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Mitra, Karan, Assistant ProfessorORCID iD iconorcid.org/0000-0003-3489-7429
Biography [eng]

 Karan Mitra is an Assistant Professor at Luleå University of Technology, Sweden. He received his Dual-badge Ph.D. from Monash University, Australia and Luleå University of Technology in 2013. He received his MIT (MT) and a PGradDipDigComm from Monash University in 2008 and 2006, respectively. He received his BIS (Hons.) from Guru Gobind Singh Indraprastha University, Delhi, India in 2004. His research interests include quality of experience modelling and prediction, context-aware computing, cloud computing and mobile and pervasive computing systems. From January 2012 to December 2013 he worked as a researcher at CSIRO, Canberra, Australia. He is a member of the IEEE and ACM.

Publications (10 of 70) Show all publications
Alzubaidi, A., Mitra, K. & Solaiman, E. (2023). A blockchain-based SLA monitoring and compliance assessment for IoT ecosystems. Journal of Cloud Computing: Advances, Systems and Applications, 12, Article ID 50.
Open this publication in new window or tab >>A blockchain-based SLA monitoring and compliance assessment for IoT ecosystems
2023 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 12, article id 50Article in journal (Refereed) Published
Abstract [en]

A Service Level Agreement (SLA) establishes the trustworthiness of service providers and consumers in several domains; including the Internet of Things (IoT). Given the proliferation of Blockchain technology, we find it compelling to reconsider the assumption of trust and centralised governance typically practised in SLA management including monitoring, compliance assessment, and penalty enforcement. Therefore, we argue that, such critical tasks should be operated by blockchain-based smart contracts in a non-repudiable manner beyond the influence of any SLA party. This paper envisions an IoT scenario wherein a firefighting station outsources end-to-end IoT operations to a specialised service provider. The contractual relationship between them is governed by an SLA which stipulates a set of quality requirements and violation consequences. The main contribution of this paper lies in designing, deploying and empirically experimenting a novel blockchain-based SLA monitoring and compliance assessment framework in the context of IoT. This is done by utilising Hyperledger Fabric (HLF), an enterprise-grade blockchain technology. Our work highlights a set of considerations and best practice at two sides, the IoT application monitoring-side and the blockchain-side. Moreover, it experimentally validates the reliability of the proposed monitoring approach, which collects relevant metrics from each IoT component and examines them against the quality requirements stated in the SLA. Finally, we propose a novel design for smart contracts at the blockchain-side, analyse and benchmark the performance, and demonstrate that the new design proves to successfully handle Multiversion Concurrency Control (MVCC) conflicts typically encountered in blockchain applications, while maintaining sound throughput and latency.

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
Blockchain, Trust, SLA, IoT, Monitoring, MVCC, Performance, Hyperledger Fabric
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-96332 (URN)10.1186/s13677-023-00409-7 (DOI)2-s2.0-85152555920 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-04-06 (hanlid);

Funder: The Engineering and Physical Sciences Research Council, EPSRC (EP/V042017/1)

Available from: 2023-04-06 Created: 2023-04-06 Last updated: 2023-11-15Bibliographically approved
Albshri, A., Alzubaidi, A., Alharby, M., Awaji, B., Mitra, K. & Solaiman, E. (2023). A conceptual architecture for simulating blockchain-based IoT ecosystems. Journal of Cloud Computing: Advances, Systems and Applications, 12, Article ID 103.
Open this publication in new window or tab >>A conceptual architecture for simulating blockchain-based IoT ecosystems
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2023 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 12, article id 103Article in journal (Refereed) Published
Abstract [en]

Recently, the convergence between Blockchain and IoT has been appealing in many domains including, but not limited to, healthcare, supply chain, agriculture, and telecommunication. Both Blockchain and IoT are sophisticated technologies whose feasibility and performance in large-scale environments are difficult to evaluate. Consequently, a trustworthy Blockchain-based IoT simulator presents an alternative to costly and complicated actual implementation. Our primary analysis finds that there has not been so far a satisfactory simulator for the creation and assessment of blockchain-based IoT applications, which is the principal impetus for our effort. Therefore, this study gathers the thoughts of experts about the development of a simulation environment for blockchain-based IoT applications. To do this, we conducted two different investigations. First, a questionnaire is created to determine whether the development of such a simulator would be of substantial use. Second, interviews are conducted to obtain participants’ opinions on the most pressing challenges they encounter with blockchain-based IoT applications. The outcome is a conceptual architecture for simulating blockchain-based IoT applications that we evaluate using two research methods; a questionnaire and a focus group with experts. All in all, we find that the proposed architecture is generally well-received due to its comprehensive range of key features and capabilities for blockchain-based IoT purposes.

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
Blockchain, IoT, Modelling, Performance, Simulation
National Category
Information Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-99410 (URN)10.1186/s13677-023-00481-z (DOI)001030557400002 ()2-s2.0-85165226902 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-08-09 (hanlid);

Funder: EPSRC (EP/V042017/1)

Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2023-11-15Bibliographically approved
Fizza, K., Banerjee, A., Jayaraman, P. P., Auluck, N., Ranjan, R., Mitra, K. & Georgakopoulos, D. (2023). A Survey on Evaluating the Quality of Autonomic Internet of Things Applications. IEEE Communications Surveys and Tutorials, 25(1), 567-590
Open this publication in new window or tab >>A Survey on Evaluating the Quality of Autonomic Internet of Things Applications
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2023 (English)In: IEEE Communications Surveys and Tutorials, ISSN 1553-877X, E-ISSN 1553-877X, Vol. 25, no 1, p. 567-590Article, review/survey (Refereed) Published
Abstract [en]

The rapid evolution of the Internet of Things (IoT) facilitates the development of IoT applications in domains such as manufacturing, smart cities, retail, agriculture, etc. Such IoT applications collect data, analyze, and extract insightful information to enable decision-making and actuation. There is an unprecedented growth of IoT applications that automate decision-making and actuation without requiring human intervention, which we term autonomic IoT applications. The increasing scale of such applications necessitates holistic measurement and evaluation of application quality. Existing literature has evaluated quality from an end-user perspective, which may be unsuitable when dealing with the complexity of modern IoT applications, especially when they are autonomic. In this paper, we refer to IoT application quality as the aggregate quantitative value of various IoT quality metrics measured at each stage of the autonomic IoT application life cycle. We present an in-depth survey of current state-of-the-art techniques and approaches for evaluating quality of IoT applications. In particular, we survey various definitions to identify the factors that contribute to understanding and evaluating quality in IoT. Furthermore, we present open issues and identify future research directions towards realizing fine-grained quality evaluation of IoT applications. We envision that the identified research directions will, in turn, enable real-time diagnostics of IoT applications and make them quality-aware. This survey can serve as the basis for designing and developing modern, resilient quality-aware autonomic IoT applications.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Autonomic, Data analysis, Internet of Things, IoT, Measurement, QoE, Quality of experience, Quality of service, Sensors, Tutorials
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-93621 (URN)10.1109/COMST.2022.3205377 (DOI)2-s2.0-85139441739 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-03-21 (joosat);

Available from: 2022-10-27 Created: 2022-10-27 Last updated: 2023-03-21Bibliographically approved
Souza Rossi, H., Mitra, K., Åhlund, C., Cotanis, I., Ögren, N. & Johansson, P. (2023). ALTRUIST: A Multi-platform Tool for Conducting QoE Subjective Tests. In: 2023 15th International Conference on Quality of Multimedia Experience (QoMEX): . Paper presented at 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), June 20-22, 2023, Ghent, Belgium (pp. 99-102). IEEE
Open this publication in new window or tab >>ALTRUIST: A Multi-platform Tool for Conducting QoE Subjective Tests
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2023 (English)In: 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2023, p. 99-102Conference paper, Published paper (Refereed)
Abstract [en]

Quality of Experience (QoE) subjective assessment often demands setting up expensive lab experiments that involve controlling several software programs and services. In addition, these experiments may pose significant challenges regarding man-agement of testbed software components as they may have to be synchronized for efficient data collection, leading to human errors or loss of time. Further, maintaining error-free repeatability between subsequent subjective tests and comprehensive data collection is essential. Therefore, this paper proposes, develops and validates ALTRUIST, a multi-platform tool that assists the experimenter in conducting subjective tests by controlling external applications, facilitates data collection and automates test execution for conducting repeatable subjective tests in broad application areas.

Place, publisher, year, edition, pages
IEEE, 2023
Series
International Workshop on Quality of Multimedia Experience, QoMEX, ISSN 2372-7179, E-ISSN 2472-7814
National Category
Computer Sciences Software Engineering
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-100658 (URN)10.1109/QoMEX58391.2023.10178508 (DOI)2-s2.0-85167362907 (Scopus ID)979-8-3503-1173-0 (ISBN)979-8-3503-1174-7 (ISBN)
Conference
2023 15th International Conference on Quality of Multimedia Experience (QoMEX), June 20-22, 2023, Ghent, Belgium
Available from: 2023-08-21 Created: 2023-08-21 Last updated: 2023-10-11Bibliographically approved
Paananen, S., Kim, J. C., Kirjavainen, E., Kalving, M., Mitra, K. & Häkkilä, J. (2023). Augmenting Indigenous Sámi Exhibition - Interactive Digital Heritage in Museum Context. In: José Abdelnour Nocera, Marta Kristín Lárusdóttir, Helen Petrie, Antonio Piccinno, Marco Winckler (Ed.), Human-Computer Interaction – INTERACT 2023: . Paper presented at 19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023 (pp. 597-617). Springer, Part II
Open this publication in new window or tab >>Augmenting Indigenous Sámi Exhibition - Interactive Digital Heritage in Museum Context
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2023 (English)In: Human-Computer Interaction – INTERACT 2023 / [ed] José Abdelnour Nocera, Marta Kristín Lárusdóttir, Helen Petrie, Antonio Piccinno, Marco Winckler, Springer, 2023, Vol. Part II, p. 597-617Conference paper, Published paper (Refereed)
Abstract [en]

Museums and cultural heritage institutions have an important role in presenting accurate information and sharing cultural knowledge, and new technologies are increasingly implemented. For the best results, the appropriateness of a specific technology must be evaluated for each context. Research has shown the need for participatory methods and local knowledge in Indigenous design contexts. We describe a case study where an Indigenous Sámi museum exhibition was augmented with interactive technology through multidisciplinary co-design work with museum experts, designers, and developers. The traditional clothing of the Sámi people was digitized by filming, and information related to it was presented as a touchscreen installation in a renewed exhibition. User tests including interactive tasks and interviews (n = 7) and a questionnaire (n = 27) were completed on-site. The installation was rated interesting and easy to use, while some users struggled to find all the features. Our study shows that a technically relatively simple digital installation can be easy-to-use but interesting for a standard museum visitor. Additionally, the work demonstrates how to build successful collaborations that highlight Indigenous cultural heritage. We discuss the implications of using technology to promote cultural heritage and identities.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14143
Keywords
Interactive systems, User studies, Digital cultural heritage, Museum exhibitions, Indigenous HCI
National Category
Human Computer Interaction Cultural Studies
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-101078 (URN)10.1007/978-3-031-42283-6_32 (DOI)978-3-031-42282-9 (ISBN)978-3-031-42283-6 (ISBN)
Conference
19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023
Available from: 2023-08-29 Created: 2023-08-29 Last updated: 2023-09-05Bibliographically approved
Minovski, D., Ögren, N., Åhlund, C. & Mitra, K. (2023). Throughput Prediction using Machine Learning in LTE and 5G Networks. IEEE Transactions on Mobile Computing, 22(3), 1825-1840
Open this publication in new window or tab >>Throughput Prediction using Machine Learning in LTE and 5G Networks
2023 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 22, no 3, p. 1825-1840Article in journal (Refereed) Published
Abstract [en]

The emergence of novel cellular network technologies, within 5G, are envisioned as key enablers of a new set of use-cases, including industrial automation, intelligent transportation, and tactile internet. The critical nature of the traffic requirements ranges from ultra-reliable communications, massive connectivity, and enhanced mobile broadband. Thus, the growing research on cellular network monitoring and prediction aims for ensuring a satisfied user-base and fulfillment of service level agreements. The scope of this study is to develop an approach for predicting the cellular link throughput of end-users, with a goal to benchmark the performance of network slices. First, we report and analyze a measurement study involving real-life cases, such as driving in urban, sub-urban, and rural areas, as well as tests in large crowded areas. Second, we develop machine learning models using lower-layer metrics, describing the radio environment, to predict the available throughput. The models are initially validated on the LTE network and then applied to a non-standalone 5G network. Finally, we suggest scaling the proposed model into the future standalone 5G network. We have achieved 93% and 84% R^2 accuracy, with 0.06 and 0.17 mean squared error, in predicting the end-user's throughput in LTE and non-standalone 5G network, respectively.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
5G, LTE, Network Slice, Throughput, QoS
National Category
Computer Sciences Communication Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-86486 (URN)10.1109/TMC.2021.3099397 (DOI)000932434400038 ()2-s2.0-85111577640 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-04-18 (joosat);

Available from: 2021-07-29 Created: 2021-07-29 Last updated: 2023-04-18Bibliographically approved
Demirbaga, U., Wen, Z., Noor, A., Mitra, K., Alwasel, K., Garg, S., . . . Ranjan, R. (2022). AutoDiagn: An Automated Real-time Diagnosis Framework for Big Data Systems. IEEE Transactions on Computers, 71(5), 1035-1048
Open this publication in new window or tab >>AutoDiagn: An Automated Real-time Diagnosis Framework for Big Data Systems
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2022 (English)In: IEEE Transactions on Computers, ISSN 0018-9340, E-ISSN 1557-9956, Vol. 71, no 5, p. 1035-1048Article in journal (Refereed) Published
Abstract [en]

Big data processing systems, such as Hadoop and Spark, usually work on large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. Several systems and methods exist to detect big data processing systems' performance degradation, perform root-cause analysis, and even overcome the issues causing such degradation. However, these solutions focus on specific problems such as straggler and inefficient resource utilization. There is a lack of a generic and extensible framework to support the real-time diagnosis of big data systems. In this paper, we propose, develop and validate AutoDiagn. This generic and flexible framework provides holistic monitoring of a big data system while detecting performance degradation and enabling root-cause analysis. We present the implementation and evaluation of AutoDiagn that interacts with a Hadoop cluster deployed on a public cloud and tested with real-world benchmark applications. Experimental results show that AutoDiagn has a small resource footprint, high throughput and low latency.

Place, publisher, year, edition, pages
USA: IEEE, 2022
Keywords
Root-cause analysis, Big data systems, QoS, Hadoop, Performance
National Category
Computer Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-83436 (URN)10.1109/TC.2021.3070639 (DOI)000778905700004 ()2-s2.0-85103783969 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-04-19 (sofila);

Funder: Turkish Ministry of National Education; UKRI (EP/T021985/1, EP/R033293/1, EP/T022582/1); National Natural Science Foundation of China (62072408); Zhejiang Provincial Natural Science Foundation ofChina (LY20F020030)

Available from: 2021-03-29 Created: 2021-03-29 Last updated: 2023-03-28Bibliographically approved
Souza Rossi, H., Ögren, N., Mitra, K., Cotanis, I., Åhlund, C. & Johansson, P. (2022). Subjective Quality of Experience Assessment in Mobile Cloud Games. In: 2022 IEEE Global Communications Conference, GLOBECOM: Proceedings: . Paper presented at 2022 IEEE Global Communications Conference (GLOBECOM 2022), December 4-8, 2022, Rio de Janeiro, Brazil (pp. 1918-1923). IEEE
Open this publication in new window or tab >>Subjective Quality of Experience Assessment in Mobile Cloud Games
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2022 (English)In: 2022 IEEE Global Communications Conference, GLOBECOM: Proceedings, IEEE, 2022, p. 1918-1923Conference paper, Published paper (Refereed)
Abstract [en]

The rise of mobile cloud gaming  (MCG) has necessitated understanding its impact on mobile network design and deployment for end users' QoE maximization. MCG is a dynamic service that requires stringent quality from network operators. Therefore, this paper investigates the subjective QoE of MCG over mobile networks played on smartphones. We conducted subjective tests (N=31); our results indicate that MCG is affected differently by QoS attributes such as packet loss (PL), round trip time (RTT) and jitter compared to cloud games and online mobile games. We identify that RTT values above 100 milliseconds significantly impact users' QoE, measured via the mean opinion score (MOS). Further, lower RTT values with high PL; and higher RTT values with low PL cause a strong negative effect on MOS. Lastly, bursty jitter seems to affect the MOS, while random jitter does not significantly impact MOS.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Subjective tests, Quality of Experience, Quality of Service, mobile games, mobile networks
National Category
Communication Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-95066 (URN)10.1109/GLOBECOM48099.2022.10001407 (DOI)2-s2.0-85146916640 (Scopus ID)978-1-6654-3540-6 (ISBN)
Conference
2022 IEEE Global Communications Conference (GLOBECOM 2022), December 4-8, 2022, Rio de Janeiro, Brazil
Available from: 2022-12-29 Created: 2022-12-29 Last updated: 2023-05-08Bibliographically approved
Minovski, D., Åhlund, C., Mitra, K. & Cotanis, I. (2021). Anomaly Detection for Discovering Performance Degradation in Cellular IoT Services. In: Lyes Khoukhi; Sharief Oteafy; Eyuphan Bulut (Ed.), Proceedings of the IEEE 46th Conference on Local Computer Networks (LCN 2021): . Paper presented at 46th IEEE Conference on Local Computer Networks (LCN 2021), Virtual, October 4-7, 2021 (pp. 99-106). IEEE
Open this publication in new window or tab >>Anomaly Detection for Discovering Performance Degradation in Cellular IoT Services
2021 (English)In: Proceedings of the IEEE 46th Conference on Local Computer Networks (LCN 2021) / [ed] Lyes Khoukhi; Sharief Oteafy; Eyuphan Bulut, IEEE, 2021, p. 99-106Conference paper, Published paper (Refereed)
Abstract [en]

Connected and automated vehicles (CAVs) are envisioned to revolutionize the transportation industry, enabling autonomous processes and real-time exchange of information among vehicles and infrastructure. To safely navigate the roadways, CAVs rely on sensor readings and data from the surrounding vehicles. Hence, a fault or anomaly arising from the hardware, software, or the network can lead into devastating consequences regarding safety. This study investigates potential performance degradation caused by anomalies, by analyzing real-life vehicles’ sensory and network-related data. The aim is to utilize unsupervised learning for anomaly detection, with a goal to describe the cause and effect of the detected anomalies from a performance perspective. The results show around 93% F1-score when detecting anomalies imposed by the cellular network and the vehicle’s sensors. Moreover, with approximately 90% F1-score we can detect anomalous predictions from a deployed network-related ML model predicting cellular throughput and describe the root-causes behind the detected anomalies.

Place, publisher, year, edition, pages
IEEE, 2021
Series
Conference on Local Computer Networks (LCN)
Keywords
Anomaly, ML, LTE, Network Performance
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-86965 (URN)10.1109/LCN52139.2021.9524931 (DOI)000766931400013 ()2-s2.0-85118439647 (Scopus ID)
Conference
46th IEEE Conference on Local Computer Networks (LCN 2021), Virtual, October 4-7, 2021
Note

ISBN för värdpublikation: 978-1-6654-1886-7

Available from: 2021-09-03 Created: 2021-09-03 Last updated: 2022-04-21Bibliographically approved
Pathak, A. K., Saguna, S., Mitra, K. & Åhlund, C. (2021). Anomaly Detection using Machine Learning to Discover Sensor Tampering in IoT Systems. In: ICC 2021 - IEEE International Conference on Communications: . Paper presented at IEEE International Conference on Communications, Montreal, Canada (Virtual), June 14-23, 2021. IEEE
Open this publication in new window or tab >>Anomaly Detection using Machine Learning to Discover Sensor Tampering in IoT Systems
2021 (English)In: ICC 2021 - IEEE International Conference on Communications, IEEE, 2021Conference paper, Published paper (Refereed)
Abstract [en]

With the rapid growth of the Internet of Things (IoT) applications in smart regions/cities, for example, smart healthcare, smart homes/offices, there is an increase in security threats and risks. The IoT devices solve real-world problems by providing real-time connections, data and information. Besides this, the attackers can tamper with sensors, add or remove them physically or remotely. In this study, we address the IoT security sensor tampering issue in an office environment. We collect data from real-life settings and apply machine learning to detect sensor tampering using two methods. First, a real-time view of the traffic patterns is considered to train our isolation forest-based unsupervised machine learning method for anomaly detection. Second, based on traffic patterns, labels are created, and the decision tree supervised method is used, within our novel Anomaly Detection using Machine Learning (AD-ML) system. The accuracy of the two proposed models is presented. We found 84% with silhouette metric accuracy of isolation forest. Moreover, the result based on 10 cross-validations for decision trees on the supervised machine learning model returned the highest classification accuracy of 91.62% with the lowest false positive rate.

Place, publisher, year, edition, pages
IEEE, 2021
Series
IEEE International Conference on Communications (ICC), E-ISSN 1938-1883
Keywords
anomaly detection, Internet of Things, smart city, sensor tampering, traffic analysis, machine learning, security
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-83314 (URN)10.1109/ICC42927.2021.9500825 (DOI)000719386003085 ()2-s2.0-85115675450 (Scopus ID)
Conference
IEEE International Conference on Communications, Montreal, Canada (Virtual), June 14-23, 2021
Note

ISBN för värdpublikation: 978-1-7281-7122-7

Available from: 2021-03-19 Created: 2021-03-19 Last updated: 2021-12-13Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-3489-7429

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