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27th Symposium on Nuclear Electronics and Computing, NEC 2019

作者： Barberis, D. Aimar, A. Alekseev, A. de Sousa Andrade, P.M. Rodrigues Beermann, T.A. Gardner, R.W. Bear, B. Garrido Korchuganova, T. Magnoni, L. Padolski, S. Schanet, E. Tsvetkov, N. Vukotić, I. Wenaus, T. Dipartimento di Fisica dell’Università di Genova e INFN Sezione di Genova Via Dodecaneso 33 GenovaI - 16146 Italy CERN Genève 23CH - 1211 Switzerland Institute of System Programming Russian Academy of Science Moscow Russia Universidad Andrés Bello Santiago Chile Plekhanov Russian University of Economics Stremyanny Lane 36 MoscowRU – 117997 Russia Bergische Universitaet Wuppertal Gaußstr. 20 WuppertalDE - 42119 Germany University of Chicago Enrico Fermi Institute 5640 S Ellis Ave ChicagoIL60637 United States Brookhaven National Laboratory UptonNY United States Fakultät für Physik Ludwig-Maximilians-Universität München Am Coulombwall 1 Garching bei MünchenDE - 85748 Germany

Over the years, ATLAS has developed a large number of monitoring and accounting tools for distributed computing applications. In advance of the increased experiment data rates and monitoring data volumes foreseen for LHC Run 3, starting in 2012, a new infrastructure has been provided by the CERN-IT Monit group, based on InfluxDB as the data store and Grafana as the display environment. ATLAS is adapting and further developing its monitoring tools to use this infrastructure for data and workflow management monitoring and accounting dashboards, expanding the range of previous possibilities with the aim of achieving a single, simpler, environment for all monitoring applications. This contribution describes the tools used, the data flows for monitoring and accounting, the problems encountered and the solutions found. Copyright © 2019 for this paper by its authors.

关键词： Monitoring

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INNOVATIONS IN COMPUTER-AIDED-DESIGN OF MARINE TURBINES USING INTERACTIVE GRAPHICS

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NAVAL ENGINEERS JOURNAL 1980年第2期92卷 207-217页

作者： GINGRICH, JK WINTER, RL Mr. John K. Gingrich joined the General Electric Company after his graduation from Lafayette College in 1948. His early assignments were with the General Engineering Laboratory and the Knolls Atomic Power Laboratory in Schenectady. N. Y. He joined the Marine Turbine and Gear Department in 1968 as Manager Engineering Administration. and assumed his present assignment as Manager Engineering Resource Planning & Administration in 1976. Mr. Gingrich was instrumental in procuring the Interactive Graphics System for the Medium Steam Department. having headed up the Study Team and is now responsible for the Interactive Graphics Systems management including new procurements. He is a member of the American Institute of Design Drafting and is currently the Chairman of the Computer-Aided Drafting Committee in that organization. Mr. Reinhold L. Winter is a graduate of the General Electric Apprentice Program and Salem State College from which he received his B.S. degree in Business Administration. He has nineteen years experience with the General Electric Company including eight years in Business Systems and Programming two years in Computer Operations Management and six years in his present position as Manager Engineering Business Systems. He was a member of the original Study Team that investigated and selected the present Applicon Interactive Graphics (IAG) equipment and currently is responsible for the management of software and hardware support for the ZAG Facility.

This paper discusses the Interactive Graphics system used by the General Electric Company, Medium Steam Turbine Department (Engineering & Manufacturing) for designing, drafting, and manufacturing applications. A brief overview of the hardware malting up the system is described, followed by a more detailed description of the actual applications. Two-dimensional applications described include a Heat Balance Analysis, Flow Diagrams, and Electrical Schematics. A more fruitful area for increased productivity gains is described in the three-dimensional or mechanical applications including turbine design & layout and bucket design. coordination of the design with manufacturing for numerical control tape generation is described through CAM and Plate Frame Cutting applications. Finally, a short review of the engineering design work using Interactive Graphics is discussed. Productivity gains of 2.6 to 1 are being realized, and the overall savings to the Medium Steam Department are outlined.

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Quality of life during the Pandemic: A cross sectional study about Attitude, Individual perspective and behavior change affecting general population in daily life 6

Quality of life during the Pandemic: A cross sectional study...

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6th Smart Cities Symposium, SCS 2022

作者： Khatoon, Fahmida Kumar, Manish Khalid, Ayesha Akbar Alshammari, Amal Daher Khan, Farida Alshammari, Rashid D. Balouch, Zahid Verma, Deepmala Mishra, Pradeep Abotaleb, Mostafa Makarovskikh, Tatiana El-Kenawy, El-Sayed M. Dutta, P.K. Marques, Joao Alexandre Biochemistry Department College of Medicine University of Hail Saudi Arabia Department of Zoology Narain College Shikohabad India Queen Elizabeth the Queen Mother Hospital East Kent NHS Foundation Trust Margate United Kingdom Dept. of Public Health Community Medicine in College of Medicine Princess Nourah Bint Abdul Rahman University Saudi Arabia Sulaiman Al-Rajhi University Saudi Arabia Department of Community Medicine Community Medicine Hail University Saudi Arabia United Medical College Jinnah University Pakistan Department of Zoology SS Jain Subodh Autonomous College Raj Jaipur India J.N.K.V.V. College of Agriculture M.P. Powarkheda India Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt School of Engineering and Technology Amity University West Bengal Kolkata India Laboratory of Applied Neurosciences - LAN/USJ University of Saint Joseph China

ISBN: (纸本)9781839538544

Quality of life in general population before and during pandemic is topic need to be address by researcher in terms of mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The study was carried out among Saudi population. Data were collected from general population using questionnaire during the period from 22 August 2021 to 10th January 2022. As a result, total 214 participants have included in this study. Among them prevalent age group include 40 years (n= 63, 29.4%) shadowed by the age group 25-35 (n= 61, 28.5%) while above 60 years group were least frequent (n= 1, 0.5%). On questioning the applicants whether they were satisfied with their health and how would they rate their quality of life, their answers were as follows: yes, or satisfied (n= 86, 40.2%), very Satisfied (n= 102, 47.7%) Dissatisfied (n= 11, 5.1%) and neither satisfied nor dissatisfied (n= 15, 7%). Due to pandemic, they were rate quality of life very good (n= 94, 43.9%), good (n= 63, 29.4 %) poor (n= 5, 2.3 %) and neither good and nor poor (n= 52, 24.3 %). During pandemic 96 participants feel no change in their weight but 110 participants respond that there is increase in coffee intake during the pandemic. Similarly increased in smoking habits and decrease rate in social activities (n=119,41.4%). The psychosomatic well-being of people has been interrupted by disturbing their social activities during pandemic. © 2022 IET Conference Proceedings. All rights reserved.

关键词： SARS

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ROLE OF SIMULATION IN RAPID PROTOTYPING FOR CONCEPT DEVELOPMENT

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NAVAL ENGINEERS JOURNAL 1991年第3期103卷 204-211页

作者： KING, JF BARTON, DE J. Fred King:is the manager of the Advanced Technology Department for Unisys in Reston Virginia. He earned his Ph.D. in mathematics from the University of Houston in 1977. He has been principal investigator of research projects in knowledge engineering pattern recognition and heuristic problem-solving. Efforts include the development of a multi-temporal multispectral classifier for identifying graincrops using LANDSAT satellite imagery data for NASA. Also as a member of the research team for a NCI study with Baylor College of Medicine and NASA he helped develop techniques for detection of carcinoma using multispectral microphotometer scans of lung tissue. He established and became technical director of the AI Laboratory for Ford Aerospace where he developed expert scheduling modeling and knowledge acquisition systems for NASA. Since joining Unisys in 1985 he has led the development of object-oriented programming environments blackboard architectures data fusion techniques using neural networks and intelligent data base systems. Douglas E. Barton:is manager of Logistics Information Systems for Unisys in Reston Virginia. He earned his B.A. degree in computer science from the College of William and Mary in 1978 and did postgraduate work in London as a Drapers Company scholar. Since joining Unisys in 1981 his work has concentrated on program management and software engineering of large scale data base management systems and design and implementation of knowledge-based systems in planning and logistics. As chairman of the Logistics Data Subcommittee of the National Security Industrial Association (NSIA) he led an industry initiative which examined concepts in knowledge-based systems in military logistics. His responsibilities also include evaluation development and tailoring of software engineering standards and procedures for data base and knowledge-based systems. He is currently program manager of the Navigation Information Management System which provides support to the Fleet Ballistic Missile Progr

A valuable technique during concept development is rapid prototyping of software for key design components. This approach is particularly useful when the optimum design approach is not readily apparent or several known alternatives need to be rapidly evaluated. A problem inherent in rapid prototyping is the lack of a "target system" with which to interface. Some alternatives are to develop test driver libraries, integrate the prototype with an existing working simulator, or build one for the specific problem. This paper presents a unique approach to concept development using rapid prototyping for concept development and scenario-based simulation for concept verification. The rapid prototyping environment, derived from artificial intelligence technology, is based on a blackboard architecture. The rapid prototype simulation capability is provided through an object-oriented modeling environment. It is shown how both simulation and blackboard technologies are used collectively to rapidly gain insight into a tenacious problem. A specific example will be discussed where this approach was used to evolve the logic of a mission controller for an autonomous underwater vehicle.

关键词： Military Engineering

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Quality of life during the pandemic: a cross sectional study about attitude, individual perspective and behavior change affecting general population in daily life

Quality of life during the pandemic: a cross sectional study...

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6th Smart Cities Symposium (SCS 2022)

作者： F. Khatoon M. Kumar A. A. Khalid A. D. Alshammari F. Khan R. D. Alshammari Z. Balouch D. Verma P. Mishra M. Abotaleb T. Makarovskikh E. M. El-kenawy P. K. Dutta J. A. Marques Biochemistry Department College of Medicine University of Hail Hail Saudi Arabia Department of Zoology Narain College Narain College Shikohabad India Queen Elizabeth the Queen Mother Hospital East Kent NHS Foundation Trust Margate Dept. of Public Health/Community Medicine in College of Medicine Princess Nourah Bint Abdul Rahman University Sulaiman Al-Rajhi University Department of Community Medicine Hail university United Medical College Jinnah University SS Jain Subodh Autonomous College Department of Zoology Jaipur (Raj) Jaipur India J.N.K.V.V. College of Agriculture Powarkheda India Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt School of Engineering and Technology Amity University Kolkata Kolkata India Laboratory of Applied Neurosciences - LAN/USJ University of Saint Joseph West Hartford China

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Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments

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Journal of Physics: Conference Series 2020年第1期1690卷

作者： A Alekseev A Kiryanov A Klimentov T Korchuganova V Mitsyn D Oleynik A Smirnov S Smirnov A Zarochentsev Compiler Technology Department Ivannikov Institute for System Programming of the RAS Moscow 109004 Russia Cloud technologies and Big Data analytics laboratory Plekhanov Russian University of Economics Moscow 117997 Russia ATLAS group Universidad Andres Bello Santiago 7550196 Chile Department of computing resources and technologies NRC 'Kurchatov Institute' - PNPI 188300 Gatchina Russia Physics Department Brookhaven National Laboratory Upton NY 11973 USA Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna 141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow 115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg 198504 Russia

The next phase of LHC Operations – High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-the-art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed.

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