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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

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Experimental study on laser cladding forming of 316 L stainless steels based on Response Surface Methodology

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Structures 2025年 79卷

作者： Ran Feng Zhukai Tang Wai-Meng Quach Jinghua Yao Ming Yan Hieng-Ho Lau School of Intelligent Civil and Ocean Engineering Harbin Institute of Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering China Department of Civil and Environmental Engineering University of Macau Macau China Xiamen Branch of Luoyang Ship Material Research Institute Xiamen 361101 China State Key Laboratory for Marine Corrosion and Protection Luoyang Ship Material Research Institute (LSMRI) Xiamen 361101 China Department of Materials Science and Engineering Shenzhen Key Laboratory for Additive Manufacturing of High-performance Materials Southern University of Science and Technology Shenzhen 518055 China Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Malaysia

In the realm of marine and coastal structures, laser cladding technology has demonstrated an immense potential in the structural and material repair. This study conducted a series of single-layer single-track laser cladding experiments on grade Q235 low-carbon steels, utilizing grade 316 L stainless steel powders under various laser powers, scanning speeds, and powder feeding rates. Subsequently, the process parameters were optimized based on the Response Surface Methodology (RSM). Furthermore, single-layer multi-track and multi-layer single-track laser cladding tests were performed to ascertain optimal overlap ratios, scanning patterns, and lifting amounts. Finally, the repair efficacy of optimized process parameters on corroded specimens was validated through standardized tensile testing. The results reveal that the laser power, scanning speed, and powder feeding rate significantly impact the formation quality and morphology of the cladding quality. Notably, the depth of the heat-affected zone (HAZ) increases proportionally with the increase in laser power and inversely with the decrease in scanning speed. Moreover, a well-matched powder feeding rate with laser energy is crucial to ensure a full and continuous morphology of the cladding tracks. Furthermore, the regression models were established, which exhibited a high predictive accuracy for the depth of the HAZ, the width-to-height ratio, and the dilution rate in laser cladding. The optimal parameters for single-layer single-track laser cladding were determined to be a laser power of 1405.9 W, a scanning speed of 12.68 mm/s, and a powder feeding rate of 0.7 rad/min. Finally, based on a comparative analysis of single-layer multi-track and multi-layer single-track laser cladding experiments, it is recommended that the overlap ratio should be maintained within the range of 31.58–42.11 %, and a lifting amount of 0.63 mm. The optimized process parameters significantly enhance the mechanical properties of corrosion-da

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Investigation of the atomic and thermal performance of Al 2 O 3 nanoparticles/octadecane as phase change materials (PCM) in circular tube with molecular dynamics simulation

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International Communications in Heat and Mass Transfer 2025年 167卷

作者： Gongxing Yan Jialing Li Ihab Omar Soheil Salahshour Rozbeh Sabetvand Riadh Marzouki School of Architecture and Engineering Xinjiang Applied Vocational ad Technical College Yili 8333200 Xinjiang China School of Artificial intellegence Chongqing Youth Vocational & Technical College Chongqing 401320 China Air Conditioning Engineering Department Faculty of Engineering Warith Al-Anbiyaa University Karbala 56001 Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran Department of Chemistry College of Science King Khalid University P.O. Box 9004 61413 Abha Saudi Arabia

Phase change materials' heat transfer mechanism may be enhanced by high thermal conductivity nanoparticles, allowing for quicker and more efficient thermal energy storage and release. This study examined the atomic and thermal characteristics of octadecane as a phase transition material inside a circular tube using molecular dynamics modeling with Al 2 O 3 nanoparticles. At 0.031 (±0.002) atoms/Å 3 , the tube walls had the highest density after 20 ns. The tube's maximum recorded temperature was 751.51 (±1.01) K, and its peak velocity was 0.0078 (±0.0001) Å/fs. After 20 ns, the thermal conductivity was 1.35 (±0.01) W/m·K and the heat flux was 3.84 (±0.01) W/m 2 . The charging and discharging times of the structure were 6.45 (±0.05) and 7.15 (±0.03) ns, respectively. This study shows how AlO₃ nanoparticles can enhance the thermal performance of octadecane in energy storage applications. It also provided important information about the atomic-level behavior of these materials during phase transitions.

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Simulation study on cosmic ray shower rate variations with LHAASO-KM2A during thunderstorms 38

Simulation study on cosmic ray shower rate variations with L...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Yang, Ci Zhou, Xunxiu Chen, Xuejian Huang, Daihui Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. School of Physical Science and Technology Southwest Jiaotong University Chengdu610031 China Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Sun Yat-sen University Zhuhai519000 China School of Physics Sun Yat-sen University Guangdong Guangzhou510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai519000 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Départeme

The Large High Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA, and WFCTA. As the major array of LHAASO, KM2A has been operating stably in shower mode. To study the near-earth atmospheric electric field (AEF) effect on the trigger event rate during thunderstorms, Monte Carlo simulations are performed with CORSIKA and G4KM2A. According to the simulations, the shower rate variations are found to be strongly dependent on the strength and polarity of the AEF. The shower rates increase with the field intensity. In positive AEF (defined as the direction pointing towards the ground), the increased amplitude is less than that in negative AEF. With the same field strength 1000 V/cm, the value exceeds 12% in a negative field, and merely is up to 6% in the positive one. The dependence of the trigger rate variation on the thickness of the AEF layer is also simulated. The shower event rate increases dramatically at small thickness, and then the trend of variation slows down with the AEF layer thickness. This indicates that the AEF with larger layer thickness has more deflection effects on the development of an extensive air shower. The shower rate variations are also found to be dependent on the primary zenith angle. Our simulation results could be useful in understanding the variation of trigger rate detected by LHAASO-KM2A during thunderstorms. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

Research Square

引用

Research Square 2021年

作者： Usmani, Raja Sher Afgun Pillai, Thulasyammal Ramiah Hashem, Ibrahim Abaker Targio Marjani, Mohsen Shaharudin, Rafiza Binti Latif, Mohd Talib School of Computer Science and Engineering Taylor’s University Selangor Malaysia College of Computing and Informatics Department of Computer Science University of Sharjah Sharjah27272 United Arab Emirates Institute for Medical Research Environmental Health Research Centre Occupational Health Unit Jalan Pahang Kuala Lumpur50588 Malaysia Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia

Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the Artificial Intelligence (AI) techniques. We propose the Enhanced Long Short-Term Memory (ELSTM) model and provide a comparison with other AI techniques, i.e., Long Short-Term Memory (LSTM), Deep Learning (DL), and Vector Autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia. © 2021, CC BY.

关键词： Long short-term memory

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Resume Designing using Speech Recognition System

Resume Designing using Speech Recognition System

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Hoshiyar Singh Kanyal Mukulit Goel R.C Tripathi Anuj Kumar Birendra Kr. Saraswat Shubham Singh Koneru Lakshmaiah Education Foundation Hyderabad India Assistant Professor Hi-Tech Institute of Engg & Technology Ghaziabad India Professor Faculty of Engineering and Computing ScienceTMU Moradabad India GL Bajaj Institute of Technology and Management Greater Noida India Hi-Tech Institute of Engg. & Technology Ghaziabad India

ISBN: (纸本)9781665439718

There is abundance of resume builder tools used to design the resume of the job seeker in the simplest way but still their resume cannot get shortlisted. By keeping this in mind so many professionals are still working on it to build the resume in the smartest way. In our proposed system, we have designed a prototype which uses the voice of the jobseeker. We have proposed a desktop application in which user is not responsible for resume format rather the application interacts with user like an assistant and collects the data from the user using voice. Our tool is based on Speech-To-Text (STT) and Text-To-Speech (TTS) and other APIs which we will discuss in the paper.

关键词： Resumes Prototypes Speech recognition Market research Mobile applications

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UseGeo - A UAV-based multi-sensor dataset for geospatial research

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ISPRS Open Journal of Photogrammetry and Remote Sensing 2024年 13卷

作者： Nex, F. Stathopoulou, E.K. Remondino, F. Yang, M.Y. Madhuanand, L. Yogender, Y. Alsadik, B. Weinmann, M. Jutzi, B. Qin, R. ITC Department of Earth Observation Science Faculty ITC University of Twente Enschede Netherlands 3D Optical Metrology (3DOM) Unit Bruno Kessler Foundation (FBK) Trento Italy Visual Computing Group University of Bath Bath United Kingdom Department of Physical Geography Faculty of Geosciences University of Utrecht Utrecht Netherlands Inter-university Department of Regional & Urban Studies and Planning (DIST) Politecnico di Torino Italy Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany Geospatial Data Analytics Laboratory The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

3D reconstruction is a long-standing research topic in the photogrammetric and computer vision communities;although a plethora of open-source and commercial solutions for 3D reconstruction have been released in the last few years, several open challenges and limitations still exist. Undoubtedly, deep learning algorithms have demonstrated great potential in several remote sensing tasks, including image-based 3D reconstruction. State-of-the-art monocular and stereo algorithms leverage deep learning techniques and achieve increased performance in depth estimation and 3D reconstruction. However, one of the limitations of such methods is that they highly rely on large training sets that are often tedious to obtain;even when available, they typically refer to indoor, close-range scenarios and low-resolution images. Especially while considering UAV (Unmanned Aerial Vehicle) scenarios, such data are not available and domain adaptation is not a trivial challenge. To fill this gap, the UAV-based multi-sensor dataset for geospatial research (UseGeo - https://***/home) is introduced in this paper. It contains both image and LiDAR data and aims to support relevant research in photogrammetry and computer vision with a useful training set for both stereo and monocular 3D reconstruction algorithms. In this regard, the dataset provides ground truth data for both point clouds and depth maps. In addition, UseGeo can be also a valuable dataset for other tasks such as feature extraction and matching, aerial triangulation, or image and LiDAR co-registration. The paper introduces the UseGeo dataset and validates some state-of-the-art algorithms to assess their usability for both monocular and multi-view 3D reconstruction. © 2024 The Authors

关键词： 3D reconstruction Deep learning LiDAR Monocular depth estimation Multi-view stereo Stereo matching UAV

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Deep Learning for COVID-19 Contamination Analysis and Prediction Using ECG Images on Raspberry Pi 4

SSRN

引用

SSRN 2022年

作者： Mhamdi, Lotfi Dammak, Oussama Cottin, Francois Bendhaou, Imed Biotechnology Institute of Monastir. Environment street. Monastir 5000 Tunisia College of first common year Department of Mathematics Um Al Qura University POBox 14035 Mecca21955 Saudi Arabia Science faculty of Gabes University of Gabes Omar Ibn AlKhattab-Zrig road Gabes6029 Tunisia Paris-Saclay University CIAMS EA 4532 Orsay91405 France Orleans University CIAMS EA 4532. Orleans45067 France Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hikma University Jeddah22246-4872 Saudi Arabia Department of Technology Higher Institute of Computer Sciences and Mathematics University of Monastir Monastir5000 Tunisia Department of Computing University of Turku TurkuFI20014 Finland

Background: The main goal of this paper is to automatically diagnose COVID-19 contamination based on the artificial intelligence approach. We used Convolutional Neural Network (CNN) deep learning algorithm for analyzing the ECG images to detect cardiac abnormalities, consequent of the contamination by the SARS-CoV-2 virus;responsible for the Covid-19 ***: We designed, trained and evaluated the performance of two deep learning models (MobileNet V2 and VGG16) in detecting and distinguishing between two different classes (healthy subjects and COVID-19 positive cases). Indeed, this virus attacks the human respiratory system, which could affect the heart system. Thus, the development of a deep learning model could be useful to help for a quick and efficient diagnosis and prediction, and for decision making among ***: The overall classification rate achieved by the models was 99.34% and 99.67% for MobileNet V2 and VGG16, ***: This approach can efficiently contribute to the diagnosis of COVID-19 contamination. Therefore, the performed deep learning model may be used for predicting abnormal cardiac activities, consequent of the contamination by the virus. © 2022, The Authors. All rights reserved.

关键词： Coronavirus

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On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations

arXiv

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arXiv 2024年

作者： Bennett, Ed Forzano, Niccolò Hong, Deog Ki Hsiao, Ho Lee, Jong-Wan Lin, C.-J. David Lucini, Biagio Piai, Maurizio Vadacchino, Davide Zierler, Fabian Swansea Academy of Advanced Computing Swansea University Bay Campus Fabian Way Wales SwanseaSA1 8EN United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park Wales SwanseaSA2 8PP United Kingdom Department of Physics Pusan National University Busan46241 Korea Republic of Extreme Physics Institute Pusan National University Busan46241 Korea Republic of Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Daejeon34126 Korea Republic of Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Centre for High Energy Physics Chung-Yuan Christian University Chung-Li32023 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Bay Campus Fabian Way Wales SwanseaSA1 8EN United Kingdom Centre for Mathematical Sciences University of Plymouth PlymouthPL4 8AA United Kingdom

We provide the first extensive, numerical study of the non-trivial problem of mixing between flavor-singlet composite states emerging in strongly coupled lattice field theories with matter field content consisting of fermions transforming in different representations of the gauge group. The theory of interest is the minimal candidate for a composite Higgs model that also accommodates a mechanism for top partial compositeness: the Sp(4) gauge theory coupled to two (Dirac) fermions transforming as the fundamental and three as the two-index antisymmetric representation of the gauge group, respectively. We apply an admixture of APE and Wuppertal smearings, as well as the generalized eigenvalue problem approach, to two-point functions involving flavor-singlet mesons, for ensembles having time extent longer than the space extent. We demonstrate that, in the region of lattice parameter space accessible to this study, both masses and mixing angles can be measured effectively, despite the presence of (numerically noisy) contributions from disconnected diagrams. © 2024, CC BY.

关键词： Fermions

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General condition of quantum teleportation by one-dimensional quantum walks

arXiv

引用

arXiv 2021年

作者： Yamagami, Tomoki Segawa, Etsuo Konno, Norio Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo Tokyo113-8656 Japan Graduate School of Environment and Information Sciences Yokohama National University Yokohama240-8501 Japan Department of Applied Mathematics Faculty of Engineering Yokohama National University Yokohama240-8501 Japan

We extend the scheme of quantum teleportation by quantum walks introduced by Wang et al. (2017). First, we introduce the mathematical definition of the accomplishment of quantum teleportation by this extended scheme. Secondly, we show a useful necessary and sufficient condition that the quantum teleportation is accomplished rigorously. Our result classifies the parameters of the setting for the accomplishment of quantum teleportation. Copyright © 2021, The Authors. All rights reserved.

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