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37th International Cosmic Ray Conference, ICRC 2021

作者： Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Morselli, A. Mrakovcic, K. Murase, K. Nagai, A. Nakamori, T. Nickel, L. LISTIC Université Savoie Mont-Blanc Polytech Annecy-Chambéry 5 chemin de bellevue Annecy74940 France Institute for Cosmic Ray Research University of Tokyo Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Italy INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France The Barcelona Institute of Science and Technology Spain IRFU CEA Université Paris-Saclay France Laboratoire d'Annecy de Physique des Particules Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS LAPP 9 chemin de bellevue Annecy74940 France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Instituto de Física de Partículas y del Cosmos Departamento de EMFTEL Universidad Complutense de Madrid Madrid Spain INAF Osservatorio di Astrofisica e Scienza Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik Germany Department of Physics TU Dortmund University Germany Saha Institute of Nuclear Physics INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Port d'Informació Científica INFN Sezione di Torino Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engin

When very-high-energy gamma rays interact high in the Earth's atmosphere, they produce cascades of particles that induce flashes of Cherenkov light. Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and convert them into shower images that can be analyzed to extract the properties of the primary gamma ray. The dominant background for IACTs is comprised of air shower images produced by cosmic hadrons, with typical noise-to-signal ratios of several orders of magnitude. The standard technique adopted to differentiate between images initiated by gamma rays and those initiated by hadrons is based on classical machine learning algorithms, such as Random Forests, that operate on a set of handcrafted parameters extracted from the images. Likewise, the inference of the energy and the arrival direction of the primary gamma ray is performed using those parameters. State-of-the-art deep learning techniques based on convolutional neural networks (CNNs) have the potential to enhance the event reconstruction performance, since they are able to autonomously extract features from raw images, exploiting the pixel-wise information washed out during the parametrization process. Here we present the results obtained by applying deep learning techniques to the reconstruction of Monte Carlo simulated events from a single, next-generation IACT, the Large-Sized Telescope (LST) of the Cherenkov Telescope Array (CTA). We use CNNs to separate the gamma-ray-induced events from hadronic events and to reconstruct the properties of the former, comparing their performance to the standard reconstruction technique. Three independent implementations of CNN-based event reconstruction models have been utilized in this work, producing consistent results. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Hadrons

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Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach

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Physical Review B 2019年第20期100卷 205411-205411页

作者： V. Torres P. Silva E. A. T. de Souza L. A. Silva D. A. Bahamon Instituto de Física Universidade Federal Fluminense Niterói Av. Litorânea sn 24210-340 RJ-Brazil MackGraphe – Graphene and Nano-Materials Research Center Mackenzie Presbyterian University Rua da Consolação 896 01302-907 São Paulo SP Brazil School of Computing and Informatics & Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Rua da Consolação 896 01302-907 São Paulo SP Brazil

The valley transport properties of a superlattice of out-of-plane Gaussian deformations are calculated using a Green's function and a machine learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation; these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counterpropagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make it difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a deep neural network can be trained to predict valley polarization with a precision similar to the Green's function but with much less computational effort.

关键词： Ballistic transport Quantum transport Strain Graphene Landauer formula Machine learning Tight-binding model

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A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems 15

A Neuro-Inspired Approach to Solve a Simultaneous Location a...

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15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

作者： Menezes, Matheus Chaves De Freitas, Edison Pignaton Cheng, Sen De Oliveira, Alexandre Cesar Muniz De Almeida Ribeiro, Paulo Rogerio Graduate Program on Computer Science Federal University of Maranhão Sao Luis Brazil Computational Neuroscience Ruhr University Bochum Institute for Neural Computation Bochum Germany Department of Informatics Federal University of Maranhão Sao Luis Maranhao Brazil Department of Computer Engineering Federal University of Maranhão Sao Luis Maranhao Brazil

ISBN: (纸本)9781538695821

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots. © 2018 IEEE.

关键词： Mapping

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Halstead Metric for Quality Measurement of Various Version of Statcato 5

Halstead Metric for Quality Measurement of Various Version o...

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5th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2018

作者： Binanto, Iwan Warnars, Harco Leslie Hendric Spits Abbas, Bahtiar Saleh Sianipar, Nesti Fronika Informatics Department Sanata Dharma University Yogyakarta55002 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Food Technology Department Bina Nusantara University Jakarta11480 Indonesia Research Interest Group Biotechnology Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538655276

Halstead metric is one of software quality measurement technique. Some studies call it as Primitive and/or Classic Software Metric, although some researchers find vagueness of it. This paper utilizing Halstead metric to measuring the quality of various versions of Statcato software. Measurements are performed in each class of each version of Statcato software. Python programming used to facilitate this. This paper find that in terms of Halstead metric, this software seems stable during its lifecycle. So, the quality of the newer versions of Statcato are better in terms of stability of Halstead metrics than the earlier version, although its feature is increasing. © 2018 IEEE.

关键词： High level languages

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Cloud computing model in higher education

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IOP Conference Series: Materials Science and Engineering 2020年第1期852卷

作者： Bagus Mulyawan Raymond Kosala Benny Ranti Suhono Harso Supangkat Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Universitas Tarumanagara Jakarta 11440 Computer Science Department Faculty of Computer Science Universitas Indonesia Indonesia 16424 Schoolof Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

Currently, higher education is institutions that should be managed as a global business institution. Higher education should improve continually its business processes according to the demands of increasing its stakeholders. of the problems faced in the development of educational, activities if related to the use of information technology for each service in higher education for stakeholders is the issue of available funds. Cloud computing also has advantages related to infrastructure and maintenance and increases operating efficiency. Most institutions are currently moving towards cloud computing technology to reduce operational costs. For achieving these objectives, the institutions need to be supported by reliable technology. This study aim to propose cloud model for education and IT Governance conceptual framework for Cloud Computing model adoption, Tarumanagara University as a study case.

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A Key Risk Indicator for the game usage lifecycle 30

A Key Risk Indicator for the game usage lifecycle

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30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017

作者： Kummer, Luiz Bernardo Martins Nievola, Júlio Cesar Paraiso, Emerson Cabrera Graduate Program in Informatics Pontificia Universidade Católica do Paraná Rua Imaculada Conceicao 1155 Curitiba-PR Brazil

ISBN: (纸本)9781577357872

Among the different types of digital games, Massively Multiplayer Online Role-Playing Games (MMORPGs) are one of the most popular. Game producers use usage data to compute metrics to analyze their game lifecycles. The most popular is the MAU (Monthly Active Users), which indicates the number of active players in each timestamp. MAU only describes how many players played a game. It cannot show how players are motivated for that game. We support that motivation is a key factor to follow and game producers should be aware of it. In this way, we are proposing in this article a new game independent Key Risk Indicator. The risk indicator is based on commitment and, in our context, can be defined as the attachment of a player to a given game. The game industry may use commitment to evaluate how attractive a game is during its usage lifecycle. A machine learning approach is presented to predict the risk indicator. We applied the method to a real dataset. The proposed approach identified risky situations where the classical approach did not. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning systems

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QRFA: A data-driven model of information-seeking dialogues

arXiv

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arXiv 2018年

作者： Vakulenko, Svitlana Revoredo, Kate Di Ciccio, Claudio de Rijke, Maarten Vienna University of Economics and Business Vienna Austria Graduate Program in Informatics Federal University of Rio de Janeiro Rio de Janeiro Brazil University of Amsterdam Amsterdam Netherlands

Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances exchanged between a user and an agent. Process mining techniques have been successfully applied to analyze structured event logs, discovering the underlying process models or evaluating whether the observed behavior is in conformance with the known process. In this paper, we apply process mining techniques to discover patterns in conversational transcripts and extract a new model of information-seeking dialogues, QRFA, for Query, Request, Feedback, Answer. Our results are grounded in an empirical evaluation across multiple conversational datasets from different domains, which was never attempted before. We show that the QRFA model better reflects conversation flows observed in real information-seeking conversations than models proposed previously. Moreover, QRFA allows us to identify malfunctioning in dialogue system transcripts as deviations from the expected conversation flow described by the model via conformance analysis. Copyright © 2018, The Authors. All rights reserved.

关键词： Data mining

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IDF2022-1112 The gut microbiome of obese postpartum women with and without gestational diabetes mellitus and of their babies

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Diabetes Research and Clinical Practice 2023年 197卷 110364-110364页

作者： Dualib, P. Taddei, C. Fernandes, G. Carvalho, C. Sparvoli, L.G. Bittencourt, C. Tande, I. Mattar, R. Ferreira, S. Dib, S. Almeida-Pititto, B. UNIFESP Department of Medicine- Escola paulista de Medicina São Paulo Brazil Universidade de São Paulo Department of Clinical and Toxicological Analysis São Paulo Brazil Instituto René Rachou – Fiocruz Minas DepaBiosystems Informatics and Genomics Group Belo Horizonte Brazil UNIFESP Graduate Program in Endocrinology and Metabology São Paulo Brazil Centro Universitário Estácio de Sá Nutrition Course Belo Horizonte Brazil UNIFESP Departament of Obstetrics- Escola Paulista de Medicina São Paulo Brazil Universidade de São Paulo Department of Epidemiology- School of Public Health São Paulo Brazil UNIFESP Department of Medicine- Escola Paulista Medicina São Paulo Brazil UNlFESP Department of Preventive Medicine- Escola Paulista de Medicina São Paulo Brazil

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Adverse events following the first, second and third doses of a COVID-19 vaccine in hemodialysis patients

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Renal failure 2023年第1期45卷 2172432页

作者： Mei-Fen Pai Kuei-Ting Tung Shih-Ping Hsu Yu-Sen Peng Wan-Yu Lin Ju-Yeh Yang Hon-Yen Wu Yen-Ling Chiu Kai-Hsiang Shu Wan-Chuan Tsai Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan. Department of Electrical Engineering Yuan Ze University Taoyuan City Taiwan. Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan. Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. Faculty of Medicine School of Medicine National Yang-Ming University Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan.

BACKGROUND:This study aimed to identify adverse events following the first three doses of COVID-19 vaccines in hemodialysis (HD) patients. Risk factors associated with postvaccination adverse events were explored. METHODS:Postvaccination adverse events in 438 HD patients who received 3 doses of COVID-19 vaccines were prospectively assessed. The adverse events among three doses were compared using generalized linear mixed models. Factors associated with adverse events were assessed with multivariate analyses. RESULTS:The vast majority of participants received Oxford/AstraZeneca ChAdOx1 as their first two doses and Moderna mRNA-1273 as their third dose. Overall, 79%, 50% and 84% of the participants experienced at least one adverse event after their first, second, and third doses, respectively. These adverse events were mostly minor, short-lived and less than 5% reported daily activities being affected. Compared with the first dose, the second dose caused a lower rate of adverse events. Compared with the first dose, the third dose elicited a higher rate of injection site reactions and a lower rate of systemic reactions. Multivariate analyses showed that every 10-year increase of age (odds ratio 0.67, 95% confidence intervals 0.57-0.79) was associated with decreased risk of adverse events, while female sex (2.82, 1.90-4.18) and arteriovenous fistula (1.73, 1.05-2.84) were associated with increased risk of adverse events. Compared with Oxford/AstraZeneca ChAdOx1, Moderna mRNA-1273 was associated with an increased risk of injection site reactions. CONCLUSIONS:COVID-19 vaccination was well tolerated in HD patients. Age, sex, dialysis vascular access and vaccine types were associated with postvaccination adverse events.

关键词： COVID-19 ChAdOx1 Hemodialysis adverse event mRNA-1273 vaccination

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Real world fracture prediction of fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OSTA) and one-minute osteoporosis risk test: An 11-year longitudinal study

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Bone Reports 2024年 20卷 101742页

作者： Sheng, Yueh-Hsuan Wu, Tai-Yin Liaw, Chen-Kun Hsiao, Sheng-Huang Kuo, Kuan-Liang Tsai, Ching-Yao Department of Family Medicine Taipei City Hospital Renai Branch No. 10 Sec. 4 Renai Rd. Daan Dist. Taipei City Taiwan Department of Family Medicine Taipei City Hospital Zhongxing Branch No. 145 Zhengzhou Rd. Datong Dist. Taipei City Taiwan Institute of Epidemiology and Preventive Medicine National Taiwan University No. 17 Xuzhou Rd. Zhongzheng Dist. Taipei City Taiwan Department of Health and Welfare University of Taipei No. 101 Sec. 2 Zhongcheng Rd. Shilin Dist Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan Department of Orthopedics Shuang Ho Hospital Taipei Medical University No. 291 Zhongzheng Rd. Zhonghe Dist. New Taipei City Taiwan Research Center of Biomedical Device College of Biomedical Engineering Graduate Institute of Biomedical Optomechatronics Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan TMU Biodesign Center Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan Department of Neurosurgery Taipei City Hospital Renai Branch No. 10 Sec. 4 Ren'ai Rd. Da'an Dist. Taipei City Taiwan Department of Psychology National Chengchi University No. 64 Sec. 2 Zhinan Rd. Wenshan Dist. Taipei City Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City Taiwan The Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen University No. 510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City Taiwan Department of Ophthalmology Taipei City Hospital No. 145 Zhengzhou Rd. Datong Dist. Taipei City Taiwan Institute of Public Health National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City Taiwan Department of Business Administration Fu Jen Catholic University No. 510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City Taiwan

Introduction: Fractures affect people's quality of life especially in the elders. One of the most important risk factors is osteoporosis. There are many screening tools to predict osteoporosis and fractures. We aimed to compare the predictive validity of three commonly used screening tools: fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OSTA) and one-minute osteoporosis risk test. Among them, OSTA and one-minute osteoporosis risk test were originally developed to predict osteoporosis risks and FRAX was to predict fracture risks. Methods: This is an 11-year longitudinal study. We enrolled 708 senior people from health examinees in Taiwan in 2010. A standardized questionnaire and blood tests were provided. Annual telephone interview was conducted to assess the real fracture status. We calculated risk scores of FRAX, OSTA, and one-minute osteoporosis risk test and compared with real-world fracture records. Results: The mean age of the participants were 74.9 (SD 6.4). There were 356 (50.3 %) men. From 2010 to 2020, a total of 105 (14.8 %) persons suffered from fractures. Compared to people without fractures, people with fractures had higher FRAX major osteoporotic fracture risk scores (14.0 % ± 7.6 % vs.11.3 % ± 5.7 %), higher hip fracture risk scores, and higher OSTA risk (5.9 % ± 1.4 % vs. 5.3 % ± 1.3 %). Cox regression analysis showed that hazard ratios for fracture of high FRAX risk was 1.53 (95 % confidence interval (CI) 1.05–2.21), and for high OSTA risk was 1.37 (95 % CI 1.04–1.82). Conclusions: Only OSTA and FRAX scores were satisfactory in predicting 10-year fractures. © 2024

关键词： Fracture risk assessment tool (FRAX) Fractures One-minute osteoporosis risk test Osteoporosis self-assessment tool for Asians (OSTA)

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