检索结果-内蒙古大学图书馆

15th International Conference on Computer science and Education, ICCSE 2020

作者： Umezawa, Katsuyuki Nakazawa, Makoto Kobayashi, Manabu Ishii, Yutaka Nakano, Michiko Hirasawa, Shigeichi Shonan Institute of Technology Dept. of Information Science Kanagawa Japan College of Aizu Dept. of Industrial Information Science Fukushima Japan Waseda University Center for Data Science Tokyo Japan Chiba University Faculty of Education Chiba Japan Waseda University Faculty of Education and Integrated Arts and Sciences Tokyo Japan Waseda University Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9781728172675

There are several difficulties encountered by learners during learning such as good or bad learning content, the difficulty level of learning content, and the degree of learning proficiency. It is possible to detect these difficulties by measuring the browsing history, editing history, and biological information such as brain waves or eye-tracking information. In this paper, we measure electroencephalograph (EEG) information during programming learning. We focus on the relationship between task response time and EEG, and try to detect careless mistakes due to the lack of attention. The results show that careless mistakes during programming learning can be detected by experiments. © 2020 IEEE.

关键词： Eye tracking

来源：评论

学校读者我要写书评

暂无评论

CHASM (CHerenkov Air Shower Model): Simulating the Cherenkov Profiles of Cosmic Ray Air Showers 37

CHASM (CHerenkov Air Shower Model): Simulating the Cherenkov...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Buckland, Isaac Bergman, Douglas Abbasi, R.U. Abe, M. Abu-Zayyad, T. Allen, M. Arai, Y. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kido, E. Kim, H.B. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Suzawa, T. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Dept. of Physics & Astronomy High Energy Astrophysics Inst. University of Utah Utah United States Department of Physics Loyola University Chicago ChicagoIL United States The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Todai Institutes for Advanced Study University of Tokyo Chiba Kashiwa Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics Sungkyunkwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique ThÃ©orique UniversitÃ© Libre de Bruxelles Brussels Belgium Depart

Reconstruction of an EAS seen using non-imaging Cherenkov detectors requires simulating the Cherenkov yield of many EAS’s with given shower parameters. Since Shower Universality parameterizes both the angular distribution and energy distribution of charged particles within a shower, one can calculate the Cherenkov photon yield (at a fixed point) from the Cherenkov cones of electrons. In this work, we compare both the CWLD (Cherenkov Width Lateral Distribution) and arrival time distributions from Cherenkov universality calculations with those from CORSIKA iact (imaging atmospheric Cherenkov telescope) simulations. Since universality calculations are much less computationally expensive than shower simulation programs like CORSIKA, reconstruction could be accomplished more efficiently using Cherenkov data. © Copyright owned by the author(s).

关键词： Angular distribution

来源：评论

学校读者我要写书评

暂无评论

Telescope Array 10-Year Monocular Spectrum Measurement 37

Telescope Array 10-Year Monocular Spectrum Measurement

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Bergman, Douglas R. Furlich, Greg Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Dept. of Physics & Astronomy High Energy Astrophysics Inst. University of Utah Utah United States Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawashi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique ThÃ©orique UniversitÃ© Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Center

Telescope Array (TA) is the largest cosmic ray detector in the northern hemisphere. We present a measurement of the cosmic ray energy spectrum for energies above 1017.5 eV using fluorescence telescopes in monocular mode. A novel weather classification scheme using machine learning was used to select data parts with good weather to ensure the quality of the fluorescence data. The data from the Black Rock Mesa (BR) and Long Ridge (LR) fluorescence telescope stations were analysed separately in monocular mode, with the calculated fluxes combined into a single spectrum. The 10-year monocular combined cosmic ray energy spectrum is in excellent agreement with previous measurements from the northern hemisphere. We present fits of the combined spectrum to a series of broken power law models. A thrice-broken power law is observed to be the best fit considering the Poisson deviance per degrees of freedom. The three breaks suggest an additional feature of the spectrum between the previously observed Ankle at 1018.7 eV and the GZK suppression at 1019.8 eV. © Copyright owned by the author(s).

关键词： Fluorescence

来源：评论

学校读者我要写书评

暂无评论

Telescope Array Combined Fit to Cosmic Ray Spectrum and Composition 37

Telescope Array Combined Fit to Cosmic Ray Spectrum and Comp...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Bergman, Douglas R. Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Dept. of Physics & Astronomy High Energy Astrophysics Inst. University of Utah Utah United States Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawashi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique ThÃ©orique UniversitÃ© Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Center

We present the result of fits of an astrophysical model of UHECR sources to the energy spectrum measured using the Telescope Array (TA) surface detectors and the composition data measured by TA fluorescence detectors in stereo mode. The astrophysical model consists of identical sources, uniformly distributed with a density which evolves with age, where groups of nuclei with varying fractions are accelerated producing a power law source with a rigidity-dependent cutoff. The model includes the propagation effects of photo-pion production, photo-nuclear disintegration and general energy loss from universal expansion. Comparison of model data with detector data in both the energy and the Xmax distributions is done via a forward-folding description of detector effects. A prediction of the cosmogenic neutrino flux at Earth resulting from the cosmic rays produced in the model with these fit values is also presented. The predicted flux is considerably higher than other similar fits to Auger data have previously predicted. © Copyright owned by the author(s).

关键词： Cosmic rays

来源：评论

学校读者我要写书评

暂无评论

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

引用

arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Cost optimization in CNN for big data of share price

引用

AIP Conference Proceedings 2023年第1期2587卷

作者： R. Saravanakumar Gokul Talele Ali Hazim Alyamoor Kaushal Kishor Bhatt Prashant Vats Malik Bader Alazzam Dept. of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India Dept. of Data Science IIIT Banglore India Dept. of Accounting Economic and Administration College University of Mosul Ninawa Iraq Dept. of Computer Science and Engineering Birla Institute of Applied Science Bhimtal Nainital Uttarakhand India Dept. of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Faculty of Computer Science and Informatics Amman Arab University Mubis-Amman Jordan

Optimization of share price in order to evaluate whether to invest in specific share or not is subject of study paper. Optimized value has been utilized to enhance the performance and accuracy when dataset is trained using CNN. Review has been given on big cap, mid cap & small cap fund which are accessible in large, medium and small amount for the purpose of investing. It has been found that share in big cap are more dependable but offers limited return. However share in case of mid cap are considered comparatively more hazardous than big cap yet return may be substantial. The suggested study has given mechanism that will make use of optimization mechanism to filter the data set considering PSO and MVO mechanism. The suggested CNN based training model is claimed high performance system with improved accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Observation of spectral structures in the flux of cosmic ray protons with CALET on the International Space Station 38

Observation of spectral structures in the flux of cosmic ray...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Kobayashi, Kazuyoshi Marrocchesi, Pier Simone Adriani, O. Akaike, Y. Asano, K. Asaoka, Y. Berti, E. Bigongiari, G. Binns, W.R. Bongi, M. Brogi, P. Bruno, A. Cannady, N. Castellini, G. Checchia, C. Cherry, M.L. Collazuol, G. de Nolfo, G.A. Ebisawa, K. Ficklin, A.W. Fuke, H. Gonzi, S. Guzik, T.G. Hams, T. Hibino, K. Ichimura, M. Ioka, K. Ishizaki, W. Israel, M.H. Kasahara, K. Kataoka, J. Kataoka, R. Katayose, Y. Kato, C. Kawanaka, N. Kawakubo, Y. Kobayashi, K. Kohri, K. Krawczynski, H.S. Krizmanic, J.F. Maestro, P. Marrocchesi, P.S. Messineo, A.M. Mitchell, J.W. Miyake, S. Moiseev, A.A. Mori, M. Mori, N. Motz, H.M. Munakata, K. Nakahira, S. Nishimura, J. Okuno, S. Ormes, J.F. Ozawa, S. Pacini, L. Papini, P. Rauch, B.F. Ricciarini, S.B. Sakai, K. Sakamoto, T. Sasaki, M. Shimizu, Y. Shiomi, A. Spillantini, P. Stolzi, F. Sugita, S. Sulaj, A. Takita, M. Tamura, T. Terasawa, T. Torii, S. Tsunesada, Y. Uchihori, Y. Vannuccini, E. Wefel, J.P. Yamaoka, K. Yanagita, S. Yoshida, A. Yoshida, K. Zober, W.V. InstitutionWaseda Research Institute of Science and Enginnearing Waseda University 17 Kikuicho Shinjuku Tokyo162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Ibaraki Tsukuba305-8505 Japan Dept. Of Physical Sciences Earth and Environment Univ. Of Siena via Roma 56 Siena53100 Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo Pisa3-56127 Italy Department of Physics University of Florence Via Sansone 1 Sesto Fiorentino50019 Italy INFN Sezione di Firenze Via Sansone 1 Sesto Fiorentino50019 Italy Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba277-8582 Japan Via Madonna del Piano 10 Sesto Fiorentino50019 Italy Department of Physics McDonnell Center for the Space Sciences Washington University One Brookings Drive St. LouisMO63130-4899 United States Heliospheric Physics Laboratory NASA GSFC GreenbeltMD20771 United States Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Astroparticle Physics Laboratory NASA GSFC GreenbeltMD20771 United States Center for Research and Exploration in Space Sciences and Technology NASA GSFC GreenbeltMD20771 United States Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States Department of Physics and Astronomy University of Padova Via Marzolo 8 Padova35131 Italy INFN Sezione di Padova Via Marzolo 8 Padova35131 Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Kanagawa Sagamihara252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Kanagawa Yokohama221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Hirosaki Aomori036-8561 Japan Yukawa Institute for Theoretical Physics Kyot

A precise measurement of the cosmic-ray proton spectrum is carried out with the Calorimetric Electron Telescope (CALET) and a sharp softening of the energy spectrum above 10 TeV is observed. CALET, located on the International Space Station, has started data taking in October 2015 and has accumulated data for more than seven years without any serious troubles. CALET is pursuing the direct measurement of the main components of high energy cosmic rays up to ~1 PeV in order to understand the cosmic ray acceleration and propagation. Thanks to the thick calorimeter that corresponds to 30 radiation lengths and to ∼1.3 proton interaction lengths, the proton analysis presented in this paper spans a broad energy range from 50 GeV to 60 TeV. Proton energy resolution is 30-40%, and the residual background is less than 10% in the E © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

来源：评论

学校读者我要写书评

暂无评论

Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

arXiv

引用

arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

来源：评论

学校读者我要写书评

暂无评论

Correlation Analysis with Experimental Procedures and Peak Structures in Xps Spectral Round-Robin Tests on Mno Powder Sample

SSRN

引用

SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

引用

SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：