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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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Link prediction of LOD by multiple label propagation algorithm considering semantic distance

Link prediction of LOD by multiple label propagation algorit...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Toshitaka Maki Kazuki Takahashi Akihiro Yamaguchi Toshihiko Wakahara Toru Kobayashi Akihisa Kodate Noboru Sonehara Intelligent Information System Engineering Fukuoka Institute of Technology FIT Fukuoka Japan Communication & Information Networking Fukuoka Institute of Technology FIT Fukuoka Japan Information and Systems Engineering Fukuoka Institute of Technology FIT Fukuoka Japan Computer and Information Sciences Program Nagasaki University Nagasaki Univ. Nagasaki Japan Department of Computer Science Tsuda College Tsuda Tokyo Japan Information and Society Research Division National Institute of Informatics NII Tokyo Japan

ISBN: (纸本)9781509023349

This paper presents link prediction of Linked Open Data (LOD) by Multiple Label Propagation Algorithm (MLPA). The current LOD do not have enough links. Therefore, the LOD have not been able to exert so much semantic characteristics. In order to solve this problem, we proposed the MLPA considering semantic distance. The MLPA can expand potential links of each data contained in the LOD. The experimental result of the MLPA shows the good performance and the validity of this algorithm is confirmed.

关键词： Prediction algorithms Semantics Metadata Algorithm design and analysis Conferences Consumer electronics Resource description framework

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Steganography algorithm multi pixel value differencing (MPVD) to increase message capacity and data security

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AIP Conference Proceedings 2017年第1期1867卷

作者： Rojali Ida Sri Rejeki Siahaan Benfano Soewito aMathematics Department School Of Computer Sciences Bina Nusantara University Jakarta 11480 Indonesia bComputer Science Program School Of Computer Sciences Bina Nusantara University Jakarta 11480 Indonesia cMagister of Information Technology Bina Nusantara University Jakarta 11530 Indonesia

Steganography is the art and science of hiding the secret messages so the existence of the message cannot be detected by human senses. The data concealment is using the Multi Pixel Value Differencing (MPVD) algorithm, utilizing the difference from each pixel. The development was done by using six interval tables. The objective of this algorithm is to enhance the message capacity and to maintain the data security.

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Designing the Application of Security Text Messages Into Audio Files Using Data Encryption Standard (DES) Algorithms Using the End Of File (EOF) Method

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Journal of Physics: Conference Series 2019年第1期1363卷 012078-012078页

作者： Pardede, A M H Anggi Pramana Zarlis, Muhammad Iskandar, Akbar Manurung, Rosida Tiurma Sriadhi, S Kurniawan, Citra Hasibuan, Abdurrozzaq Nurintan Asyiah Siregar Sari, Ayu Esteka Mulyaningsih, Indrya Teguh Hidayat Iskandar Alam Ibrahim Listyorini, Tri Winarno, Edy Tulus STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Department of Informatics Engineering Universitas Muhammadiyah Sorong Sorong Indonesia Department of English Education Universitas Muhammadiyah Sorong Sorong Indonesia Department of Informatics Universitas Muria Kudus Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia

There are several ways to deal with security issues of confidential data sent via the internet, including using cryptographic techniques and steganography. Steganography is the science and art of concealing information / messages in a media in such a way that its existence is not detected by other parties who are not entitled to the information. Instead, cryptography disguises the meaning of a message, but does not hide that there is a message because the file looks suspicious. The Steganography technique used here was End Of File (EOF). The EOF technique described how to add data or files at the end of the image file. For this technique, the size of data or files that will be hidden could be larger than the size of the image file. The hidden data will be inserted at the end of the file so that it will not affect the image. This steganography application is also equipped with a cryptographic function Data Encryption Standard (DES) at the time of insertion of data that functions as a generator code and encrypts data, so that the security of a data in the file is more protected and protected from those who are not entitled to know the data.

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Byzantine fault tolerance for collaborative editing with commutative operations

Byzantine fault tolerance for collaborative editing with com...

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2016 IEEE International Conference on Electro information technology, EIT 2016

作者： Zhao, Wenbing Babi, Mamdouh William, Yang Luo, Xiong Zhu, Yueqin Jack, Yang Chaomin, Luo Yang, Mary Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States Texas Advanced Computing Center University of Texas at Austin 10100 Burnet Road AustinTX78758 United States School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Development Research Center of China Geological Survey Ministry of Land and Resources Beijing100037 China Division of Biostatistics and Biomathematics Massachusetts General Hospital and Harvard Medical School BostonMA United States Department of Electrical and Computer Engineering University of Detroit Mercy DetroitMI48221 United States Department of Information Science George Washington Donaghey College of Engineering and Information Technology Joint Bioinformatics Program of University of Arkansas at Little Rock University of Arkansas for Medical Sciences 2801 S. University Avenue Little RockAR72204 United States

ISBN: (纸本)9781467399852

In this paper, we present a study on how to achieve Byzantine fault tolerance for collaborative editing systems with commutative operations. Recent research suggests that Conflict-free Replicated Data Types (CRDTs) can be used to construct collaborative editing systems where concurrent update operations are commutative. This new approach is shown to avoid the complex issue of conflict resolution for concurrent updates to a shared document. The shared document is often modeled as a linear text buffer where each basic element is assigned a globally unique and totally ordered identifier. The linear text buffer constructed this way would constitute as a CRDT, which would make concurrent update operations issued by different users commutative. State convergence at all users can be achieved automatically as long as the users could receive the same set of operations irrespective of their relative ordering. However, it is not straightforward to guarantee state convergence in the presence of malicious users and external adversaries. In this paper, we carefully analyze the threats towards this type of systems, and propose a lightweight solution to achieve Byzantine fault tolerance with low runtime overhead. We define a set of correctness properties for such systems and prove that the proposed Byzantine fault tolerance mechanisms guarantee these properties. © 2016 IEEE.

关键词： Fault tolerance

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Gene classification of dengue virus type based on codon usage

Gene classification of dengue virus type based on codon usag...

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International computer science and Engineering Conference (ICSEC)

作者： Panuwat Mekha Khukrit Osathanunkul Nutnicha Teeyasuksaet Program of Computer Science Maejo University Chiang Mai Thailand Department of Information Technology Payap University Chiang Mai Thailand The Fifth Regional Livestock Office Department of Livestock Development Chiang Mai Thailand

Dengue virus infection or dengue fever is caused by the dengue virus (DENV). It is transmitted to humans by mosquitoes. There are four serotypes classified together based on their surface antigens. Each serotype can provide specific immunity and short-term cross-immunity in human. Several studies have examined the classification of dengue molecules into four major classes including methods such as machine learning using codon usage as features. In this work we directly classify dengue molecules using their primary sequences. Thus, we have compared different methods for data classification to classify sequences of dengue molecules. The method was tested on 372 dengue sequences from major classes. Using a tenfold cross-validation, the neural network yields 96.22% of prediction accuracy for classifying dengue classes.

关键词： Support vector machines Artificial neural networks Kernel Radio frequency Niobium Decision trees

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Author Correction: The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics

npj biodiversity

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npj biodiversity 2024年第1期3卷 31页

作者： Ann M Mc Cartney Giulio Formenti Alice Mouton Diego De Panis Luísa S Marins Henrique G Leitão Genevieve Diedericks Joseph Kirangwa Marco Morselli Judit Salces-Ortiz Nuria Escudero Alessio Iannucci Chiara Natali Hannes Svardal Rosa Fernández Tim De Pooter Geert Joris Mojca Strazisar Jonathan M D Wood Katie E Herron Ole Seehausen Phillip C Watts Felix Shaw Robert P Davey Alice Minotto José M Fernández Astrid Böhne Carla Alegria Tyler Alioto Paulo C Alves Isabel R Amorim Jean-Marc Aury Niclas Backstrom Petr Baldrian Laima Baltrunaite Endre Barta Bertrand BedHom Caroline Belser Johannes Bergsten Laurie Bertrand Helena Bilandija Mahesh Binzer-Panchal Iliana Bista Mark Blaxter Paulo A V Borges Guilherme Borges Dias Mirte Bosse Tom Brown Rémy Bruggmann Elena Buena-Atienza Josephine Burgin Elena Buzan Alessia Cariani Nicolas Casadei Matteo Chiara Sergio Chozas Fedor Čiampor Angelica Crottini Corinne Cruaud Fernando Cruz Love Dalen Alessio De Biase Javier Del Campo Teo Delic Alice B Dennis Martijn F L Derks Maria Angela Diroma Mihajla Djan Simone Duprat Klara Eleftheriadi Philine G D Feulner Jean-François Flot Giobbe Forni Bruno Fosso Pascal Fournier Christine Fournier-Chambrillon Toni Gabaldon Shilpa Garg Carmela Gissi Luca Giupponi Jessica Gomez-Garrido Josefa González Miguel L Grilo Björn Grüning Thomas Guerin Nadege Guiglielmoni Marta Gut Marcel P Haesler Christoph Hahn Balint Halpern Peter W Harrison Julia Heintz Maris Hindrikson Jacob Höglund Kerstin Howe Graham M Hughes Benjamin Istace Mark J Cock Franc Janžekovič Zophonias O Jonsson Sagane Joye-Dind Janne J Koskimäki Boris Krystufek Justyna Kubacka Heiner Kuhl Szilvia Kusza Karine Labadie Meri Lähteenaro Henrik Lantz Anton Lavrinienko Lucas Leclère Ricardo Jorge Lopes Ole Madsen Ghislaine Magdelenat Giulia Magoga Tereza Manousaki Tapio Mappes Joao Pedro Marques Gemma I Martinez Redondo Florian Maumus Shane A McCarthy Hendrik-Jan Megens Jose Melo-Ferreira Sofia L Mendes Matteo Montagna Joao Moreno Mai-Britt Mosbech Mónica Moura Zuzana Musilova Eugene Myers Will J N Genomics Institute University of California Santa Cruz CA USA. anmmccar@ucsc.edu. The Vertebrate Genome Laboratory The Rockefeller University New York NY USA. Department of Biology University of Florence Sesto Fiorentino Italy. InBios-Conservation Genetics Laboratory University of Liege Liege Belgium. Leibniz Institut für Zoo und Wildtierforschung Berlin Germany. Berlin Center for Genomics in Biodiversity Research Berlin Germany. Department of Biology University of Antwerp Antwerp Belgium. Institute of Zoology University of Cologne Cologne Germany. Department of Chemistry Life Sciences and Environmental Sustainability University of Parma Parma Italy. Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra) Barcelona Spain. Naturalis Biodiversity Center Leiden The Netherlands. Neuromics Support Facility VIB Center for Molecular Neurology VIB Antwerp Belgium. Neuromics Support Facility Department of Biomedical Sciences University of Antwerp Antwerp Belgium. Tree of Life Wellcome Sanger Institute Hinxton Cambridge UK. School of Biology and Environmental Science University College Dublin Belfield Ireland. Aquatic Ecology & Evolution Institute of Ecology & Evolution University of Bern Bern Switzerland. Department of Fish Ecology & Evolution Eawag Kastanienbaum Switzerland. Department of Biological and Environmental Science University of Jyvaskyla Jyvaskyla Finland. The Earlham Institute Norwich Research Park Norwich UK. Digital Science London UK. Barcelona Supercomputing Center Spanish National Bioinformatics Institute ELIXIR Spain Getafe Spain. Leibniz Institute for the Analysis of Biodiversity Change Museum Koenig Bonn Bonn Germany. CE3C-Centre for Ecology Evolution and Environmental Changes & CHANGE-Global Change and Sustainability Institute Faculdade de Ciências Universidade de Lisboa Campo Grande Lisboa Portugal. Centro Nacional de Análisis Genómico (CNAG) Barcelona Spain. Universitat de Barcelona (UB) Barcelona Spain. CIBIO C

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Retraction: LUC Algorithm in Visual Cryptography (J. Phys.: Conf. Ser. 1114 012090)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Ratnadewi Christian Dio Alpha G Darmawan Napitupulu Heri Nurdiyanto Nuning Kurniasih Abdurrozaq Hasibuan GS Achmad Daengs Rosida Tiurma Manurung Indrya Mulyaningsih Robbi Rahim Castaka Agus Sugianto Dahlan Abdullah Department of Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Banten Indonesia STMIK Dharma Wacana Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Universitas Islam Sumatera Utara Indonesia University of 45 Surabaya Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Bandung Indonesia Department of Education Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Politeknik TEDC Bandung Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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LUC Algorithm in Visual Cryptography

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Journal of Physics: Conference Series 2018年第1期1114卷

Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero.

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