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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Peng Li Xiaoming Wang Lichen Zhang Junling Lu Feng Zhang Zaobo He Key Laboratory Modern Teaching Technology Ministry of Education Xian China School of Computer Science Shaanxi Normal University Xian China Department of Computer Science Georgia State University Atlanta GA

ISBN: (纸本)9781509039371

Opportunistic Network has become an international hotspot in according with the popularity of various mobile intelligent terminals. But the nodes with limited communication capabilities cannot satisfy the growing of large content data transmission. Aim to these issues, we propose a novel communication capacity calculation model of Opportunistic Network based on the classical Random Direction mobile model. We observe that different size of fragments in large content represents different delivery delays. Considering that the large content are always the multimedia data, it would necessarily produce additional head-part of every fragment, and this will affect the whole overhead of network. We define the restrain facts model of overhead and put forward an optimal fragment size algorithm. Finally, we designed and evaluated the methods and algorithms in simulate circumstance of Opportunistic Networks. The experiments results show that the average value of contact duration and total times of contacts analyzed from logs are all in according with the contact duration expectation and contact times calculated from the model. The optimal size of fragment is located at the well position in the proper range of fragment size.

关键词： Mobile communication Mobile computing Big data Bandwidth Multimedia communication Social network services Mathematical model

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A second update on mapping the human genetic architecture of COVID-19

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Nature 2023年第7977期621卷 E7-E26页

作者： Masahiro Kanai Christine Stevens Benjamin M. Neale Mark Daly Andrea Ganna Amy Trankiem Mary K. Balaconis Samuli Ripatti Lindo Nkambul Mark J. Daly Sam Bryant Matthew Solomonson Shea J. Andrews Mattia Cordioli Juha Karjalainen Juha Mehtonen Matti Pirinen Andrea Corbetta Bradley Jermy Kati Donner Katja Kivinen Aarno Palotie Mari Kaunisto Kumar Veerapen Konrad J. Karczewski Alicia R. Martin Elizabeth G. Atkinson Kristin Tsuo Nikolas Baya Rahul Gupta Raymond K. Walters Duncan S. Palmer Gopal Sarma Nathan Cheng Wenhan Lu Claire Churchhouse Jacqueline I. Goldstein Daniel King Wei Zhou Cotton Seed Hilary Finucane F. Kyle Satterstrom Gita A. Pathak Akiko Iwasaki Karolina Chwialkowska Karolina Chwiałkowska Brooke N. Wolford Hajar Fauzan Ahmad Shea Andrews Kathrin Aprile von Hohenstaufen Puoti Palwende R. Boua Guillaume Butler-Laporte Celia M. T. Greenwood Bartha M. Knoppers Mark Lathrop Carmen L. Cadilla Francesca Colombo Nicole Dueker Chloe Kirk Atanu Kumar Dutta Yasser M. El-Sherbiny Jehan J. El-Jawhari Doaa Shahin Ahmed Elzeiny Tamer A. Mansour Madonna M. Eltoukhy Attia A. S. Mohamed Sahar Esmaeeli Annika Faucon Israel Fernández Cadenas Israel Fernandez-Cadenas Laia Llucià-Carol Natalia Cullell Elena Muiño Jara Cárcel-Márquez Margherita Francescatto Laurent Francioli Lindokuhle Nkambule Jatin Arora Soumya Raychaudhuri Caroline Cusick Lude Franke Macarena Fuentes Macarena Fuentes-Guajardo David Gómez Cabrero Emi N. Harry Philip Jansen József L. Szentpéteri Réka Várnai Elżbieta Kaja Athanasios Kousathanas Dorote Pasko Mark J. Caulfield Alex Stuckey Christopher A. Odhams Susan Walker Daniel Rhodes Richard H. Scott Loukas Moutsianas Afshan Siddiq Peter Goddard Sally Donovan Tala Zainy Fiona Maleady-Crowe Linda Todd Shahla Salehi Greg Elgar Georgia Chan Prabhu Arumugam Christine Patch Augusto Rendon Tom A. Fowler Jose E. Krieger Sanjay K. Patel Audrey Lemaçon Sophie Limou Pietro Lió Eirini Marouli Charles Hinds Minttu M. Marttila Carolina Medina-Gómez Isabelle Migeotte Soumyajit Mondal Andrés Moreno-Estrada Leire Moya Tomok Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Public Health Faculty of Medicine University of Helsinki Helsinki Finland Analytic & Translational Genetics Unit Massachusetts General Hospital Boston MA USA Stanley Center for Psychiatric Research & Program in Medical and Population Genetics Cambridge UK Institute for Molecular Medicine Finland (FIMM) Univerisity of Helsinki Helsinki Finland University of California San Francisco San Francisco CA USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Department of Psychiatry School of Medicine Yale University New Haven CT USA Veteran Affairs Connecticut Healthcare System West Haven CT USA Yale University New Haven CT USA Centre for Bioinformatics and Data Analysis Medical University of Bialystok Bialystok Poland Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Pahang Malaysia Icahn School of Medicine at Mount Sinai New York NY USA Genolier Innovation Network and Hub Swiss Medical Network Genolier Healthcare Campus Genolier Switzerland Immediate Independent Multidisciplinary Med & Biotech Research Association Milan Italy Clinical Research Unit of Nanor

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Expert System to Diagnosing Heart Attack Disease Using Certainty Factor Method

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

作者： Muhammad Iqbal Nurul Fatia Sara Surya Evi Susilawati Parwito Zefri Azharman Anita Dewi Moelyaningrum Rinandita Wikansari Andri Nugraha Arfriani Maifizar Nur Mukarromah Abdurrozzaq Hasibuan Dian Ade Kurnia I Ketut Sudarsana Department of Computer System Universitas Pembangunan Panca Budi Medan Indonesia Department of Pharmacy University of Dharma Andalas Padang Indonesia Faculty of Education and Teaching Training Universitas Islam Sumatera Utara MedanIndonesia Departement of Agrotechnology Universitas Ratu Samban Bengkulu Indonesia Department of Industrial Engineering University of Putera Batam Batam Indonesia Departement of Environmental Health and Ocupational Health and safety School of Public Health University of Jember Indonesia Department of International Trade for ASEAN and China Region Politeknik APP Jakarta Indonesia Department of Nursing STIKes Karsa Husada Garut Indonesia Department of Sociology Faculty of Social and Political Sciences Universitas Teuku Umar Meulaboh Indonesia Faculty of Health Science Universitas Muhammadiyah of Surabaya Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Departement Of Information Technology STMIK IKMI Cirebon Jawa Barat Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Indonesia

Coronary Heart Disease is one of many diseases that cause death and still being a health problem for both developed and developing countries. It is estimated to double in the next two decades, making it the biggest leading cause of death in 2020. The causes of coronary heart disease contain age and sex, with the incidence of males is more than in females but the incidence of females will increase after menopause (the end of the menstrual cycle) around the age of 50 years. This is due to estrogen (steroid compounds) have a protective effect on the onset of atherosclerosis. In this study the author wants to create a system that can detect heart disease by users before ensuring himself to the doctor to diagnose heart disease which is very dangerous. The system that the author will design used the certainty factor method to provide an assessment of the symptoms of user diagnosis. The application was designed with the desktop-based visual studio software assistance that is easy to use by everyone because it is familiar with the windows display design.

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Evolution and role of vacancy clusters at grain boundaries of ZnO:Al during accelerated degradation of Cu(In,Ga)Se2 solar cells revealed by positron annihilation

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Physical Review Materials 2018年第10期2卷 105403-105403页

作者： Wenqin Shi Mirjam Theelen Andrea Illiberi Nicolas Barreau Stefan J. van der Sar Maik Butterling Henk Schut Werner Egger Marcel Dickmann Christoph Hugenschmidt Miro Zeman Ekkes Brück Stephan W. H. Eijt Fundamental Aspects of Materials and Energy Faculty of Applied Sciences Delft University of Technology Mekelweg 15 NL-2629 JB Delft Netherlands TNO/Solliance Thin Film Technology High Tech Campus 21 NL-5656 AE Eindhoven Netherlands Institut des Matériaux Jean Rouxel (IMN)-UMR 6502 Université de Nantes CNRS 2 rue de la Houssinière BP 32229 F-44322 Nantes Cedex 3 France Neutron and Positron Methods in Materials Faculty of Applied Sciences Delft University of Technology Mekelweg 15 NL-2629 JB Delft Netherlands Institut für Angewandte Physik und Messtechnik Universität der Bundeswehr München D-85579 Neubiberg Germany Physics Department and Heinz Maier-Leibnitz Zentrum (MLZ) Technische Universität München D-85748 Garching Germany Photovoltaic Materials and Devices Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 NL-2628 CD Delft Netherlands

Positron annihilation lifetime spectroscopy (PALS) and Doppler broadening positron annihilation spectroscopy (DB-PAS) depth profiling demonstrate pronounced growth of vacancy clusters at the grain boundaries of as-deposited Al-doped ZnO films deposited as transparent conductive oxide (TCO) on Cu(In,Ga)Se2 (CIGS) solar cells upon accelerated degradation at 85∘C/85% relative humidity. Quantitative fractions of positrons trapped either in the vacancy clusters at the grain boundaries or in Zn monovacancies inside the grains of ZnO:Al were obtained by detailed analysis of the PALS data using a positron trapping model. The time and depth dependence of the positron Doppler depth profiles can be accurately described using a planar diffusion model, with an extracted diffusion coefficient of 35nm2/hour characteristic for in-diffusion of molecules such as H2O and CO2 into ZnO:Al TCO films via the grain boundaries, where they react with the ZnO:Al. This leads to increased open volume at the grain boundaries that imposes additional transport barriers and may lead to charge carrier trapping and nonradiative recombination. Simultaneously, a pronounced increase in series resistance and a strong reduction in efficiency of the ZnO:Al capped CIGS solar cells is observed on a remarkably similar timescale. This strongly indicates that these atomic-scale processes of molecular in-diffusion and creation of open volume at the grain boundaries play a key role in the degradation of the solar cells.

关键词： Diffusion Electrical properties Grain boundaries Optoelectronics Solid-state chemistry Vacancies Solar cells Thin films Positron annihilation spectroscopy

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Commissioning of the J-PET detector for studies of decays of positronium atoms

Commissioning of the J-PET detector for studies of decays of...

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作者： Czerwiński, E. Dulski, K. Bialas, P. Curceanu, C. Gajos, A. Glowacz, B. Gorgol, M. Hiesmayr, B.C. Jasińska, B. Kisielewska, D. Korcyl, G. Kowalski, P. Kozik, T. Krawczyk, N. Krzemień, W. Kubicz, E. Mohammed, M. Niedźwiecki, Sz. Palka, M. Pawlik-Niedźwiecka, M. Raczyński, L. Rudy, Z. Sharma, N.G. Sharma, S. Shopa, R.Y. Silarski, M. Skurzok, M. Wieczorek, A. Wiślicki, W. Zgardzińska, B. Zieliński, M. Moskal, P. Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Lojasiewicza 11 Kraków30-348 Poland INFN Laboratori Nazionali di Frascati CP 13 Via E. Fermi 40 Frascati00044 Italy Department of Nuclear Methods Institute of Physics Maria Curie-Sklodowska University pl. Marii Curie-Sklodowskiej 1 Lublin20-031 Poland Faculty of Physics University of Vienna Boltzmanngasse 5 Vienna1090 Austria Swierk Computing Center National Centre for Nuclear Research Otwock-Swierk05-400 Poland High Energy Physics Division National Center for Nuclear Research Otwock-Swierk05-400 Poland Department of Physics College of Education for Pure Sciences University of Mosul Mosul Iraq

The Jagiellonian Positron Emission Tomograph (J-PET) is a detector for medical imaging of the whole human body as well as for physics studies involving detection of electron-positron annihilation into photons. J-PET has high angular and time resolution, and allows for measurement of spin of the positronium and the momenta and polarization vectors of annihilation quanta. In this article, we present the potential of the J-PET system for the background rejection in the decays of positronium atoms.

关键词： Positrons

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Estimating and locating the volumes of multiple primary non-small cell stage-I bronchogenic carcinomas from medical images using specially devised morphological operations

Estimating and locating the volumes of multiple primary non-...

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International Conference on Information science (ICIS)

作者： Appudurai Ramalingam Somaskandan Suthakar Medical Physicist Teaching Hospital Jaffna Sri Lanka Department of Computer Science University of Jaffna Jaffna Sri Lanka

ISBN: (纸本)9781509019885

Lung tumour segmentation is one of the challenging tasks in medical image processing due to the high diversity in appearance of tumour tissue among different cases. In this paper, a novel morphological based semi-automatic method for estimating and locating the volumes of multiple primary non-small cell stage-I bronchogenic carcinomas in medical CT DICOM images is proposed. Various morphological image processing techniques along with a sequence of other image processing methods are used. Experiments are carried out on real datasets from various CT DICOM images of lung tumours. The results verify that this method is promising and accurate with the sensitivity of 93.3% and the specificity of 90.0%.

关键词： Computed tomography Lungs Cancer Tumors Image segmentation Morphology

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Beginning students may be less capable of proportional reasoning than they appear to be

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teaching MATHEMATICS AND ITS APPLICATIONS 2015年第1期34卷 26-34页

作者： Glaeser, Kathrin Riegler, Peter Center for successful teaching and learning (ZeLL) ‡Department of Computer Science Ostfalia University of Applied Sciences Germany

We analyse students' answers to a set of tasks designed to gain information about their ability to reason proportionally. These tasks have been particularly designed to control for false positive, i.e. that students arrive at the correct answer for the wrong reasons, an effect we actually observe with respect to students' ability to reason proportionally in a mathematics bridging course. We describe the rationale behind the test design and discuss implications for bridging courses and test designs in general.

关键词： Students

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Multi-modal multi-scale deep learning for large-scale image annotation

arXiv

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arXiv 2017年

作者： Niu, Yulei Lu, Zhiwu Wen, Ji-Rong Xiang, Tao Chang, Shih-Fu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China School of Electronic Engineering and Computer Science Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Department of Electrical Engineering Columbia University New YorkNY10027 United States

Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept;2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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A Cognitive Robot Control Architecture for Autonomous Execution of Surgical Tasks

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Journal of Medical Robotics Research 2016年第4期1卷

作者： Preda, Nicola Ferraguti, Federica De Rossi, Giacomo Secchi, Cristian Muradore, Riccardo Fiorini, Paolo Bonfè, Marcello Engineering Department University of Ferrara Italy Department of Science and Methods for Engineering University of Modena and Reggio Emilia Italy Department of Computer Science University of Verona Italy

The research on medical robotics is starting to address the autonomous execution of surgical tasks, without effective intervention of humans apart from supervision and task configuration. This paper addresses the complete automation of a surgical robot by combining advanced sensing, cognition and control capabilities, developed according to rigorous assessment of surgical requirements, formal specification of robotic system behavior and software design and implementation based on solid tools and frameworks. In particular, the paper focuses on the cognitive control architecture and its development process, based on formal modeling and verification methods as best practices to ensure safe and reliable behavior. Full implementation of the proposed architecture has been tested on an experimental setup including a novel robot specifically designed for surgical applications, but adaptable to different selected tasks (i.e. needle insertion, wound suturing). © 2016 World Scientific Publishing Company.

关键词： cognitive systems Surgical robotics task modeling

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Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study

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The Lancet. Planetary health 2021年第7期5卷 e415-e425页

作者： Qi Zhao Yuming Guo Tingting Ye Antonio Gasparrini Shilu Tong Ala Overcenco Aleš Urban Alexandra Schneider Alireza Entezari Ana Maria Vicedo-Cabrera Antonella Zanobetti Antonis Analitis Ariana Zeka Aurelio Tobias Baltazar Nunes Barrak Alahmad Ben Armstrong Bertil Forsberg Shih-Chun Pan Carmen Íñiguez Caroline Ameling César De la Cruz Valencia Christofer Åström Danny Houthuijs Do Van Dung Dominic Royé Ene Indermitte Eric Lavigne Fatemeh Mayvaneh Fiorella Acquaotta Francesca de'Donato Francesco Di Ruscio Francesco Sera Gabriel Carrasco-Escobar Haidong Kan Hans Orru Ho Kim Iulian-Horia Holobaca Jan Kyselý Joana Madureira Joel Schwartz Jouni J K Jaakkola Klea Katsouyanni Magali Hurtado Diaz Martina S Ragettli Masahiro Hashizume Mathilde Pascal Micheline de Sousa Zanotti Stagliorio Coélho Nicolás Valdés Ortega Niilo Ryti Noah Scovronick Paola Michelozzi Patricia Matus Correa Patrick Goodman Paulo Hilario Nascimento Saldiva Rosana Abrutzky Samuel Osorio Shilpa Rao Simona Fratianni Tran Ngoc Dang Valentina Colistro Veronika Huber Whanhee Lee Xerxes Seposo Yasushi Honda Yue Leon Guo Michelle L Bell Shanshan Li Department of Epidemiology School of Public Health Cheeloo College of Medicine Shandong University Jinan China Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia. Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia. Electronic address: yuming.guo@monash.edu. Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia. Department of Public Health Environments and Society London School of Hygiene and Tropical Medicine London UK Centre for Statistical Methodology London School of Hygiene and Tropical Medicine London UK Centre on Climate Change and Planetary Health London School of Hygiene and Tropical Medicine London UK. Shanghai Children's Medical Centre Shanghai Jiao Tong University Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China Center for Global Health Nanjing Medical University Nanjing China School of Public Health and Social Work Queensland University of Technology Brisbane QLD Australia. Laboratory of Management in Science and Public Health National Agency for Public Health of the Ministry of Health Chisinau Moldova. Institute of Atmospheric Physics Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic. Institute of Epidemiology Helmholtz Zentrum München-German Research Center for Environmental Health Neuherberg Germany. Faculty of Geography and Environmental Sciences Hakim Sabzevari University Sabzevar Iran. Institute of Social and Preventive Medicine University of Bern Bern Switzerland Oeschger Center for Climate Change Res

BACKGROUND:Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. methods:In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. FINDINGS:Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to a

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