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Image-Guided Morphological Measurement for the Circumferential Residual Strains in Aortic Arch of Rabbit

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Image-Guided Morphological Measurement for the Circumferenti...

作者： Xiaojun Zhang Yangyang Shi Qin Zhu Youhong Tang Xiaoyang Li School of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology Wuhan Technical College of Communications School of Computer Science Engineering and MathematicsFlinders University

The physiological functions and pathological features of the vessel are related to its mechanical environment,and the mechanical properties of vessel have played the important role in the cardiovascnlar and cerebrovascular *** order to quantify residual strains in aortic arch ultimately,elliptic curve fitness method which combines computerized measurements of images,are *** the cross-sectional configuration parameters of rabbit aortic arch in the non-loaded state,such as semi major/minor axis,thickness of arterial wall,were *** results of the experiment and measurement indicate that aortic arch in the non-loaded state is elliptic cross-section in its curved section but is circular cross section in its straight ***,the whole aortic arch is cone-shaped in axial direction,which agrees well with the physiologic ***,circumferential distribution of averaged thickness is nearly uniform in the non-loaded ***,axial distribution of averaged thickness is not uniform any *** these results are the necessary information for further research on the mechanical features of the vessel,and corresponding numerical investigation of residual strains in vessels will be carried out in subsequent work.

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Front Cover: General Doping Chemistry of Carbon Materials (ChemNanoMat 4/2023)

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ChemNanoMat 2023年第4期9卷

作者： Dr. Mohammad Boshir Ahmed Jahangir Alom Md. Saif Hasan Md. Asaduzzaman Md. Saifur Rahman Raqibul Hossen Dr. Mohammad Abu Hasan Johir Prof. Dr. Mohammad Taufiq Alam Prof. Dr. John L. Zhou Prof. Dr. Yanqiu Zhu Dr. Masoumeh Zargar School of Material Science and Engineering Gwangju Institute of Science and Technology 61005 Gwangju Republic of Korea Department of Applied Chemistry and Chemical Engineering University of Rajshahi 6205 Rajshahi Bangladesh Department of Biomedical Engineering Texas A&M University College Station TX 77843 United States School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology 61005 Gwangju Republic of Korea Center for Green Technology School of Civil and Environmental Engineering University of Technology Sydney 15 Broadway 2007 Sydney NSW Australia College of Engineering Mathematics and Physical Sciences University of Exeter Exceter city EX4 4QF United Kingdom School of Engineering Edith Cowan University 6027 Joondalup WA Australia

Preventive and Reactive Cyber Defense Dynamics with Ergodic Time-dependent Parameters Is Globally Attractive

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arXiv 2020年

作者： Yujuan, Han Wenlian, Lu Shouhuai, Xu College of Information Engineering Shanghai Maritime University China School of Mathematical Sciences Fudan University ChinaShanghai Center for Mathematical Sciences Fudan University Chinaand Shanghai Key Laboratory for Contemporary Applied Mathematics China Department of Computer Science Univer-sity of Texas at San Antonio United States

Cybersecurity dynamics is a mathematical approach to modeling and analyzing cyber attack-defense interactions in networks. In this paper, we advance the state-of-the-art in characterizing one kind of cybersecurity dynamics, known as preventive and reactive cyber defense dynamics, which is a family of highly nonlinear system models. We prove that this dynamics in its general form with time-dependent parameters is globally attractive when the time-dependent parameters are ergodic, and is (almost) periodic when the time-dependent parameters have the stronger properties of being (almost) periodic. Our results supersede the state-of-the-art ones, including that the same type of dynamics but with time-independent parameters is globally convergent. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

Revisiting two well studied galaxy samples using alternative gravity

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Journal of Physics: Conference Series 2021年第1期1956卷

作者： James G. O'Brien Thomas L. Chiarelli William M. Kerin Jon Belanger Department of Mathematics Physics and Computer Science Springfield College Springfield MA 01109 USA Department of Electromechanical Engineering Wentworth Institute of Technology Boston MA 02115 USA Department of Applied Mathematics Wentworth Institute of Technology Boston MA 02115 USA

In the last few years, alternative gravity theories have seen increased interest due to the lack of observational evidence of dark matter. Further, new empirical patterns found in rotation curve data such as the Radial Acceleration Rule (RAR) have given new testable features for gravitational theories. In this paper, we revisit two popular surveys of galaxies (Randriamampandry et al 2013 and Bottema et al 2015) which when published were shown to be problematic for alternative gravity. Here, we apply the most recent observational parameters to the surveys and provide fits of Conformal Gravity, MOND as well as the RAR rotation curve formalism and show how these theories can apply to the new findings. We also provide the fits to the RAR and Tully-Fisher relation for each theory and discuss how the RAR may allow for some confining of parameters in the fitting procedure.

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First Results from the REAL-time Transient Acquisition backend (REALTA) at the Irish LOFAR station

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arXiv 2021年

作者： Murphy, Pearse C. Callanan, P. McCauley, J. McKenna, D.J. Fionnagáin, D.Ó. Louis, C.K. Redman, M.P. Cañizares, L.A. Carley, E.P. Maloney, S.A. Coghlan, B. Daly, M. Scully, J. Dooley, J. Gajjar, V. Giese, C. Brennan, A. Keane, E.F. Maguire, C.A. Quinn, J. Mooney, S. Ryan, A.M. Walsh, J. Jackman, C.M. Golden, A. Ray, T.P. Doyle, J.G. Rigney, J. Burton, M. Gallagher, P.T. School of Physics Trinity College Dublin Dublin 2 Ireland Astronomy & Astrophysics Section Dublin Institute for Advanced Studies D02 XF86 Ireland Department of Physics University College Cork Cork Ireland Astrophysics Research Group School of Mathematics Statistics and Applied Mathematics National University of Ireland Galway University Road GalwayH91 TK33 Ireland Centre for Astronomy School of Physics National University of Ireland Galway University Road GalwayH91 TK33 Ireland School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Deptartment of Computer and Software Engineering Athlone Institute of Technology Athlone Ireland Department of Astronomy University of California BerkeleyCA94720 United States School of Physics University College Dublin Belfield Dublin 4 Ireland Armagh Observatory and Planetarium College Hill ArmaghBT61 9DG United Kingdom Astrophysics Research Centre School of Mathematics and Physics Queen's University Belfast BelfastBT7 1NN United Kingdom

Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts (FRBs), rotating radio transients (RRATs), the search for extraterrestrial intelligence (SETI), Jupiter, and the Sun. © 2021, CC BY.

关键词： Interferometers

Roadmap on Nonlocality in Photonic Materials and Metamaterials

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arXiv 2025年

作者： Monticone, Francesco Mortensen, N. Asger Fernández-Domínguez, Antonio I. Luo, Yu Tserkezis, Christos Khurgin, Jacob B. Shahbazyan, Tigran V. Chaves, André J. Peres, Nuno M.R. Wegner, Gino Busch, Kurt Hu, Huatian della Sala, Fabio Zhang, Pu Ciracì, Cristian Aizpurua, Javier Babaze, Antton Borisov, Andrei G. Chen, Xue-Wen Christensen, Thomas Yan, Wei Yang, Yi Hohenester, Ulrich Huber, Lorenz Wubs, Martijn de Liberato, Simone Gonçalves, P.A.D. de Abajo, F. Javier García Hess, Ortwin Tarasenko, Illya Cox, Joel D. Jelver, Line Dias, Eduardo J.C. Sánchez, Miguel Sánchez Margetis, Dionisios Gómez-Santos, Guillermo Stauber, Tobias Tretyakov, Sergei Simovski, Constantin Pakniyat, Samaneh Gómez-Díaz, J. Sebastián Bondarev, Igor V. Biehs, Svend-Age Boltasseva, Alexandra Shalaev, Vladimir M. Krasavin, Alexey V. Zayats, Anatoly V. Alù, Andrea Song, Jung-Hwan Brongersma, Mark L. Levy, Uriel Long, Olivia Y. Guo, Cheng Fan, Shanhui Bozhevolnyi, Sergey I. Overvig, Adam Prudêncio, Filipa R. Silveirinha, Mário G. Gangaraj, S. Ali Hassani Argyropoulos, Christos Huidobro, Paloma A. Galiffi, Emanuele Yang, Fan Pendry, John B. Miller, David A.B. School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States POLIMA Center for Polariton-driven Light–Matter Interactions University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid MadridE-28049 Spain Universidad Autónoma de Madrid MadridE-28049 Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Physics Jackson State University JacksonMS39217 United States Department of Physics Aeronautics Institute of Technology SP So Jos dos Campos12228-900 Brazil Departamento de Física Universidade do Minho BragaP-4710-057 Portugal Av Mestre José Veiga Braga4715-330 Portugal Max-Born-Institut Berlin12489 Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik Berlin12489 Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 Arnesano73010 Italy Via Monteroni Campus Unisalento Lecce73100 Italy School of physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Donostia International Physics Center DIPC Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Electricity and Electronics University of the Basque Country Leioa48940 Spain Department of Applied Physics University of the Basque Country Donostia20018 Spain Université Paris-Saclay CNRS Institut des Scienc

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces–can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward–toward new scientific discoveries and technological advancements. Copyright © 2025, The Authors. All rights reserved.

关键词： Nanocrystals

Task scheduling algorithm based on Virtual machine availability awareness in cloud platform

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Journal of engineering Science and Technology Review 2018年第5期11卷 161-169页

作者： Li, Zhixin Liu, Lei Tong, Zeyu College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Technology and Engineering Changchun Institute Of Technology Changchun130012 China Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore21218 United States

Virtual machine (VM) availability awareness is a key task scheduling technology in a cloud platform. However, the dynamic change and uncertainty of VM availability constitute difficulties for task scheduling, and quality requirements of task services cannot be satisfied and thus seriously affect the task scheduling capacity of the cloud platform. A task scheduling algorithm based on VM availability awareness was proposed to solve the unmatching problem between VM availability and quality of service (QoS) to improve task scheduling capacity of VMs. This algorithm combined available task processing capacities of VMs and task requirement features to establish a differential entropy model between VM availability and task availability requirement. Task availability matching and scheduling was realized through the principle of maximum entropy, and task scheduling was optimized from the aspect of balance of server workloads. Finally, a comparative verification between the task scheduling algorithm and Random and Minmin algorithms was implemented. Results demonstrate that through the task scheduling algorithm based on VM availability awareness, the task execution speed of VMs is higher than those of Random and Minmin algorithms by 28% and 6%, respectively. Therefore, task execution speed of the cloud platform is significantly elevated, and server workloads are more balanced than those in Random and Minmin algorithms. Within the same time, QoS satisfaction rate is higher than those of Random and Minmin algorithms by 10% and 2%, respectively. QoS task requirement is satisfied while task completion time is reduced. This study concludes that the VM availability awareness method satisfies the task requirements and improves task processing performance of the VM in the cloud platform. Relevant conclusions can provide technical support for task scheduling of the VM in the cloud platform. © 2018 Eastern Macedonia and Thrace Institute of Technology.

关键词： Virtual machine

Author Correction: AI models collapse when trained on recursively generated data

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Nature 2025年第8058期640卷 E6页

作者： Ilia Shumailov Zakhar Shumaylov Yiren Zhao Nicolas Papernot Ross Anderson Yarin Gal OATML Department of Computer Science University of Oxford Oxford UK. ilia.shumailov@chch.ox.ac.uk. Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UK. zs334@cam.ac.uk. Department of Electrical and Electronic Engineering Imperial College London London UK. University of Toronto Toronto Ontario Canada. Vector Institute Toronto Ontario Canada. Department of Computer Science and Technology University of Cambridge Cambridge UK. School of Informatics University of Edinburgh Edinburgh UK. OATML Department of Computer Science University of Oxford Oxford UK. yarin@cs.ox.ac.uk.

Why is the winner the best?

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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