Physical Review A 2019年第2期100卷 023838-023838页

作者： Wen-An Li Guang-Yao Huang School of Physics and Electronic Engineering Guangzhou University Guangzhou 510006 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China

We theoretically study radiation pressure induced generation of the frequency components at the sum sideband in an optomechanical system containing an optical parametric amplifier (OPA). It is shown that an OPA inside a cavity can considerably enhance the amplitude of sum sideband even with low power input fields. We find a new matching condition for the upper sum sideband generation. The height and width of the new peak can be adjusted by the nonlinear gain of the OPA. Furthermore, the lower sum sideband generation can be enhanced with several orders of magnitude by tuning the nonlinear gain parameter and the phase of the field pumping the OPA. The enhanced sum sideband may have potential applications to the manipulation of light in an on-chip optomechanical device and the sensitive sensing for precision measurement in the weak optomechanical coupling regime.

关键词： high-order harmonic generation Optomechanics Quantum optics

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Design and Implementation of a Portable Laser Calibration System for LHAASO-WFCTA 38

Design and Implementation of a Portable Laser Calibration Sy...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Yuan, Guotao Sun, Qinning Jin, Min Xia, Junji Liu, Jing Min, Zhen Zhu, Fengrong Chen, Long Wang, Yang Liu, Yu Zhang, Yong Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. School of Physical Science and Technology Southwest Jiaotong University Sichuan Province Chengdu611756 China Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Institute of HighEnergy Physics Chinese Academy of Sciences Sichuan Province Chengdu610000 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica dell’Università di Napoli ‘Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan Uni

The Wide Field of View Cherenkov Telescope Array (WFCTA), situated in the harsh environment of Daocheng, is established to investigate ultrahigh energy cosmic rays. To enhance the calibration of WFCTA, a portable laser calibration system has been developed, which provides specified energy and direction of the YAG laser. Special designs, including power system, north calibration, and rotating system, have been implemented to ensure the proper functioning of the portable laser calibration system. The aluminum alloy of the entire frame and the vibration damper provide robust anti-impact ability and safety during transportation to different observation locations. A temperature control facility has been employed to meet the operational temperature requirement of electronic equipment. Various sensors have been utilized to monitor environmental parameters, the energy of YAG laser pulses, azimuth and pitch angles, and other parameters in real-time. The auto-control program is communicated wirelessly to avoid any manual interference with the system. The field experiment results have confirmed the reliability of the system and its ability to meet the calibration requirements of WFCTA. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Laser safety

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