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Book Review: Quantitative Methods and Socio-Economic Applications in GIS, 2nd edition, F. Wang, CRC Press, Boca Raton, FL, 301 pages, 2015, £76.99 $119.95 ISBN 9781466584723

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Applied Spatial Analysis and Policy 2015年第2期9卷 283-285页

作者： Higgs, Gary GIS Research Centre and Wales Institute of Social and Economic Research Data and Methods (WISERD) Faculty of Computing Engineering & Science University of South Wales Pontypridd UK

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A MapReduce Optimization Method on Hadoop Cluster

A MapReduce Optimization Method on Hadoop Cluster

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International Conference on Industrial Informatics - computing Technology, Intelligent Technology, Industrial Information Integration

作者： Xiaodong Wu Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

ISBN: (纸本)9781467383134

The MapReduce parallel and distributed computing framework has been widely applied in both academia and industry. MapReduce applications are divided into two steps: Map and Reduce. Then, the input data is divided into splits, which can be concurrently processed, and the amount of the splits determines the number of map tasks. In this paper, we present a regression-based method to compute the number of Map tasks as well as Reduce tasks such that the performance of the MapReduce application can be improved. The regression analysis is used to predict the executing time of MapReduce applications. Experimental results show that the proposed optimization method can effectively reduce the execution time of the applications.

关键词： MapReduce Hadoop Optimization Polynomial Regression

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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Unresolved Gamma-Ray Sky through its Angular Power Spectrum

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Physical Review Letters 2018年第24期121卷 241101-241101页

作者： M. Ackermann M. Ajello L. Baldini J. Ballet G. Barbiellini D. Bastieri R. Bellazzini E. Bissaldi R. D. Blandford R. Bonino E. Bottacini J. Bregeon P. Bruel R. Buehler E. Burns S. Buson R. A. Cameron R. Caputo P. A. Caraveo E. Cavazzuti S. Chen G. Chiaro S. Ciprini D. Costantin A. Cuoco S. Cutini F. D’Ammando P. de la Torre Luque F. de Palma A. Desai S. W. Digel N. Di Lalla M. Di Mauro L. Di Venere F. Fana Dirirsa C. Favuzzi A. Franckowiak Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Giglietto F. Giordano M. Giroletti D. Green I. A. Grenier L. Guillemot S. Guiriec D. Horan G. Jóhannesson M. Kuss S. Larsson L. Latronico J. Li I. Liodakis F. Longo F. Loparco P. Lubrano J. D. Magill S. Maldera D. Malyshev A. Manfreda M. N. Mazziotta I. Mereu P. F. Michelson W. Mitthumsiri T. Mizuno M. E. Monzani A. Morselli I. V. Moskalenko M. Negro E. Nuss M. Orienti E. Orlando M. Palatiello V. S. Paliya D. Paneque M. Persic M. Pesce-Rollins V. Petrosian F. Piron T. A. Porter G. Principe S. Rainò R. Rando M. Razzano S. Razzaque A. Reimer O. Reimer D. Serini C. Sgrò E. J. Siskind G. Spandre P. Spinelli D. J. Suson H. Tajima M. Takahashi J. B. Thayer L. Tibaldo D. F. Torres E. Troja T. M. Venters G. Vianello K. Wood M. Yassine G. Zaharijas S. Ammazzalorso N. Fornengo M. Regis Deutsches Elektronen Synchrotron DESY D-15738 Zeuthen Germany Department of Physics and Astronomy Clemson University Kinard Lab of Physics Clemson South Carolina 29634-0978 USA Università di Pisa and Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d’Astrophysique CEA Saclay F-91191 Gif sur Yvette France Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica e Astronomia “G. Galilei” Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari I-70126 Bari Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford California 94305 USA Istituto Nazionale di Fisica Nucleare Sezione di Torino I-10125 Torino Italy Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 I-35122 Padova Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 F-34095 Montpellier France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 F-91128 Palaiseau France NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA Center for Research and Exploration in Space Science and Technology (CRESST) and NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 I-20133 Milano Italy Italian Space Agency Via del Politecnico snc 00133 Roma Ita

The gamma-ray sky has been observed with unprecedented accuracy in the last decade by the Fermi -large area telescope (LAT), allowing us to resolve and understand the high-energy Universe. The nature of the remaining unresolved emission [unresolved gamma-ray background (UGRB)] below the LAT source detection threshold can be uncovered by characterizing the amplitude and angular scale of the UGRB fluctuation field. This Letter presents a measurement of the UGRB autocorrelation angular power spectrum based on eight years of Fermi-LAT Pass 8 data products. The analysis is designed to be robust against contamination from resolved sources and noise systematics. The sensitivity to subthreshold sources is greatly enhanced with respect to previous measurements. We find evidence (with ∼3.7σ significance) that the scenario in which two classes of sources contribute to the UGRB signal is favored over a single class. A double power law with exponential cutoff can explain the anisotropy energy spectrum well, with photon indices of the two populations being 2.55±0.23 and 1.86±0.15.

关键词： Gamma ray astronomy

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Single-dish and VLBI observations of Cygnus X-3 during the 2016 giant flare episode

arXiv

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arXiv 2017年

作者： Egron, E. Pellizzoni, A. Giroletti, M. Righini, S. Stagni, M. Orlati, A. Migoni, C. Melis, A. Concu, R. Barbas, L. Buttaccio, S. Cassaro, P. de Vicente, P. Nski, M.P. Gawro Lindqvist, M. Maccaferri, G. Stanghellini, C. Wolak, P. Yang, J. Navarrini, A. Loru, S. Pilia, M. Bachetti, M. Iacolina, M.N. Buttu, M. Corbel, S. Rodriguez, J. Markoff, S. Wilms, J. Pottschmidt, K. Bel, M. Cadolle Kalemci, E. Belloni, T. Grinberg, V. Marongiu, M. Vargiu, G.P. Trois, A. INAF Osservatorio Astronomico di Cagliari Via della Scienza 5 Selargius09047 Italy INAF Istituto di Radio Astronomia di Bologna Via P. Gobetti 101 Bologna40129 Italy Spain INAF Istituto di Radioastronomia Sezione di Noto Contrada Renna Bassa Noto96017 Italy Torú n Centre for Astronomy N. Copernicus University Gagarina 11 Torú n87-100 Poland Department of Earth and Space Sciences Chalmers University of Technology Onsala Space Observatory Onsala439 92 Sweden Agenzia Spaziale Italiana Via del Politecnico snc Roma00133 Italy Laboratoire AIM UMR 7158 CEA/CNRS Université Paris Diderot CEA DRF/IRFU/DAp Gif-sur-Yvette91191 France Station de Radioastronomie de Nançay Observatoire de Paris PSL Research University CNRS Univ. Orléans Nançay18330 France Anton Pannekoek Institute for Astronomy University of Amsterdam PO Box 94249 Amsterdam1090 GE Netherlands Friedrich Alexander Universität Erlangen-Nürnberg Sternwartstr. 7 Bamberg96049 Germany CRESST and NASA Goddard Space Flight Center Astrophysics Science Division Code 661 GreenbeltMD20771 United States Center for Space Science and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Max Planck Computing and Data Facility Garching85748 Germany Faculty of Engineering and Natural Sciences Sabancı University Orhanlı -Tuzla Istanbul34956 Turkey INAF Osservatorio Astronomico di Brera via E. Bianchi 46 Merate23807 Italy ESA/ESTEC Keplerlaan 1 Noordwijk2201 AZ Netherlands Department of Physics and Earth Sciences University of Ferrara via Saragat 1 Ferrara44122 Italy

In September 2016, the microquasar Cygnus X-3 underwent a giant radio flare, which was monitored for 6 days with the Medicina Radio Astronomical Station and the Sardinia Radio Telescope. Long observations were performed in order to follow the evolution of the flare on a hourly scale, covering six frequency ranges from 1.5 GHz to 25.6 GHz. The radio emission reached a maximum of 13.2 ± 0.7 Jy at 7.2 GHz and 10±1 Jy at 18.6 GHz. Rapid flux variations were observed at high radio frequencies at the peak of the flare, together with rapid evolution of the spectral index: α steepened from 0.3 to 0.6 (with Sν ∝ ν−α) within 5 hours. This is the first time that such fast variations are observed, giving support to the evolution from optically thick to optically thin plasmons in expansion moving outward from the core. Based on the Italian network (Noto, Medicina and SRT) and extended to the European antennas (Torun, Yebes, Onsala), VLBI observations were triggered at 22 GHz on five different occasions, four times prior to the giant flare, and once during its decay phase. Flux variations of 2-hour duration were recorded during the first session. They correspond to a mini-flare that occurred close to the core ten days before the onset of the giant flare. From the latest VLBI observation we infer that four days after the flare peak the jet emission was extended over 30 mas. Copyright © 2017, The Authors. All rights reserved.

关键词： Stars

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Search for gamma-ray emission from local primordial black holes with the Fermi large area telescope

arXiv

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arXiv 2018年

作者： Ackermann, M. Atwood, W.B. Baldini, L. Ballet, J. Barbiellini, G. Bastieri, D. Bellazzini, R. Berenji, B. Bissaldi, E. Blandford, R.D. Bloom, E.D. Bonino, R. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Charles, E. Chekhtman, A. Cheung, C.C. Chiaro, G. Ciprini, S. Cohen-Tanugi, J. Conrad, J. Costantin, D. D’Ammando, F. de Palma, F. Digel, S.W. Di Lalla, N. Di Mauro, M. Di Venere, L. Favuzzi, C. Fegan, S.J. Focke, W.B. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Guillemot, L. Guiriec, S. Horan, D. Jóhannesson, G. Johnson, C. Kensei, S. Kocevski, D. Kuss, M. Larsson, S. Latronico, L. Li, J. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Magill, J.D. Maldera, S. Malyshev, D. Manfreda, A. Mazziotta, M.N. McEnery, J.E. Meyer, M. Michelson, P.F. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Ojha, R. Omodei, N. Orienti, M. Orlando, E. Ormes, J.F. Palatiello, M. Paliya, V.S. Paneque, D. Persic, M. Pesce-Rollins, M. Piron, F. Principe, G. Rainò, S. Rando, R. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Ritz, S. Sánchez-Conde, M. Sgrò, C. Siskind, E.J. Spada, F. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Thayer, J.G. Thayer, J.B. Torres, D.F. Tosti, G. Troja, E. Valverde, J. Vianello, G. Wood, K. Wood, M. Zaharijas, G. Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany Santa Cruz Institute for Particle Physics Department of Physics Department of Astronomy and Astrophysics University of California at Santa Cruz Santa CruzCA95064 United States Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Laboratoire AIM CEA-IRFU CNRS Université Paris Diderot Service d’Astrophysique CEA Saclay Gif sur YvetteF-91191 France Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy California State University Los Angeles Department of Physics and Astronomy Los AngelesCA90032 United States Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France NASA Goddard Space Flight Center GreenbeltMD20771 United States INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy Italian Space Agency Via del Politecnico snc Roma00133 Italy College of Science George Mason University Fairfax Naval Researc

Black holes with masses below approximately 1015 g are expected to emit gamma rays with energies above a few tens of MeV, which can be detected by the Fermi Large Area Telescope (LAT). Although black holes with these masses cannot be formed as a result of stellar evolution, they may have formed in the early Universe and are therefore called Primordial Black Holes (PBHs). Previous searches for PBHs have focused on either short timescale bursts or the contribution of PBHs to the isotropic gamma-ray emission. We show that, in case of individual PBHs, the Fermi LAT is most sensitive to PBHs with temperatures above approximately 16 GeV and masses 6×1011 g, which it can detect out to a distance of about 0.03 pc. These PBHs have a remaining lifetime of months to years at the start of the Fermi mission. They would appear as potentially moving point sources with gamma-ray emission that becomes spectrally harder and brighter with time until the PBH completely evaporates. In this paper, we develop a new algorithm to detect the proper motion of a gamma-ray point sources, and apply it to 318 unassociated point sources at high galactic latitude in the third Fermi-LAT source catalog (3FGL). None of unassociated point sources with spectra consistent with PBH evaporation show significant proper motion. Using the non-detection of PBH candidates, we derive a 99% confidence limit on PBH evaporation rate in the vicinity of the Earth ρPBH 3 pc−3yr−1. This limit is similar to the limits obtained with ground-based gamma-ray observatories. Copyright © 2018, The Authors. All rights reserved.

关键词： Gamma rays

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SRC: Smart Reminder Clock

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IOP Conference Series: Materials science and Engineering 2016年第1期160卷

作者： Shahreen Kasim Hanayanti Hafit Tan Hua Leong Rathiah Hashim Husni Ruslai Kamaruzzaman Jahidin Mohammad Syafwan Arshad Soft Computing and data Mining Centre Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia GATES IT Solution Sdn Bhd Lot 4 Kompleks Usahawan Teknologi MARA Industrial Centre Technovation Park UTM Jalan Pontian Lama 81300 Skudai Johor Darul Ta'zim. Terap Consultancy Sdn Bhd No. 92 Jalan Waja Indah 2 Taman Waja Indah 09000 Kulim Kedah Darul Aman

Nowadays, some people facing the problem to wake up in the morning. This was result to absence of the classes, meetings, and even exams. The aim of this project is to develop an android application that can force the user to wake up. The method used in this application are pedometer and Short Message Service (SMS) function. This application need the user to take their smartphone and walk about 10 steps to disable it, when the alarm clock is activated. After that, when the alarm clock was rang, this alarm application has automatically send a message to the users' friends or parents phone to wake them up.

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BBIS: Beacon Bus Information System

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IOP Conference Series: Materials science and Engineering 2016年第1期160卷

作者： Shahreen Kasim Hanayanti Hafit Kong Pei Juin Zehan Afizah Afif Rathiah Hashim Husni Ruslai Kamaruzzaman Jahidin Mohammad Syafwan Arshad Soft Computing and data Mining Centre Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia GATES IT Solution Sdn Bhd Lot 4 Kompleks Usahawan Teknologi MARA Industrial Centre Technovation Park UTM Jalan Pontian Lama 81300 Skudai Johor Darul Ta'zim. Terap Consultancy Sdn Bhd No. 92 Jalan Waja Indah 2 Taman Waja Indah 09000 Kulim Kedah Darul Aman

Lack of bus information for example bus timetable, status of the bus and messy advertisement on bulletin board at the bus stop will give negative impact to tourist. Therefore, a real-time update bus information bulletin board provides all information needed so that passengers can save their bus information searching time. Supported with Android or iOS, Beacon Bus Information System (BBIS) provides bus information between Batu Pahat and Kluang area. BBIS is a system that implements physical web technology and interaction on demand. It built on Backend-as-a-Service, a cloud solution and Firebase non relational database as data persistence backend and syncs between user client in the real-time. People walk through bus stop with smart device and do not require any application. Bluetooth Beacon is used to achieve smart device's best performance of data sharing. Intellij IDEA 15 is one of the tools that that used to develop the BBIS system. Multi-language included front end and backend supported Integration development environment (IDE) helped to speed up integration process.

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