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Noise subtraction from KAGRA O3GK data using Independent Component Analysis

Classical and Quantum Gravity 2023年第8期40卷 085015-085015页

作者： H Abe T Akutsu M Ando A Araya N Aritomi H Asada Y Aso S Bae Y Bae R Bajpai K Cannon Z Cao E Capocasa M Chan C Chen D Chen K Chen Y Chen C-Y Chiang Y-K Chu S Eguchi M Eisenmann Y Enomoto R Flaminio H K Fong Y Fujii Y Fujikawa Y Fujimoto I Fukunaga D Gao G-G Ge S Ha I P W Hadiputrawan S Haino W-B Han K Hasegawa K Hattori H Hayakawa K Hayama Y Himemoto N Hirata C Hirose T-C Ho B-H Hsieh H-F Hsieh C Hsiung H-Y Huang P Huang Y-C Huang Y-J Huang D C Y Hui S Ide K Inayoshi Y Inoue K Ito Y Itoh C Jeon H-B Jin K Jung P Jung K Kaihotsu T Kajita M Kakizaki M Kamiizumi N Kanda T Kato K Kawaguchi C Kim J Kim J C Kim Y-M Kim N Kimura T Kiyota Y Kobayashi K Kohri K Kokeyama A K H Kong N Koyama C Kozakai J Kume Y Kuromiya S Kuroyanagi K Kwak E Lee H W Lee R Lee M Leonardi K L Li P Li L C -C Lin C-Y Lin E T Lin F-K Lin F-L Lin H L Lin G C Liu L-W Luo M Ma’arif E Majorana Y Michimura N Mio O Miyakawa K Miyo S Miyoki Y Mori S Morisaki N Morisue Y Moriwaki K Nagano K Nakamura H Nakano M Nakano Y Nakayama T Narikawa L Naticchioni L Nguyen Quynh W-T Ni T Nishimoto A Nishizawa S Nozaki Y Obayashi W Ogaki J J Oh K Oh M Ohashi T Ohashi M Ohkawa H Ohta Y Okutani K Oohara S Oshino S Otabe K-C Pan A Parisi J Park F E Pe na Arellano S Saha Y Saito K Sakai T Sawada Y Sekiguchi L Shao Y Shikano H Shimizu K Shimode H Shinkai T Shishido A Shoda K Somiya I Song R Sugimoto J Suresh T Suzuki H Tagoshi H Takahashi R Takahashi S Takano H Takeda M Takeda K Tanaka T Tanaka S Tanioka A Taruya T Tomaru T Tomura L Trozzo T Tsang J-S Tsao S Tsuchida T Tsutsui D Tuyenbayev N Uchikata T Uchiyama A Ueda T Uehara K Ueno G Ueshima T Ushiba M H P M van Putten J Wang T Washimi C Wu H Wu T Yamada K Yamamoto T Yamamoto K Yamashita R Yamazaki Y Yang S Yeh J Yokoyama T Yokozawa T Yoshioka H Yuzurihara S Zeidler M Zhan H Zhang Y Zhao Z-H Zhu The KAGRA Collaboration Graduate School of Science Tokyo Institute of Technology Meguro-ku Tokyo 152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Earthquake Research Institute The University of Tokyo Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Gravitational Wave Science Project Hirosaki University Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences Yuseong-gu Daejeon 34047 Republic of Korea School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Beijing 100875 People’s Republic of China Department of Applied Physics Fukuoka University Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy National Tsing Hua University Hsinchu 30013 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University Zhongli District Taoyuan City 32001 Taiwan Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan Institute of Physics Academia Sinica Nankang Taipei 11529 Taiwan Univ. Grenoble Alpes Laboratoire d’Annecy de Physique des Particules (LAPP) Université Savoie M

During April 7–21 2020, KAGRA conducted its first scientific observation in conjunction with the GEO600 detector. The dominant noise sources during this run were found to be suspension control noise in the low-frequency range and acoustic noise in the mid-frequency range. In this study, we show that their contributions in the observational data can be reduced by a signal processing method called independent component analysis (ICA). The model of ICA is extended from that studied in the initial KAGRA data analysis to account for frequency dependence, while the linearity and stationarity of the coupling between the interferometer and the noise sources are still assumed. We identify optimal witness sensors in the application of ICA, leading to successful mitigation of these two dominant contributions. We also analyze the stability of the transfer functions for the entire two weeks of data to investigate the applicability of the proposed subtraction method in gravitational wave searches.

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Adaptive, online models to detect and estimate gross error in SPNDs

Adaptive, online models to detect and estimate gross error i...

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Indian control Conference (ICC)

作者： Arindam Bhattacharyya Vivek Yogi Sugandha Singla Mani Bhushan Mahendra G. Kelkar Akhilanand P. Tiwari Mahitosh Pramanik Madhu N. Belur Department of Electrical Engineering Department of Chemical Engineering Indian Institute of Technology Bombay India Nuclear Power Corporation of India Ltd Mumbai India Reactor Control Systems Design Section Bhabha Atomic Research Center Mumbai India

ISBN: (纸本)9781509017966

In-core flux measurement is critical for monitoring and power regulation of a large core nuclear reactor. Self Powered Neutron Detectors (SPNDs) are used for measuring the neutron flux in a nuclear reactor. In this paper we propose an online method for SPND gross error detection, identification and estimation. The method uses linear models which are extracted from data and which adapt continuously in time. This adaption is made possible by use of recursive PCA. However, unlike existing recursive PCA approaches which make approximations to achieve recursion, our proposed approach does not make any approximation. We term our technique as `Recursive Principal Component Analysis: Exact Computation' (RPCA-EC). Use of recursion ensures that the computational requirements of RPCA-EC are low thereby facilitating online implementation. Continuous adaption ensures that the model evolves to adequately capture the time varying relationships amongst the SPNDs. The relationships amongst the SPNDs vary with time due to significant variations in the neutron flux profiles in the reactor with varying operating power levels in the reactor. We apply our proposed method on data taken from an operating nuclear reactor in India and compare results with alternate implementations. Results show that the false alarm rate of our implementation is reasonable thereby indicating that the model adapts to time varying relationships. Performance in presence of gross errors is also satisfactory.

关键词： Detectors Inductors Neutrons Principal component analysis Adaptation models Covariance matrices Computational modeling

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Overview of KAGRA : Data transfer and management

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Progress of Theoretical and Experimental Physics 2023年第10期2023卷

作者： Akutsu, T Ando, M Arai, K Arai, Y Araki, S Araya, A Aritomi, N Asada, H Aso, Y Bae, S Bae, Y Baiotti, L Bajpai, R Barton, M A Cannon, K Cao, Z Capocasa, E Chan, M Chen, C Chen, K Chen, Y Chiang, C-Y Chu, H Chu, Y-K Eguchi, S Enomoto, Y Flaminio, R Fujii, Y Fujikawa, Y Fukunaga, M Fukushima, M Gao, D Ge, G Ha, S Hagiwara, A Haino, S Han, W-B Hasegawa, K Hattori, K Hayakawa, H Hayama, K Himemoto, Y Hiranuma, Y Hirata, N Hirose, E Hong, Z Hsieh, B Huang, G-Z Huang, H-Y Huang, P Huang, Y-C Huang, Y Hui, D C Y Ide, S Ikenoue, B Imam, S Inayoshi, K Inoue, Y Ioka, K Ito, K Itoh, Y Izumi, K Jeon, C Jin, H-B Jung, K Jung, P Kaihotsu, K Kajita, T Kakizaki, M Kamiizumi, M Kanda, N Kang, G Kawaguchi, K Kawai, N Kawasaki, T Kim, C Kim, J Kim, J C Kim, W S Kim, Y-M Kimura, N Kita, N Kitazawa, H Kobayashi, Y Kojima, Y Kokeyama, K Komori, K Kong, A K H Kotake, K Kozakai, C Kozu, R Kumar, R Kume, J Kuo, C Kuo, H-S Kuromiya, Y Kuroyanagi, S Kusayanagi, K Kwak, K Lee, H K Lee, H W Lee, R Leonardi, M Li, K L Lin, L C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H L Liu, G C Luo, L-W Majorana, E Marchio, M Michimura, Y Mio, N Miyakawa, O Miyamoto, A Miyazaki, Y Miyo, K Miyoki, S Mori, Y Morisaki, S Moriwaki, Y Nagano, K Nagano, S Nakamura, K Nakano, H Nakano, M Nakashima, R Nakayama, Y Narikawa, T Naticchioni, L Negishi, R Quynh, L Nguyen Ni, W-T Nishizawa, A Nozaki, S Obuchi, Y Ogaki, W Oh, J J Oh, K Oh, S H Ohashi, M Ohashi, T Ohishi, N Ohkawa, M Ohta, H Okutani, Y Okutomi, K Oohara, K Ooi, C Oshino, S Otabe, S Pan, K Pang, H Parisi, A Park, J Arellano, F E Peña Pinto, I Sago, N Saito, S Saito, Y Sakai, K Sakai, Y Sakuno, Y Sasaki, Y Sato, S Sato, T Sawada, T Sekiguchi, T Sekiguchi, Y Shao, L Shibagaki, S Shimizu, R Shimoda, T Shimode, K Shinkai, H Shishido, T Shoda, A Somiya, K Son, E J Sotani, H Sugimoto, R Suresh, J Suzuki, T Tagoshi, H Takahashi, H Takahashi, R Takamori, A Takano, S Takeda, H Takeda, M Tanaka, H Tanaka, K Tanaka, T Tanioka, S Martin, E N Tapia San Telada, S Tomaru, T Tomigami, Y Tomura, T Travasso, F Trozzo, L Tsang, T Ts Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Hirosaki University 3 Bunkyo-cho Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University No. 19 Xinjiekou Street Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Rd. Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy Nation

KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located in Kamioka, Gifu prefecture, Japan. In this article, we describe the KAGRA data management system, i.e., recording of data, transfer from the KAGRA experiment site to computing resources, as well as data distribution to tier sites, including international sites in Taiwan and Korea. The amount of KAGRA data exceeded 1.0 PiB and increased by about 1.5 TB per day during operation in 2020. Our system has succeeded in data management, and has achieved performance that can withstand observations after 2023, that is, a transfer rate of 20 MB s-1or more and file storage of sufficient capacity for petabyte class. We also discuss the sharing of data between the global gravitational-wave detector network with other experiments, namely LIGO and Virgo. The latency, which consists of calculation of calibrated strain data and transfer time within the global network, is very important from the view of multi-messenger astronomy using gravitational waves. Real-time calbrated data delivered from the KAGRA detector site and other detectors to our computing system arrive with about 4–15 seconds of latency. These latencies are sufficiently short compared to the time taken for gravitational wave event search computations. We also established a high-latency exchange of offline calibrated data that was aggregated with a better accuracy compared with real-time data.

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Combining deep convolutional neural network and SVM to SAR image target recognition 10

Combining deep convolutional neural network and SVM to SAR i...

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Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017

作者： Gao, Fei Huang, Teng Wang, Jun Sun, Jinping Yang, Erfu Hussain, Amir School of Electronic and Information Engineering Beihang University Beijing China Space Mechatronic Systems Technology Laboratory Department of Design Manufacture and Engineering Management University of Strathclyde GlasgowG1 1XJ United Kingdom Cognitive Signal-Image and Control Processing Research Laboratory School of Natural Sciences University of Stirling StirlingFK9 4LA United Kingdom

ISBN: (纸本)9781538630655

To address the challenging problem on target recognition from synthetic aperture radar (SAR) images, a novel method is proposed by combining Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM). First, an improved DCNN is employed to learn the features of SAR images. Then, a SVM is utilized to map the leant features into the output labels. To enhance the feature extraction capability of DCNN, a class of separation information is also added to the cross-entropy cost function as a regularization term. As a result, this explicitly facilitates the intra-class compactness and separability in the process of feature learning. Numerical experiments are performed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. The results demonstrate that the proposed method can achieve an average accuracy of 99.15% on ten types of targets. © 2017 IEEE.

关键词： Synthetic aperture radar

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Design of Low-cost Position Differential Positioning System Based on STM32

Design of Low-cost Position Differential Positioning System ...

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Chinese Automation Congress

作者： Haijun Fu Ming Qiu Xisong Dong Gang Xiong Haonan Xiong Xiang Su Shichao Chen Zhen Shen Xiujiang Guo School of Artificial Intelligence Beijing Union University Beijing China The Cloud Computing Center Chinese Academy of Sciences Dongguan China The Qingdao Academy of Intelligent Industries Macau University of Science and Technology Qingdao China Beijing Union University Beijing China School of Electrical Engineering and Electronic Information Xihua University Chengdu China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Department of Computer Science University of Helsinki Fin Finland Measurement Center Station PetroChina Huabei Oilfield Company Renqiu China

There exist some obstacles to realize high-precision positioning with using the position differential technique due to the limiting factors. However, with the advancement of chip performance and the establishment of multiple satellite navigation systems, position differential positioning can be realized to meet the civilian high-precision positioning demand while significantly reducing costs. This paper is to explore the engineering application and propose a new method. Through the experiments and data analysis, we have found the error correlation between two receivers in a small area and designed a set of positioning system, and programmed in the Keil μVision5 in C language. The system consists of the current mainstream low-cost STM32, UBLOX NEO 6M chip, NRF24L01 chip, GPS antenna and power supply module. Each two receivers can form a differential positioning system. At present, the system in the low-speed status is, in the urban environment, basically reaching within 0.5 - 3 m of the positioning error.

关键词： Position differential positioning Low cost Error correlation GPS STM32

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Characterising acute and chronic care needs: insights from the Global Burden of Disease Study 2019

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Nature communications 2025年第1期16卷 4235-4235页

作者： Cecilia Anza-Ramirez J. Jaime Miranda Benedetta Armocida Jorge César Correia Harriette Gillian Christine Van Spall David Beran Amirali Aali Kalkidan Hassen Abate Semagn Mekonnen Abate Cristiana Abbafati Mohsen Abbasi-Kangevari Zeinab Abbasi-Kangevari Zahra Aryan Sina Azadnajafabad Mohammadreza Azangou-Khyavy Seyyed-Hadi Ghamari Mohammad Keykhaei Javad Khanali Sara Momtazmanesh Fateme Montazeri Shayan Rahmani Mohammad-Mahdi Rashidi Nazila Rezaei Negar Rezaei Sahar Saeedi Moghaddam Hossein Hassanian-Moghaddam Ali-Asghar Kolahi Ali Nikoobar Hedayat Abbastabar Ahmed M. Abdel-Azeem Michael Abdelmasseh Juan Sanabria Sherief Abd-Elsalam Ahmed Abdelwahab Gholamreza Abdoli Mohammad Abdollahi Meriem Abdoun Deldar Morad Abdulah Abu Yousuf Md Abdullah Ame Mehadi Abdurehman Getachew Abebe Aidin Abedi Chirag P. Doshi Vida Abedi Hassan Abidi Richard Gyan Aboagye Hassan Abolhassani Nima Rezaei Lucas Guimarães Abreu Michael R. M. Abrigo Yonas Derso Abtew Hiwa Abubaker Ali Eman Abu-Gharbieh Hiba Jawdat Barqawi Rabih Halwani Maha Mohamed Saber-Ayad Iman M. Talaat Ahmed Abu-Zaid Manfred Mario Kokou Accrombessi Juan Manuel Acuna Denberu Eshetie Adane Tigist Demssew Adane Isaac Yeboah Addo Giovanni Addolorato Oluwafemi Atanda Adeagbo Victor Adekanmbi Miracle Ayomikun Adesina Isaac Iyinoluwa Olufadewa Olatunji O. Adetokunboh Daniel Adedayo Adeyinka Qorinah Estiningtyas Sakilah Adnani Aanuoluwapo Adeyimika Afolabi Olayinka Stephen Ilesanmi Muhammad Sohail Afzal Saira Afzal Gina Agarwal Prerna Agarwal Pradyumna Agasthi Anurag Agrawal Marcela Agudelo-Botero Adolfo Martinez-Valle Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Sohail Ahmad Tauseef Ahmad Ali Ahmadi Siamak Sabour Keivan Ahmadi Sanjay Basu Sepideh Ahmadi Ali Ahmed Shaun Wen Huey Lee Ayman Ahmed Haroon Ahmed Jivan Qasim Ahmed Gahin Abdulraheem Tayib Yahya Yahya Luai A. Ahmed Iffat Elbarazi Syed Mahboob Shah Janardhana P. Aithala Marjan Ajami Budi Aji Hossein Akbarialiabad Yibeltal Akelew Meisam Akhlaghdoust Elham Jamshidi Addis Aklilu Azene Tesfaye Melat Woldemariam Maxwell Ak CRONICAS Center of Excellence in Chronic Diseases Universidad Peruana Cayetano Heredia Lima Peru School of Medicine Universidad Peruana Cayetano Heredia Lima Peru Sydney School of Public Health Faculty of Medicine and Health University of Sydney Sydney NSW Australia Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK Division of Tropical and Humanitarian Medicine University of Geneva Geneva Switzerland Department of Cardiovascular Endocrine-metabolic Diseases and Aging Istituto Superiore di Sanità Rome Italy Division of Endocrinology Diabetology Nutrition and Therapeutic Patient Education Department of Medicine Geneva University Hospitals Geneva Switzerland Department of Medicine and Department of Health Research Evidence and Impact McMaster University Hamilton ON Canada Research Institute of St. Joe’s Hamilton ON Canada Population Health Research Institute Hamilton Canada Geneva University Hospitals Geneva Switzerland Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran Department of Nutrition and Dietetics Jimma University Jimma Ethiopia Anesthesiology Department Dilla University Addis Ababa Ethiopia Department of Juridical and Economic Studies La Sapienza University Rome Italy Non-Communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Brigham and Women’s Hospital Harvard University Boston MA USA Students’ Scientific Research Center (SSRC) Tehran University of Medical Sciences Tehran Iran School of Medicine Tehran University of Medical Sciences Tehran Iran School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Institute Tehran University of Medical Sciences Tehran Iran Chapter of Addiction Medicine University of Sydney Sydney NSW Australia Advanced Diagnostic and Intervent

Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly. © 2025. The Author(s).

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Room temperature continuous-wave excited biexciton emission in CsPbBr3 nanocrystals

arXiv

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arXiv 2018年

作者： Chen, Jie Zhang, Qing Du, Wenna Mi, Yang Shang, Qiuyu Shi, Jia Liu, Pengchong Sui, Xinyu Wu, Xianxin Wang, Rui Peng, Bo Zhong, Haizheng Xing, Guichuan Qiu, Xiaohui Sum, Tze Chien Liu, Xinfeng Division of Nanophotonics CAS Key Laboratory of Standardization and Measurement for Nanotechnology CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing100190 China University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing100049 China Department of Materials Science and Engineering College of Engineering Peking University Beijing100871 China Research Center for Wide Band Semiconductor Peking University China National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of Materials Science & Engineering Beijing Institute of Technology China Institute of Applied Physics and Materials Engineering University of Macau China School of Physical & Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore

Biexcitons are a manifestation of many-body excitonic interactions crucial for quantum information and quantum computation in the construction of coherent combinations of quantum states. However, due to their small binding energy and low transition efficiency, most biexcitons in conventional semiconductors exist either at cryogenic temperature or under femtosecond pulse laser excitation. Here we demonstrate room temperature, continuous wave driven biexciton states in CsPbBr3 perovskite nanocrystals through coupling with a plasmonic nanogap. The room temperature CsPbBr3 biexciton excitation fluence (~100 mW/cm2) is reduced by ~1013 times in the Ag nanowire-film nanogaps. The giant enhancement of biexciton emission is driven by coherent biexciton-plasmon Fano interference. These results provide new pathways to develop high efficiency non-blinking single photon sources, entangled light sources and lasers based on biexciton states. Copyright © 2018, The Authors. All rights reserved.

关键词： Cesium compounds

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Comparison of various methods to extract ringdown frequency from gravitational wave data

arXiv

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arXiv 2018年

作者： Nakano, Hiroyuki Narikawa, Tatsuya Oohara, Ken-Ichi Sakai, Kazuki Shinkai, Hisa-Aki Takahashi, Hirotaka Tanaka, Takahiro Uchikata, Nami Yamamoto, Shun Yamamoto, Takahiro S. Faculty of Law Ryukoku University Kyoto612-8577 Japan Institute for Cosmic Ray Research University of Tokyo Chiba277-8582 Japan Department of Physics Kyoto University Kyoto606-8502 Japan Graduate School of Science and Technology Niigata University Niigata950-2181 Japan Department of Electronic Control Engineering National Institute of Technology Nagaoka College Niigata940-8532 Japan Faculty of Information Science & Technology Osaka Institute of Technology Kitayama Hirakata City Osaka573-0196 Japan Department of Information and Management Systems Engineering Nagaoka University of Technology Niigata940-2188 Japan Earthquake Research Institute University of Tokyo Tokyo113-0032 Japan Center for Gravitational Physics Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan

The ringdown part of gravitational waves in the final stage of merger of compact objects tells us the nature of strong gravity and hence can be used for testing theories of gravity. The ringdown waveform, however, fades out in a very short time with a few cycles, and hence it is challenging to extract the ringdown frequency and its damping time scale. We here propose to build up a suite of mock data of gravitational waves to compare the performance of various approaches developed to detect the dominant quasi-normal mode from an excited black hole after merger. In this paper we present our initial results of comparisons of the following five methods;(1) plain matched filtering with ringdown part (MF-R) method, (2) matched filtering with both merger and ringdown parts (MF-MR) method, (3) Hilbert-Huang transformation (HHT) method, (4) autoregressive modeling (AR) method, and (5) neural network (NN) method. After comparing the performances of these methods, we discuss our future projects. Copyright © 2018, The Authors. All rights reserved.

关键词： Merging

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GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo during the Second Part of the Third Observing Run

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Physical Review X 2023年第4期13卷 041039-041039页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith S. Akcay T. Akutsu S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud M. Arogeti S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi C. P. L. Berry D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. Brandt J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer G. S. Cabourn Davies L. Cadonati G. Cagn LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India University College Dublin Dublin 4 Ireland Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut L

The third Gravitational-Wave Transient Catalog (GWTC-3) describes signals detected with Advanced LIGO and Advanced Virgo up to the end of their third observing run. Updating the previous GWTC-2.1, we present candidate gravitational waves from compact binary coalescences during the second half of the third observing run (O3b) between 1 November 2019, 15∶00 Coordinated Universal Time (UTC) and 27 March 2020, 17∶00 UTC. There are 35 compact binary coalescence candidates identified by at least one of our search algorithms with a probability of astrophysical origin pastro>0.5. Of these, 18 were previously reported as low-latency public alerts, and 17 are reported here for the first time. Based upon estimates for the component masses, our O3b candidates with pastro>0.5 are consistent with gravitational-wave signals from binary black holes or neutron-star–black-hole binaries, and we identify none from binary neutron stars. However, from the gravitational-wave data alone, we are not able to measure matter effects that distinguish whether the binary components are neutron stars or black holes. The range of inferred component masses is similar to that found with previous catalogs, but the O3b candidates include the first confident observations of neutron-star–black-hole binaries. Including the 35 candidates from O3b in addition to those from GWTC-2.1, GWTC-3 contains 90 candidates found by our analysis with pastro>0.5 across the first three observing runs. These observations of compact binary coalescences present an unprecedented view of the properties of black holes and neutron stars.

关键词： Gravitational waves

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