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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer Science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Ginting, Tinus Manurung, Hotler Harahap, Astro Julida Ongko, Erianto Hartono, H. Abdullah, Dahlan Erliana, Cut Ita Saddhono, Kundharu Sriadhi, S. Supriyono, S. Iskandar, Akbar Saleh, A.A. Sudarsana, I. Ketut Department of Mechanical Engineering Akademi Teknologi Industri Immanuel Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Statistics Financial and Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia

Based on the formulation of the problem to be discussed by the writer is to know: the influence of 0.4 mm spark gap variation, and standard spark plug type, to CO and HC, the influence of 0.5 mm spark plug gap variation, and variation of platinum spark plug on CO and HC, influence of spark gap variation 0,6mm, and variation of iridium spark plug type to CO and HC emission Method used to obtain data needed to support the writer that is research method with experiment, literature study method or literature study The result of this research concludes that there is spark gap influence 0.4 mm, with variations of standard plug, platinum and iridium spark plugs against CO and HC emissions, where the lowest CO value is 0.27% and HC 98 ppm, 0.5 mm spark plug with lowest CO 0.15% and HC 46 ppm, 0.6 mm spark plug gap the lowest CO value of 0.19% and HC 24 ppm. The results of this research will be disseminated using the website. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach

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Physical Review B 2019年第20期100卷 205411-205411页

作者： V. Torres P. Silva E. A. T. de Souza L. A. Silva D. A. Bahamon Instituto de Física Universidade Federal Fluminense Niterói Av. Litorânea sn 24210-340 RJ-Brazil MackGraphe – Graphene and Nano-Materials Research Center Mackenzie Presbyterian University Rua da Consolação 896 01302-907 São Paulo SP Brazil School of Computing and Informatics & Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Rua da Consolação 896 01302-907 São Paulo SP Brazil

The valley transport properties of a superlattice of out-of-plane Gaussian deformations are calculated using a Green's function and a machine learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation; these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counterpropagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make it difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a deep neural network can be trained to predict valley polarization with a precision similar to the Green's function but with much less computational effort.

关键词： Ballistic transport Quantum transport Strain Graphene Landauer formula Machine learning Tight-binding model

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A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems 15

A Neuro-Inspired Approach to Solve a Simultaneous Location a...

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15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

作者： Menezes, Matheus Chaves De Freitas, Edison Pignaton Cheng, Sen De Oliveira, Alexandre Cesar Muniz De Almeida Ribeiro, Paulo Rogerio Graduate Program on Computer Science Federal University of Maranhão Sao Luis Brazil Computational Neuroscience Ruhr University Bochum Institute for Neural Computation Bochum Germany Department of Informatics Federal University of Maranhão Sao Luis Maranhao Brazil Department of Computer Engineering Federal University of Maranhão Sao Luis Maranhao Brazil

ISBN: (纸本)9781538695821

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots. © 2018 IEEE.

关键词： Mapping

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Cloud computing model in higher education

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IOP Conference Series: Materials Science and engineering 2020年第1期852卷

作者： Bagus Mulyawan Raymond Kosala Benny Ranti Suhono Harso Supangkat Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Universitas Tarumanagara Jakarta 11440 Computer Science Department Faculty of Computer Science Universitas Indonesia Indonesia 16424 Schoolof Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

Currently, higher education is institutions that should be managed as a global business institution. Higher education should improve continually its business processes according to the demands of increasing its stakeholders. of the problems faced in the development of educational, activities if related to the use of information technology for each service in higher education for stakeholders is the issue of available funds. Cloud computing also has advantages related to infrastructure and maintenance and increases operating efficiency. Most institutions are currently moving towards cloud computing technology to reduce operational costs. For achieving these objectives, the institutions need to be supported by reliable technology. This study aim to propose cloud model for education and IT Governance conceptual framework for Cloud Computing model adoption, Tarumanagara University as a study case.

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A Blockchain Approach for the Future Renewable Energy Transaction

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： Sandi Rahmadika Diena Rauda Ramdania Maisevli Harika Interdisciplinary Program of Information Security Graduate School PKNU Pukyong National University Busan South Korea Informatics Engineering UIN Sunan Gunung Djati Bandung Indonesia Computer Engineering and Informatics Politeknik Negeri Bandung Indonesia

Blockchain technology holds great promise to rewire the current financial system which relies on the third party. Every transaction is recorded in a secure and transparent for the parties in the blockchain network. Blockchain technology in the energy sector becomes an interesting topic among researchers at this moment. The use of blockchain allows producers and consumers to trade energy transactions through smart grids because of a decentralized energy trading system. The trading activities without a third party involved would reduce the cost of a transaction thus it brings to a new level of quality of service in the trading system. In this paper, we propose an architectural model for decentralized energy trading system among the neighbors that allows the producer who has the surplus energy to conduct a trading activity with his/her neighbors. The transactions manage by the miners in the same blockchain network. Moreover, we analyze the security issues from various attacks and presenting the performance of the selected attack that might occur in the model.

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Adverse events following the first, second and third doses of a COVID-19 vaccine in hemodialysis patients

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Renal failure 2023年第1期45卷 2172432页

作者： Mei-Fen Pai Kuei-Ting Tung Shih-Ping Hsu Yu-Sen Peng Wan-Yu Lin Ju-Yeh Yang Hon-Yen Wu Yen-Ling Chiu Kai-Hsiang Shu Wan-Chuan Tsai Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan. Department of Electrical Engineering Yuan Ze University Taoyuan City Taiwan. Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan. Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. Faculty of Medicine School of Medicine National Yang-Ming University Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan.

BACKGROUND:This study aimed to identify adverse events following the first three doses of COVID-19 vaccines in hemodialysis (HD) patients. Risk factors associated with postvaccination adverse events were explored. METHODS:Postvaccination adverse events in 438 HD patients who received 3 doses of COVID-19 vaccines were prospectively assessed. The adverse events among three doses were compared using generalized linear mixed models. Factors associated with adverse events were assessed with multivariate analyses. RESULTS:The vast majority of participants received Oxford/AstraZeneca ChAdOx1 as their first two doses and Moderna mRNA-1273 as their third dose. Overall, 79%, 50% and 84% of the participants experienced at least one adverse event after their first, second, and third doses, respectively. These adverse events were mostly minor, short-lived and less than 5% reported daily activities being affected. Compared with the first dose, the second dose caused a lower rate of adverse events. Compared with the first dose, the third dose elicited a higher rate of injection site reactions and a lower rate of systemic reactions. Multivariate analyses showed that every 10-year increase of age (odds ratio 0.67, 95% confidence intervals 0.57-0.79) was associated with decreased risk of adverse events, while female sex (2.82, 1.90-4.18) and arteriovenous fistula (1.73, 1.05-2.84) were associated with increased risk of adverse events. Compared with Oxford/AstraZeneca ChAdOx1, Moderna mRNA-1273 was associated with an increased risk of injection site reactions. CONCLUSIONS:COVID-19 vaccination was well tolerated in HD patients. Age, sex, dialysis vascular access and vaccine types were associated with postvaccination adverse events.

关键词： COVID-19 ChAdOx1 Hemodialysis adverse event mRNA-1273 vaccination

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Rainfall forecasting using PSPline and rice production with ocean-atmosphere interaction 2

Rainfall forecasting using PSPline and rice production with ...

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2nd International Conference on Eco engineering Development 2018, ICEED 2018

作者： Caraka, Rezzy Eko Supatmanto, Budi Darmawan Tahmid, Muhammad Soebagyo, Joko Mauludin, M. Ali Iskandar, Akbar Pardamean, Bens Bina Nusantara University Jakarta11480 Indonesia Indonesia Indonesia Agency for Meteorology Climatology and Geophysics Indonesia College of Technology Wastukancana Purwakarta Indonesia Laboratory of Sociology and Extension Padjadjaran University Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The role of climate can be affected by plants. The weather can accelerate and multiply the existence of various plant pests and diseases, accelerate the growth and development of grass among plants, and encourage the emergence of infection and significant damage to plants. The elements of climate that affect the growth of plants are one of them is rainfall. In this paper, we performed the simulation using the non-parametric penalized spline (PSPLINE) method and studied the effect on rice production in Lampung. It can be concluded that the increasing fluctuation, frequency, and intensity of climate anomalies in the last decade caused by the ENSO phenomenon have an impact on changes in distribution patterns, intensity, and period of the wet season so that the start of the rainy season and the dry season becomes too late. As a result, there is a seasonal shift from normal average conditions that can ultimately have severe implications for food crops. In a nutshell penalized spline gives high accuracy with R 2 = 96.227% and MAPE = 1.62%. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Rain

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The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

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四川生理科学杂志 2022年第7期44卷 1299-1299页

作者： Jonathan E Pekar Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA New York City Public Health Laboratory New York City Department of Health and Mental Hygiene New York NY 11101 USA Department of Microbiology Institute for Viral Diseases Biosafety Center College of Medicine Korea University Seoul South Korea BK21 Graduate Program Department of Biomedical Sciences Korea University College of Medicine Seoul 02841 Republic of Korea National Public Health Laboratory National Centre for Infectious Diseases Singapore Malaysia Genome and Vaccine Institute Jalan Bangi 43000 Kajang Selangor Malaysia Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology and Immunology School of Medicine Tulane University New Orleans LA 70112 USA Zalgen Labs Frederick MD 21703 USA Global Virus Network (GVN) Baltimore MD 21201 USA Sydney Institute for Infectious Diseases School of Life and Environmental Sciences and School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia Institute of Evolutionary Biology University of Edinburgh King’s Buildings Edinburgh EH9 3FL UK Department of Biomathematics David Geffen School of Medicine Un

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October-8 December), while the separate introduction of lineage A likely occurred within weeks of this event.

关键词： epidemiology likely acute

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Multiwavelength study of GRB 201216C using the sub-TeV emission detected by MAGIC 38

Multiwavelength study of GRB 201216C using the sub-TeV emiss...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Fukami, Satoshi Berti, Alessio Loporchio, Serena Nava, Lara Suda, Yusuke Noda, Koji Asano, Katsuaki Bošnjak, Željka Longo, Francesco Jordana-Mitjans, Nuria Melandri, Andrea Mundell, Carole Shrestha, Manisha Steele, Iain Abe, H. Abe, S. Abhir, J. Acciari, V.A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Arcaro, C. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Burelli, I. Busetto, G. Campoy-Ordaz, A. Carosi, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Ceribella, G. Chai, Y. Chilingarian, A. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Emery, G. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Grau, R. Green, D. Green, J.G. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hrupec, D. Hütten, M. Imazawa, R. Inada, T. Iotov, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez Lezáun, M. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Machado de Oliveira Fraga, B. Majumdar, P. Makariev, M. Maneva, G. Mang, N. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Institute for Particle Physics and Astrophysics ETH Zürich Otto-Stern-Weg 5 Zürich8093 Switzerland Institute for Cosmic Ray Research The University of Tokyo Kashiwanoha 5-1-5 Kashiwa Japan Max Planck Institut for Physics Föhringer Ring 6 Munich Germany INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari BariI-70125 Italy RomeI-00136 Italy Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia INFN Sezione di Trieste via Valerio 2 Trieste Italy Department of Physics University of Bath Claverton Down BathBA2 7AY United Kingdom INAF-Osservatorio Astronomico di Brera Via E. Bianchi 46 LC MerateI-23807 Italy European Space Agency European Space Astronomy Centre Villanueva de la Canñada Madrid28692 Spain Astrophysics Research Institute Liverpool John Moores University Liverpool Science Park IC2 146 Brownlow HillL3 5RF United Kingdom Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721-0065 United States The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Udine INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik MünchenD-80805 Germany Università di Padova INFN PadovaI-35131 Italy Bellaterra BarcelonaE-08193 Spain Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Pola

Recently, several gamma-ray bursts (GRBs) have been detected in the very-high-energy (VHE) gamma-ray energy range by ground-based gamma-ray experiments such as MAGIC, H.E.S.S., and LHASSO. For some GRBs, the VHE emission is consistent with synchrotron self-Compton (SSC) emission from high-energy electrons accelerated in the forward shock of the relativistic jet. However, more statistics are needed to further constrain the emission models. GRB 201216C is a long bright GRB detected in a broad energy range from radio to VHE. The redshift is estimated to be 1.1, making the GRB the most distant source detected in the VHE energy range. MAGIC started the observation 56 seconds after the GRB was triggered by the Swift-BAT telescope. We performed a detailed analysis and detected the signal with about 6 sigma in the first 20 minutes. MAGIC continued the observation for 2.2 hours on the same night and 4 hours on the next night. No signal was detected later than 40 minutes after the GRB trigger. We have performed modelling of the multi-wavelength emission using the MAGIC data. We analysed simultaneous optical data from Liverpool Telescope with MAGIC and included the results in the modelling. The sub-TeV emission is consistent with the single-zone SSC model in the forward shock. In this presentation, we show the final results of the MAGIC data analysis of GRB 201216C and discuss the emission mechanism of the multi-wavelength data. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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