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Blockchain Use Cases in Digital Sectors: A Review of the Literature

Blockchain Use Cases in Digital Sectors: A Review of the Lit...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Shiroq Al-Megren Shada Alsalamah Lina Altoaimy Hessah Alsalamah Leili Soltanisehat Emad Almutairi Alex ‘Sandy’ Pentland College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Engineering Management and Systems Engineering Old Dominion University Norflok VA USA National Center for Cybersecurity Technologies King Abdulaziz City of Science and Technology Riyadh Saudi Arabia Media Lab Massachusetts Institute of Technology Cambridge USA

Blockchain technology is a ledger system that is popularly known as the backbone of the Bitcoin cryptocur-rency. Since its conception, the potential beneficial applications of blockchain in other digital sectors have been lauded in the literature, and related challenges have been disputed. In this study, the literature is reviewed for frameworks and use cases that fully realize the applicability of blockchain beyond financial applications and cryptocurrencies. A network analysis of the literature was performed to identify the most popularly documented digital sectors in this context, which include the Internet of Things (IoT), healthcare, supply chain management, and government sectors. For each sector, this review documents use cases in which an attempt is made to implement blockchain solutions. The main purpose of this paper is to probe each sector for the growing maturity of blockchain technology and to document the unique benefits and challenges arising from the use of this technology. The findings show that despite the growing reputation of blockchain technology, its implementation within these four sectors remains in infancy because the use cases lack concrete evaluations of its effectiveness and plausibility. Nevertheless, the categorization of current blockchain use cases demonstrates current applications and sector-specific concerns that suggest future directions for further research.

关键词： Blockchain Internet of Things Bitcoin Government Distributed ledger Peer-to-peer computing

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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An Enhanced Binary Particle-Swarm Optimization (E-BPSO) Algorithm for Service Placement in Hybrid Cloud Platforms

arXiv

引用

arXiv 2018年

作者： Abbes, Wissem Kechaou, Zied Hussain, Amir Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. University of Sousse ISITCom Sousse4011 Tunisia REGIM-Lab REsearch Groups on Intelligent Machines BP 1173 Sfax3038 Tunisia School of Computing Edinburgh Napier University United Kingdom Department of Computer Science College of Computers and Information Technology Taif University P.O. Box. 11099 Taif21944 Saudi Arabia College of Applied Computer Science King Saud University Almuzahmiyah Campus Riyadh Saudi Arabia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Nowadays, hybrid cloud platforms stand as an attractive solution for organizations intending to implement combined private and public cloud applications, in order to meet their profitability requirements. However, this can only be achieved through the utilization of available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud solution, while allocating others to the public cloud. In this context, the present work is set to help minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. Several evolutionary algorithms have been applied to solve the service placement problem and are used when dealing with complex solution spaces to provide an optimal placement and often produce a short execution time. The standard BPSO algorithm is found to display a significant disadvantage, namely, of easily trapping into local optima, in addition to demonstrating a noticeable lack of robustness in dealing with service placement problems. Hence, to overcome critical shortcomings associated with the standard BPSO, an Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm is proposed, consisting of a modification of the particle position updating equation, initially inspired from the continuous PSO. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art approaches in terms of both cost and execution time, using a real benchmark. Copyright © 2018, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Secrecy performance analysis of SIMO underlay cognitive radio systems with outdated CSI

引用

IET Communications 2017年第12期11卷 1961-1969页

作者： Lei, Hongjiang Zhang, Jianming Park, Ki-Hong Ansari, Imran Shafique Pan, Gaofeng Alouini, Mohamed-Slim Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electrical and Computer Engineering (ECEN) Texas A and M University at Qatar (TAMUQ) Education City Doha Qatar Chongqing Key Laboratory of Non-linear Circuits and Intelligent Information Processing Southwest University Chongqing 400715 China

This study investigates the secrecy outage performance of a single-input multiple-output underlay cognitive radio network (CRN) with outdated channel state information (CSI). The confidential messages are transmitted from transmitter to the destination, while a multi-antenna eavesdropper exists. The maximal ratio combining and selection combining schemes are utilised at the receivers to improve the quality of the received signal-to-noise ratio. The exact and asymptotic closed-form expressions of secrecy outage probability are derived, and simulation results are provided to verify the authors' proposed analytical results. The results reveal that imperfect CSI of main channels deteriorates the secrecy outage performance while that of eavesdropping and interfering channels has contrary effect, and only a unity diversity order can be obtained in underlay CRNs with imperfect CSI. © The Institution of engineering and Technology.

关键词： antenna arrays cognitive radio diversity reception probability radio networks telecommunication security

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Abstracts

引用

JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Cloud service resource allocation with particle swarm optimization algorithm 12th

Cloud service resource allocation with particle swarm optimi...

引用

12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Cheng, Shi Guo, Lantian Yang, Tao Feng, Jiqiang Sun, Yifei Shao, Chang Duan, Qiqi School of Computer Science Shaanxi Normal University Xi’an China Shenzhen Research Institute Northwestern Polytechnical University Shenzhen China School of Automation Northwestern Polytechnical University Xi’an China Institute of Intelligent Computing Science Shenzhen University Shenzhen China School of Physics and Information Technology Shaanxi Normal University Xi’an China Shenzhen Key Lab of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9789811071782

Cloud service resource allocation is an essential task in cloud computing. The cloud service resource allocation problem is modeled as an optimization problem, and is solved via different particle swarm optimization (PSO) variants in this paper. The aim of our method is to minimize the delay and the price at the same time. Based on the experimental results, it could be conducted that the good performance could be achieved via PSO algorithms. The future research is to utilize PSO algorithms on solving more real-world problems, especially with other quality of service problems. © Springer Nature Singapore Pte Ltd 2017.

关键词： Swarm intelligence

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From social to individuals: A parsimonious path of multi-level models for crowdsourced preference aggregation

arXiv

引用

arXiv 2018年

作者： Xu, Qianqian Xiong, Jiechao Cao, Xiaochun Huang, Qingming Yao, Yuan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing1000190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Tencent AI Lab Shenzhen518057 BICMR-LMAM-LMEQF-LMP School of Mathematical Sciences Peking University Beijing100871 China University of Chinese Academy of Sciences Institute of Computing Technology of Chinese Academy of Sciences Beijing100190 China Department of Mathematics and by courtesy Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or social utility function which generates their comparison behaviors in experiments. However, in reality annotators are subject to variations due to multi-criteria, abnormal, or a mixture of such behaviors. In this paper, we propose a parsimonious mixed-effects model, which takes into account both the fixed effect that the majority of annotators follows a common linear utility model, and the random effect that some annotators might deviate from the common significantly and exhibit strongly personalized preferences. The key algorithm in this paper establishes a dynamic path from the social utility to individual variations, with different levels of sparsity on personalization. The algorithm is based on the Linearized Bregman Iterations, which leads to easy parallel implementations to meet the need of large-scale data analysis. In this unified framework, three kinds of random utility models are presented, including the basic linear model with L2 loss, Bradley-Terry model, and Thurstone-Mosteller model. The validity of these multi-level models are supported by experiments with both simulated and real-world datasets, which shows that the parsimonious multi-level models exhibit improvements in both interpretability and predictive precision compared with traditional HodgeRank. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

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引用

作者： Nguyen, Linh Anh Nguyen, Thi Hong Khanh Nguyen, Ngoc-Thanh Ha, Quang-Thuy Division of Knowledge and System Engineering for ICT Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Institute of Informatics University of Warsaw Warsaw Poland Electric Power University Hoang Quoc Viet Tu Liem Hanoi Viet Nam Department of Information Systems Faculty of Computer Science and Management Wroclaw University of Science and Technology Str. Wyb. Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Information Technology VNU University of Engineering and Technology Xuan Thuy Hanoi Viet Nam

We introduce comparisons w.r.t. information between interpretations in paraconsistent description logics and use them to define bisimilarity for such logics. This notion is useful for concept learning in description logics when inconsistencies occur. We give preservation results and the Hennessy-Milner property for comparisons w.r.t. information in paraconsistent description logics. As consequences, we obtain also invariance results and the Hennessy-Milner property for bisimilarity in paraconsistent description logics. © 2017 - IOS Press and the authors. All rights reserved.

关键词： Data description

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Exploring nutritional supplement use for countering respiratory tract infections through an X (formerly Twitter)-based survey

Current Research in Biotechnology

引用

Current Research in Biotechnology 2025年 9卷

作者： Singla, Rajeev K. Mondal, Himel Singla, Shailja De, Ronita Behzad, Sahar Găman, Mihnea-Alexandru Sai Chandragiri, Siva Cenanovic, Merisa Patra, Jayanta Kumar Depew, Jennifer R. Boyina, Hemanth Kumar Maigoro, Abdulkadir Yusif Lee, Soojin Atrooz, Omar M. Das, Gitishree Schultz, Fabien Abdallah, Emad Mohamed Chopra, Hitesh Ahmad, Jamil Gautam, Rupesh K. Patnaik, Sourav S. Goh, Bey Hing Babiaka, Smith B. Vats, Sharad Okoh, Michael P. Ahmed, Atallah F. Dubey, Ankit Kumar Lordan, Ronan Subramani, Parasuraman Aiya Singh, Amit Kumar Alvarez-Suarez, José M. Chellappan, Dinesh Kumar Paswan, Shravan Kumar Semwal, Prabhakar Khan, Johra Sheshe, Sadeeq Sethiya, Neeraj Kumar Karpiński, Tomasz M. Riaz, Muhammad Emam-Djomeh, Zahra Gupta, Girish Kumar Madaan, Reecha Kumar, Suresh Choudhary, Neeraj Parisi, Salvatore Willschke, Harald Pirgozliev, Vasil Rayan, Rehab A. Ritschl, Valentin Mondal, Shaikat Zengin, Gokhan Verma, Pritt Kapoor, Bhupinder Gulati, Monica Balla, Gareeballah Osman Adam Le, Dan Khoa Pittalà, Valeria El-Demerdash, Amr Khalid, Garba M. Simal-Gandara, Jesus Alzahrani, Qushmua E. Russo, Gian Luigi Kharat, Kiran R. Bishayee, Anupam Wang, Dongdong Orhan, Ilkay Erdogan Ullah, Hammad Heinrich, Michael Baral, Bikash Tzvetkov, Nikolay T. Yeung, Andy Wai Kan Dias-Ferreira, João M.L. Olea, Scarlett Perez Mohanta, Yugal Kishore Kureshi, Azazahemad A. Supuran, Claudiu T. Rani, Neeraj Gundamaraju, Rohit Mulholland, Eoghan Joseph Lonardo, Sara Di Dinkova-Kostova, Albena T. González-Burgos, Elena Hritcu, Lucian Badhe, Pravin Singh, Abhilasha Al-Rimawi, Fuad Sureda, Antoni Abiri, Rambod Braidy, Nady Kapral, Lorenz Abdullahi, A.N. Medina, Christhian Delfino Villanueva Sheridan, Helen Lucarini, Massimo Durazzo, Alessandra Giampieri, Francesca Barreca, Davide Maria, Witkowska Anna Andrade, José Carlos Fimognari, Carmela Akram, Faizan Tikhonov, Aleksei Battino, Maurizio Oladipupo, Akolade R. Emerald, Mila Efferth, Thomas Amrani, Said Echeverría, Javier Maria Louka, Anna Tripathi, Surya Kant Fiebich, Bernd L. Es-Safi, Nour Eddine Khan Department of Critical Care Medicine and Institutes for Systems Genetics Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu China Department of Physiology All India Institute of Medical Sciences Jharkhand Deoghar India iGlobal Research and Publishing Foundation New Delhi India Evidence-based Phytotherapy and Complementary Medicine Research Center Alborz University of Medical Sciences Karaj Iran Department of Hematology Center for Clinical and Basic Research (CCBR Clinic) Bucharest Romania Department of Cellular and Molecular Pathology Stefan S. Nicolau Institute of Virology Romanian Academy Bucharest Romania Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK United States Sarajevo Bosnia and Herzegovina Research Institute of Integrative Life Sciences Dongguk University-Seoul Goyang-Si South Korea United States University Institute of Pharma Sciences Chandigarh University Punjab Mohali 140413 India Department of Microbiology and Molecular Biology College of Bioscience and Biotechnology Chungnam National University Daejeon 34134 South Korea Department of Biological Sciences Mutah University Jordan Ethnopharmacology & Zoopharmacognosy Research Group Department of Agriculture and Food Sciences Neubrandenburg University of Applied Sciences Germany Department of Biology College of Science Qassim University Buraydah 51452 Saudi Arabia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai 602105 India Department of Human Nutrition The University of Agriculture Peshawar Khyber Pakhtunkhwa 25120 Pakistan Department of Pharmacology Indore Institute of Pharmacy IIST Campus Rau (M.P.) Indore 453331 India The University of Texas Southwestern Medical Center Dallas TX United States Sunway Biofunctional Molecules Discovery Centre (SBMDC) School of Medical and Life Sciences Sunway University S

Background: Respiratory tract infections are a common health issue, driving interest in preventive strategies like nutritional supplements, while evidence on their usage and effectiveness remains limited. In this context, social media platforms, particularly X (formerly Twitter), provide a unique opportunity to gather large-scale public health-related data. Objectives: In this study, we aimed to survey participants’ uses and opinions on nutritional supplements in prevention or treatment of respiratory tract infections, by using X. Methods: A survey was conducted between 1st and 15th December 2022. A single open-ended question “Which are the best dietary supplements to counteract respiratory infections?“ was asked. One week after the start of the survey, a poll was posted to get more relevant information and boost the survey's reach. Total endorsements were calculated for each tweet posted as the total sum of replies, retweets, and likes. Results: The open-ended question received a total of 118 retweets, 39 quotes, and 371 likes, while the poll received 56 retweets, 13 quotes, and 67 likes. A total of 495 replies, 2,251 retweets, 5,118 likes, and 148 quotes were received for the question and its related tweets. Vitamin D (1,607 endorsements), zinc (1,347 endorsements), vitamin C (803 endorsements), magnesium (694 endorsements), and honey (661 endorsements) were the nutritional supplements that received most endorsements. Conclusion: Various foods, drinks, and natural ingredients have been suggested as potentially helpful for counteracting respiratory infections. Approximately half of respondents indicated using such supplements for themselves. The result of this study supports the idea that the X platform can be used as an effective survey tool to study global health-related behaviours and trends. © 2025 The Authors

关键词： Bacteria COVID-19 Nutritional supplements Phytomedicine Respiratory tract infections X (formerly Twitter)

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