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IEEE International Conference on system Engineering and technology

作者： Nor Bakiah Abd Warif Mohd. Yamani Idna Idris Ainuddin Wahid Abdul Wahab Rosli Salleh Department of Computer System and Technology Faculty of Computer Science & Information Technology University of Malaya

ISBN: (纸本)9781467367141

The advancement in digital image tampering has encouraged studies in the image forensics fields. The image tampering can be found over various image formats such as Joint Photographic Experts Group (JPEG). JPEG is the most common format that supported by devices and applications. Therefore, researchers have been studying the implementation of JPEG algorithm in the image forensics. In this paper, the Error Level Analysis (ELA) technique was evaluated with different types of image tampering, including JPEG compression, image splicing, copy-move and image retouching. From the experiment, the ELA showed reliability with JPEG compression, image splicing and image retouching forgery.

关键词： Image forensics JPEG compression Error level analysis FORENSIC PHOTOGRAPHY JPEG image format Imagery (Psychotherapy) Image images Retouching

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, 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 Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Transparent solar antenna of 28 GHz using transparent conductive oxides (TCO) thin film

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Journal of Physics: Conference Series 2017年第1期852卷

作者： N I Mohd Ali N Misran M F Mansor M F Jamlos Department of Electrical Electronic and System Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia Faculty of Engineering Technology Universiti Malaysia Perlis Kampus UniCITI Alam Sungai Chuchuh 02100 Padang Besar Perlis Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Kampus Tetap Pauh Putra 02600 Arau Perlis Malaysia

This paper presents the analysis of 28GHz solar patch antenna using the variations of transparent conductive oxides (TCO) thin film as the radiating patch. Solar antenna is basically combining the function of antenna and solar cell into one device and helps to maximize the usage of surface area. The main problem of the existing solar antenna is the radiating patch which made of nontransparent material, such as copper, shadowing the solar cell and degrades the total solar efficiency. Hence, by using the transparent conductive oxides (TCO) thin film as the radiating patch, this problem can be tackled. The TCO thin film used is varied to ITO, FTO, AgHT-4, and AgHT-8 along with glass as substrate. The simulation of the antenna executed by using computer Simulation technology (CST) Microwave Studio software demonstrated at 28 GHz operating frequency for 5G band applications. The performance of the transparent antennas is compared with each other and also with the nontransparent patch antenna that using Rogers RT5880 as substrate, operating at the same resonance frequency and then, the material that gives the best performance is identified.

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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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Enterotypes in the landscape of gut microbial community composition (vol 3, pg 8, 2017)

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NATURE MICROBIOLOGY 2018年第3期3卷 388-388页

作者： Costea, Paul I. Hildebrand, Falk Arumugam, Manimozhiyan Backhed, Fredrik Blaser, Martin J. Bushman, Frederic D. de Vos, Willem M. Ehrlich, S. Dusko Fraser, Claire M. Hattori, Masahira Huttenhower, Curtis Jeffery, Ian B. Knights, Dan Lewis, James D. Ley, Ruth E. Ochman, Howard O'Toole, Paul W. Quince, Christopher Relman, David A. Shanahan, Fergus Sunagawa, Shinichi Wang, Jun Weinstock, George M. Wu, Gary D. Zeller, Georg Zhao, Liping Raes, Jeroen Knight, Rob Bork, Peer European Molecular Biology Laboratory Heidelberg Germany. VIB Center for Microbiology VIB Belgium. Laboratory of Microbiology Vrije Universiteit Brussel Brussels Belgium. The Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark. Wallenberg Laboratory Department of Molecular and Clinical Medicine Institute of Medicine Sahlgrenska Academy University of Gothenburg Gothenburg Sweden. Novo Nordisk Foundation Center for Basic Metabolic Research Section for Metabolic Receptology and Enteroendocrinology Faculty of Health Sciences University of Copenhagen Copenhagen Denmark. New York University Langone Medical Center New York NY USA. Department of Microbiology Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA. RPU Immunobiology Department of Bacteriology & Immunology University of Helsinki Helsinki Finland. Laboratory of Microbiology Wageningen University Wageningen The Netherlands. Metagenopolis Institut National de la Recherche Agronomique Jouy en Josas France. King's College London Centre for Host-Microbiome Interactions Dental Institute Central Office Guy's Hospital London UK. Institute for Genome Sciences at the University of Maryland School of Medicine Baltimore MD USA. Graduate School of Advanced Science and Engineering Waseda University Tokyo Japan. Department of Biostatistics Harvard School of Public Health Boston MA USA. APC Microbiome Institute University College Cork Cork Ireland. Biotechnology Institute University of Minnesota Saint Paul MN USA. Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA. Center for Clinical Epidemiology and Biostatistics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. MPI Department of Microbiome Science Tübingen Germany. Department of Integrative Biology University of Texas Austin TX USA. Warwick Medical School University

Population stratification is a useful approach for a better understanding of complex biological problems in human health and wellbeing. The proposal that such stratification applies to the human gut microbiome, in the form of distinct community composition types termed enterotypes, has been met with both excitement and controversy. In view of accumulated data and re-analyses since the original work, we revisit the concept of enterotypes, discuss different methods of dividing up the landscape of possible microbiome configurations, and put these concepts into functional, ecological and medical contexts. As enterotypes are of use in describing the gut microbial community landscape and may become relevant in clinical practice, we aim to reconcile differing views and encourage a balanced application of the concept.

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LifeTime and improving European healthcare through cell-based interceptive medicine (vol 587, pg 377, 2020)

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NATURE 2021年第7852期592卷 E8-E8页

作者： Rajewsky, Nikolaus Almouzni, Genevieve Gorski, Stanislaw A. Aerts, Stein Amit, Ido Bertero, Michela G. Bock, Christoph Bredenoord, Annelien L. Cavalli, Giacomo Chiocca, Susanna Clevers, Hans De Strooper, Bart Eggert, Angelika Ellenberg, Jan Fernandez, Xose M. Figlerowicz, Marek Gasser, Susan M. Hubner, Norbert Kjems, Jorgen Knoblich, Jurgen A. Krabbe, Grietje Lichter, Peter Linnarsson, Sten Marine, Jean-Christophe Marioni, John C. Marti-Renom, Marc A. Netea, Mihai G. Nickel, Dorthe Nollmann, Marcelo Novak, Halina R. Parkinson, Helen Piccolo, Stefano Pinheiro, Ines Pombo, Ana Popp, Christian Reik, Wolf Roman-Roman, Sergio Rosenstiel, Philip Schultze, Joachim L. Stegle, Oliver Tanay, Amos Testa, Giuseppe Thanos, Dimitris Theis, Fabian J. Torres-Padilla, Maria-Elena Valencia, Alfonso Vallot, Celine van Oudenaarden, Alexander Vidal, Marie Voet, Thierry Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Charité-Universitätsmedizin Berlin Germany Berlin Institute of Health (BIH) Berlin Germany German Center for Cardiovascular Research (DZHK) Partner Site Berlin Berlin Germany Institute for Biology Humboldt University of Berlin Berlin Germany Cardiovascular and Metabolic Sciences Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany Institut Curie CNRS PSL Research University Sorbonne Université Nuclear Dynamics Unit Equipe Labellisée Ligue contre le cancer Paris France VIB Center for Brain and Disease Research Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Department of Neurosciences KU Leuven Leuven Belgium UK Dementia Research Institute at UCL University College London London UK Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK Department of Immunology Weizmann Institute of Science Rehovot Israel Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology Barcelona Spain CNAG-CRG Centre for Genomic Regulation Barcelona Institute of Science and Technology Barcelona Spain Universitat Pompeu Fabra Barcelona Spain ICREA Barcelona Spain CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Department of Laboratory Medicine Medical University of Vienna Vienna Austria Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases Vienna Austria Department of Medical Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Institute of Human Genetics UMR 9002 CNRS and University of Montpellier Montpellier France Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy Department of Oncology and Hemato

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RETRACTED ARTICLE: Support vector regression methodology for prediction of output energy in rice production

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Stochastic Environmental Research and Risk Assessment 2015年第8期29卷 2115-2126页

作者： Yousefi, Marziye Khoshnevisan, Benyamin Shamshirband, Shahaboddin Motamedi, Shervin Md. Nasir, Mohd Hairul Nizam Arif, Muhammad Ahmad, Rodina Department of Agricultural Machinery Engineering Faculty of Agricultural Engineering and Technology University of Tehran Karaj Iran Department of Computer System and Information Technology Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Institute of Ocean and Earth Sciences (IOES) University of Malaya Kuala Lumpur Malaysia Department of Software Engineering Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia

The increase in world population has led to a significant increase in food demand throughout the world, so agricultural policy makers in all countries try to estimate their annual food requirements in advance in order to provide food security for their people. In order to achieve this goal, this study developed a novel predictive model based on the energy inputs employed during the production season. Rice caters more than 30 % of the calorie requirement for the Asian countries. In Iran too rice is one of the most important agricultural products. Therefore, objective of this study was to develop a model based on artificial intelligence for predicting the output energy in rice production. Such a model could help farmers and policy makers. This model employed the polynomial and radial basis function (RBF) as the kernel function for support vector regression (SVR). The input energies from different sources used during rice production were given as the inputs to the model, and the output energy was chosen as the output of the model. In order to achieve generalized performance, SVRpoly and SVRrbf tried to minimize the generalization error bound, instead of minimizing the training error. The results show that the proposed model improves the predictive accuracy and capability of generalization. Results show that SVRs can serve as a promising alternative for existing prediction models.

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Can electric devices be identified from their signatures of waveform? 12

Can electric devices be identified from their signatures of ...

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2015 12th Annual IEEE Consumer Communications and Networking Conference, CCNC 2015

作者： Kushiro, Noriyuki Ide, Taichi Tomonaga, Kazuki Ogawa, Yuki Higuma, Toshiyasu Faculty of Computer Science and System Engineering Kyushu Institute of Technology KIT Iizuka Fukuoka Japan Living Environment Systems Laboratory Mitsubishi Electric Corp. Kamakura Kanagawa Japan

ISBN: (纸本)9781479963904

Life event sensor is an activity recognition sensor both for measuring electric power consumption and for detecting usage of electronic devices by utilizing a high frequency current waveform as a signature of each device. To discuss the feasibility of the sensor, we have constructed a long-term field data collection system and installed the system in five occupied houses. Discussion about feasibility of the life event sensor on long-term field data and experimental data on a test bed, which can simulate several conditions of power line, is described in this paper. © 2015 IEEE.

关键词： Information management

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