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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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Image Segmentation via Improving Clustering Algorithms with Density and Distance

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Procedia Computer Science 2015年 55卷 1015-1022页

作者： Zhensong Chen Zhiquan Qi Fan Meng Limeng Cui Yong Shi School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing 100190 China Sino-Danish Center for Education and Research Beijing 100190 China Research Center on Fictitious Economy and Data Science CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Image segmentation problem is a fundamental task and process in computer vision and image processing applications. It is well known that the performance of image segmentation is mainly influenced by two factors: the segmentation approaches and the feature presentation. As for image segmentation methods, clustering algorithm is one of the most popular approaches. However, most current clustering-based segmentation methods exist some problems, such as the number of regions of image have to be given prior, the different initial cluster centers will produce different segmentation results and so on. In this paper, we present a novel image segmentation approach based on DP clustering algorithm. Compared with the current methods, our method has several improved advantages as follows: 1) This algorithm could directly give the cluster number of the image based on the decision graph; 2) The cluster centers could be identified correctly; 3) We could simply achieve the hierarchical segmentation according to the applications requirement. A lot of experiments demonstrate the validity of this novel segmentation algorithm.

关键词： Image Segmentation Clustering Algorithm Feature Representation

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Factor decomposition of regional carbon emissions from energy consumption for China

Factor decomposition of regional carbon emissions from energ...

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3rd International Conference on Energy and Environmental Protection, ICEEP 2014

作者： Qi, Ya-Wei School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783038351375

This paper calculates the carbon emissions from energy consumption of 30 provinces in China through 2000-2010, and research correlation of factors such as regional economic gap and regional characteristics of carbon emissions in the process of regional economic coordinated development. The LMDI decomposition model is used to decompose the growth rate of China's carbon emissions into 4 types of driving factors, i.e. GDP, industrial pollutants emission intensity, industrial structure and imbalance of regional economic development, to analyze influence of scale effect, technical effect, industrial structure effect and regional spatial structure effect on carbon emissions in the process of China's regional economic development. The results show that: The scale effect is determinants of carbon emissions increasing. The technical effect is the most important force to inhibit the increment of carbon emissions. Industrial structure effect and regional spatial structure effect on carbon emissions are not yet stable, but have a certain pull impact on increasing carbon emissions. © (2014) Trans Tech Publications, Switzerland.

关键词： Energy utilization

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A hybrid model for explaining older adults' continuance intention toward SNSs 8

A hybrid model for explaining older adults' continuance inte...

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8th International Conference on Management of e-Commerce and e-Government, ICMeCG 2014

作者： Liu, Wei Jiangxi Key Laboratory of Data and Knowledge Engineering Institute of Information Resources Management Jiangxi University of Finance and Economics Nanchang360013 China

ISBN: (纸本)9781479965434

Due to the rapid growth of social net services (SNSs), research into SNSs continuance usage has recently emerged as an important issue in information systems adaption. This study develops an integrated model based on the expectation-confirmation model (ECM), the technology acceptance model (TAM), and the theory of planned behavior (TPB), and apply them into the context of older adults' continuance intention toward SNSs. The hypothesized model is validated empirically using a sample collected from 250 older adults who had prior experience with SNSs and was tested against the proposed research model using structure equation modeling. Analysis results demonstrate that satisfaction has the most significant effect on older adults' continuance intention, followed by perceived usefulness, attitude, subject norm and perceived behavioral control. The results of these findings for SNSs practitioners are discussed at the end of this work. © 2014 IEEE.

关键词： Adoption model continuance intention Older adults SNSs

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Semi-automatic online tagging with K-Medoid clustering

Semi-automatic online tagging with K-Medoid clustering

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作者： Hu, He Du, Xiaoyong School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MoE No. 59 Zhongguancun St. Beijing100872 China

Online tagging is crucial for the acquisition and organization of web knowledge. We present TYG (Tag-as-You-Go) in this paper, a web browser extension for online tagging of personal knowledge on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can automatically group similar HTML DOM nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios. © 2014 World Scientific Publishing Company.

关键词： Websites

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INK: A cloud-based system for efficient top-k interval keyword search 14

INK: A cloud-based system for efficient top-k interval keywo...

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23rd ACM International Conference on Information and knowledge Management, CIKM 2014

作者： Li, Rui Zhang, Xiao Zhou, Xin Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education Renmin University of China Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781450325981

It is insufficient to search temporal text by only focusing on either time attribute or keywords today as we pay close attention to the evolution of event with time. Both temporal and textual constraints need to be considered in one single query, called Top-k Interval Keyword Query (TIKQ). In this paper, we presents a cloud-based system named INK that supports efficient execution of TIKQs with appropriate effectiveness on Hadoop and HBase. In INK, an Adaptive Index Selector (AIS) is devised to choose the better execution plan for various TIKQs adaptively based on the proposed cost model, and leverage two novel hybrid index modules (TriI and IS-Tree) to combine keyword and interval filtration seamlessly.

关键词： Search engines

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Can web news media sentiments improve stock trading signal prediction? 18

Can web news media sentiments improve stock trading signal p...

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18th Pacific Asia Conference on Information Systems, PACIS 2014

作者： Lin, Nana Xu, Wei Zhang, Xinwei Lv, Siqi School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China School of Information Beijing China

The technical analysis and machine learning have been integrated in stock trading signal forecasting. And it has been proved that there are some weaknesses in technical analysis because of the complex environment in the stock market. In our prediction system, web news media sentiment analysis is regarded as a supplementary way to cover the shortage of technical analysis. It is considered to bring the stock market sentiment which reflects the subjective information of investors into the prediction system. Web news media sentiment indicators (WNMS) are designed to bring the information about stock market sentiment in our system. The WNMS is generated by analyzing the variance of sentiment elements from the news in the Stock Timely Rain Sector of Sina Finance and Economics Website and it is imported into the prediction system as features combined with common feature indicators (CFI). GMKL is applied to establish the relationship between the trading signals generated by piecewise linear representation (PLR) and the features of the trading signals (SCFI). Comparative experiments are adopted in nine stocks from Shanghai and Shenzhen Stock Exchange to determine the effect of PLR-GMKL and WNMS in prediction. From the aspects of the prediction accuracy and the profit, the final comparative results show that the PLR-GMKL model performs better than the PLR-WSVM model. And the prediction system performs best when adding WNMS into features and using PLRGMKL model.

关键词： Financial markets

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A fast algorithm to building a fuzzy rough classifier 13

A fast algorithm to building a fuzzy rough classifier

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13th International Conference on Machine Learning and Cybernetics, ICMLC 2014

作者： Tsang, Eric C. C. Zhao, Suyun The Macau University of Science and Technology Taipa Macau China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing100872 China

ISBN: (纸本)9783662456514

In this paper, by strict mathematic reasoning, we discover the relation between the similarity relation and lower approximation. Based on this relation, we design a fast algorithm to build a rule based fuzzy rough classifier. Finally, the numerical experiments demonstrate the efficiency and the affectivity of the proposed algorithm. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Rough set theory

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Personalized Financial News Recommendation Algorithm Based on Ontology

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Procedia Computer Science 2015年 55卷 843-851页

作者： Rui Ren Lingling Zhang Limeng Cui Bo Deng Yong Shi School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

To deal with the challenge of information overload, in this paper, we propose a financial news recommendation algorithm which help users find the articles that are interesting to read. To settle the ambiguity problem, a new presented OF-IDF method is employed to represent the unstructured text data in the form of key concepts, synonyms and synsets which are all stored in the domain ontology. For users, the recommendation algorithm build the profiles based on their behaviors to detect the genuine interests and predict current interests automatically and in real time by applying the thinking of relevance feedback. Finally, the experiment conducted on a financial news dataset demonstrates that the proposed algorithm significantly outperforms the performance of a traditional recommender.

关键词： news recommendation algorithm ontology relevence feedback OF-IDF

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HDROP: Detecting ROP attacks using performance monitoring counters

HDROP: Detecting ROP attacks using performance monitoring co...

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10th International Conference on Information Security Practice and Experience, ISPEC 2014

作者： Zhou, Hongwei Wu, Xin Shi, Wenchang Yuan, Jinhui Liang, Bin Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Information Engineering University Zhengzhou China

ISBN: (纸本)9783319063195

Combining short instruction sequences originated only from existing code pieces, Return Oriented Programming (ROP) attacks can bypass the code-integrity effort model. To defeat this kind of attacks, current approaches check every instruction executed on a processor, which results in heavy performance overheads. In this paper, we propose an innovative approach, called HDROP, to detecting the attacks. It utilizes the observation that ROP attacks often make branch predictor in modern processors fail to determine the accurate branch destination. With the support of PMC (Performance Monitoring Counters) that is capable of counting performance events, we catch the abnormal increase in branch mis-prediction and detect the existence of ROP attacks. In HDROP, each basic unit being checked consists of hundreds of instructions rather than a single one, which effectively avoids significant performance overheads. The prototype system we developed on commodity hardware shows that HDROP succeeds in detecting ROP attacks, and the performance tests demonstrate that our approach has acceptably lower overheads. © 2014 Springer International Publishing.

关键词： Computers

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