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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Zheng, Mengyu Zhou, Chuan Wu, Jia Pan, Shirui Shi, Jinqiao Guo, And Li Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Centre for Artificial Intelligence University of Technology Sydney Australia

ISBN: (纸本)9781509060146

Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt shallow models, which cannot capture the highly non-linear relationship between vertexes in a bipartite graph composed of users and items. To address this issue, in this paper we propose a joint deep structure embedding approach FraudNE for fraud detection that (a) can preserve the highly non-linear structural information of networks, (b) is robust to sparse networks, (c) embeds different types of vertexes jointly in the same latent space. It is worth mentioning that we can detect multiple fraudulent groups without the number of groups as a priori. Compared with baselines, our method achieved significant accuracy improvement. © 2018 IEEE.

关键词： Network embeddings

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The Needs, Challenges, and Priorities for Advancing Global Flood Research

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Wiley Interdisciplinary Reviews: Water 2025年第3期12卷

作者： Samadi, Vidya Fowler, Hayley J. Lamond, Jessica Wagener, Thorsten Brunner, Manuela Gourley, Jonathan Moradkhani, Hamid Popescu, Ioana Wasko, Conrad Wright, Daniel Wu, Huan Zhang, Ke Arias, Paola A. Duan, Qingyun Nazemi, Ali van Oevelen, Peter J. Prein, Andreas F. Roundy, Joshua K. Saberian, Mostafa Umutoni, Lisa Agricultural Sciences Department Clemson University Clemson SC United States Artificial Intelligence Research Institute for Science and Engineering (AIRISE) School of Computing Clemson University Clemson SC United States School of Engineering and Tyndall Centre for Climate Change Research Newcastle University Newcastle upon Tyne United Kingdom Centre for Architecture and Built Environment Research University of the West of England Bristol United Kingdom Institute of Environmental Science and Geography University of Potsdam Potsdam Germany Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland WSL Institute for Snow and Avalanche Research SLF Davos Dorf Switzerland Climate Change Extremes and Natural Hazards in Alpine Regions Research Center CERC Davos Dorf Switzerland NOAA/National Severe Storms Laboratory Norman OK United States Center for Complex Hydrosystems Research and Department of Civil Construction and Environmental Engineering The University of Alabama Tuscaloosa AL United States IHE Delft Institute for Water Education Delft Netherlands Faculty of Civil Engineering and Geosciences Delft University of Technology Delft Netherlands School of Civil Engineering The University of Sydney Sydney NSW Australia Civil and Environmental Engineering University of Wisconsin-Madison Madison WI United States Southern Marine Science and Engineering Laboratory and School of Atmospheric Sciences Sun Yat-Sen University Guangdong China College of Hydrology and Water Resources Hohai University Jiangsu Nanjing China GIGA Group School of Environment University of Antioquia Antioquia Colombia Center for Hydrometeorological Predictions College of Hydrology and Water Resources Hohai University Jiangsu Nanjing China Department of Building Civil and Environmental Engineering Concordia University Montreal QC Canada International GEWEX Project Office Fairfax VA United States Department of Atmospheric Oceanic and Earth Sciences George Mason

In recent years, numerous flood events have caused loss of life, widespread disruption, and damage across the globe. These devastating impacts highlight the importance of a better understanding of flood generating processes, their impacts, and their variability under climate and landscape changes. Here, we argue that the ability to better model flooding is underpinned by the grand challenge of understanding flood generation mechanisms and potential impacts. To address this challenge, the World Meteorological Organization-Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP) aims to establish a Global Flood Crosscutting project to propagate flood modeling and research knowledge across regions and to synthesize results at the global scale. This paper outlines a framework for understanding the dynamics and impacts of runoff generation processes and a rationale for the role of a Global Flood Crosscutting project to address these challenges. Within this Global Flood Crosscutting project, we will establish a common terminology and methods to enable the global research community to exchange knowledge and experiences, and to design experiments toward developing actionable recommendations for more effective flood management practices and policies for improved resilience. This harmonization of rich perspectives across disciplines will foster the co-production of knowledge primed to advance flood research, particularly in the current period of heightened climate variability and rapid change. It will create a new transdisciplinary paradigm for flood science, wherein different dimensions of mechanistic understanding and processes are rigorously considered alongside socioeconomic impacts, early warning communications, and longer-term adaptation to alleviate flood risks in society. © 2025 The Author(s). WIREs Water published by Wiley Periodicals LLC.

关键词： effective flood management flood mechanistic understanding flood modeling GEWEX knowledge exchange

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Why is the winner the best?

arXiv

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arXiv 2023年

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M.D. Isensee, F. Adler, T.J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D.G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulmé, A. Jannin, P. Kavur, A.E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martín-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Rädsch, T. Rafael-Patiño, J. Bawa, V. Singh Speidel, S. Sudre, C.H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M.H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Brüngel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C.M. Fuller, C. Gaire, R.R. Galdran, A. Faura, Á. García Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martinčič, T. Morais, P. Naser, M.A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Štepec, D. Subedi, R. Tochon, G. Torres, H.R. Urien, H. Vilaça, J.L. Wahid, K.A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jäger, P.F. Kopp-Schneider, A. Maier-Hein, L. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Germany Heidelberg Germany School of Computing Faculty of Engineering and Physical Sciences University of Leeds Leeds United Kingdom Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington SeattleWA United States Department of Computer Science University College London London United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Sierre Switzerland Harvard Medical School Brigham and Women’s Hospital BostonMA United States School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Essen Germany University of Nebraska Medical Center OmahaNE United States Department of Internal Medicine III Heidelberg University Hospital Heidelberg Germany Neurobiology Research Unit Copenhagen University Hospital Rigshospitalet C

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. © 2023, CC BY.

关键词： Benchmarking

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人工智能赋能癌症免疫治疗——创新探索与未来机遇（英文）

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Engineering 2025年第1期 12-16页

作者： Wei Xiang Lu Yu Xiaoyuan Chen Marco J.Herold Australian Centre for Artificial Intelligence in Medical Innovation La Trobe University School of Computing Engineering and Mathematical Sciences La Trobe University Department of Diagnostic Radiology Yong Loo Lin School of Medicine National University of Singapore Department of Chemical and Biomolecular Engineering College of Design and Engineering National University of Singapore Department of Biomedical Engineering College of Design and Engineering National University of Singapore Department of Pharmacy and Pharmaceutical Sciences Faculty of Science National University of Singapore Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Nanomedicine Translational Research Program Yong Loo Lin School of Medicine National University of Singapore Theranostics Center of Excellence(TCE) Yong Loo Lin School of Medicine National University of Singapore Institute of Molecular and Cell Biology Agency for Science Technology and Research(A*STAR) Olivia Newton-John Cancer Research Institute School of Cancer Medicine La Trobe University The Walter and Eliza Hall Institute of Medical Research Department of Medical Biology University of Melbourne

1. Introduction Cancer continues to be a major cause of global mortality rates, with conventional treatments such as chemotherapy and radiotherapy exhibiting inconsistent efficacy, high costs, and considerable side effects. Over the past decade, a promising alternative has emerged: cancer immunotherapy, which leverages the body's immune system to identify and eradicate cancer cells [1].

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The 2025 Roadmap to Ultrafast Dynamics: Frontiers of Theoretical and Computational Modelling

arXiv

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arXiv 2025年

作者： Caruso, Fabio Sentef, Michael A. Attaccalite, Claudio Bonitz, Michael Draxl, Claudia De Giovannini, Umberto Eckstein, Martin Ernstorfer, Ralph Fechner, Michael Grüning, Myrta Hübener, Hannes Joost, Jan-Philip Juraschek, Dominik M. Karrasch, Christoph Kennes, Dante Marvin Latini, Simone Lu, I-Te Neufeld, Ofer Perfetto, Enrico Rettig, Laurenz Pela, Ronaldo Rodrigues Rubio, Angel Rudzinski, Joseph F. Ruggenthaler, Michael Sangalli, Davide Schüler, Michael Shallcross, Samuel Sharma, Sangeeta Stefanucci, Gianluca Werner, Philipp Institute of Theoretical Physics and Astrophysics University of Kiel Leibnizstrasse 15 Kiel24116 Germany Institute for Theoretical Physics Bremen Center for Computational Materials Science University of Bremen Bremen28359 Germany Luruper Chaussee 149 Hamburg22761 Germany CNRS Aix-Marseille Université Centre Interdisciplinaire de Nanoscience de Marseille UMR 7325 Campus de Luminy Marseille13288 cedex 9 France Physics Department CSMB Adlershof Humboldt-Universität zu Berlin Berlin12489 Germany Università degli Studi di Palermo Dipartimento di Fisica e Chimica—Emilio Segrè PalermoI-90123 Italy Institute for Theoretical Physics University of Hamburg Notkestraße 9-11 Hamburg22607 Germany The Hamburg Centre for Ultrafast Imaging Hamburg Germany Fritz Haber Institute Max Planck Society Faradayweg 4-6 Berlin14195 Germany Institut für Optik und Atomare Physik Technische Universität Berlin Straße des 17 Juni 135 Berlin10623 Germany Max Planck Institute for the Structure and Dynamics of Matter Hamburg Germany School of Mathematics and Physics Queen’s University Belfast University Road BelfastBT7 1NN United Kingdom Max Planck Institute for the Structure and Dynamics of Matter Center for Free Electron Laser Science Hamburg22761 Germany Department of Applied Physics and Science Education Eindhoven University of Technology Eindhoven Netherlands Technische Universität Braunschweig Institut für Mathematische Physik Mendelssohnstr. 3 Braunschweig38106 Germany Institut für Theorie der Statistischen Physik RWTH Aachen University JARA – Fundamentals of Future Information Technology Aachen52056 Germany Max Planck Institute for the Structure and Dynamics of Matter Center for Free Electron Laser Science Hamburg22761 Germany Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Max Planck Institute for the Structure and Dynamics of Matter Center for Free-Electron Laser Science Luruper Chaussee 149 Hamburg22761 Germany Schulich Fac

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This Roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science. Contributions in this Roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green’s function approaches, quantum and ab initio simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Time-resolved spectroscopy

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Artificial Intelligence for the Electron Ion Collider (AI4EIC)

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computing and Software for Big science 2024年第1期8卷 1-21页

作者： Allaire, C. Ammendola, R. Aschenauer, E.-C. Balandat, M. Battaglieri, M. Bernauer, J. Bondì, M. Branson, N. Britton, T. Butter, A. Chahrour, I. Chatagnon, P. Cisbani, E. Cline, E.W. Dash, S. Dean, C. Deconinck, W. Deshpande, A. Diefenthaler, M. Ent, R. Fanelli, C. Finger, M. Fol, E. Furletov, S. Gao, Y. Giroux, J. Waduge, N. C. Gunawardhana Hassan, O. Hegde, P.L. Hernández-Pinto, R.J. Blin, A. Hiller Horn, T. Huang, J. Jalotra, A. Jayakodige, D. Joo, B. Junaid, M. Kalantarians, N. Karande, P. Kriesten, B. Elayavalli, R. Kunnawalkam Li, Y. Lin, M. Liu, F. Liuti, S. Matousek, G. McEneaney, M. McSpadden, D. Menzo, T. Miceli, T. Mikuni, V. Montgomery, R. Nachman, B. Nair, R.R. Niestroy, J. Oregon, S. A. Ochoa Oleniacz, J. Osborn, J.D. Paudel, C. Pecar, C. Peng, C. Perdue, G.N. Phelps, W. Purschke, M.L. Rajendran, H. Rajput, K. Ren, Y. Renteria-Estrada, D.F. Richford, D. Roy, B.J. Roy, D. Saini, A. Sato, N. Satogata, T. Sborlini, G. Schram, M. Shih, D. Singh, J. Singh, R. Siodmok, A. Stevens, J. Stone, P. Suarez, L. Suresh, K. Tawfik, A.-N. Acosta, F. Torales Tran, N. Trotta, R. Twagirayezu, F.J. Tyson, R. Volkova, S. Vossen, A. Walter, E. Whiteson, D. Williams, M. Wu, S. Zachariou, N. Zurita, P. Université Paris-Saclay CNRS/IN2P3 IJCLAB Orsay 91400 France Sezione di Roma Tor Vergata INFN Rome 00133 Italy Brookhaven National Lab Upton 11973 NY United States Meta Menlo Park 935025 CA United States National Institute for Nuclear Physics Genoa 16146 Italy Center for Frontiers in Nuclear Science Stony Brook University Stony Brook 11790-3800 NY United States Stony Brook University Stony Brook 11794 NY United States National Institute for Nuclear Physics Catania 95125 Italy Drexel University Philadelphia 19104 PA United States Messiah University Mechanicsburg 17055 PA United States Jefferson Lab Newport News 293606 VA United States Laboratory Nuclear and High-Energy Physics CNRS Paris 75005 France University of Michigan Ann Arbor 48104 MI United States National Institute for Nuclear Physics Roma Rome 00185 Italy Indian Institute of Technology Bombay Maharashtra Mumbai 400076 India Massachusetts Institute of Technology Cambridge 021339 MA United States Department of Physics University of Manitoba Winnipeg R3T 2N2 MB Canada William & amp Charles University Faculty of Mathematics and Physics Prague 18000 Czech Republic Accelerators and Beam Physics Group CERN Geneva 1211 Switzerland University of Regina Regina S4S0A2 SK Canada University of Virginia Charlottesville 22904 VA United States University of Victoria Victoria V8P 5C2 Canada Central University of Karnataka Kalaburagi 585367 India Facultad de Ciencias Físico-Matemáticas Universidad Autónoma de Sinaloa Ciudad Universitaria Sinaloa Culiacán 80000 Mexico Institute for Theoretical Physics TübingenUniversity Tübingen 72076 Germany The Catholic University of America Washington 20064 DC United States University of Jammu Jammu and Kashmir Jammu 180006 India Hampton University Hampton 23668 VA United States Oak Ridge National Laboratory Oak Ridge 37830 TN United States Virginia Union University Richmond 23220 VA United States Lawrence Livermore N

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments. © The Author(s) 2024.

关键词： Artificial Intelligence Deep learning EIC ePIC Machine learning Physics QCD

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GLOBAL CLIMATE

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Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

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Spatio-temporal regression analysis on shorelines data extracted from historical satellite images

Spatio-temporal regression analysis on shorelines data extra...

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International Conferences on WWW/Internet, ICWI 2018 and Applied computing 2018

作者： Manaf, Syaifulnizam Abd Mustapha, Norwati Sulaiman, Md Nasir Husin, Nor Azura Shafri, Helmi Zulhaidi Mohd Hamid, Mohd Radzi Abdul Intelligent Computing Research Group Faculty of Computer Science and Information Technology Universiti Putra Malaysia SerdangSelangor Malaysia Geospatial Information Science Research Centre Faculty of Engineering Universiti Putra Malaysia Serdang Selangor Malaysia Coastal Management and Oceanography Research Centre National Hydraulic Research Institute of Malaysia Seri Kembangan Selangor Malaysia

ISBN: (纸本)9789898533821

With the increasing number of sensors of the Earth observation satellites, data were collected at all times all over our planet. The longest satellite mission was Landsat, with the first operation starting from early 1970’s and still operations until today. Landsat satellites collect a vast amount of images with the latest Landsat 8 satellites alone collect 1,200 images per day at approximately 1 GB per image. However, acquiring the suitable images that cover the area of study was not directly proportional to the number of images produced. It depended on the chosen area and the weather conditions while capturing the images. The chosen images considered cleaned from thick cloud and haze that required minimal pre-processing tasks. In the case of coastal zone monitoring, the information about conditions of coastal areas provided whether affected by natural or human activities. Monitoring coastal zones could be applied by monitoring shorelines, which are an important criterion to measure boundary of a country. In this research, Pontian district was chosen as the area of study. It has been identified as one of the most vulnerable places in Malaysia due to sea-level rise. For extraction, satellite image classification using Artificial Neural Network was employed to classify land and water classes. Then, historical shorelines were extracted for the year 1977, 1989, 2000, 2008, 2014 and 2017 covering a medium-term of 40 years’ period. Rates of change statistics were calculated using the End Point Rate method. Approximately 753 transects were cast at left and right of shorelines along the entire coast at 50 m interval. The prediction of year 2017 were assessed using the Linear Regression and the End Point Rate methods. Among the two, Linear Regression was used to project the future positions based on historical shoreline data because it attained the lowest error after validating model of 2017 of 1977 to 2014 data with 2017 extracted data. Using the same prediction model, fut

关键词： Regression analysis

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Author Correction: Sub-femtonewton force sensing in solution by super-resolved photonic force microscopy

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Nature Photonics 2024年 998页

作者： Xuchen Shan Dajing Wang Jinlong Shi Hongyan Zhu Xiaolan Zhong Baolei Liu Weichang Hao Fan Wang Lei Ding Qian Peter Su Shihui Wen Dayong Jin Lei Jiang Chaohao Chen Yang Wang Zhaocun Huang Lingqian Chang Shen S. J. Wang Peter John Reece Wei Ren Xunyu Lu Jie Lu Lingdong Sun School of Physics Beihang University Beijing People’s Republic of China Science and Technology Center for Quantum Biology National Institute of Extremely-Weak Magnetic Field Infrastructure Hangzhou People’s Republic of China School of Biomedical Engineering Faculty of Engineering and Information Technology University of Technology Sydney Sydney New South Wales Australia Centre for Atomaterials and Nanomanufacturing School of Science RMIT University Melbourne Victoria Australia Institute for Biomedical Materials and Devices (IBMD) School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney Sydney New South Wales Australia Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo People’s Republic of China Key Laboratory of Bio-Inspired Materials and Interfaces Sciences Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing People’s Republic of China Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials Engineering Research School of Physics The Australian National University Canberra Australian Capital Territory Australia Key Laboratory of Biomechanics and Mechanobiology (Ministry of Education) Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing People’s Republic of China Computing and Information Systems Department Melbourne School of Engineering University of Melbourne Melbourne Victoria Australia School of Physics The University of New South Wales Sydney New South Wales Australia School of Chemistry & Chemical Engineering Shaanxi Normal University Xi’an People’s Republic of China Particles and Catalysis Research Laboratory School of Chemical Engineering The University of New South Wales Sydney New South Wales Australia Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology

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Correction to Heating and Evaporation of Droplets of Multicomponent and Blended Fuels: A Review of Recent Modeling Approaches

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Energy & Fuels 2022年第3期36卷 1746页

作者： Mansour Al Qubeissi Sergei S. Sazhin Nawar Al-Esawi Ruslana Kolodnytska Bidur Khanal Mohammad Ghaleeh Ahmed Elwardany Institute for Future Transport and Cities Coventry University Coventry CV1 5FB United Kingdom School of Mechanical Aerospace and Automotive Engineering Faculty of Engineering Environment and Computing Coventry University Coventry CV1 2JH United Kingdom Advanced Engineering Centre School of Architecture Technology and Engineering University of Brighton Brighton BN2 4GJ United Kingdom Faculty of Arts Science and Technology University of Northampton Northampton NN1 5PH United Kingdom Automotive Engineering Department Zhytomyr Polytechnic State University Zhytomyr 10005 Ukraine Fuels and Combustion Engines Laboratory Energy Resources Engineering Department Egypt-Japan University of Science and Technology New Borg El-Arab 21934 Egypt Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria 21544 Egypt

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