research-article

AIS-based maritime anomaly traffic detection: : A review

Authors:

Claudio V. Ribeiro,

Daniel de OliveiraAuthors Info & Claims

Volume 231, Issue C

https://doi.org/10.1016/j.eswa.2023.120561

Published: 30 November 2023 Publication History

Abstract

Maritime transportation plays an essential role in global trade. Due to the huge number of vessels worldwide, there is also a non-negligible volume of Maritime incidents such as collisions/sinking and illegal events (e.g., piracy, smuggling, and unauthorized fishing). Electronic equipment/systems, such as radars and Automatic Identification Systems (AIS), have contributed to improving maritime situational awareness. AIS provides one of the fundamental sources of vessel kinematics and static data. Today, many approaches are focused on automatically detecting the vessels’ traffic behavior and discovering useful patterns and deviations from those data. These studies contribute to detecting suspicious activities and anomalous trajectories, whose developed techniques could be applied in the surveillance systems, helping the authorities to anticipate proper actions. Several concerns and difficulties are involved in the analyses of vessel kinematics data: how to deal with big data generated, inconsistencies, irregular updates, dynamic data, unlabeled data, and evaluation. This article presents the approaches, constraints, and challenges in maritime traffic anomaly detection research, presenting a review, a taxonomy, and a discussion of the proposed approaches.

Highlights

•

Several concerns are involved in the analyses of vessel kinematics data.

•

Manually detecting anomalous behavior is a tedious and error-prone task.

•

Anomaly detection is a fundamental task in different domains of knowledge.

•

In the maritime domain, the nonconforming patterns are referred to as anomalies.

References

[1]

Aarsæther K.G., Moan T., Estimating navigation patterns from AIS, The Journal of Navigation 62 (4) (2009) 587–607.

[2]

Amigo D., Sánchez Pedroche D., García J., Molina J.M., Review and classification of trajectory summarisation algorithms: From compression to segmentation, International Journal of Distributed Sensor Networks 17 (10) (2021).

[3]

Anneken M., Fischer Y., Beyerer J., Evaluation and comparison of anomaly detection algorithms in annotated datasets from the maritime domain, in: 2015 SAI intelligent systems conference, IEEE, 2015, pp. 169–178.

[4]

Avram V., Glässer U., Shahir H.Y., Anomaly detection in spatiotemporal data in the maritime domain, in: 2012 IEEE international conference on intelligence and security informatics, IEEE, 2012, pp. 147–149.

[5]

Balduzzi, M., Pasta, A., & Wilhoit, K. (2014). A security evaluation of AIS automated identification system. In Proceedings of the 30th annual computer security applications conference (pp. 436–445).

[6]

Brax C., Karlsson A., Andler S.F., Johansson R., Niklasson L., Evaluating precise and imprecise state-based anomaly detectors for maritime surveillance, in: 2010 13th International conference on information fusion, IEEE, 2010, pp. 1–8.

[7]

Carbonell J.G., Michalski R.S., Mitchell T.M., An overview of machine learning, in: Machine learning, Elsevier, 1983, pp. 3–23.

[8]

Castaldo F., Palmieri F.A., Bastani V., Marcenaro L., Regazzoni C., Abnormal vessel behavior detection in port areas based on dynamic bayesian networks, in: 17th International conference on information fusion, IEEE, 2014, pp. 1–7.

[9]

Cazzanti L., Pallotta G., Mining maritime vessel traffic: Promises, challenges, techniques, in: OCEANS 2015-Genova, IEEE, 2015, pp. 1–6.

[10]

Cheboli D., Anomaly detection of time series, University of Minnesota, 2010.

[11]

Chen C.-H., Khoo L.P., Chong Y.T., Yin X.F., Knowledge discovery using genetic algorithm for maritime situational awareness, Expert Systems with Applications 41 (6) (2014) 2742–2753.

Digital Library

[12]

Clapin H., Visual representation and taxonomy, in: Paton R., Neilson I. (Eds.), Visual representations and interpretations, Springer, 1999, pp. 313–321,.

[13]

De Vries G.K.D., Van Someren M., Machine learning for vessel trajectories using compression, alignments and domain knowledge, Expert Systems with Applications 39 (18) (2012) 13426–13439.

Digital Library

[14]

Deng F., Guo S., Deng Y., Chu H., Zhu Q., Sun F., Vessel track information mining using AIS data, in: 2014 International conference on multisensor fusion and information integration for intelligent systems, IEEE, 2014, pp. 1–6.

[15]

Dobrkovic, A., Iacob, M.-E., & van Hillegersberg, J. (2015). Using machine learning for unsupervised maritime waypoint discovery from streaming AIS data. In Proceedings of the 15th international conference on knowledge technologies and data-driven business (pp. 1–8).

[16]

Dobrkovic A., Iacob M.-E., van Hillegersberg J., Maritime pattern extraction and route reconstruction from incomplete AIS data, International Journal of Data Science and Analytics 5 (2–3) (2018) 111–136.

[17]

Dobrkovic A., Iacob M.-E., Van Hillegersberg J., Maritime pattern extraction from AIS data using a genetic algorithm, in: 2016 IEEE international conference on data science and advanced analytics, IEEE, 2016, pp. 642–651.

[18]

Elias P.A.N.d.C., Representação Semântica de Trajetórias para Detecção de Anomalias Marítimas, Instituto Tecnológico de Aeronáutica - São josé dos Campos, 2014.

[19]

EMSA P.A.N.d.C., Annual overview of marine casualties and incidents, European Maritime Safety Agency, 2019.

[20]

Ester M., Kriegel H.-P., Sander J., Xu X., et al., A density-based algorithm for discovering clusters in large spatial databases with noise, in: Kdd. Vol. 96. No. 34, 1996, pp. 226–231.

[21]

Fiorini M., Capata A., Bloisi D.D., AIS data visualization for maritime spatial planning (MSP), International Journal of E-Navigation and Maritime Economy 5 (2016) 45–60.

[22]

Fischer Y., Beyerer J., Ontologies for probabilistic situation assessment in the maritime domain, in: 2013 IEEE international multi-disciplinary conference on cognitive methods in situation awareness and decision support, IEEE, 2013, pp. 102–105.

[23]

Flach P., Machine learning: The art and science of algorithms that make sense of data, Cambridge University Press, 2012.

Digital Library

[24]

Fooladvandi F., Brax C., Gustavsson P., Fredin M., Signature-based activity detection based on Bayesian networks acquired from expert knowledge, in: 2009 12th International conference on information fusion, IEEE, 2009, pp. 436–443.

[25]

Fu P., Wang H., Liu K., Hu X., Zhang H., Finding abnormal vessel trajectories using feature learning, IEEE Access 5 (2017) 7898–7909.

[26]

George J., Crassidis J., Singh T., Fosbury A.M., Anomaly detection using context-aided target tracking, Journal of Advances in Information Fusion 6 (1) (2011) 39–56.

[27]

Goerlandt F., Kujala P., Traffic simulation based ship collision probability modeling, Reliability Engineering & System Safety 96 (1) (2011) 91–107.

[28]

Goodman L.A., Snowball sampling, The Annals of Mathematical Statistics (1961) 148–170.

[29]

Goudossis A., Katsikas S.K., Towards a secure automatic identification system (AIS), Journal of Marine Science and Technology 24 (2) (2019) 410–423.

[30]

Huang W., Liu X., Gill E.W., Ocean wind and wave measurements using X-band marine radar: A comprehensive review, Remote Sensing 9 (12) (2017) 1261.

[31]

Idiri B., Napoli A., The automatic identification system of maritime accident risk using rule-based reasoning, in: 2012 7th International conference on system of systems engineering, IEEE, 2012, pp. 125–130.

[32]

IMO B., International convention for the safety of life at sea, International Maritime Organization, 2003.

[33]

Iphar C., Ray C., Napoli A., Uses and misuses of the automatic identification system, in: OCEANS 2019-Marseille, IEEE, 2019, pp. 1–10.

[34]

Iphar C., Ray C., Napoli A., Data integrity assessment for maritime anomaly detection, Expert Systems with Applications 147 (2020).

[35]

Kazemi S., Abghari S., Lavesson N., Johnson H., Ryman P., Open data for anomaly detection in maritime surveillance, Expert Systems with Applications 40 (14) (2013) 5719–5729.

[36]

Kessler G., Protected AIS: a demonstration of capability scheme to provide authentication and message integrity, TransNav: International Journal on Marine Navigation and Safety of Sea Transportation 14 (2) (2020).

[37]

Kowalska K., Peel L., Maritime anomaly detection using Gaussian process active learning, in: 2012 15th International conference on information fusion, IEEE, 2012, pp. 1164–1171.

[38]

Lane R.O., Nevell D.A., Hayward S.D., Beaney T.W., Maritime anomaly detection and threat assessment, in: 2010 13th International conference on information fusion, IEEE, 2010, pp. 1–8.

[39]

Last P., Analysis of automatic identification system data for maritime safety, (Ph.D. thesis) Information Resource Center der Jacobs University Bremen, 2017, URL: http://urn-resolving.de/urn:nbn:de:gbv:579-opus-1006908.

[40]

Laxhammar, R., & Falkman, G. (2010). Conformal prediction for distribution-independent anomaly detection in streaming vessel data. In Proceedings of the first international workshop on novel data stream pattern mining techniques (pp. 47–55).

[41]

Laxhammar R., Falkman G., Sequential conformal anomaly detection in trajectories based on hausdorff distance, in: 14th International conference on information fusion, IEEE, 2011, pp. 1–8.

[42]

Laxhammar R., Falkman G., Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories, Annals of Mathematics and Artificial Intelligence 74 (1–2) (2015) 67–94.

[43]

Laxhammar R., Falkman G., Sviestins E., Anomaly detection in sea traffic-a comparison of the gaussian mixture model and the kernel density estimator, in: 2009 12th International conference on information fusion, IEEE, 2009, pp. 756–763.

[44]

Le Guillarme N., Lerouvreur X., Unsupervised extraction of knowledge from S-AIS data for maritime situational awareness, in: Proceedings of the 16th international conference on information fusion, IEEE, 2013, pp. 2025–2032.

[45]

Lei P.-R., A framework for anomaly detection in maritime trajectory behavior, Knowledge and Information Systems 47 (1) (2016) 189–214.

[46]

Liu B., de Souza E.N., Hilliard C., Matwin S., Ship movement anomaly detection using specialized distance measures, in: 2015 18th International conference on information fusion, IEEE, 2015, pp. 1113–1120.

[47]

Liu B., de Souza E.N., Matwin S., Sydow M., Knowledge-based clustering of ship trajectories using density-based approach, in: 2014 IEEE international conference on big data, IEEE, 2014, pp. 603–608.

[48]

Malik A., Maciejewski R., Maule B., Ebert D.S., A visual analytics process for maritime resource allocation and risk assessment, in: 2011 IEEE conference on visual analytics science and technology, IEEE, 2011, pp. 221–230.

[49]

Mano J.-P., Georgé J.-P., Gleizes M.-P., Adaptive multi-agent system for multi-sensor maritime surveillance, in: Advances in practical applications of agents and multiagent systems, Springer, 2010, pp. 285–290.

[50]

Mantecón T., Casals D., Navarro-Corcuera J.J., del Blanco C.R., Jaureguizar F., Deep learning to enhance maritime situation awareness, in: 2019 20th International radar symposium, IEEE, 2019, pp. 1–8.

[51]

Mascaro S., Nicholso A.E., Korb K.B., Anomaly detection in vessel tracks using Bayesian networks, International Journal of Approximate Reasoning 55 (1) (2014) 84–98.

[52]

Mazzarella F., Vespe M., Damalas D., Osio G., Discovering vessel activities at sea using AIS data: Mapping of fishing footprints, in: 17th International conference on information fusion, IEEE, 2014, pp. 1–7.

[53]

McAbee A., Scrofani J., Tummala M., Garren D., McEachen J., Traffic pattern detection using the Hough transformation for anomaly detection to improve maritime domain awareness, in: 17th International conference on information fusion, IEEE, 2014, pp. 1–6.

[54]

Murphy K.P., Machine learning: A probabilistic perspective, MIT Press, 2012.

Digital Library

[55]

Nguyen D., Vadaine R., Hajduch G., Garello R., Fablet R., A multi-task deep learning architecture for maritime surveillance using ais data streams, in: 2018 IEEE 5th international conference on data science and advanced analytics, IEEE, 2018, pp. 331–340.

[56]

Nishizaki C., Terayama M., Okazaki T., Shoji R., Development of navigation support system to predict new course of ship, in: 2018 World automation congress, IEEE, 2018, pp. 1–5.

[57]

Osekowska E., Axelsson S., Carlsson B., Potential fields in maritime anomaly detection, in: Proceedings of the 3rd international conference on models and technologies for intelligent transport systems, TUD Press, 2013.

[58]

Pallotta G., Horn S., Braca P., Bryan K., Context-enhanced vessel prediction based on Ornstein-Uhlenbeck processes using historical AIS traffic patterns: Real-world experimental results, in: 17th International conference on information fusion, IEEE, 2014, pp. 1–7.

[59]

Pallotta G., Jousselme A.-L., Data-driven detection and context-based classification of maritime anomalies, in: 2015 18th International conference on information fusion, IEEE, 2015, pp. 1152–1159.

[60]

Pallotta G., Vespe M., Bryan K., Traffic knowledge discovery from AIS data, in: Proceedings of the 16th international conference on information fusion, IEEE, 2013, pp. 1996–2003.

[61]

Pallotta G., Vespe M., Bryan K., Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction, Entropy 15 (6) (2013) 2218–2245.

[62]

Park C.Y., Laskey K.B., Costa P.C., An extended maritime domain awareness probabilistic ontology derived from human-aided multi-entity Bayesian networks learning, in: STIDS, 2016, pp. 28–36.

[63]

Perera L.P., Oliveira P., Soares C.G., Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction, IEEE Transactions on Intelligent Transportation Systems 13 (3) (2012) 1188–1200.

[64]

Petersen K., Vakkalanka S., Kuzniarz L., Guidelines for conducting systematic mapping studies in software engineering: An update, Information & Software Technology 64 (2015) 1–18,.

Digital Library

[65]

Radon A.N., Wang K., Glässer U., Wehn H., Westwell-Roper A., Contextual verification for false alarm reduction in maritime anomaly detection, in: 2015 IEEE international conference on big data, IEEE, 2015, pp. 1123–1133.

[66]

Riveiro M., Evaluation of normal model visualization for anomaly detection in maritime traffic, ACM Transactions on Interactive Intelligent Systems (TiiS) 4 (1) (2014) 1–24.

[67]

Riveiro M., Falkman G., Interactive visualization of normal behavioral models and expert rules for maritime anomaly detection, in: 2009 Sixth international conference on computer graphics, imaging and visualization, IEEE, 2009, pp. 459–466.

[68]

Riveiro M., Pallotta G., Vespe M., Maritime anomaly detection: A review, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8 (5) (2018).

[69]

Roy J., Davenport M., Exploitation of maritime domain ontologies for anomaly detection and threat analysis, in: 2010 International WaterSide security conference, IEEE, 2010, pp. 1–8.

[70]

Sakhuja V., Indian ocean and the safety of sea lines of communication, Strategic Analysis 25 (5) (2001) 689–702,.

[71]

Sánchez Pedroche D., Amigo D., García J., Molina J.M., Architecture for trajectory-based fishing ship classification with AIS data, Sensors 20 (13) (2020) 3782.

[72]

Scheepens R., Hurter C., Van De Wetering H., Van Wijk J.J., Visualization, selection, and analysis of traffic flows, IEEE Transactions on Visualization and Computer Graphics 22 (1) (2015) 379–388.

[73]

Shahir H.Y., Glässer U., Nalbandyan N., Wehn H., Maritime situation analysis: A multi-vessel interaction and anomaly detection framework, in: 2014 IEEE joint intelligence and security informatics conference, IEEE, 2014, pp. 192–199.

[74]

Shahir H.Y., Glasser U., Shahir A.Y., Wehn H., Maritime situation analysis framework: Vessel interaction classification and anomaly detection, in: 2015 IEEE international conference on big data, IEEE, 2015, pp. 1279–1289.

[75]

Sidibé A., Shu G., Study of automatic anomalous behaviour detection techniques for maritime vessels, The Journal of Navigation 70 (4) (2017) 847–858.

[76]

Sidibé, A., Shu, G., Ma, Y., & Wanqi, W. (2018). Big Data Framework for Abnormal Vessel Trajectories Detection using Adaptive Kernel Density Estimation. In Proceedings of the 2nd international conference on big data research (pp. 43–46).

[77]

Silveira P., Teixeira A., Soares C.G., Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal, The Journal of Navigation 66 (6) (2013) 879–898.

[78]

Simsir U., Ertugrul S., Prediction of manually controlled vessels’ position and course navigating in narrow waterways using Artificial Neural Networks, Applied Soft Computing 9 (4) (2009) 1217–1224.

[79]

Smith M., Reece S., Roberts S., Psorakis I., Rezek I., Maritime abnormality detection using Gaussian processes, Knowledge and Information Systems 38 (3) (2014) 717–741.

[80]

Smith M., Reece S., Roberts S., Rezek I., Online maritime abnormality detection using gaussian processes and extreme value theory, in: 2012 IEEE 12th international conference on data mining, IEEE, 2012, pp. 645–654.

[81]

Snidaro L., Visentini I., Bryan K., Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks, Information Fusion 21 (2015) 159–172.

[82]

Soares Júnior A., Moreno B.N., Times V.C., Matwin S., Cabral L.d.A.F., GRASP-UTS: an algorithm for unsupervised trajectory segmentation, International Journal of Geographical Information Science 29 (1) (2015) 46–68.

[83]

Soleimani B.H., De Souza E.N., Hilliard C., Matwin S., Anomaly detection in maritime data based on geometrical analysis of trajectories, in: 2015 18th International conference on information fusion, IEEE, 2015, pp. 1100–1105.

[84]

Spaccapietra S., Parent C., Damiani M.L., de Macedo J.A., Porto F., Vangenot C., A conceptual view on trajectories, Data & Knowledge Engineering 65 (1) (2008) 126–146.

[85]

Tan K.-M.T., Learning and prediction of relational time series, (Ph.D. thesis) Naval Postgraduate School, Calhoun, Monterey, California, 2013, URL: https://calhoun.nps.edu/handle/10945/32907.

[86]

Terroso-Saenz F., Valdes-Vela M., Skarmeta-Gomez A.F., A complex event processing approach to detect abnormal behaviours in the marine environment, Information Systems Frontiers 18 (4) (2016) 765–780.

[87]

UNCTAD F., Handbook of statistics, United Nations Conference on Trade and Development, 2019, Available at https://unctad.org/en/PublicationsLibrary/tdstat44_en.pdf.

[88]

Vandecasteele A., Napoli A., Spatial ontologies for detecting abnormal maritime behaviour, in: 2012 Oceans-Yeosu, IEEE, 2012, pp. 1–7.

[89]

Venskus J., Kurmis M., Andziulis A., Lukošius Ž., Voznak M., Bykovas D., Self-learning adaptive algorithm for maritime traffic abnormal movement detection based on virtual pheromone method, in: 2015 International symposium on performance evaluation of computer and telecommunication systems, IEEE, 2015, pp. 1–6.

[90]

Venskus J., Treigys P., Bernatavičienė J., Medvedev V., Voznak M., Kurmis M., et al., Integration of a self-organizing map and a virtual pheromone for real-time abnormal movement detection in marine traffic, Informatica 28 (2) (2017) 359–374.

[91]

Venskus J., Treigys P., Bernatavičienė J., Tamulevičius G., Medvedev V., Real-time maritime traffic anomaly detection based on sensors and history data embedding, Sensors 19 (17) (2019) 3782.

[92]

Wang X., Liu X., Liu B., de Souza E.N., Matwin S., Vessel route anomaly detection with Hadoop MapReduce, in: 2014 IEEE international conference on big data, IEEE, 2014, pp. 25–30.

[93]

Wang G., Malik A., Yau C., Surakitbanharn C., Ebert D.S., TraSeer: A visual analytics tool for vessel movements in the coastal areas, in: 2017 IEEE international symposium on technologies for homeland security, IEEE, 2017, pp. 1–6.

[94]

Wen Y.T., Lai C.H., Lei P.R., Peng W.C., Routeminer: Mining ship routes from a massive maritime trajectories, in: 2014 IEEE 15th international conference on mobile data management. Vol. 1, IEEE, 2014, pp. 353–356.

[95]

Weng J., Yang D., Qian T., Huang Z., Combining zero-inflated negative binomial regression with MLRT techniques: an approach to evaluating shipping accident casualties, Ocean Engineering 166 (2018) 135–144.

[96]

Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th international conference on evaluation and assessment in software engineering (pp. 1–10).

[97]

Yan R., Wang S., Study of data-driven methods for vessel anomaly detection based on AIS data, in: Smart transportation systems 2019, Springer, 2019, pp. 29–37.

[98]

Yan W., Wen R., Zhang A.N., Yang D., Vessel movement analysis and pattern discovery using density-based clustering approach, in: 2016 IEEE international conference on big data, IEEE, 2016, pp. 3798–3806.

[99]

Zhang Y.-q., Shi G.-y., Li S., Zhang S.-k., Vessel trajectory online multi-dimensional simplification algorithm, The Journal of Navigation 73 (2) (2020) 342–363.

[100]

Zhang S.-k., Shi G.-y., Liu Z.-j., Zhao Z.-w., Wu Z.-l., Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity, Ocean Engineering 155 (2018) 240–250.

[101]

Zhao L., Shi G., Maritime anomaly detection using density-based clustering and recurrent neural network, The Journal of Navigation 72 (4) (2019) 894–916.

[102]

Zhao L., Shi G., Yang J., Ship trajectories pre-processing based on AIS data, The Journal of Navigation 71 (5) (2018) 1210–1230.

[103]

Zhen R., Jin Y., Hu Q., Shao Z., Nikitakos N., Maritime anomaly detection within coastal waters based on vessel trajectory clustering and Naïve Bayes Classifier, The Journal of Navigation 70 (3) (2017) 648–670.

[104]

Zhen R., Riveiro M., Jin Y., A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance, Ocean Engineering 145 (2017) 492–501.

[105]

Zhu F., Mining ship spatial trajectory patterns from AIS database for maritime surveillance, in: 2011 2nd IEEE international conference on emergency management and management sciences, IEEE, 2011, pp. 772–775.

[106]

Zissis D., Xidias E.K., Lekkas D., A cloud based architecture capable of perceiving and predicting multiple vessel behaviour, Applied Soft Computing 35 (2015) 652–661.

[107]

Zor C., Kittler J., Maritime anomaly detection in ferry tracks, in: 2017 IEEE international conference on acoustics, speech and signal processing, IEEE, 2017, pp. 2647–2651.

Recommendations

Unsupervised maritime anomaly detection for intelligent situational awareness using AIS data
Abstract
With the mandatory implementation of the automatic identification system and the rapid advancement of relevant satellite communication technologies, a vast amount of vessel trajectory data has been amassed. It has catalyzed advancements in the ...
A novel anomaly detection approach to identify intentional AIS on-off switching

An anomaly detection algorithm to identify AIS on-off switching is proposed.The algorithm exploits the AIS message Received Signal Strength Indicator.Machine Learning algorithms are used to build normality models.AIS reception is characterized by using ...
Towards an immunity-based anomaly detection system for network traffic

This paper proposes an immunity-based anomaly detection system for network traffic. The system is inspired by the specificity and diversity of the immune system; the system has a user-specific agent for every user, and diverse agents make a decision ...

Comments

Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 231, Issue C

Nov 2023

1599 pages

ISSN:0957-4174

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 30 November 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Oct 2024

Other Metrics

View Author Metrics

Citations

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents