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Accurate machine-learning based modeling of apparent viscosity of waxy oils mixed with polymeric wax inhibitors

Flow Measurement and Instrumentation 2025年

作者： Hongyan Lei Muntadher Abed Hussein Tarak Vora Anupam Yadav Asha Rajiv Aman Shankhyan Sachin Jaidka Mehul Manu Issa Mohammed Kadhim Zainab Jamal Hamoodah Fadhil Faez Mehrdad Mottaghi College of Computer and Electrical Engineering Hunan University of Arts and Science Changde hunan 415000 China Department of sciecnes Al-Manara College for Medical Sciences (Maysan) Iraq Marwadi University Research Center Department of Civil Engineering Faculty of Engineering & Technology Marwadi University Rajkot-360003 Gujarat India Department of Computer engineering and Application GLA University Mathura-281406 India Department of Physics & Electronics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Physics Department of Applied Sciences Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Department of Allied Science (Physics) Graphic Era Hill University Bhimtal India Graphic Era Deemed to be University Dehradun Uttarakhand-248002 India Department of Medical Laboratories Technology Al-Nisour University College Nisour Seq. Karkh Baghdad Iraq Mazaya University college An Nasiriyah Dhi Qar Iraq Department of Dentistry College of Dentistry The Islamic University Najaf Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Babylon Babylon Iraq Faculty of chemistry Kabul University Kabul Afghanistan

While producing and then transporting waxy oils through pipeline network systems, wax precipitation commonly leads to issues such as wax build-up, gel creation, and decreased flow, negatively affecting pipeline efficiency. To address these problems, the petroleum industry frequently uses polymeric flow improvers to enhance the rheological properties, thereby increasing the mobility and flowability of oils. Experimental methods to measure oil viscosity are both expensive and time-consuming; hence, having reliable data driven frameworks are necessary in this regard. This work presents the use of accurate artificial intelligence methods including K-nearest neighbors, convolutional neural network, multi-layer perceptron artificial neural network, decision tree, support vector machine, adaptive boosting, random forest and ensemble learning to create smart methods for calculating the apparent viscosity of waxy crude oils that are doped with polymeric additives by actual databank sourced from prior studies. A databank containing 526 datapoints was used to both prepare and validate these machine learning algorithms. The predicted results were then evaluated against actual lab data using both graphical and statistical methods. The outcomes showed a strong agreement between the determined and real values for the constructed models. These developed models offer a way of accurate estimation capability bypassing the need for costly, labor-intensive, and time-consuming experimental procedures.

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Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s = 5.02 TeV

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Journal of High Energy Physics 2025年第1期2025卷 1-49页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Van Mechelen, P. Bols, E.S. D’Hondt, J. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Putte, S. Vannerom, D. Clerbaux, B. Dansana, S. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, J. Zhang, H. Aga Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Venezuela Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The inclusive jet cross section is measured as a function of jet transverse momentum pT and rapidity y. The measurement is performed using proton-proton collision data at s = 5.02 TeV, recorded by the CMS experiment a... 详细信息

The inclusive jet cross section is measured as a function of jet transverse momentum p_T and rapidity y. The measurement is performed using proton-proton collision data at s = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb⁻¹. The jets are reconstructed with the anti-k_T algorithm using a distance parameter of R = 0.4, within the rapidity interval |y| < 2, and across the kinematic range 0.06 < p_T< 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling α_S. © The Author(s) 2025.

关键词： Hadron-Hadron Scattering Jet Physics QCD

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Person re-identification in aerial imagery

arXiv

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arXiv 2019年

作者： Zhang, Shizhou Zhang, Qi Yang, Yifei Wei, Xing Wang, Peng Jiao, Bingliang Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), visual surveillance by utilizing the UAV platform has been very attractive. Most of the research works for UAV captured visual data are mainly focused on the tasks of object detection and tracking. However, limited attention has been paid to the task of person Re-identification (ReID) which has been widely studied in ordinary surveillance cameras with fixed emplacements. In this paper, to facilitate the research of person ReID in aerial imagery, we collect a large scale airborne person ReID dataset named as Person ReID for Aerial Imagery (PRAI-1581), which consists of 39,461 images of 1581 person identities. The images of the dataset are shot by two DJI consumer UAVs flying at an altitude ranging from 20 to 60 meters above the ground, which covers most of the real UAV surveillance scenarios. In addition, we propose to utilize subspace pooling of convolution feature maps to represent the input person images. Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently. We conduct extensive experiments on the proposed dataset and the experimental results demonstrate that re-identify persons in aerial imagery is a challenging problem, where our method performs favorably against state of the arts. Our dataset can be accessed via https://***/stormyoung/PRAI-1581. Copyright © 2019, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Higher-order moments of the elliptic flow distribution in PbPb collisions at sNN = 5.02 TeV

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Journal of High Energy Physics 2024年第2期2024卷 1-45页

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Paulitsch, P. Pitters, F.M. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Rejeb Sfar, H. Van Mechelen, P. Bols, E.S. D’Hondt, J. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Moortgat, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Doninck, W. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Makarenko, I. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Mondal, K. Taliercio, A. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Thakur, S. Cheng, T. Javaid, T. Mittal, M. Yuan, L. Ahmad, M. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Milosevic, V. Monti, F. Sharma, R. Tao, J. Thomas-Wilsker Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The hydrodynamic flow-like behavior of charged hadrons in high-energy lead-lead collisions is studied through multiparticle correlations. The elliptic anisotropy values based on different orders of multiparticle cumulants, v₂{2k}, are measured up to the tenth order (k = 5) as functions of the collision centrality at a nucleon-nucleon center-of-mass energy of s_NN = 5.02 TeV. The data were recorded by the CMS experiment at the LHC and correspond to an integrated luminosity of 0.607 nb⁻¹. A hierarchy is observed between the coefficients, with v₂{2} > v₂{4} ≳ v₂{6} ≳ v₂{8} ≳ v₂{10}. Based on these results, centrality-dependent moments for the fluctuation-driven event-by-event v₂ distribution are determined, including the skewness, kurtosis and, for the first time, superskewness. Assuming a hydrodynamic expansion of the produced medium, these moments directly probe the initial-state geometry in high-energy nucleus-nucleus collisions. © The Author(s) 2024.

关键词： Event-by-Event fluctuation Harmonic Flow Heavy Ion Experiments Relativistic Heavy Ion Physics

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Measurement of the Higgs boson production via vector boson fusion and its decay into bottom quarks in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2024年第1期2024卷 173页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Van Mechelen, P. Bols, E.S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, J. Zhang, H. Agapitos, A. Ban, Y. Levin, A. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

A measurement of the Higgs boson (H) production via vector boson fusion (VBF) and its decay into a bottom quark-antiquark pair (bb¯) is presented using proton-proton collision data recorded by the CMS experiment at s = 13 TeV and corresponding to an integrated luminosity of 90.8 fb⁻¹. Treating the gluon-gluon fusion process as a background and constraining its rate to the value expected in the standard model (SM) within uncertainties, the signal strength of the VBF process, defined as the ratio of the observed signal rate to that predicted by the SM, is measured to be μ_Hbb¯^qqh=1.01_−0.46^+0.55. The VBF signal is observed with a significance of 2.4 standard deviations relative to the background prediction, while the expected significance is 2.7 standard deviations. Considering inclusive Higgs boson production and decay into bottom quarks, the signal strength is measured to be μ_Hbb¯^incl.=0.99_−0.41^+0.48, corresponding to an observed (expected) significance of 2.6 (2.9) standard deviations. © The Author(s) 2024.

关键词： B Physics Hadron-Hadron Scattering Higgs Physics

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Gowl: A fast ontology-mediated query answering

Gowl: A fast ontology-mediated query answering

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2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018

作者： Meng, Chenhong Zhang, Xiaowang Xiao, Guohui Feng, Zhiyong Qi, Guilin School of Computer Science and Technology Tianjin University Tianjin300350 China School of Computer Software Tianjin University Tianjin300350 China Faculty of Computer Science Free University of Bozen-Bolzano BolzanoI-39100 Italy School of Computer Science and Engineering Southeast University Nanjing211189 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China

This poster shows an ontology-mediated query answering system (gOWL) based on pure materialization to avoid query rewriting online. gOWL shows that answering queries over a partial materialization of the chase is complete for a large fragment of practical queries with bounded depth. From a system engineering perspective, the materialization approach allows us to design a modular architecture to integrate off-the-shelf efficient SPARQL query engines. The poster will be based on DBpedia and UOBM datasets. We will compare the performance of gOWL with PAGOdA, Ontop, and Pellet (with speedup up to three orders of magnitude). © 2018 CEUR-WS. All rights reserved.

关键词： Ontology

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A method of calculating the mean life-time of solar granules 29

A method of calculating the mean life-time of solar granules

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第29届中国控制与决策会议

作者： Jianshu Wang Song Feng Yunnan Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (纸本)9781509046584

Solar granulation is a kind of morphological characteristics of solar photosphere caused by convective *** of the mechanism of convective motion,when we calculated the mean life-time of solar granules,we can get the time of convective *** are many methods based on the classification of solar granules and tracking solar granules to calculate the mean life-time of solar *** a result of the existence time of solar granules is very short and the evolution is more complex,identifying the fragmentation and merging of solar granules will get us into *** this paper,we present a power spectrum analysis based on periodic analysis method to calculate the mean life-time of solar ***,we use the κ-ω filter to remove the 5-minute *** removing the 5-minute oscillations can we more accurately calculate the mean life-time of solar ***,we use gradient transformation to remove the direct current component(DC component) and reduce spectral energy leakage with the Hamming ***,using the function of Periodogram calculates the mean life-time of solar *** order to verify the accuracy of the method,we set three data to calculate the mean life-time of solar *** final results of calculating the mean life-time of solar granules are 9.24 min,9.04 min and 10.67 min *** our method is effective and accurate compared with the results of other literature.

关键词： Solar granules k-ω filter Power spectrum The mean life-time

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Sunspot Umbral Oscillation Analysis Using Wavelet Transform 29

Sunspot Umbral Oscillation Analysis Using Wavelet Transform

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第29届中国控制与决策会议

作者： Zeyuan Du Song Feng Yunnan Key Laboratory of Computer Technology Application/Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (纸本)9781509046584

Umbral oscillation is one kind of basic phenomena appearing in *** measurement the oscillation period is quiet important for understanding the structure and evolution of sunspot.A long line of work indicates that there are two kinds of typical oscillations in sunspot umbral area:3-minute oscillations and 5-minute *** high quality observational sunspot data set obtained with the HINODE/Solar Optical Telescope in the Ca II H line on 20 August 2013 was employed to study the oscillation period of the chromosphere above a *** this paper,we used two kinds of wavelet transform methods to analyze sunspot umbral *** method based on continuous wavelet transform(CWT) was applied to analyze umbral oscillation period and frequency changing over *** other method based on cross wavelet transform(XWT) was used to analyze phase relationship between the two time *** outcomes demonstrate the viability and efficiency of the wavelet transform as a analyzing tool by accurately calculating period and studying phase difference.

关键词： Sunspot Umbral oscillation period Phase relationship Continuous wavelet transform Cross wavelet transform

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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

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Scientific Reports 2025年第1期15卷 1-9页

作者： Altalbawy, Farag M. A. Alkhayyat, Ahmad Jhala, Ramdevsinh Yadav, Anupam Ramachandran, T. Shankhyan, Aman Karthikeyan, A. Thatoi, Dhirendra Nath Sinitsin, Vladimir Vladimirovich Department of Chemistry University College of Duba University of Tabuk Tabuk Saudi Arabia Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Center Department of Mechanical Engineering Faculty of Engineering & Technology Marwadi University Gujarat Rajkot 360003 India Department of Computer Engineering and Application GLA University Mathura 281406 India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Mechanical Engineering Siksha ’O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar 751030 India Research Laboratory of Technical Self-Diagnostics and Self-Control of Devices and Systems Department of Scientific and Innovative Activities Research Institute of Mechanical Engineering South Ural State University Lenin prospect 76 Chelyabinsk 454080 Russian Federation

This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles. To enhance model efficacy, the Political Optimizer (PO) algorithm was utilized for fine-tuning purposes. The findings substantiate the capability of the optimized frameworks in delivering precise temperature estimations. GPR yielded a notable R² value of 0.998, reflecting a strong concordance between the estimated and actual measurements. In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R² value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. The study’s findings offer valuable insights for decision-making processes in a variety of domains that rely on accurate temperature forecasting. © The Author(s) 2025.

关键词： Gaussian process regression Heat transfer K-Nearest neighbors Multi-layer perceptron Nanofluid

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Implementation of Mobile game for Religion Learning

Implementation of Mobile game for Religion Learning

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Edy Sulistiyo Vindy Nizirwan Anwar Diana Teresia Spits Warnars Setya Chendra Wibawa Goenawan Brotosaputro Dedy Prasetya Kristiadi Harco Leslie Hendric Spits Warnars Kiyota Hashimoto Electrical Engineering Department Universitas Negeri Surabaya Jawa Timur Indonesia Game Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Esa Unggul University Jakarta Indonesia Information Systems Department Bina Nusantara University Jakarta Indonesia Informatics Department Universitas Negeri Surabaya Jawa Timur Indonesia Information System Department Universitas Budi Luhur Jakarta Indonesia Computer Science department Raharja University Tangerang Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Technology and Environment Prince Songkla University Phuket Campus Thailand

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player's view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3.

关键词： Games Land mobile radio Education computer science Operating systems Information systems Media

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