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FAIR Convergence Matrix:Optimizing the Reuse of Existing FAIR-Related Resources

Data Intelligence 2020年第1期2卷 158-170,313页

作者： Hana Pergl Sustkova Kristina Maria Hettne Peter Wittenburg Annika Jacobsen Tobias Kuhn Robert Pergl Jan Slifka Peter McQuilton Barbara Magagna Susanna-Assunta Sansone Markus Stocker Melanie Imming Larry Lannom Mark Musen Erik Schultes GO FAIR International Support and Coordination Office LeidenThe Netherlands Centre for Digital Scholarship Leiden University LibrariesLeidenThe Netherlands Max Planck Computing and Data Facility Gießenbachstraße 285748 GarchingGermany Leiden University Medical Center Leiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK Environment Agency Austria A-1090 ViennaAustria TIB Leibniz Information Centre for Science and Technology HannoverGermany SURF Utrecht 3511 EPThe Netherlands Corporation for National Research Initiatives(CNRI) RestonVirginia 20191USA Stanford Center for Biomedical Informatics Research StanfordCA 94305USA

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by *** by now widely accepted,the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR *** different communities have their own,often well-established implementation preferences and priorities for data reuse,coordinating a broadly accepted,widely used FAIR implementation approach remains a global *** an effort to accelerate broad community convergence on FAIR implementation options,the GO FAIR community has launched the development of the FAIR Convergence *** Matrix is a platform that compiles for any community of practice,an inventory of their self-declared FAIR implementation choices and *** Convergence Matrix is itself a FAIR resource,openly available,and encourages voluntary participation by any self-identified community of practice(not only the GO FAIR Implementation Networks).Based on patterns of use and reuse of existing resources,the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.

关键词： FAIR Implementation Choices and Challenges Convergence FAIR Communities

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

arXiv

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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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Smoking status prediction using bio-signals

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AIP Conference Proceedings 2024年第1期3135卷

作者： Obaidah Alquran Mohammed Alslatie Niveen Alawneh Hiam Alquran Wan Azani Mustafa Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid 21163 Jordan King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman 11855 Jordan Department of English Colleage of Litreture Jadara University Irbid Jordan Faculty of Electronic Engineering & Technology Pauh Putra Campus Universiti Malaysia Perlis (UniMAP) Arau 02600 Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra 02600 Arau Perlis Malaysia

Smoking is considered as one of the most common causes of death and diseases around the world. Its negative effect on the health of individuals and groups leads to preventable mortality and morbidity in many countries. This study aims to utilize bio-signals and their related features to predict the smoking status among patients using machine learning. The main part of this study is to use two different methods of features selection: Principal component analysis (PCA) and Ant Lion Optimizer (ALO) followed by a comparison of the effectiveness between both two methods. we used: accuracy, sensitivity, specificity, and precision measures for comparison, and the results showed that ALO algorithm outperformed with an accuracy of 98.1%, a sensitivity of 98.2%, a specificity of 97.9%, and a precision of 97.9%.

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The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv

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arXiv 2023年

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics Agh University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine Mevis Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan Forxai Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAB Canada Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

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A novel 3-D chaotic system with line equilibrium: Dynamical analysis, coexisting attractors, offset boosting control and circuit design 3

A novel 3-D chaotic system with line equilibrium: Dynamical ...

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3rd Indonesian Operations Research Association - International Conference on Operations Research 2018, IORA-ICOR 2018

作者： Sambas, A. Vaidyanathan, S. Zhang, S. Mujiarto Sukono Mamat, M. Subiyanto Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Research and Development Centre Vel Tech University Avadi Chennai India School of Physics and Opotoelectric Engineering Xiangtan University Hunan China Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu Malaysia Department of Marine Science Faculty of Fishery and Marine Science Universitas Padjadjaran Indonesia

A 3-D new chaotic system with five nonlinearities is proposed in this paper. A novel feature of our chaotic system is that there is no linear term in it. We also show that the chaotic system consists of equilibrium points on the z-axis (line equilibrium) as well as two equilibrium points on the (x, y)-plane. The dynamical properties of the new chaotic system are described in terms of phase portraits, bifurcation diagram, Lyapunov exponents, coexisting attractors, coexisting bifurcation and offset boosting control. Finally, an electronic circuit realization of the new chaotic system is presented in detail to confirm the feasibility of the theoretical chaotic model. © Published under licence by IOP Publishing Ltd.

关键词： Chaotic systems

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On Rearranging Physical Spaces for Enhancing Social Distancing Measures to Combat the COVID-19 Infection Rates

On Rearranging Physical Spaces for Enhancing Social Distanci...

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Internet of Things and Intelligent Applications (ITIA), International Conference on

作者： Hassan Ugail Irfan Mehmood Riya Aggarwal Andrés Iglesias Patricia Suárez Muazzam Maqsood Farhan Aadil Almudena Campuzano Lotti Tajouri Sarah Gleghorn Khasrouf Taif Carlton Reeve Tao Wan Rob Redman Centre for Visual Computing University of Bradford United Kingdom School of Engineering University of Newcastle Australia Applied Mathematics and Computational Sciences University of Cantabria Spain COMSATS University Islamabad Pakistan University of Amsterdam The Netherlands Faculty of Health Sciences & Medicine Bond University Australia Skipton Girls’ High School United Kingdom

ISBN: (数字)9781728193014

ISBN: (纸本)9781728193021

With the COVID-19 crisis, social distancing has become a critical element of our lives. In this regard, the physical settings within the shared spaces in our cities - ranging from public transport systems to meeting rooms - require significant re-planning. This comes with tremendous challenges to the government bodies and institutions, business and transport operators and primarily to anyone who provides a service to the general public. An essential requirement of social distancing is the rearrangement or redesign of existing physical spaces in order to ensure the necessary physical distancing measures among people are in place. In this paper, we discuss the challenges of rearranging physical spaces for enhancing social distancing. We describe the essential measures that are required to achieve effective social distancing in order to minimise the rate of infection from the COVID-19 disease. We show examples through which we demonstrate how venues can be arranged for social distancing enabled meetings and functions. We examine how social distancing guidelines have unfolded in transportation systems. We explain how social distancing can be enhanced within general public areas. We discuss the shortcomings of the current ad-hoc solutions for enabling social distancing within physical spaces. We also outline a potential technological solution that can be utilised to address the challenge of enabling complex social distancing measures on physical spaces during the unfolding COVID-19 crisis and beyond.

关键词： Diseases Social factors Human factors COVID-19 Ventilation Viruses (medical) Buildings

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Volumetric occupancy mapping with probabilistic depth completion for robotic navigation

arXiv

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arXiv 2020年

作者： Popović, Marija Thomas, Florian Papatheodorou, Sotiris Funk, Nils Vidal-Calleja, Teresa Leutenegger, Stefan Smart Robotics Lab Department of Computing Imperial College London United Kingdom Cluster of Excellence PhenoRob Institute of Geodesy and Geoinformation University of Bonn Germany Centre for Autonomous Systems Faculty of Engineering and IT University of Technology Sydney Australia Smart Robotics Lab Department of Informatics Technical University of Munich Germany

In robotic applications, a key requirement for safe and efficient motion planning is the ability to map obstaclefree space in unknown, cluttered 3D environments. However, commodity-grade RGB-D cameras commonly used for sensing fail to register valid depth values on shiny, glossy, bright, or distant surfaces, leading to missing data in the map. To address this issue, we propose a framework leveraging probabilistic depth completion as an additional input for spatial *** introduce a deep learning architecture providing uncertainty estimates for the depth completion of RGB-D images. Our pipeline exploits the inferred missing depth values and depth uncertainty to complement raw depth images and improve the speed and quality of free space mapping. Evaluations on synthetic data show that our approach maps significantly more correct free space with relatively low error when compared against using raw data alone in different indoor environments;thereby producing more complete maps that can be directly used for robotic navigation tasks. The performance of our framework is validated using real-world data. © 2020, CC BY.

关键词： Motion planning

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A review of detection and classification cervical cell images

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AIP Conference Proceedings 2023年第1期2608卷

作者： Nadzirah Nahrawi Wan Azani Mustafa Syed Zulkarnain Syed Idrus Mohd Aminudin Jamlos Shahrina Ismail Hiam Alquran Ali Mohammad Alqudah Faculty of Electrical Engineering Technology Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra 02600 Arau Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra 02600 Arau Perlis Malaysia Faculty of Applied and Human Sciences Universiti Malaysia Perlis (UniMAP) Jalan Alor Setar-Kangar 01000 Kangar Perlis Malaysia Faculty of Electronic Engineering Technology University of Malaysia Perlis Campus Pauh Putra 02600 Arau Perlis Malaysia Faculty of Science and Technology Universiti Sains Islam Malaysia (USIM) Bandar Baru Nilai 71800 Negeri Sembilan Malaysia Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid 21163 Jordan

Cervical cancer is a very prevalent disease among women all over the world. Cervical cancer can form in the cervix cells found in the lower uterus. Women all over the world are at death risk as a result of this type of cancer. Cervical cancer has seven stages: normal intermediate, normal superficial, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. Doctors in hospitals find it difficult to recognise cancer cells as it is challenging to view a nucleus through the naked eye. A normal cell’s nucleus is smaller than an abnormal cell’s nucleus. It is possible to calculate the size of the abnormal nucleus with the naked eye in order to assess the stages of cervical cancer. A tool for identifying and quantifying Pap smear cell images to detect cervical cancer has recently been suggested by several researchers. This method has the potential to increase detection and classification precision, resulting in improved results with balanced data and samples. A comprehensive study of nucleus detection cervical cancer classification techniques was conducted in this paper. As a result of the findings, the function database, detection and classification process, and device performance were all investigated for further evaluation.

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Assessing responsible innovation training

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Journal of Responsible Technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian Webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

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