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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Spatio-temporal Factorization of Log data for Understanding Network Events

Spatio-temporal Factorization of Log Data for Understanding ...

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IEEE International Conference on Computer Communications

作者： Tatsuaki Kimura Keisuke Ishibashi Tatsuya Mori Hiroshi Sawada Tsuyoshi Toyono Ken Nishimatsu Akio Watanabe Akihiro Shimoda Kohei Shiomoto NTT Network Technology Laboratories NTT Corporation Tokyo Japan NTT Machine Learning and Data Science Center NTT Corporation Kyoto Japan School of Fundamental Science and Engineering Waseda University Tokyo Japan

Understanding the impacts and patterns of network events such as link flaps or hardware errors is crucial for diagnosing network anomalies. In large production networks, analyzing the log messages that record network events has become a challenging task due to the following two reasons. First, the log messages are composed of unstructured text messages generated by vendor-specific rules. Second, network equipment such as routers, switches, and RADIUS severs generate various log messages induced by network events that span across several geographical locations, network layers, protocols, and services. In this paper, we have tackled these obstacles by building two novel techniques: statistical template extraction (STE) and log tensor factorization (LTF). STE leverages a statistical clustering technique to automatically extract primary templates from unstructured log messages. LTF aims to build a statistical model that captures spatial-temporal patterns of log messages. Such spatial-temporal patterns provide useful insights into understanding the impacts and root cause of hidden network events. This paper first formulates our problem in a mathematical way. We then validate our techniques using massive amount of network log messages collected from a large operating network. We also demonstrate several case studies that validate the usefulness of our technique.

关键词： Tensile stress data mining Conferences Computers Mathematical model Protocols Hidden Markov models

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Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

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Applied Informatics 2014年第1期1卷 1-21页

作者： Hiroyuki Akama Brian Murphy Miao Mei Lei Massimo Poesio Graduate School of Decision Science and Technology Tokyo Institute of Technology Meguro-ku Tokyo Japan Knowledge & Data Engineering EEECS Queen's University Belfast Belfast Northern Ireland Machine Learning Department Carnegie Mellon University Pittsburgh USA Centre for Mind/Brain Sciences University of Trento Rovereto Italy School of Computer Science and Electronic Engineering University of Essex Colchester UK

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

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Norbert wiener panel session

Norbert wiener panel session

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IEEE International Conference on Systems, Man and Cybernetics

作者： William B. Rouse Chelsea C. White Keith Hipel Yacov Y. Haimes Lotfi A. Zadeh Mo Jamshidi Dimitar P. Filev Jose del R. Millan Lawrence O. Hall Wiliam A. Gruver Georgia Institute of Technology USA “Control and Communication in Animals Machines Organizations and Societies” Georgia Institute of Technology USA “Trends in Global Supply Chains and Logistics” University of Waterloo Canada “Tackling Climate Change: A System of Systems Engineering Perspective” University of Virginia USA TBA University of California at Berkeley USA “Wiener's Cybernetics and Shannon's Information Theory: A Personal Perspective” University of Texas at San Antonio USA “Big Data Analytic Based Models of System of Systems: Applications to Energy Finances and Medicine” Ford Motor Company USA “Evolving Embedded Systems in Cyber-Physical Applications” Swiss Federal Institute of Technology in Lausanne Switzerland “Brain-Machine Interfaces: Perception-Action Closed Loop” South Florida University USA “Do we need new learning algorithms or just the ability to blend existing ones?” Simon Fraser University Canada Algorithmic Trading of the Financial Markets: Research and Personal Opportunities”

The 2014 IEEE International Conference on Systems, Man, and Cybernetics is dedicated to Norbert Wiener, the Father of Cybernetics, in honor of the 120th anniversary of his birth.

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On-orbit measurements of the ISS atmosphere by the vehicle cabin atmosphere monitor

On-orbit measurements of the ISS atmosphere by the vehicle c...

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41st International Conference on Environmental Systems 2011, ICES 2011

作者： Darrach, M.R. Chutjian, A. Bornstein, B.J. Croonquist, A.P. Garkanian, V. Haemmerle, V.R. Hofman, J. Heinrichs, W.M. Karmon, D. Kenny, J. Kidd, R.D. Lee, S. MacAskill, J.A. Madzunkov, S.M. Mandrake, L. Rust, T.M. Schaefer, R.T. Thomas, J.L. Toomarian, N. Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States Atomic and Molecular Physics Group JPL/Caltech United States Instrument Autonomy Group JPL/Caltech United States MicroDevices Group JPL/CalTech United States Optical Communications Group JPL/Caltech United States Processing Algorithms and Calibration Engineering JPL/Caltech United States Inst System Engineering JPL/Caltech United States Instruments and Science Data Systems JPL/Caltech United States Instrument Integration and Test JPL/Caltech United States Planetary Chemistry and Astrobiology JPL/Caltech United States High Capacity Computing and Modeling JPL/Caltech United States Machine Learning and Instrument Autonomy Group JPL/Caltech United States SpaceX 1 Rocket Rd Hawthorne CA 90250 United States Advanced Computer Systems and Technology JPL/Caltech United States Advanced Instrument Concepts JPL/Caltech United States

ISBN: (纸本)9781600869488

We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) during operations aboard the International Space Station (ISS). VCAM is an autonomous environmental monitor based on a miniature gas chromatograph/mass spectrometer. It was flown to the ISS on shuttle mission STS-131 and commenced operations on 6/10/10. VCAM provides measurements of ppb-to-ppm levels of volatile trace-gas constituents, and of the atmospheric major constituents (nitrogen, oxygen, argon, and carbon dioxide) in a space vehicle or station. It is designed to operate autonomously and maintenance-free, approximately once per day, with a self-contained gas supply sufficient for a one-year lifetime. VCAM is designed to detect and identify 90% of the target compounds at their 180-day Spacecraft Maximum Allowable Concentration levels. © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Gases

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CLEER - AN AI-SYSTEM DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

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NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： Artificial Intelligence

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Advances in Self-Organizing Maps, learning Vector Quantization, Clustering and data Visualization 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Alfredo Vellido Karina Gibert Cecilio Angulo José David Martín Guerrero

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