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epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study (vol 15, pg 613, 2020)

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LANCeT INFeCTIOUS DISeASeS 2020年第7期20卷 148-148页

作者： bi, Qifang Wu, Yongsheng Mei, Shujiang Ye, Chenfei Zou, Xuan Zhang, Zhen Liu, Xiaojian Wei, Lan Truelove, Shaun A. Zhang, Tong Gao, Wei Cheng, Cong Tang, Xiujuan Wu, Xiaoliang Wu, Yu Sun, binbin Huang, Suli Sun, Yu Zhang, Juncen Ma, Ting Lessler, Justin Feng, Tiejian [a]Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA [b]Department of International Health Johns Hopkins Bloomberg School of Public Health Baltimore MD USA [c]Department of Public Health Information Shenzhen China [d]Department of Communicable Diseases Control and Prevention Shenzhen China [e]Department of School Health Shenzhen China [f]Department of Environment and Health Shenzhen China [g]Shenzhen Center for Disease Control and Prevention Shenzhen China [h]School of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China [i]School of Computer Science Harbin Institute of Technology at Shenzhen Shenzhen China [j]Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

background Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures. Methods From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk. Findings Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20–22). Cases were isolated on average 4·6 days (95% CI 4·1–5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1–2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49–26·33] for household contacts and 7·06 [1·43–34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1–13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The ob.erved reproductive number (R) was 0·4 (95% CI 0·3–0·5), with a mean serial interval of 6·3 days (95% CI 5·2–7·6). Interpretation Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the

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Status of the belle II Pixel Detector 10

Status of the Belle II Pixel Detector

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10th International Workshop on Semiconductor Pixel Detectors for Particles and Imaging, Pixel 2022

作者： Giakoustidis, G. Abudinen, F. Ackermann, K. Ahlburg, P. Albalawi, M. Alonso, O. Andricek, L. Ayad, R. babu, V. baur, A. bernlochner, F. bilka, T. bolz, A. bozek, A. Camien, C. Caldwell, A. Cao, L. Chekelian, V. Dieguez, A. Dingfelder, J. Doležal, Z. Fras, M. Frey, A. Gabriel, M. Gadow, K. Gessler, T. Getzkow, D. Gioi, L. Greenwald, D. Heck, M. Hensel, M. Hoek, M. Huber, S. Kandra, J. Kapusta, P. Karl, R. Kehl, J. Kiesling, C. Kisielewski, b. Kittlinger, D. Klose, D. Kodyš, P. Koffmane, C. Konorov, I. Krein, M. Krivokuca, S. Kuhr, T. Kurz, S. Kvasnička, P. Lange, J.S. Lautenbach, K. Leis, U. Leitl, P. Levit, D. Liemann, G. Liu, Q. Liu, Z. Lück, T. Mariñas, C. McCarney, S. Moser, H.G. Moya, D. Müller, F.J. Müller, F. Niebuhr, C. Ninkovic, J. Paschen, b. Paul, S. Peric, I. Pitzl, D. Rabusov, A. Reif, M. Reiter, S.P. Richter, R. Ritter, M. Ritzert, M. Sanchez, J.G. Scavino, b. Schaller, G. Schmitz, J. Schnecke, M. Schopper, F. Schreeck, H. Schwenker, b. Schwickardi, M. Sedlmeyer, R. Sfienti, C. Simon, F. Skambraks, S. Skorupa, J. Soloviev, Y. Spruck, b. Stefkova, S. Stever, R. Tafelmayer, e. Takahashi, M. Vila, I. Virto, A.L. Vogt, S. Wang, C. Wieduwilt, P. Windel, H. Ye, H. Zhao, J. Žlebčík, R. University of Barcelona C/Marti Franques 1. Barcelona08028 Spain Institute of High Energy Physics CAS 19B Yuquan Road Shijingshan District Beijing China University of Bonn Bonn53115 Germany Justus-Liebig-Universität Gießen Gießen35392 Germany II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen37073 Germany Deutsches Elektronen-Synchrotron Hamburg22607 Germany Institute for Computer Engineering Heidelberg University Heidelberg69117 Germany Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Karlsruhe76344 Germany H. Niewodniczanski Institute of Nuclear Physics Krakow31-342 Poland Johannes Gutenberg University Mainz Mainz55099 Germany Ludwig Maximilians University Munich80539 Germany Technical University of Munich Arcisstrasse 21 MunichD-80333 Germany Max Planck Institute for Physics MunichD-80805 Germany Halbleiterlabor der Max-Planck-Gesellschaft Otto-Hahn-Ring 6 MunichD-81739 Germany Faculty of Mathematics and Physics Charles University Prague121 16 Czech Republic Avd. de los Castros s/n Santander39005 Spain Department of Physics Faculty of Science University of Tabuk Tabuk71451 Saudi Arabia Edificio Institutos de Investigación Apartado de Correos 22085 ValenciaE-46071 Spain

The belle II experiment at the Super KeK b-factory (SuperKeKb) in Tsukuba, Japan, has b.en collecting e+e− collision data since March 2019. Operating at a record-b.eaking luminosity of up to 4.7 × 1034 cm−2 s−1, data corresponding to 424 fb−1 has since b.en recorded. The belle II Vertex Detector (VXD) is central to the belle II detector and its physics program and plays a crucial role in reconstructing precise primary and decay vertices. It consists of the outer four-layer Silicon Vertex Detector (SVD) using doub.e-sided silicon strips and the inner two-layer PiXel Detector (PXD) based on the Depleted P-channel Field effect Transistor (DePFeT) technology. The PXD DePFeT structure combines signal generation and amplification within the pixels with a minimum pitch of (50 × 55) µm2. A high gain and a high Signal-to-Noise Ratio (SNR) allow thinning the pixels to 75 µm while retaining a high pixel hit efficiency of about 99 % for the belle II PXD. As a consequence, the material budget of the full detector is kept low at ≈ 0.21%XX0 per layer in the acceptance region. This also includes contributions from the control, Analog-to-Digital Converter (ADC), and data processing Application Specific Integrated Circuits (ASICs) as well as from cooling and support structures. This article presents the experience gained from four years of operating the PXD;the first full-scale detector employing the DePFeT technology in High energy Physics. Overall, The PXD performance has met the expectations. Operating in the intense SuperKeKb.environment poses many challenges that will also be discussed. The current PXD system remains incomplete with only 20 out of 40 modules having b.en installed. A full replacement has b.en constructed and is currently in its final testing stage before it will be installed into belle II during the ongoing long shutdown that will last throughout 2023. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivativ

关键词： Pixels

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benchmarking bias: expanding clinical AI model card to incorporate bias reporting of social and non-social factors

arXiv

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arXiv 2023年

作者： Heming, Carolina A.M. Abdalla, Mohamed Mohanna, Shahram Ahluwalia, Monish Zhang, Linglin Trivedi, Hari Woo, MinJae Fine, benjamin Gichoya, Judy Wawira Celi, Leo Anthony Seyyed-Kalantari, Laleh University of Iowa Iowa CityIA United States Institute for Better Health Trillium Health Partners 100 Queensway West 6th floor MississaugaONL5B 1B8 Canada Kingston Health Sciences Centre Queen's University 76 Stuart St. KingstonONK7L 2V7 Canada School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Vector Institute of Artificial Intelligence 661 University Ave Suite 710 TorontoONM5G 1M1 Canada Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Harvard Medical School 25 Shattuck St BostonMA02115 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada Department of Medicine University of Alberta Walter C Mackenzie Health Sciences Centre St. NW Edmonton Alberta 8440 112 Canada

The model facts labels are often used to communicate artificial intelligence (AI) model performance to the end users for proper deployment and often report the overall performance averaged across all populations. However, the overall performance is not generalizab.e to different subgroups of patients. In this paper, we highlight the importance of currently unforeseen aspects, essential for AI models in healthcare, that are not included in the model fact cards. We suggest including through bias and fairness analysis outcomes in model fact labels in the health care AI model including: (a) socio-demographic fairness factors (e.g., sexes, race, socioeconomic status, etc.) and (b) Technical, environmental and biological bias Factors (e.g. disease-dependent, anatomical, or instrumental factors, etc.). The socio-demographic fairness factors have b.en explored in fairness analysis of AI models in healthcare but have not often b.en listed in model fact labels. besides, the technical, environmental, and biological bias factors have not often b.en explored in the bias analysis of AI models in healthcare and have not b.en reported in model fact cards. Here, we demonstrate disparities with respect to both sets of factors. We suggest that clinical AI model reporting cards need to be expanded to communicate such a wider range of disparities to end users for precise patient care. Analysis and communication of such a wider range of biases identify potential drivers of bias, which pave the way to debiasing. As well, it highlights the model caveats and guides the end user and policymaker on how, when, and under what circumstances the model works or fails. Copyright © 2023, The Authors. All rights reserved.

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Automated control and optimisation of laser driven ion acceleration

arXiv

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arXiv 2023年

作者： Loughran, b. Streeter, M.J.V. Ahmed, H. Astbury, S. balcazar, M. borghesi, M. bourgeois, N. Curry, C.b. Dann, S.J.D. DiIorio, S. Dover, N.P. Dzelzanis, T. ettlinger, O.C. Gauthier, M. Giuffrida, L. Glenn, G.D. Glenzer, S.H. Green, J.S. Gray, R.J. Hicks, G.S. Hyland, C. Istokskaia, V. King, M. Margarone, D. McCusker, O. McKenna, P. Najmudin, Z. Parisuaña, C. Parsons, P. Spindloe, C. Symes, D.R. Thomas, A.G.R. Treffert, F. Xu, N. Palmer, C.A.J. School of Mathematics and Physics Queen's University Belfast BelfastBT7 1NN United Kingdom Central Laser Facility STFC Rutherford Appleton Laboratory DidcotOX11 0QX United Kingdom Gérard Mourou Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G1H9 Canada The John Adams Institute for Accelerator Science Imperial College London LondonSW7 2AZ United Kingdom ELI Beamlines Centre Institute of Physics Czech Academy of Sciences Za Radnicí 835 Dolní Břežany252 41 Czech Republic Department of Applied Physics Stanford University StanfordCA94305 United States Department of Physics SUPA University of Strathclyde GlasgowG4 0NG United Kingdom Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague Prague Czech Republic Department of Mechanical Engineering Stanford University StanfordCA94305 United States Institut für Kernphysik Technische Universität Darmstadt Karolinenplatz 5 Darmstadt64289 Germany

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has b.en the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuab.e opportunity for efficient source optimisation. Here, an automated, HRR-compatib.e system produced high fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop bayesian optimisation of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually-optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources. © 2023, CC bY.

关键词： Proton beams

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bigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer e bouchard Lin Gu Weidong Cai Shuiwang Ji badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou erhan bas eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-bellido Jinhyun Kim edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

bigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a sub.et of the availab.e imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enab.e generalizab.e benchmarking. Together with image quality features, we pooled the data in an interactive web application that enab.es users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data sub.ets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We ob.erved that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Umpire Gesture Detection and Recognition using HOG and Non-Linear Support Vector Machine (NL-SVM) Classification of Deep Features in Cricket Videos

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Journal of Physics: Conference Series 2021年第1期2070卷

作者： Suvarna Nandyal Suvarna Laxmikant Kattimani Department of Computer Science & Engineering P. D. A. College of Engineering (Affiliated to Visvesvaraya Technological University Belagavi-590018) Kalaburgi-585102 Karnataka India Department of Computer Science & Engineering B.L.D.E.A's V.P. Dr. P. G. Halakatti College of Engineering and Technology (Affiliated to Visvesvaraya Technological University Belagavi-590018) Vijayapur-586103 Karnataka India

Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behaviour analysis etc., to virtual reality. In recent years, there has b.en increased interest in video summarization and automatic sports highlights generation in the game of Cricket. In Cricket, the Umpire has the authority to make important decisions about events on the field. The Umpire signals important events using unique hand on signals and gestures. The primary intention of our work is to design and develop a new robust method for Umpire Action and Non-Action Gesture Identification and Recognition based on the Umpire Segmentation and the proposed Histogram Oriented Gradient (HOG) feature extraction oriented Non-Linear Support Vector Machine (NL-SVM) classification of Deep Features. Primarily the 80% of Umpire action and non-action images in a cricket match, about 1, 93, 000 frames, the Histogram of Oriented Gradient Deep Features are calculated and trained the system having six gestures of Umpire pose. The proposed HOG Feature extraction oriented Non-Linear Support Vector Machine classification method achieves the maximal accuracy of 97.95%, the maximal sensitivity of 98.87%, the maximal specificity of 98.89% and maximal Precision of 97.02% which indicates its superiority.

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Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops

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Journal of big Data 2025年第1期12卷

作者： Nainwal, Mukta Satpathi, Anurag Shamsi, Saqib Salem, Ali Nain, Ajeet Singh Vishwakarma, Dinesh Kumar elbeltagi, Ahmed el-Hendawy, Salah Mattar, Mohamed A. Department of Agrometeorology College of Agriculture G.B. Pant University of Agriculture and Technology Uttarakhand Pantnagar263145 India Department of Computer Science G.B. Pant University of Agriculture and Technology Uttarakhand Pantnagar263145 India Structural Diagnostics and Analysis Research Group Faculty of Engineering and Information Technology University of Pécs 2 7624 Boszorkány út Pécs Hungary Department of Irrigation and Drainage Engineering G.B. Pant University of Agriculture and Technology Uttarakhand Pantnagar263145 India Agricultural Engineering Department Faculty of Agriculture Mansoura University Mansoura35516 Egypt Department of Plant Production College of Food and Agricultural Sciences King Saud University P.O. Box 2460 Riyadh11451 Saudi Arabia Department of Agricultural Engineering College of Food and Agriculture Sciences King Saud University P.O. Box 2460 Riyadh11451 Saudi Arabia Division of Agrometeorology Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir 190025 Jammu and Kashmir Shalimar Srinagar India

Timely identification and management of plant diseases are critical for sustaining crop yields and ensuring food security. This study proposes an innovative approach to detect Rhizoctonia aerial blight (RAb) caused by Rhizoctonia solani, a prevalent disease-causing substantial loss in soybean crops in Uttarakhand, India. by integrating smartphone imageries into sophisticated algorithms, our automated system offers a scalab.e solution for disease detection. We evaluated nine machine learning algorithms, including logistic regression, Support Vector Machine (SVM), VGG-16 (with and without augmentation), ResNet-18 (with, without augmentation and larger image sizes) and ResNet-34 (with and without augmentation), for disease classification. Results demonstrate the effectiveness of our approach, with classification accuracies ranging from 66.77% (logistic regression) to 95.64% (ResNet-34). Particularly, the ResNet-34 model with data augmentation achieved the highest accuracy, showcasing its potential for accurate disease detection. Leveraging advanced computer technologies and smartphone imaging, this study presents a practical solution for enhancing crop management practices and minimizing yield losses due to diseases. The source code for our implementation is availab.e in supportive file. © The Author(s) 2025.

关键词： Support vector machines

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Scalar product lattice computation for efficient privacy-preserving systems

arXiv

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arXiv 2020年

作者： Rahulamathavan, Yogachandran Dogan, Safak Lu, Rongxing Rajarajan, Muttukrishnan Kondoz, Ahmet Institute for Digital Technologies Loughborough University London London United Kingdom Faculty of Computer Science University of New Brunswick FrederictonNBE3B 5A3 Canada Information Security Group School of Engineering and Mathematical Sciences City University London LondonEC1V 0HB United Kingdom

Privacy-preserving applications allow users to perform on-line daily actions without leaking sensitive information. Privacy-preserving scalar product is one of the critical algorithms in many private applications. The state-of-the-art privacy-preserving scalar product schemes use either computationally intensive homomorphic (public-key) encryption techniques such as Paillier encryption to achieve strong security (i.e., 128-bit) or random masking technique to achieve high efficiency for low security. In this paper, lattice structures have b.en exploited to develop an efficient privacy-preserving system. The proposed scheme is not only efficient in computation as compared to the state-of-the-art but also provides high degree of security against quantum attacks. Rigorous security and privacy analyses of the proposed scheme have b.en provided along with a concrete set of parameters to achieve 128-bit and 256 - bit security. Performance analysis shows that the scheme is at least five orders faster than the Paillier schemes and at least twice as faster than the existing randomisation technique at 128-bit security. Copyright © 2020, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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Detection of millimeter-wave coronal emission in a quasar at cosmological distance using microlensing

arXiv

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arXiv 2025年

作者： Rybak, M. Sluse, D. Gupta, K.K. Millon, M. behar, e. Courbin, F. McKean, J.P. Stacey, H.R. Leiden Observatory Leiden University P.O. Box 9513 Leiden2300 RA Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands SRON Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333 CA Netherlands STAR Institute University of Liège Quartier Agora Allée du six Aout 19c Liège4000 Belgium Sterrenkundig Observatorium Universiteit Gent Krijgslaan 281 S9 GentB-9000 Belgium Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States Institute for Particle Physics and Astrophysics ETH Zurich Wolfgang-Pauli-Strasse 27 ZurichCH-8093 Switzerland Physics Department Technion Haifa32000 Israel ICC University of Barcelona Marti i Franques 1 Barcelona08028 Spain ICREA Pg. Lluis Companys 23 Barcelona08010 Spain Kapteyn Astronomical Institute University of Groningen Postbus 800 GroningenNL-9700 AV Netherlands P.O. Box 443 Krugersdorp1740 South Africa Department of Physics University of Pretoria Lynnwood Road Hatfield Pretoria0083 South Africa European Southern Observatory Karl-Schwarzschild-Straße 2 Garching bei MünchenD-85748 Germany

Determining the nature of emission processes at the heart of quasars is critical for understanding environments of supermassive black holes. One of the key open questions is the origin of long-wavelength emission from radio-quiet quasars. The proposed mechanisms span a broad range, from central star formation to dusty torus, low-power jets, or coronal emission from the innermost accretion disk. Distinguishing between these scenarios requires probing spatial scales ≤0.01 pc, beyond the reach of any current millimetre-wave telescope. Fortunately, in gravitationally lensed quasars, compact mm-wave emission might be microlensed by stars in the foreground galaxy, providing strong constraints on the source size. We report a striking change in rest-frame 1.3-mm flux-ratios in RXJ1131-1231, a quadruply-lensed quasar at z = 0.658, ob.erved by the Atacama Large Millimeter/submillimeter Array (ALMA) in 2015 and 2020. The ob.erved flux-ratio variability is consistent with microlensing of a very compact source with a half-light radius ≤50 astronomical units. The compactness of the source leaves coronal emission as the most likely scenario. Furthermore, the inferred mm-wave and X-ray luminosities follow the characteristic Güdel-benz relationship for coronal emission. These ob.ervations represent the first unambiguous evidence for coronae as the dominant mechanism for long-wavelength emission in radio-quiet quasars. © 2025, CC bY.

关键词： Millimeter waves

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Confined variational calculation of o-Ps–He scattering properties

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Physical Review A 2020年第4期101卷 042705-042705页

作者： M.-S. Wu J.-Y. Zhang X. Gao Y. Qian H.-H. Xie K. Varga Z.-C. Yan U. Schwingenschlögl State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Chinese Academy of Sciences Wuhan 430071 China Beijing Computational Science Research Center Beijing 100193 China Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Department of Computer Science and Technology East China Normal University Shanghai 200062 China LSEC ICMSEC Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Department of Physics and Astronomy Vanderbilt University Nashville Tennessee 37235 USA Department of Physics University of New Brunswick Fredericton New Brunswick Canada E3B 5A3

High-precision quantum-mechanical calculations have b.en developed to investigate positronium (Ps) scattering. Positronium scattering experiments are a powerful tool to study positronium-matter interactions, but the theoretical description of these experiments needs better accuracy. We have developed an ab initio confined variational approach that can reach higher collision energy, includes higher orbital momenta and uses small confining radii. Calculation of the Ps–He momentum-transfer cross section shows that the experimental Doppler broadening spectroscopy results are questionab.e. The energy dependence of the pickoff annihilation rate is also calculated, demonstrating an important role of the so far neglected P-wave contribution.

关键词： electron & positron scattering Positronium Positrons

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