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Estimating earthquake peak ground acceleration and intensity using short-time Fourier and wavelet transform techniques: a case study at Odisha, India

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Arabian Journal of Geosciences 2022年第11期15卷 1-16页

作者： Jena, Ratiranjan Al-Amri, Abdullah Malulud, Khairul Nizam Abdul Shanableh, Abdallah Al-Ruzouq, Rami Center for Advanced Modeling and Geospatial Information Systems School of Civil and Environmental Engieering Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia GIS & Remote Sensing Center Research Institute of Sciences and Engineering University of Sharjah Sharjah United Arab Emirates Civil and Environmental Engineering Department University of Sharjah Sharjah United Arab Emirates Department of Geology and Geophysics College of Science King Saud University Riyadh Saudi Arabia Earth Observation Center Institute of Climate Change Universiti Kebangsaan Malaysia Bangi Malaysia

Seismic hazard assessment is indispensable to improve and update the earthquake peak ground acceleration (PGA) and intensity map. Although a lot of work has been done on earthquake hazard assessment using various mathematical functions and spatial technologies, however, limited studies could be seen on estimating PGA and intensity variation using the powerful wavelets tool. This paper analyzed the location-frequency characteristics of PGA, intensity, and their magnitude values based on short-time Fourier transform (STFT) and continuous wavelet transform (CWT) techniques. The models were implemented in the Indian state of Odisha. Earthquake ground motion is a random process that depends on lithology, amplification factors, structural features, and complicated tectonics of a given location. Four STFTs, namely Rectangle, Welch, Hanning, and Hamming and four CWT techniques such as Morlet, Paul (k=2), derivative of Gaussian DOG (k=2), and DOG (k=6) were implemented to study earthquake PGA in horizontal directions and their motion intensities. This study would help to locate the long-period ground motion in all the event locations. This research could help in identifying the areas, where the power of long-period ground motions could trigger structural damage. This study concludes that Hanning and Hamming’s windows are superior to others in STFT, and DOG (k=6) is better than others in CWT analysis. Detailed seismic wave analysis and long-period ground motions study are required to minimize the structural damage during destructive earthquakes. Hence, wavelet techniques could be useful to specify the seismically active locations and local structural features.

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Flexible Surface Acoustic Wave (Saw) Magnetic Sensor Based on Terfenol-D Grating-Arrayed Thin Film

SSRN

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SSRN 2024年

作者： Qadir, Akeel Muhammad, Fayyaz Shafique, Shareen Haider, Zeeshan Zhimin, Yang Chuanjiao, Hou Xu, Xiaoqing Han, Gao Shi, Tianjin Karim, Shahid Xu, Hongsheng Farooq, Umar SchoolofInformation Engineering Xi’an Eurasia University 710065 China Key Laboratory of Micro and Nano Electro-Mechanical Systems Department of Microsystems Engineering NorthwesternPolytechnicalUniversity Xi'An710072 China Islamabad Pakistan Department of Microelectronic Science and Engineering Laboratory of Clean Energy Storage and Conversion School of Physical Science and Technology Ningbo Collaborative Innovation Center of Nonlinear Calamity System of Ocean andAtmosphere Ningbo University Ningbo315211 China Institute ofbiomedicalandhealthengineering Shenzhen Institute ofAdvancedTechnology China Hangzhou Hikvision DigitalTechnology Co. Ltd Hangzhou310052 China Faculty ofScience andTechnology ILMA University Karachi Pakistan Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits Anhui University HefeiAnhui230601 China

In recent years, research on the development of SAW based magnetic sensors has gained substantial interest since magnetic sensors are vital measuring components in numerous industrial, engineering and scientific applications. Literature survey revealed that SAW magnetic sensors are generally developed using rigid substrates which make them unsuitable to be mounted on uneven or bent surfaces. In this paper, SAW magnetic sensor design based on Terfenol-D grating-arrayed thin film is presented. The design is implemented on flexible Lithium Niobate (LiNbO3) substrate in order to address the design limitations associated with the conventional SAW magnetic sensors that are implemented on rigid substrates. The experimental results obtained suggest that the proposed SAW magnetic sensor is durable and have tendency to undergo mechanical deformation under stress while keeping the high performance. © 2024, The Authors. All rights reserved.

关键词： Thin films

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Classification of Lung Cancer by Using Machine Learning Algorithms

Classification of Lung Cancer by Using Machine Learning Algo...

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International Conference on engineering Technology and their Applications (IICETA)

作者： Joman Al-Tawalbeh Baraa Alshargawi Hiam Alquran Waleed Al-Azzawi Wan Azani Mustafa Ahmed Alkhayyat Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Medical technical college Al-Farahidi University Baghdad Iraq Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Pauh Putra Campus Advanced Computing Centre of Excellence Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Due to the structure of cancer cells, where the majority of the cells are overlapped with each other, early diagnosis of lung cancer is a difficult challenge, so the cause of lung cancer remains unknown and prevention is difficult, early discovery of lung cancer is the only approach to cure it. The classification of lung cancer is a crucial process, based on signs that appear on patients; cancers could easily be predicted and treated. This paper uses KNN, SVM, Naïve Bayes and narrow neural network (NNN) classifiers. KNN showed 85.87% accuracy. SVM, Naïve Bayes and NNN showed 92.6%, 90.3% and 90% accuracy respectively. Results came after the fact that among the four classifiers SVM was the most accurate and much better to classify our data.

关键词： Support vector machines Machine learning algorithms Lung cancer Medical treatment Artificial neural networks

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Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed

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Geoscience Frontiers 2019年第2期10卷 389-403页

作者： J.H.Abdulkareem B.Pradhan W.N.A.Sulaiman N.R.Jamil Department of Environmental Science Faculty of Environmental StudiesUniversiti Putra Malaysia (UPM) Department of Soil Science Institute for Agricultural Research/Faculty of AgricultureAhmadu Bello University Department of Civil Engineering Faculty of EngineeringGeospatial Information Science Research Center (GISRC)Universiti Putra Malaysia (UPM) Centre for Advanced Modeling and Geospatial Information Systems (CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology Sydney Department of Energy and Mineral Resources Engineering Choongmu-gwanSejong University

The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing,wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, *** causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover(LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km^2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation(USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor,topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential(reversible soil loss) or 0-1 t ha^(-1) yr^(-1) soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC *** from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions(1984-2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significance of LULC in the control of erosion. Maps generated from the study may be usef

关键词： Soil erosion USLE Landuse/cover changes GIS Malaysia

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Electric vehicle impact on energy industry, policy, technical barriers, and power systems

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International Journal of Thermofluids 2022年 13卷

作者： Abo-Khalil, Ahmed G. Abdelkareem, Mohammad Ali Sayed, Enas Taha Maghrabie, Hussein M. Radwan, Ali Rezk, Hegazy Olabi, A.G. Dept. of Sustainable and Renewable Energy Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering Assuit University Assuit71515 Egypt Sustainable Energy & Power Systems Research Centre RISE University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Faculty of Engineering Minia University Elminia Egypt Center for Advanced Materials Research University of Sharjah PO Box 27272 Sharjah United Arab Emirates Department of Mechanical Engineering Faculty of Engineering South Valley University Qena83521 Egypt Mechanical Power Engineering Department Mansoura University El-Mansoura35516 Egypt College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz University Saudi Arabia Mechanical Engineering and Design Aston University School of Engineering and Applied Science Aston Triangle BirminghamUKB4 7ET United Kingdom

The proliferation of eco-friendly electric vehicles is becoming an irresistible trend. As governments worldwide tighten regulations on fuel efficiency and exhaust gas, the position of electric vehicles is expected to become more and more solid. The essence of this study is to cover the main components of the EV, the needed infrastructure, and investigate the impacts of the high insertion in the power systems. Moreover, this work presents an evaluation for the electric vehicles in energy distribution networks with a focus on adapting the capacity of charging stations. It was possible to process the data to confine the impact of the demand increase in distribution generators. The study allows evaluating the effect of random variables related to the distances previously traveled by vehicles and the instant of their connection to the electricity grid. These effects were compared considering these variables in deterministic terms and allowed to conclude the importance of using these more realistic methods to calculate the capacity of distributed generators to supply this new demand. © 2022 The Author(s)

关键词： Electric vehicles

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Cosmic Ray Composition in the Second Knee Region as Measured by the TALE Hybrid Detector 37

Cosmic Ray Composition in the Second Knee Region as Measured...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Fujita, Keitaro 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. Graduate School of Science Osaka City University 3-3-138 Sugimoto Sumiyoshi-ku Osaka558-8585 Japan 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 Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Cent

The Telescope Array Low-energy Extension (TALE) experiment is a hybrid air shower detector for the observation of air showers induced by cosmic rays with energy above 1016 eV. The TALE detector consists of a Fluorescence Detector (FD) station with 10 FD telescopes located at the TA Middle Drum FD Station (itself made up of 14 FD telescopes), and a Surface Detector (SD) array made up of 80 scintillation counters, including 40 with 400 m spacing and 40 with 600 m spacing. A triggering system for the TALE-SD using an external trigger from the TALE-FD, a so-called hybrid trigger, allows for a lower energy threshold. The TALE hybrid trigger system has been working since 2018. Here we present an estimate of the performance of hybrid detection using a Monte Carlo simulation, and a first measurement of the cosmic ray composition and spectrum using the TALE-Hybrid detector. © Copyright owned by the author(s).

关键词： Cosmic rays

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Robustness of a U-net model for different image processing types in segmentation of the mammary gland region 16

Robustness of a U-net model for different image processing t...

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16th International Workshop on Breast Imaging, IWBI 2022

作者： Yamamuro, Mika Asai, Yoshiyuki Hashimoto, Naomi Yasuda, Nao Kimura, Hiroto Yamada, Takahiro Nemoto, Mitsutaka Kimura, Yuichi Handa, Hisashi Yoshida, Hisashi Abe, Koji Tada, Masahiro Habe, Hitoshi Nagaoka, Takashi Nin, Seiun Ishii, Kazunari Lee, Yongbum Radiology Center Kindai University Hospital 377-2 Ono-higashi Osaka Osaka-sayama589-8511 Japan Division of Positron Emission Tomography Institute of Advanced Clinical Medicine Kindai University 377-2 Ono-higashi Osaka Osaka-sayama589-8511 Japan Department of Computational Systems Biology Kindai University Faculty of Biology-Oriented Science and Technology 930 Nishimitani Wakayama Kinokawa649-6433 Japan Department of Informatics Kindai University Faculty of Science and Engineering 3-4-1 Kowakae Osaka Higashi-osaka577-8502 Japan Department of Radiology Kindai University Faculty of Medicine 377-2 Ono-higashi Osaka Osaka-sayama589-8511 Japan Graduate School of Health Sciences Niigata University 2-746 Asahimachidori Chuouku Niigata951-8518 Japan

ISBN: (纸本)9781510655843

Many studies have assessed breast density in clinical practice. However, calculation of breast density requires segmentation of the mammary gland region, and deep learning has only recently been applied. Thus, the robustness of the deep learning model for different image processing types has not yet been reported. We investigated the accuracy of segmentation of the U-net for mammograms made with various image processing types. We used 478 mediolateral oblique view mammograms. The mammograms were divided into 390 training images and 88 testing images. The ground truth of the mammary gland region made by mammary experts was used for the training and testing datasets. Four types of image processing (Types 1-4) were applied to the testing images to compare breast density in the segmented mammary gland regions with that of ground truths. The shape agreement between ground truth and the segmented mammary gland region by U-net of Types 1-4 was assessed using the Dice coefficient, and the equivalence or compatibility of breast density with ground truth was assessed by Bland-Altman analysis. The mean Dice coefficients between the ground truth and U-net were 0.952, 0.948, 0.948, and 0.947 for Types 1, 2, 3, and 4, respectively. By Bland-Altman analysis, the equivalence of breast density between ground truth and U-net was confirmed for Types 1 and 2, and compatibility was confirmed for Types 3 and 4. We concluded that the robustness of the U-net for segmenting the mammary gland region was confirmed for different image processing types. © 2022 SPIE.

关键词： Mammography

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The status of the TALE surface detector array and a TALE infill project 37

The status of the TALE surface detector array and a TALE inf...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Iwasaki, Aoi Ogio, Shoichi 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. Globus, N. 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. Graduate School of Science Osaka City University 3-3-138 Sugimoto Sumiyoshi-ku Osaka Japan Nambu Yoichiro Institute of Theoretical and Experimental Physics Osaka City University 3-3-138 Sugimoto Sumiyoshi-ku Osaka Japan 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 Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Theorique Universit

Routine hybrid observations of the surface detectors (SD) in conjunction with the fluorescence detectors (FD) of the Telescope Array Low-energy Extension (TALE) began in November 2018. In this presentation, we will describe the simulation studies of detector aperture and resolution of the TALE SD, and report on the latest observation results other than the energy spectrum. We are also in the process of expanding the experiment by 50 SDs, with even smaller nearest-neighbor spacing, in order lower the energy threshold to match that of the Cherenkov-dominated events seen by the FD. Details of the upgrade and expected performance of this new extension will be discussed. © Copyright owned by the author(s).

关键词： Fluorescence

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Reconstruction of Air Shower Events Measured by the Surface Detectors of the TAx4 Experiment 37

Reconstruction of Air Shower Events Measured by the Surface ...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Jeong, Hyomin 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. Sungkyunkwan University Physics Department Seobu-ro Suwon-si2066 Korea Republic of Cooperation Center for Cosmic Ray Research Seobu-ro Suwon-si2066 Korea Republic of 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 Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of P

The source, propagation and acceleration mechanism of ultra-high-energy cosmic rays (UHECR) have been investigated since the first discovery of the UHECRs to solve the mystery of the extremely high-energy universe. The Telescope Array times 4 (TAx4) experiment, which currently consists of 257 Surface Detectors (SDs) and 2 Fluorescence Detector (FD) stations, had been built in Utah, USA in 2019. The TAx4 SDs observe secondary particles in an extensive air shower induced by the UHECR, and we reconstruct arrival direction and energy of the UHECR using the signal intensities and timings measured by the SDs. We present the reconstruction procedure of the UHECR and the preliminary energy spectrum above 10 EeV measured by the TAx4 SDs. © Copyright owned by the author(s).

关键词： Fluorescence

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Defect engineering approaches for metal oxide semiconductor-based chemiresistive gas sensing

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Coordination Chemistry Reviews 2025年 541卷

作者： Kumar, Amit Mazumder, Julaiba Tahsina Joyen, Kenza Favier, Frédéric Mirzaei, Ali Kim, Jin-Young Kwoka, Monika Bechelany, Mikhael Jha, Ravindra Kumar Kumar, Mahesh Kim, Hyoun Woo Kim, Sang Sub Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur 342030 India Department of Electronics Assam Skill University Assam Mangaldai 784125 India European Institut of Membranes (IEM) – UMR 5635 University of Montpellier ENSCM CNRS Montpellier 34090 France Institut Charles Gerhardt Montpellier (ICGM) – UMR 5253 University of Montpellier ENSCM CNRS Montpellier 34090 France Department of Materials Science and Engineering Shiraz University of Technology Shiraz 715557-13876 Iran Customized manufacturing R&D department Korea Institute of Industrial Technology Gyeonggi-do 15014 South Korea Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland Functional Materials Group Gulf University for Science and Technology (GUST) Mubarak Al-Abdullah 32093 Kuwait Department of Electronics and Electrical Engineering & Centre for Intelligent Cyber-Physical Systems Indian Institute of Technology Guwahati Guwahati 781039 India Division of Materials Science and Engineering Hanyang University Seoul 04763 South Korea Department of Materials Science and Engineering Inha University Incheon 22212 South Korea

Defect engineering in metal oxides presents a promising approach for tailoring material properties. This strategy enhances gas sorption, catalysis, and control over key physical characteristics such as bandgap, magnetic behavior, and electrical conductivity. Despite its potential, the role of defect engineering in advancing metal oxide semiconductor (MOS) gas-sensing performance remains underexplored. This review introduces defect engineering strategies, emphasizing their applications in gas sensing. Gas sensors play a vital role in environmental monitoring, industrial safety, healthcare, and in improving energy efficiency. The demand for advanced gas sensors has never been more critical, given the need for real-time, accurate, and cost-effective detection of pollutant gases. MOS-based gas sensors are widely used in air quality monitoring, industrial safety, and health diagnostics. Vacancies and defect architectures in MOS have been widely studied for their role in sensing performance, as they fundamentally influence sensor efficiency. The effectiveness of MOS sensors largely depends on the type and concentration of defects. This review introduces vacancies and defects in MOS, followed by an in-depth discussion of defect types, factors influencing defect formation, and their role in charge transport. Additionally, it examines the correlation between elemental chemical properties and defect chemistry. Special attention is given to the defect chemistry of metal oxides, including TiO₂, ZnO, Co₃O₄, ZrO₂, WO₃, and CeO₂. It concludes with an examination of surface engineering methods for defect control to improve gas-sensing capabilities. © 2024

关键词： Chemiresistive gas sensing Defect engineering Metal-oxide semiconductor

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