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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, shengbo Liang, Kongming Zhang, Ziji siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, sabine Jeong, Hyun-su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, sebastien sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, shao-Chieh Juan, Chun-Jung Hu, tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, susanne Kofler, Florian Ezhov, ivan shit, suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan s. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRi (DWi) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable Ai algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 ischemic stroke Lesion segmentation (isLEs) challenge. isLEs’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. this accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). the algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NiHss and 90-day mRs) on par with or exceeding expert-derived results, underlining its clinical relevance. this study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWi across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Measurement of the and tH production rates in the H → decay channel using proton-proton collision data at = 13 teV

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Journal of High Energy Physics 2025年第2期2025卷 1-75页

作者： Hayrapetyan, A. tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, t. Chatterjee, s. Damanakis, K. Dragicevic, M. Hussain, P. s. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, i. schieck, J. schöfbeck, R. schwarz, D. sonawane, M. templ, s. Waltenberger, W. Wulz, C.-E. Darwish, M. R. Janssen, t. Van Mechelen, P. Breugelmans, N. D’Hondt, J. Dansana, s. De Moor, A. Delcourt, M. Heyen, F. Lowette, s. Makarenko, i. Müller, D. tavernier, s. tytgat, M. Van Onsem, G. P. Van Putte, s. Vannerom, D. Bilin, B. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, s. shahzad, M. A. thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. samalan, A. skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, i. s. Giammanco, A. Guzel, A. O. Jain, sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. tran, t. t. Wertz, s. Alves, G. A. Alves Gallo Pereira, M. Coelho, E. Correia silva, G. Hensel, C. Menezes De Oliveira, t. Moraes, A. Rebello teles, P. soeiro, M. Vilela Pereira, A. Aldá Júnior, W. L. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E. M. Da silveira, G. G. De Jesus Damiao, D. Fonseca De souza, s. Gomes De souza, R. Macedo, M. Martins, J. Mora Herrera, C. Mundim, L. Nogima, H. Pinheiro, J. P. santoro, A. sznajder, A. thiel, M. Bernardes, C. A. Calligaris, L. Fernandez Perez tomei, t. R. Gregores, E. M. Maietto silverio, i. Mercadante, P. G. Novaes, s. F. Orzari, B. Padula, sandra s. Aleksandrov, A. Antchev, G. Hadjiiska, R. iaydjiev, P. Misheva, M. shopova, M. sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. shumka, E. Keshri, s. thakur, s. Cheng, t. Javaid, t. Yuan, L. Hu, Z. Liang, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. s. Chen, M. iemmi, F. Jiang, C. H. Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Bruxelles Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Nanjing China The University of Iowa Iowa City U.S.A. Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Hangzhou

An analysis of the production of a Higgs boson (H) in association with a top quark-antiquark pair ( $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ ) or a single top quark (tH) is presented. The Higgs boson decay into a bottom quark-antiquark pair (H → $$ \textrm{b}\overline{\textrm{b}} $$ ) is targeted, and three different final states of the top quark decays are considered, defined by the number of leptons (electrons or muons) in the event. The analysis utilises proton-proton collision data collected at the CERN LHC with the CMS experiment at $$ \sqrt{s} $$ = 13 TeV in 2016–2018, which correspond to an integrated luminosity of 138 fb−1. The observed $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ production rate relative to the standard model expectation is 0.33 ± 0.26 = 0.33 ± 0.17(stat) ± 0.21(syst). Additionally, the $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ production rate is determined in intervals of Higgs boson transverse momentum. An upper limit at 95% confidence level is set on the tH production rate of 14.6 times the standard model prediction, with an expectation of $$ {19.3}_{-6.0}^{+9.2} $$ . Finally, constraints are derived on the strength and structure of the coupling between the Higgs boson and the top quark from simultaneous extraction of the $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ and tH production rates, and the results are combined with those obtained in other Higgs boson decay channels.

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Erratum to ’ severe plastic deformation for producing superfunctional ultrafine-grained and heterostructured materials: An interdisciplinary review’ [J. Alloys Compd. 1002 (2024) 174667]

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Journal of Alloys and Compounds 2025年 1034卷

作者： Kaveh Edalati Anwar Q. Ahmed saeid Akrami Kei Ameyama Valery Aptukov Rashid N. Asfandiyarov Maki Ashida Vasily Astanin Andrea Bachmaier Victor Beloshenko Elena V. Bobruk Krzysztof Bryła José María Cabrera Amanda P. Carvalho Nguyen Q. Chinh in-Chul Choi Robert Chulist Jorge M. Cubero-sesin Gregory Davdian Muhammet Demi.t.s Yuntian t. Zhu WPI International Institute for Carbon-Neutral Energy Research (WPI-I2CNER) Kyushu University Fukuoka 819-0395 Japan Department of Materials Physics Eötvös Loránd University ELTE Pázmány Péter sétány 1/A Budapest H-1117 Hungary College of Science University of Kufa Najaf 54001 Iraq Institutes of Innovation for Future Society Nagoya University Nagoya 464-8603 Japan College of Science and Engineering Ritsumeikan University Kusatsu 525-8577 Japan Perm State University Bukireva Perm 614068 Russia Ufa University of Science and Technology 32 Zaki Validi str Ufa 450076 Russia Institute of Molecule and Crystal Physics UFRC RAS 71 pr. Oktyabrya Ufa 450054 Russia Seikei University 3-3-1 Kichijoji-Kitamachi Musashino-shi Tokyo 180-8633 Japan Erich Schmid Institute of Materials Science of the Austrian Academy of Sciences Leoben 8700 Austria Donetsk Institute for Physics and Engineering Named after O.O. Galkin National Academy of Sciences of Ukraine Pr. Nauki 46 Kyiv 02000 Ukraine Faculty of Mechanical Engineering Cracow University of Technology Warszawska 24 Kraków 31-155 Poland Department of Materials Science and Engineering EEBE Universidad Politécnica de Catalunya c/Eduard Maristany 10-14 Barcelona 08019 Spain Fundació CIM-UPC c/Llorens i Artigas 12 Barcelona 08028 Spain Department of Metallurgical and Materials Engineering Universidade Federal de Minas Gerais Belo Horizonte MG 31270-901 Brazil School of Materials Science and Engineering Kumoh National Institute of Technology Gumi 39177 Republic of Korea Institute of Metallurgy and Materials Science Polish Academy of Sciences Krakow 30-059 Poland Centro de Investigación y Extensión en Materiales (CIEMTEC) Escuela de Ciencia e Ingeniería de los Materiales Instituto Tecnológico de Costa Rica Cartago 159-7050 Costa Rica Osipyan Institute of Solid State Physics of the Russian Academy of Sciences Ac. Osipyan Str. 2 Chernogolovka 142432 Russia Department of Aerospace Engineering Samsun University Samsun 55420

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Fungal diversity notes 1387-1511:taxonomic and phylogenetic contributions on genera and species of fungal taxa

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Fungal Diversity 2021年第6期111卷 1-335页

作者： saranyaphat Boonmee Dhanushka N.Wanasinghe Mark s.Calabon Naruemon Huanraluek sajini K.U.Chandrasiri Gareth E.B.Jones Walter Rossi Marco Leonardi sanjay K.singh shiwali Rana Paras N.singh Deepak K.Maurya Ajay C.Lagashetti Deepika Choudhary Yu-Cheng Dai Chang-Lin Zhao Yan-Hong Mu Hai-sheng Yuan shuang-Hui He Rungtiwa Phookamsak Hong-Bo Jiang María P.Martín Margarita Dueñas Mteresa telleria izabela L.Kałucka Andrzej M.Jagodziński Kare Liimatainen Diana s.Pereira Alan J.L.Phillips Nakarin suwannarach Jaturong Kumla surapong Khuna saisamorn Lumyong tarynn B.Potter Roger G.shivas Adam H.sparks Niloofar Vaghefi Mohamed A.Abdel-Wahab Faten A.Abdel-Aziz Guo-Jie Li Wen-Fei Lin Upendra singh Rajendra P.Bhatt Hyang Burm Lee thuong t.t.Nguyen Paul M.Kirk Arun Kumar Dutta Krishnendu Acharya VVenkateswara sarma M.Niranjan Kunhiraman C.Rajeshkumar Nikhil Ashtekar sneha Lad Nalin N.Wijayawardene Darbe J.Bhat Rong-Ju Xu subodini N.Wijesinghe Hong-Wei shen Zong-Long Luo Jing-Yi Zhang Phongeun sysouphanthong Naritsada thongklang Dan-Feng Bao Janith V.s.Aluthmuhandiram Jafar Abdollahzadeh Alireza Javadi Francesco Dovana Muhammad Usman Abdul Nasir Khalid Asha J.Dissanayake Anusha telagathoti Maraike Probst Ursula Peintner isaac Garrido-Benavent Lilla Bóna Zsolt Merényi Lajos Boros Bratek Zoltán JBenjamin stielow Ning Jiang Cheng-Ming tian Esmaeil shams Farzaneh Dehghanizadeh Adel Pordel Mohammad Javan-Nikkhah teodor t.Denchev Cvetomir M.Denchev Martin Kemler Dominik Begerow Chun-Ying Deng Emma Harrower tohir Bozorov tutigul Kholmuradova Yusufjon Gafforov Aziz Abdurazakov Jian-Chu Xu Peter E.Mortimer Guang-Cong Ren Rajesh Jeewon sajeewa s.N.Maharachchikumbura Chayanard Phukhamsakda Ausana Mapook Kevin D.Hyde Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand School of Science Mae Fah Luang UniversityChiang Rai 57100Thailand CAS Key Laboratory for Plant Biodiversity and Biogeography of East Asia(KLPB) Kunming Institute of BotanyChinese Academy of ScienceKunming 650201YunnanPeople’s Republic of China CIFOR-ICRAF China Program World Agroforestry(ICRAF)Kunming 650201YunnanPeople’s Republic of China Honghe Center for Mountain Futures Kunming Institute of BotanyHonghe CountyKunming 654400YunnanPeople’s Republic of China Department of Botany and Microbiology College of ScienceKing Saud UniversityP.O.Box 2455Riyadh 11451Saudi Arabia Section Environmental Sciences Department MeSVAUniversity of L’Aquila67100 CoppitoAQItaly National Fungal Culture Collection of India(NFCCI) Biodiversity and Palaeobiology GroupMACS Agharkar Research InstituteG.G.Agarkar RoadPune 411004India Institute of Microbiology School of Ecology and Nature ConservationBeijing Forestry UniversityBeijing 100083People’s Republic of China College of Biodiversity Conservation Southwest Forestry UniversityKunming 650224People’s Republic of China CAS Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110164People’s Republic of China University of the Chinese Academy of Sciences Beijing 100049People’s Republic of China Centre for Mountain Futures(CMF) Kunming Institute of BotanyKunming 650201YunnanPeople’s Republic of China Department of Mycology Real Jardín Botánico-CSICPlaza de Murillo 228014 MadridSpain Department of Algology and Mycology Faculty of Biology and Environmental ProtectionUniversity of LodzBanacha 12/1690-237ŁódźPoland Institute of Dendrology Polish Academy of SciencesParkowa 562-035 KórnikPoland Jodrell Laboratory Royal Botanic GardensKewRichmond TW93DSSurreyUK Faculdade de Ciências Biosystems and Integrative Sciences Institute(BioISI)Universidade de LisboaCampo Grande1749-016 LisbonPortugal Dep

this article is the 13th contribution in the Fungal Diversity Notes series,wherein 125 taxa from four phyla,ten classes,31 orders,69 families,92 genera and three genera incertae sedis are treated,demonstrating worldwide and geographic *** taxa described and illustrated in the present study include three new genera,69 new species,one new com-bination,one reference specimen and 51 new records on new hosts and new geographical *** new genera,Cylindrotorula(torulaceae),scolecoleotia(Leotiales genus incertae sedis)and Xenovaginatispora(Lindomycetaceae)are introduced based on distinct phylogenetic lineages and unique *** described species are Aspergillus lan-naensis,Cercophora dulciaquae,Cladophialophora aquatica,Coprinellus punjabensis,Cortinarius alutarius,***-latus,***,Coryneum fagi,Cruentomycena uttarakhandina,Cryptocoryneum rosae,Cyathus uniperidiolus,Cylindrotorula indica,Diaporthe chamaeropicola,Didymella azollae,Diplodia alanphillipsii,Dothiora coronicola,Efibula rodriguezarmasiae,Erysiphe salicicola,Fusarium queenslandicum,Geastrum gorgonicum,***,Helicosporium sexualis,Helminthosporium chiangraiensis,Hongkongmyces kokensis,Hydrophilomyces hydraenae,Hygrocybe boertmannii,Hyphoderma australosetigerum,Hyphodontia yunnanensis,Khaleijomyces umikazeana,Laboulbenia divisa,Laboulbenia triarthronis,Laccaria populina,Lactarius pallidozonarius,Lepidosphaeria strobelii,Longipedicellata megafusiformis,Lophiotrema lincangensis,Marasmius benghalensis,***,***,Mariannaea camelliae,Mel-anographium smilaxii,Microbotryum polycnemoides,Mimeomyces digitatus,Minutisphaera thailandensis,Mortierella solitaria,Mucor harpali,Nigrograna jinghongensis,Odontia huanrenensis,***,Paraconiothyrium ajrekarii,Par-afuscosporella niloticus,Phaeocytostroma yomensis,Phaeoisaria synnematicus,Phanerochaete hainanensis,Pleopunctum thailandicum,Pleurotheciella dimorphospora,Pseudochaetosphaeronema

关键词： 72 new taxa One new combination One reference specimen 51 new records Agaricomycetes Ascomycota Bartheletiomycetes Basidiomycota Dothideomycetes Eurotiomycetes Exobasidiomycetes Laboulbeniomycetes Leotiomycetes Mortierellomycetes Mortierellomycota Mucoromycetes Mucoromycota Phylogeny sordariomycetes taxonomy

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An expert survey to assess the current status and future challenges of energy system analysis

arXiv

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arXiv 2021年

作者： scheller, Fabian Wiese, Frauke Weinand, Jann Michael Dominković, Dominik Franjo McKenna, Russell Energy Economics and System Analysis Division of Sustainability Department of Technology Management and Economics Technical University of Denmark Akademivej 2800 Kgs Lyngby Denmark Abteilung Energie- und Umweltmanagement Europa-Universität Flensburg Flensburg24943 Germany Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Applied Mathematics and Computer Science Technical University of Denmark Matematiktorvet 2800 Kgs Lyngby Denmark School of Engineering University of Aberdeen King's College AberdeenAB24 3FX United Kingdom ETH Zurich Zürich8092 Switzerland

Decision support systems like computer-aided energy system analysis (EsA) are considered one of the main pillars for developing sustainable and reliable energy transformation strategies. Although today's diverse tools can already support decision-makers in a variety of research questions, further developments are still necessary. intending to identify opportunities and challenges in the field, we classify modelling capabilities (32), methodologies (15) implementation issues (15) and management issues (7) from an extensive literature review. Based on a quantitative expert survey of energy system modellers (N=61) mainly working with simulation and optimisation models, the status of development and the complexity of realisation of those modelling topics are assessed. While the rated items are considered to be more complex than actually represented, no significant outliers are determinable, showing that there is no consensus about particular aspects of EsA that are lacking development. Nevertheless, a classification of the items in terms of a specially defined modelling strategy matrix identifies capabilities like land-use planning patterns, equity and distributional effects and endogenous technological learning as "low hanging fruits" for enhancement, as well as a large number of complex topics that are already well implemented. the remaining "tough nuts" regarding modelling capabilities include non-energy sector and social behaviour interaction effects. in general, the optimisation and simulation models differ in their respective strengths, justifying the existence of both. While methods were generally rated as quite well developed, combinatorial optimisation approaches, as well as machine learning, are identified as important research methods to be developed further for EsA. Highlights: - Quantitative expert survey about actual modelling characteristics and future realisation complexities. - Evaluation of modelling capabilities and methodologies, and implementation a

关键词： Decision support systems

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Adaptive sleep scheduling for Cluster Based Wireless sensor Networks for Precision Agriculture 1

Adaptive Sleep Scheduling for Cluster Based Wireless Sensor ...

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1st international Conference On Advances in Communication and Computing technology, iCACCt 2018

作者： sponde, s. Lomte, s.s. Computer Science and Engineering Dept D. I. E. M. S. Aurangabad Maharashtra India M. S. Bidve College of Engineering Latur Maharashtra India

ISBN: (纸本)9781538609262

Wireless sensor networks are collection of spatially distributed autonomous sensors called as nodes, which are responsible for monitoring physical or environmental conditions such as temperature, pressure, humidity, etc. and cooperatively passes their data through the network to a main locations. But one major constraint of this network is energy consumption as the sensor nodes are equipped with limited battery power. to minimize energy consumption many algorithms have been applied. the objective of the proposed algorithm Adaptive sleep scheduling algorithm is to schedule the nodes into sleep and active state by comparing its energy with threshold. Entire network is divided into cluster with cluster head is the node with highest energy. the Cluster head is responsible for that state of the node either active or sleep. the proposed algorithm is evaluated and simulated with Ns2 and the results are analyzed to show better performance of the algorithm. © 2018 iEEE.

关键词： Precision agriculture

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Measurements of inclusive and differential cross sections for top quark production in association with a Z boson in proton-proton collisions at = 13 teV

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Journal of High Energy Physics 2025年第2期2025卷 1-53页

作者： Hayrapetyan, A. tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, t. Chatterjee, s. Damanakis, K. Dragicevic, M. Hussain, P. s. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, i. schieck, J. schöfbeck, R. schwarz, D. sonawane, M. Waltenberger, W. Wulz, C.-E. Janssen, t. Van Laer, t. Van Mechelen, P. Breugelmans, N. D’Hondt, J. Dansana, s. De Moor, A. Delcourt, M. Heyen, F. Lowette, s. Makarenko, i. Müller, D. tavernier, s. tytgat, M. Van Onsem, G. P. Van Putte, s. Vannerom, D. Bilin, B. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, s. shahzad, M. A. thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, i. s. Giammanco, A. Guzel, A. O. Jain, sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. tran, t. t. Wertz, s. Alves, G. A. Alves Gallo Pereira, M. Coelho, E. Correia silva, G. Hensel, C. Menezes De Oliveira, t. Mora Herrera, C. Moraes, A. Rebello teles, P. soeiro, M. Vilela Pereira, A. Aldá Júnior, W. L. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E. M. Da silveira, G. G. De Jesus Damiao, D. Fonseca De souza, s. Gomes De souza, R. Laux Kuhn, t. Macedo, M. Martins, J. Mundim, L. Nogima, H. Pinheiro, J. P. santoro, A. sznajder, A. thiel, M. Bernardes, C. A. Calligaris, L. Fernandez Perez tomei, t. R. Gregores, E. M. Maietto silverio, i. Mercadante, P. G. Novaes, s. F. Orzari, B. Padula, sandra s. Aleksandrov, A. Antchev, G. Hadjiiska, R. iaydjiev, P. Misheva, M. shopova, M. sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. shumka, E. Keshri, s. Laroze, D. thakur, s. Cheng, t. Javaid, t. Yuan, L. Hu, Z. Liang, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. s. Chen, M. iemmi, F. Jiang, C. H. Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Bruxelles Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Nanjing China The University of Iowa Iowa City U.S.A. Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engine

Measurements are presented of inclusive and differential cross sections for Z boson associated production of top quark pairs ( $$ \textrm{t}\overline{\textrm{t}}\textrm{Z} $$ ) and single top quarks (tZq or tWZ). The data were recorded in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb−1. Events with three or more leptons, electrons or muons, are selected and a multiclass deep neural network is used to separate three event categories, the $$ \textrm{t}\overline{\textrm{t}}\textrm{Z} $$ and tWZ processes, the tZq process, and the backgrounds. A profile likelihood approach is used to unfold the differential cross sections, to account for systematic uncertainties, and to determine the correlations between the two signal categories in one global fit. The inclusive cross sections for a dilepton invariant mass between 70 and 110 GeV are measured to be 1.14 ± 0.07 pb for the sum of $$ \textrm{t}\overline{\textrm{t}}\textrm{Z} $$ and tWZ, and 0.81 ± 0.10 pb for tZq, in good agreement with theoretical predictions.

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Research on industrial internet of things security Architecture and Protection strategy

Research on Industrial Internet of Things Security Architect...

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2019虚拟现实与智能系统国际会议

作者： Hanlin Chen Ming Hu Hui Yan Ping Yu Platform of Jilin Province Science and Technology （S＆T） Department "Jilin Province S＆T Innovation Center for PhysicalSimulation and Security of Water resources and Electric Power Engineering" Changchun Institute of Technology College of Computer Science and Technology Changchun University of Technology

industrial internet of things（iiot） is a fusion of industrial automation systems and iot *** features comprehensive sensing,interconnected transmission,intelligent processing,self-organization and *** applications span intelligent transportation,smart factories,and *** areas such as power grid and intelligent environment *** the widespread application of iiot technology,the cyber security threats to industrial i.t.systems are increasing day by day,and information security issues have become a major challenge in the development *** order to protect the industrial i.t.system jfrom network attacks,this paper aims to study the industrial iot information security protection technology,and the typical architecture of industrial internet of things system,and analyzes the network security threats faced by industrial internet of things system according to the different levels of the architecture,and designs the security protection strategies applied to different levels of structures based on the specific means of network attack.

关键词： industrial iot security threats security strategy

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Empirical Determination of the Lithium 6707.856 Å Wavelength in Young stars

arXiv

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arXiv 2023年

作者： Campbell-White, Justyn Manara, Carlo F. sicilia-Aguilar, Aurora Frasca, Antonio Nielsen, Louise D. schneider, P. Christian Nisini, Brunella Bayo, Amelia Ercolano, Barbara Ábrahám, Péter Claes, Rik Fang, Min Fedele, Davide Gameiro, Jorge Filipe Gangi, Manuele Kóspál, Ágnes Maucó, Karina Petr-Gotzens, Monika G. Rigliaco, Elisabetta Robinson, Connor siwak, Michal tychoniec, Lukasz Venuti, Laura European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany SUPA School of Science and Engineering University of Dundee Nethergate DundeeDD1 4HN United Kingdom INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Hamburg Observatory Gojenbergsweg 11 Hamburg21029 Germany INAF - Osservatorio Astronomico di Roma: Monte Porzio Catone Lazio Italy Instituto de Física y Astronomía Universidad de Valparaíso Chile University Observatory Faculty of Physics Ludwig-Maximilians-Universität München Scheinerstr. 1 Munich81679 Germany Excellence Cluster’Origins’ Boltzmannstr. 2 Garching85748 Germany Konkoly-Thege Miklós út 15-17 Budapest1121 Hungary CSFK MTA Centre of Excellence Konkoly-Thege Miklós út 15-17 Budapest1121 Hungary ELTE Eötvös Loránd University Institute of Physics Pázmány Péter sétány 1/A Budapest1117 Hungary Purple Mountain Observatory Chinese Academy of Sciences 10 Yuanhua Road Nanjing210023 China INAF-Osservatorio Astrofisico di Arcetri L.go E. Fermi 5 FirenzeI-50125 Italy Instituto de Astrofísica e Ciências do Espaço Universidade do Porto CAUP Rua das Estrelas Porto4150-762 Portugal Departamento de Física e Astronomia Faculdade de Ciências Universidade do Porto rua do Campo Alegre 687 Porto4169-007 Portugal ASI Italian Space Agency Via del Politecnico snc Rome00133 Italy Max Planck Institute for Astronomy Königstuhl 17 Heidelberg69117 Germany Instituto de Astronom ia Universidad Nacional Autónoma de México Carr. Tijuana-Ensenada km107 C.I.C.E.S.E. B.C. Ensenada22860 Mexico INAF - Osservatorio Astronomico di Padova Vicolo dell’osservatorio 5 Padova35122 Italy Department of Physics & Astronomy Amherst College C025 Science Center 25 East Drive AmherstMA01002 United States SETI Institute 339 Bernardo Ave Suite 200 Mountain ViewCA94043 United States

Absorption features in stellar atmospheres are often used to calibrate photocentric velocities for kinematic analysis of further spectral lines. the Li feature at ∼6708 Å is commonly used, especially in the case of young stellar objects for which it is one of the strongest absorption lines. However, this is a complex line comprising two isotope fine-structure doublets. We empirically measure the wavelength of this Li feature in a sample of young stars from the PENELLOPE/VLt programme (using X-shooter, UVEs and EsPREssO data) as well as HARPs data. For 51 targets, we fit 314 individual spectra using the stAR-MELt package, resulting in 241 accurately fitted Li features, given the automated goodness-of-fit threshold. We find the mean air wavelength to be 6707.856 Å, with a standard error of 0.002 Å (0.09 km/s) and a weighted standard deviation of 0.026 Å (1.16 km/s). the observed spread in measured positions spans 0.145 Å, or 6.5 km/s, which is up to a factor of six higher than typically reported velocity errors for high-resolution studies. We also find a correlation between the effective temperature of the star and the wavelength of the central absorption. We discuss how exclusively using this Li feature as a reference for photocentric velocity in young stars could potentially be introducing a systematic positive offset in wavelength to measurements of further spectral lines. if outflow tracing forbidden lines, such as [O i] 6300 Å, are actually more blueshifted than previously thought, this then favours a disk wind as the origin for such emission in young stars. © 2023, CC BY.

关键词： Lithium

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Workflow Phase Detection in Fluoroscopic images Using Convolutional Neural Networks

Workflow Phase Detection in Fluoroscopic Images Using Convol...

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Workshop on Bildverarbeitung fur die Medizin, 2019

作者： Arbogast, Nikolaus Kurzendorfer, tanja Breininger, Katharina Mountney, Peter toth, Daniel Narayan, srinivas A. Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthcare GmbH Forchheim Germany Siemens Healthineers Medical Imaging Technologies Princeton United States School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Siemens Healthineers Frimley United Kingdom

ISBN: (纸本)9783658253257

in image guided interventions, the radiation dose to the patient and personnel can be reduced by positioning the blades of a collimator to block off unnecessary X-rays and restrict the irradiated area to a region of interest. in a certain stage of the operation workflow phase detection can define objects of interest to enable automatic collimation. Workflow phase detection can be beneficial for clinical time management or operating rooms of the future. in this work, we propose a learningbased approach for an automatic classification of three surgical workflow phases. Our data consists of 24 congenital cardiac interventions with a total of 2985 fluoroscopic 2D X-ray images. We compare two different convolutional neural network architectures and investigate their performance regarding each phase. Using a residual network, a class-wise averaged accuracy of 86.14% was achieved. the predictions of the trained models can then be used for context specific collimation. © 2019, springer Berlin Heidelberg. All rights reserved.

关键词： Convolution

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