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Blending effects of boron carbide and lanthanum(III) oxide reinforcements on the structural, physical, mechanical, and radiation shielding properties of 316L-stainless steel composites for nuclear applications

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Annals of Nuclear Energy 2025年 221卷

作者： Ghada ALMisned Nihal Yayla M.Gökhan Albayrak Ömer Güler Duygu Sen Baykal Hessa Alkarrani H.O. Tekin Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Munzur University Department of Strategic Raw Materials and Advanced Technology Applications Tunceli Turkey Fırat University Engineering Faculty Metallurgical and Materials Engineering Department Elazığ Turkey Rare Earth Elements Application and Research Center Munzur University Tunceli 62000 Turkey Istanbul Nisantasi University Faculty of Engineering and Architecture Mechatronics Engineering 34398 Istanbul Turkey Research Institute for Medical and Health Sciences University of Sharjah Sharjah 27272 Sharjah United Arab Emirates Medical Diagnostic Imaging Department College of Health Sciences University of Sharjah Sharjah 27272 United Arab Emirates Istinye University Faculty of Engineering and Natural Sciences Computer Engineering Department Istanbul 34396 Turkey

Four composite samples were fabricated, maintaining 10 % B 4 C and varying La 2 O 3 content from 0 % to 20 %. X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDX) analyses confirmed the successful incorporation of reinforcements, preserving the austenitic structure of 316L-SS. Elastic modulus calculations revealed a gradual reduction from 225.0 GPa to 215.0 GPa with increasing La 2 O 3 due to clustering and reduced homogeneity. Gamma-ray shielding properties demonstrated significant improvements, particularly at low and intermediate photon energies, with Sample 4 with 20 % La 2 O 3 addition, achieving a 13.6 % higher linear attenuation coefficients (LAC) at 0.015 MeV. Neutron shielding highlighted Sample 4 as one of the most effective materials, outperforming conventional alloys. This study demonstrates the potential of 316L Stainless-Steel (316L-SS) + B 4 C + La 2 O 3 composites as multifunctional materials for radiation-intensive applications, offering superior attenuation and structural stability. It can be concluded that optimization of reinforcement distribution would play an important role in balancing mechanical and shielding performance in next-generation nuclear materials.

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Bottom quark energy loss and hadronization with B+ and nuclear modification factors using pp and PbPb collisions at = 5.02 TeV

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Journal of High Energy physics 2025年第2期2025卷 1-40页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P. S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M. R. Janssen, T. Van Mechelen, P. Breugelmans, N. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Heyen, F. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Bilin, B. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Shahzad, M. A. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, I. S. Giammanco, A. Guzel, A. O. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Alves Gallo Pereira, M. Coelho, E. Correia Silva, G. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Vilela Pereira, A. Aldá Júnior, W. L. 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. Gomes De Souza, R. Macedo, M. Martins, J. Mora Herrera, C. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Maietto Silverio, I. 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. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liang, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Bruxelles 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 de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina 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 Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Nanjing China The University of Iowa Iowa City USA Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou Ch

The production cross sections of $$ {\textrm{B}}_{\textrm{s}}^0 $$ and B+ mesons are reported in proton-proton (pp) collisions recorded by the CMS experiment at the CERN LHC with a center-of-mass energy of 5.02 TeV. The data sample corresponds to an integrated luminosity of 302 pb−1. The cross sections are based on measurements of the $$ {\textrm{B}}_{\textrm{s}}^0 $$ → J/ψ(μ+μ−)ϕ(1020)(K+K−) and B+ → J/ψ(μ+μ−)K+ decay channels. Results are presented in the transverse momentum (pT) range 7–50 GeV/c and the rapidity interval |y| < 2.4 for the B mesons. The measured pT-differential cross sections of B+ and $$ {\textrm{B}}_{\textrm{s}}^0 $$ in pp collisions are well described by fixed-order plus next-to-leading logarithm perturbative quantum chromodynamics calculations. Using previous PbPb collision measurements at the same nucleon-nucleon center-of-mass energy, the nuclear modification factors, RAA, of the B mesons are determined. For pT > 10 GeV/c, both mesons are found to be suppressed in PbPb collisions (with RAA values significantly below unity), with less suppression observed for the $$ {\textrm{B}}_{\textrm{s}}^0 $$ mesons. In this pT range, the RAA values for the B+ mesons are consistent with those for inclusive charged hadrons and D0 mesons. Below 10 GeV/c, both B+ and $$ {\textrm{B}}_{\textrm{s}}^0 $$ are found to be less suppressed than either inclusive charged hadrons or D0 mesons, with the $$ {\textrm{B}}_{\textrm{s}}^0 $$ RAA value consistent with unity. The RAA values found for the B+ and $$ {\textrm{B}}_{\textrm{s}}^0 $$ are compared to theoretical calculations, providing constraints on the mechanism of bottom quark energy loss and hadronization in the quark-gluon plasma, the hot and dense matter created in ultrarelativistic heavy ion collisions.

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Towards Automated BIM and BEM Model Generation using a B-Rep-based Method with Topological Map

Towards Automated BIM and BEM Model Generation using a B-Rep...

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2024 Mid-term Symposium on Spatial Information to Empower the Metaverse

作者： Roman, Oscar Mazzacca, Gabriele Farella, Elisa Mariarosaria Remondino, Fabio Bassier, Maarten Agugiaro, Giorgio Trento Italy University of Trento Trento Italy Department of Mathematics Computer Science and Physics University of Udine Italy Department of Civil Engineering TC Construction - Geomatics KU Leuven Faculty of Engineering Technology Ghent Belgium School of Architecture and Built Environment Delft University of Technology Netherlands

In many countries, recent boosts in the construction and renovation sectors and energy efficiency directives are driving a growing interest in the built environment among designers and maintainers. In this context, customized software solutions tailored for Building Information Modelling (BIM) and Building Energy Modelling (BEM) are proving to be indispensable for optimizing operational efficiency within the architecture, Engineering, Construction, Owner, and Operator (AECOO) sector and for facilitating the generation of buildings' Digital Twins (DTs). These DTs rely on accurate geometry and ancillary information (semantics, sensors, etc.) to define properties of single elements, enabling crucial simulations in structural conditions or energy needs. However, BIM and BEM model creation and their enrichment with semantic information are highly time-consuming and prone to manual errors. Hence, there is an increasing demand for automatic methods featuring a high level of geometric accuracy to reconstruct building elements, such as walls, floors, and openings captured via 3D reality-based surveying. This paper introduces an automated method for creating Boundary Representation (B-Rep) models from 3D surveying data for the generation of digital building replicas. The method is based on the detection and computation of topological elements from 3D reality-based point clouds. It proves valuable for architectural or design workflows and for conducting energy or quality system simulations. © Author(s) 2024.

关键词： Architectural design

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Unmilled Rice Moisture Content Sensor for Rice Mill Factory Applications 12

Unmilled Rice Moisture Content Sensor for Rice Mill Factory ...

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12th International Electrical Engineering Congress, iEECON 2024

作者： Seewattanapon, Somporn Chudpooti, Nonchanutt Akkaraekthalin, Prayoot Rajamangala University Of Technology Suvarnabhumi Faculty Of Engineering And Architecture Nonthaburi Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Industrial Physics And Medical Instrumentation Bangkok10800 Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Departmemnt Of Electrical And Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This research presents a novel electromagnetic-based sensor for inline measurement of unmilled rice paddy moisture content in the rice mill factory. The proposed sensor has been designed based on the modified planar dipole structure at the operating frequency of 440 MHz. The unmilled rice dielectric constant properties have been studied and applied for characterizing the moisture content. The unmilled rice moisture content sensor system then has been designed consisting of the proposed sensor, a microwave signal generator, a microwave signal detector, and a microcontroller. The moisture content sensor system has been implemented and used for measuring the unmilled rice paddy moisture content in a real rice mill factory in Udon Thani province, Thailand. When compared with the commercial rice moisture content tool based on a capacitance technique, the proposed rice moisture content sensor system gives almost same results, which only about 4% difference has been found. © 2024 IEEE.

关键词： Moisture control

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Erratum to: Search for resonant and nonresonant production of pairs of dijet resonances in proton-proton collisions at = 13 TeV

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Journal of High Energy physics 2025年第5期2025卷 1-23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. 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. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Makarenko, I. Malara, A. Paredes, S. Pétré, L. Postiau, N. Starling, E. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Niedziela, M. Rendón, C. Roskas, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I. S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Mondal, K. Prisciandaro, J. 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. Torres Da Silva De Araujo, F. 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. 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. Kapo Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Université Libre de Bruxelles Bruxelles Belgium Vrije Universiteit Brussel Brussel 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 de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil The University of the State of Amazonas Manaus 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 Beihang University Beijing China University of Chinese Academy of Sciences Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Department of Physics Nanjing China The University of Iowa Iowa City USA 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 Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University 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 Prag

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Effects of the Flatness Network Parameter Threshold on the Performance of the Rectified Linear Unit Memristor-Like Activation Function in Deep Learning

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SN computer Science 2024年第8期5卷 1-10页

作者： Tchepgoua Mbakop, Marcelle Mboupda Pone, Justin Roger Chassem Kamdem, Priva Kengne, Romanic Research Unit of Condensed Matter Electronics and Signal Processing (RU-MACETS) Department of Physics Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer (RU-AIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Research Unit of Fundamental Computer Sciences and Artificial Intelligence (RU-FCSAI) Department of Computer Sciences Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon

In this contribution, we improve of the performance of the Rectified Linear Unit Memristor Like Activation Function with the implication to help training process of CNN without a lot of epochs by computing the best value of the flatness network parameter (p). In this regards the flatness network parameter threshold (p) has been investigated and a good performance of the activation function has been discovered at p = 4.5. We firstly used the MNIST and the CIFAR-10 datasets to trained and test the Alex-Net architecture model of convolutional neural network (CNN) and we showed better performances of Rectified Linear Unit Memristor-like Activation Function compared to those of the literature. We noticed that the performance of Alex-Net also improved and the better performance was recorded when p = 4.5 with 99.50%, 99.25%, 98.81% for training, validation and testing accuracy respectively when using the MNIST. These results open the outcome to reduce the training time of neural networks when this activation function is used. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Activation function Artificial intelligence Deep learning Flatness network parameter threshold Memristive windows function Neural network

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Implementation trade-offs of the density matrix renormalization group algorithm on kilo-processor architectures

Implementation trade-offs of the density matrix renormalizat...

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2013 European Conference on Circuit Theory and Design, ECCTD 2013

作者： Nemes, Csaba Barcza, Gergely Nagy, Zoltan Legeza, Örs Szolgay, Peter Faculty of Information Technology Péter Pâzmâny Catholic University Budapest Hungary Cellular Sensory and Wave Computing Laboratory Computer and Research Automation Institute Hungarian Academy of Sciences Budapest Hungary Department of Theoretical Solid State Physics Wigner Research Centre for Physics Hungarian Academy of Sciences Budapest Hungary

ISBN: (纸本)9783000437854

Numerical analysis of strongly correlated quantum lattice models has a great importance in quantum physics. The exponentially growing size of the Hilbert space makes these computations difficult, however sophisticated algorithms have been developed to balance the size of the effective Hilbert space and the accuracy of the simulation. One of these methods is the density matrix renormalization group (DMRG) algorithm which has become the leading numerical tool in the study of low dimensional lattice problems of current interest. In the algorithm a high computational problem can be translated to a list of dense matrix operations, which makes it an ideal application to fully utilize the computing power residing in both current multi-core processors and novel kilo-processor architectures. © 2013 TU Dresden.

关键词： Economic and social effects

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Measurement of the inclusive cross sections for W and Z boson production in proton-proton collisions at = 5.02 and 13 TeV

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Journal of High Energy physics 2025年第4期2025卷 1-52页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P. S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M. R. Janssen, T. Van Mechelen, P. Bols, E. S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Mota Amarilo, K. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I. S. Giammanco, A. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Coelho, E. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. 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. Gomes De Souza, R. Martins, J. Mora Herrera, C. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. 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. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Kapoor, A. Liao, H. Liu, Z.-A. Sharma, R. Song, J. N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Levin, A. Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Bruxelles 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 de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Estadual PaulistaUniversidade 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 Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Nanjing China The University of Iowa Iowa City Iowa USA Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong 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 Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical

Measurements of fiducial and total inclusive cross sections for W and Z boson production are presented in proton-proton collisions at $$ \sqrt{s} $$ = 5.02 and 13 TeV. Electron and muon decay modes (ℓ = e or μ) are studied in the data collected with the CMS detector in 2017, in dedicated runs with reduced instantaneous luminosity. The data sets correspond to integrated luminosities of 298 ± 6 pb−1 at 5.02 TeV and 206 ± 5 pb−1 at 13 TeV. Measured values of the products of the total inclusive cross sections and the branching fractions at 5.02 TeV are σ(pp → W + X) $$ \mathcal{B} $$ (W → ℓν) = 7300 ± 10 (stat) ± 60 (syst) ± 140 (lumi) pb, and σ(pp → Z+X) $$ \mathcal{B} $$ (Z → ℓ+ℓ−) = 669 ± 2 (stat) ± 6 (syst) ± 13 (lumi) pb for the dilepton invariant mass in the range of 60–120 GeV. The corresponding results at 13 TeV are 20480 ± 10 (stat) ± 170 (syst) ± 470 (lumi) pb and 1952 ± 4 (stat) ± 18 (syst) ± 45 (lumi) pb. The measured values agree with cross section calculations at next-to-next-to-leading-order in perturbative quantum chromodynamics. Fiducial and total inclusive cross sections, ratios of cross sections of W+ and W− production as well as inclusive W and Z boson production, and ratios of these measurements at 5.02 and 13 TeV are reported.

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Improved Classification of Benign and Malignant Breast Lesions Based on Contrast Manipulation and Gradient Map 10

Improved Classification of Benign and Malignant Breast Lesio...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Nastase, Iulia-Nela Anghelache Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

This study proposes an approach to enhance the contrast breast ultrasound (BUS) images with the preservation of the finer details. The main idea is to use the relevant and most important features in the classification of BUS images as benign or malignant. Among the contrast manipulation techniques, both linear under- and overexposure methods, contrast limiting adaptive histogram equalization (CLAHE) method and homomorphic filtering are used. The gradient maps of original images are generated for further analysis. Also, the gradient maps of original images are improved by the local mean subtraction and divisive normalization. Eight structural features are extracted from each gradient map image type. The extracted features feed a Random Forest (RF) classifier to determine their relevance for BUS images classification. An accuracy of 97.12% has been obtained for improved gradient map images and 93.03% for gradient map images. © 2022 IEEE.

关键词： Image classification

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Melanoma Detection using a Random Forest Algorithm 10

Melanoma Detection using a Random Forest Algorithm

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Damian, Felicia-Anisoara Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

In recent years, statistical evidence has identified melanoma as a major cause of skin cancer deaths. In this study, we propose a skin analysis lesions based on colour features (C) and Hu (H) moments to differentiate benign (nevi) and malignant (melanoma) lesions and, a classification based on Random Forest algorithm. Colour features are found as follows. Pre-processing for hair removing using the Dull-razor method;image segmentation based on the optimal colour threshold set on each RGB colour channel (red, green, blue);23 colour features extraction using the minimum and maximum RGB intensity and Hu moments;Random Forest (RF) algorithm to accurately differentiate benign and malignant skin lesions. This study used three datasets: 7-Point, Med-Node, PH2. RF classifier performance in terms of accuracy was: 94% for the 7-point dataset with H5, H7, H6, H2, C08 selected as relevant features;98.1% for the Med-Node dataset and H7, H5, H6, H2, H1 ACC the relevant features;and, 96.5% for the PH2 dataset and H5, H6, H7, C03, H1 selected as relevant features, respectively. Shape features based on Hu moments are found to improve the classification performance of the skin lesions compared to the colour features. © 2022 IEEE.

关键词： Image segmentation

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