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IEEE Sensors

作者： Rosangela Winter Jean Carlos Cardozo da Silva Cicero Martelli Eduardo Nunes dos Santos Marco Jose da Silva Graduate Program in Electrical and Computer Engineering (CPGEI) Federal University of Technology - Parana 3165 Curitiba Parana Brazil 80230-901

ISBN: (纸本)9781509010134

Experimental investigation of two-phase flows is important in many areas of research and industrial application. We introduce a probe based on electrical impedance and Fiber Bragg Grating-based strain measurements, which is able to monitor two-phase flow in pipes. The prototype probe presented consists of an optical fiber with a FBG for sensing encapsulated by a hollow stainless-steel cylinder, which is interrogated for strain and conductance measurements. Raw data is converted to flow parameters such as gas bubble length and velocity. Initial results in a two-phase flow test bench show that the combined optical-electrical technique is feasible and promising to be applied in two-phase flow monitoring.

关键词： flow measurement fiber Bragg grating impedance sensor optical sensor Fiber Bragg gratings TWO PHASE FLOW Flow measurement Probes optical sensors Strain measurement impedance Optical Fibers

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Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

引用

Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

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Atacama Large Aperture Submillimeter Telescope (AtLAST) Science: Surveying the distant Universe

arXiv

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arXiv 2024年

作者： van Kampen, Eelco Bakx, Tom De Breuck, Carlos Chen, Chian-Chou Dannerbauer, Helmut Magnelli, Benjamin Montenegro-Montes, Francisco Miguel Okumura, Teppei Pu, Sy-Yun Rybak, Matus Saintonge, Amélie Cicone, Claudia Hatziminaoglou, Evanthia Hilhorst, Juliëtte Klaassen, Pamela Lee, Minju Lovell, Christopher C. Lundgren, Andreas Di Mascolo, Luca Mroczkowski, Tony Sommovigo, Laura Booth, Mark Cordiner, Martin A. Ivison, Rob Johnstone, Doug Liu, Daizhong Maccarone, Thomas J. Smith, Matthew Thelen, Alexander E. Wedemeyer, Sven European Southern Observatory Karl-Schwarzschild-Str. 2 Garching bei München85748 Germany Department of Space Earth & Environment Chalmers University of Technology Chalmersplatsen 4 GothenburgSE-412 96 Sweden No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan Tenerife La LagunaE-38205 Spain Universidad de La Laguna Dpto. Astrofísica Tenerife La LagunaE-38206 Spain Université Paris-Saclay Université Paris-Cité CEA CNRS AIM Gif-sur-Yvette91191 France Ciudad Universitaria Madrid28040 Spain Graduate Institute of Astronomy National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Faculty of Electrical Engineering Mathematics and Computer Science Mekelweg 4 Delft2628 CD Netherlands Leiden Observatory Leiden University Niels Bohrweg 2 Leiden2333 CA Netherlands SRON - Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333 CA Netherlands Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Auf dem Hügel 69 BonnD-53121 Germany Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern Oslo0315 Norway Department of Astronomy Yale University New HavenCT06511 United States UK Astronomy Technology Centre Royal Observatory Edinburgh Blackford Hill EdinburghEH9 3HJ United Kingdom Denmark DTU-Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK2800 Denmark Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom Aix Marseille Univ CNRS CNES LAM Marseille France Laboratoire Lagrange Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Blvd de l'Observatoire CS 34229 Nice06304 cedex 4 France Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 Trieste34131 Italy INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 Trieste34131 Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Triest

During the most active period of star formation in galaxies, which occurs in the redshift range 1 8 and the Hubble constant with high accuracy, and demonstrate the power of the line-intensity mapping method in the mm/sub-mm wavebands to constrain the cosmic expansion history at high redshifts, as good examples of what can uniquely be done by AtLAST in this research field. © 2024, CC BY.

关键词： Galaxies

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Evaluation of an Instrument to Identify Factors that Impact the Motivation and Engagement of Undergraduate Students in Computing

Evaluation of an Instrument to Identify Factors that Impact ...

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Frontiers in Education (FIE) Conference

作者： Pablo Schoeffel Raul Sidnei Wazlawick Vinicius Faria Culmant Ramos Adilson Vahldick Marcelo de Souza Graduate Program in Computer Science Federal University of Santa Catarina Florianópolis Brazil Department of Informatics and Statistics Federal University of Santa Catarina Florianópolis Brazil Department of Information and Communication Technologies Federal University of Santa Catarina Araranguá Brazil Departament of Software Engineering University of Santa Catarina State Ibirama Brazil

ISBN: (纸本)9781538611753;9781538611746

This Research Paper presents the evaluation of an instrument to identify the impact of motivation and engagement factors in undergraduate students in computing. Although researches indicate a direct impact of motivation and engagement on student performance and retention, few studies have been found that address which factors are relevant in this process. The instrument is a questionnaire based on the compilation of several works of the literature containing 48 items divided into 6 groups: personal and demographic data, general perception about motivation, perception about the university, student behavior, perception about program and perception about classes/teachers. The questionnaire evaluation is based on a case study with 112 undergraduate students in Software engineering. As a result, we found that the questionnaire can be considered reliable (Cronbach's alpha = .8904). Considering the validity of constructs, we found an acceptable degree of correlation between the most pair of items in each group (averaging 63%). We also found that the item-total correlation coefficient was only not adequate for one factor group, indicating satisfactory correlation for all other items. Finally, we found that the number of factors is coherent, but there are several items from different groups strongly correlated, indicating the need for a reorganization.

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Correction to: Comparison of 12 surrogates to characterize CT radiation risk across a clinical population

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European radiology 2021年第10期31卷 8068页

作者： Francesco Ria Wanyi Fu Jocelyn Hoye W Paul Segars Anuj J Kapadia Ehsan Samei Carl E. Ravin Advanced Imaging Labs Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. francesco.ria@duke.edu. Clinical Imaging Physics Group Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. francesco.ria@duke.edu. Carl E. Ravin Advanced Imaging Labs Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. Clinical Imaging Physics Group Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. Medical Physics Graduate Program Departments of Radiology Physics Biomedical Engineering and Electrical and Computer Engineering Duke University 2424 Erwin Road Suite 302 Durham NC 27710 USA.

A Correction to this paper has been published: https://***/10.1007/s00330-021-07903-z

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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Binding a hopfion in chiral magnet nanodisk

arXiv

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arXiv 2018年

作者： Liu, Yizhou Lake, Roger K. Zang, Jiadong Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Department of Physics and Materials Science Program University of New Hampshire DurhamNH03824 United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sci-ences Beijing100190 China

Hopfions are three-dimensional (3D) topological textures characterized by the integer Hopf invariant QH. Here, we present the realization of a zero-field, stable hopfion spin texture in a magnetic system consisting of a chiral magnet nanodisk sandwiched by two films with perpendicular magnetic anisotropy. The preimages of the spin texture and numerical calculations of QH show that the hop-fion has QH = 1. Furthermore, another non-trivial state that includes a monopole{antimonopole pair (MAP) is also stabilized in this system. By applying an external magnetic field, hopfion and MAP states with the same polarization can be switched between each other. The topological transition between the hopfion and the MAP state involves a creation (annihilation) of the MAP and twist of the preimages. Our work paves the way to study non-trivial 3D topological spin textures and stimulates more investigations in the field of 3D spintronics. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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Identification of Pre-University Factors that Affect the Initial Motivation of Students in Computing programs: A multi-institutional case study

Identification of Pre-University Factors that Affect the Ini...

引用

Frontiers in Education (FIE) Conference

ISBN: (纸本)9781538611753;9781538611746

This Research to Practice Paper presents the results of the evaluation of pre-university factors that impact the initial motivation of undergraduate students in computing. Although there are studies in the literature that have investigated some previous factors, this paper replicates a previous work that aims to consolidate several pre-university factors and, as the main differential, uses the AMS (Academic Motivation Scale), a scale already consolidated in the literature to measure students' initial motivation, and evaluate the relation between motivation and candidate factors. We applied a questionnaire to 159 students from different computing programs in ten universities, which evaluates 20 factors divided into 4 groups: personal and demographic data, taste and knowledge of the program and area, computing experience, and school performance. To evaluate the correlation between factors and motivation, we used Spearman's coefficient, t-student test, and ANOVA to evaluate the correlation between factors and motivation. As main results, we found significant variation in the initial motivation according to following factors: taste for programming and technology, knowledge about the undergraduate program content, correct perception about computing professionals, knowledge and experience in computer programming, and general school performance.

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Deep learning in radiology: An overview of the concepts and a survey of the state of the art

arXiv

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arXiv 2018年

作者： Mazurowski, Maciej A. Buda, Mateusz Saha, Ashirbani Bashir, Mustafa R. Department of Radiology Duke University DurhamNC United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Duke Medical Physics Program Duke University DurhamNC United States Center for Advanced Magnetic Resonance Development Duke University DurhamNC United States

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mostly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms. We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review the broad range of utilized deep learning algorithms. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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When brands fight over bands: Sociality in the cognitive radio ecosystem

When brands fight over bands: Sociality in the cognitive rad...

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2017 IEEE International Conference on Communications, ICC 2017

作者： Wisniewska, Anna Khan, Bilal Al-Fuqaha, Ala Dombrowski, Kirk Shattal, Mohammad Abu Computer Science Department City University of New York Graduate Center New YorkNY10016 United States Department of Sociology University of Nebraska-Lincoln LincolnNE68583 United States Computer Science Department Western Michigan University KalamazooMI49008 United States Electrical and Computer Engineering Department Western Michigan University KalamazooMI49008 United States

ISBN: (纸本)9781467389990

As wireless devices continue to proliferate, spectrum management is essential to a healthy and functioning digital ecosystem. Here we present an evolutionary analysis of how interbrand relationships can be expected to evolve in the cognitive radio domain over long time scales. We find that a range of trajectories are possible, and that the eventual outcomes depend on a variety of system parameters including the number of users and transmission band switching costs. Starting from previous bio-socially inspired fair spectrum sharing protocols, we put forward an extended model of secondary user etiquette that allows for a range of inter-group dynamics to arise in the natural course of competition over and co-use of spectrum resources. We show that as populations grow, increases in transmission switching costs lead to evolutionary pressures toward increasing antagonism between brands, and that in such scenarios devices tend to segregate by brand across bands. Understanding the drivers behind emerging inter-brand dynamics from an evolutionary perspective is an important input to the long term view of the successful application of distributed spectrum access and cognitive radio. © 2017 IEEE.

关键词： Switches Cognitive radio Electronic mail Convergence Steady-state Ecosystems Sociology

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