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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Adriano Luiz de Paula Joaquim José Barroso Mirabel Cerqueira Rezende Division of Materials AMR Institute of Aeronautics and Space IAE Sao Jose dos Campos Brazil Laboratory of Computing and Applied Mathematics LAC National Institute of Space Research (INPE) Sao Jose dos Campos Brazil Associated Plasma Laboratory National Institute of Space Research (INPE) Sao Jose dos Campos Brazil Division of Materials AMR Institute of Aeronautics and Space lAB USA

The present report branches out into two related topics. The first is concerned with the implementation of a computational modeling to predict the behavior of electromagnetic materials in confined environment by using electromagnetic three-dimensional simulation. The second topic re-examines the Nicolson-Ross-Weir mathematical model to retrieve the constitutive parameters (complex permittivity and permeability) of a Teflon ® sample from the measurement of scattering coefficients. Recognizing the importance of this issue for the development of radar absorbing materials (RAMs), the present study contributes for the characterization of electromagnetic materials and, consequently, for the processing of RAMs in the microwave range.

关键词： Computational modeling Predictive models Mathematical model Permittivity measurement Permeability measurement Electromagnetic measurements Electromagnetic scattering Radar scattering Scattering parameters Character recognition

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On the verification of adaptive three-dimensional multiresolution computations of the magnetohydrodynamic equations

arXiv

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arXiv 2019年

作者： Gomes, Anna Karina Fontes Domingues, Margarete Oliveira Mendes, Odim Schneider, Kai Post-graduation Program in Applied Computing National Institute for Space Research São José dos Campos Brazil Associated Laboratory for Computing and Applied Mathematics National Institute for Space Research São José dos Campos Brazil Space Geophysics Division National Institute for Space Research São José dos Campos Brazil Institut de Mathématiques de Marseille Aix-Marseille Université CNRS Centrale Marseille I2M UMR 7373

Magnetohydrodynamics is an important tool to study the dynamics of plasma Space Physics. In this context, we introduce a three-dimensional magnetohydrodynamic solver with divergence-cleaning in the adaptive multiresolution CARMEN code. The numerical scheme is based on a finite volume discretization that ensures the conservation of physical quantities. The adaptive multiresolution approach allows for automatic identification of local structures in the numerical solution and thus provides an adaptive mesh refined only in regions where the solution needs more improved resolution. We assess the three-dimensional magnetohydrodynamic CARMEN code and compare its results with the ones from the well-known FLASH code. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetohydrodynamics

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Chronnet: A network-based model for spatiotemporal data analysis

arXiv

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arXiv 2020年

作者： Ferreira, Leonardo N. Vega-Oliveros, Didier A. Cotacallapa, Moshé Cardoso, Manoel F. Quiles, Marcos G. Zhao, Liang Macau, Elbert E.N. National Institute for Space Research Associated Laboratory for Computing and Applied Mathematics São José Dos Campos - SP Brazil Humboldt University Department of Physics Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany Indiana University School of Informatics Computing and Engineering BloomingtonIN United States University of São Paulo Department of Computing and Mathematics Ribeirão Preto - SP Brazil National Institute for Space Research Center for Earth System Science São José dos Campos - SP Brazil Federal University of São Paulo Institute of Science and Technology São José Dos Campos - SP Brazil

The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a network-based model for spatiotemporal data analysis called chronnet. It consists of dividing a geometrical space into grid cells represented by nodes connected chronologically. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. This representation permits the use of network science and graphing mining tools to extract information from spatiotemporal data. The chronnet construction process is fast, which makes it suitable for large data sets. In this paper, we describe how to use our model considering artificial and real data. For this purpose, we propose an artificial spatiotemporal data set generator to show how chronnets capture not just simple statistics, but also frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Additionally, we analyze a real-world data set composed of global fire detections, in which we describe the frequency of fire events, outlier fire detections, and the seasonal activity, using a single chronnet. Copyright © 2020, The Authors. All rights reserved.

关键词： Data handling

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The effect of time series distance functions on functional climate networks

arXiv

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arXiv 2019年

作者： Ferreira, Leonardo Nascimento Ferreira, Nicole C.R. Macau, Elbert E.N. Donner, Reik V. Associated Laboratory for Computing and Applied Mathematics National Institute for Space Research Av. dos Astronautas 1758 Jardim da Granja SP São José Dos CamposCEP: 12227-010 Brazil Department of Physics Humboldt University Newtonstraße 15 Berlin12489 Germany The Leibniz Association Telegrafenberg A56 Potsdam14473 Germany Center for Weather Forecast and Climatic Studies National Institute for Space Research Rodovia Presidente Dutra Km 40 SP Cachoeira PaulistaCEP: 12630-000 Brazil Institute of Science and Technology Federal University of São Paulo Av. Cesare Monsueto Giulio Lattes 1201 - Eugênio de Melo SP São José dos Campos Brazil Department of Water Environment Construction and Safety Magdeburg-–Stendal University of Applied Sciences Breitscheidstr. 2 Magdeburg39114 Germany

Complex network theory provides an important tool for the analysis of complex systems such as the Earth’s climate. In this context, functional climate networks can be constructed using a spatiotemporal climate dataset and a suitable time series distance function. The resulting coarse-grained view on climate variability consists of representing distinct areas on the globe (i.e., grid cells) by nodes and connecting pairs of nodes that present similar time series. One fundamental concern when constructing such a functional climate network is the definition of a metric that captures the mutual similarity between time series. Here we study systematically the effect of 29 time series distance functions on functional climate network construction based on global temperature data. We observe that the distance functions previously used in the literature commonly generate very similar networks while alternative ones result in rather distinct network structures and reveal different long-distance connection patterns. These patterns are highly important for the study of climate dynamics since they generally represent pathways for the long-distance transportation of energy and can be used to forecast climate variability on subseasonal to interannual or even decadal scales. Therefore, we propose the measures studied here as alternatives for the analysis of climate variability and to further exploit their complementary capability of capturing different aspects of the underlying dynamics that may help gaining a more holistic empirical understanding of the global climate system. © 2019, CC BY-SA.

关键词： Time series

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Correction to: The effect of time series distance functions on functional climate networks

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The European Physical Journal Special Topics 2021年第1期230卷 9-9页

作者： Ferreira, Leonardo N. Ferreira, Nicole C. R. Macau, Elbert E. N. Donner, Reik V. Associated Laboratory for Computing and Applied Mathematics National Institute for Space Research São José Dos Campos Brazil Department of Physics Humboldt University Berlin Germany Research Department IV-Complexity Science Potsdam Institute for Climate Impact Research (PIK)-Member of the Leibniz Association Potsdam Germany Center for Weather Forecast and Climatic Studies National Institute for Space Research Cachoeira Paulista Brazil Institute of Science and Technology Federal University of São Paulo São José dos Campos Brazil Department of Water Environment Construction and Safety Magdeburg-Stendal University of Applied Sciences Magdeburg Germany

Due to technical problems this article has been published before corrections were implemented.

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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