{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:12:58Z","timestamp":1743102778731,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030503703"},{"type":"electronic","value":"9783030503710"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-50371-0_27","type":"book-chapter","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T17:03:40Z","timestamp":1592499820000},"page":"371-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Detecting Critical Transitions in the Human Innate Immune System Post-cardiac Surgery"],"prefix":"10.1007","author":[{"given":"Alva","family":"Presbitero","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rick","family":"Quax","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valeria V.","family":"Krzhizhanovskaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter M. A.","family":"Sloot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1097\/00000542-200207000-00030","volume":"97","author":"JG Laffey","year":"2002","unstructured":"Laffey, J.G., Boylan, J.F., Cheng, D.C.H.: The systemic inflammatory response to cardiac surgery. Anesthesiology 97, 215\u2013252 (2002). https:\/\/doi.org\/10.1097\/00000542-200207000-00030","journal-title":"Anesthesiology"},{"key":"27_CR2","doi-asserted-by":"publisher","unstructured":"Paparella, D., Yau, T.M., Young, E.: Cardiopulmonary bypass induced inflammation: pathophysiology and treatment. Update (2002). https:\/\/doi.org\/10.1016\/S1010-7940(01)01099-5","DOI":"10.1016\/S1010-7940(01)01099-5"},{"key":"27_CR3","doi-asserted-by":"publisher","DOI":"10.1136\/heart.89.7.767","author":"L Nalysnyk","year":"2003","unstructured":"Nalysnyk, L.: Adverse events in coronary artery bypass graft (CABG) trials: a systematic review and analysis. Heart (2003). https:\/\/doi.org\/10.1136\/heart.89.7.767","journal-title":"Heart"},{"key":"27_CR4","doi-asserted-by":"publisher","unstructured":"Rong, L.Q., Di Franco, A., Gaudino, M.: Acute respiratory distress syndrome after cardiac surgery (2016). https:\/\/doi.org\/10.21037\/jtd.2016.10.74","DOI":"10.21037\/jtd.2016.10.74"},{"key":"27_CR5","doi-asserted-by":"publisher","unstructured":"Rubenfeld, G.D., Herridge, M.S.: Epidemiology and outcomes of acute lung injury. Chest (2007). https:\/\/doi.org\/10.1378\/chest.06-1976","DOI":"10.1378\/chest.06-1976"},{"key":"27_CR6","doi-asserted-by":"publisher","unstructured":"Weissman, C.: Pulmonary complications after cardiac surgery. In: Seminars in Cardiothoracic and Vascular Anesthesia (2004). https:\/\/doi.org\/10.1177\/108925320400800303","DOI":"10.1177\/108925320400800303"},{"key":"27_CR7","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.162.15.1689","author":"P Jong","year":"2002","unstructured":"Jong, P., Vowinckel, E., Liu, P.P., Gong, Y., Tu, J.V.: Prognosis and determinants of survival in patients newly hospitalized for heart failure: a population-based study. Arch. Intern. Med. (2002). https:\/\/doi.org\/10.1001\/archinte.162.15.1689","journal-title":"Arch. Intern. Med."},{"key":"27_CR8","doi-asserted-by":"publisher","unstructured":"Lloyd-Jones, D., et al.: Heart disease and stroke statistics - 2010 update: A report from the American heart association (2010). https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.109.192666","DOI":"10.1161\/CIRCULATIONAHA.109.192666"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.3389\/fimmu.2018.02342","volume":"9","author":"A Presbitero","year":"2018","unstructured":"Presbitero, A., Mancini, E., Brands, R., Krzhizhanovskaya, V.V., Sloot, P.M.A.: Supplemented alkaline phosphatase supports the immune response in patients undergoing cardiac surgery: clinical and computational evidence. Front. Immunol. 9, 2342 (2018). https:\/\/doi.org\/10.3389\/fimmu.2018.02342","journal-title":"Front. Immunol."},{"key":"27_CR10","first-page":"319","volume":"76","author":"K Poelstra","year":"1997","unstructured":"Poelstra, K., Bakker, W.W., Klok, P.A., Hardonk, M.J., Meijer, D.K.: A physiologic function for alkaline phosphatase: endotoxin detoxification. Lab. Invest. 76, 319\u2013327 (1997)","journal-title":"Lab. Invest."},{"key":"27_CR11","doi-asserted-by":"publisher","first-page":"214","DOI":"10.2174\/187221309789257388","volume":"3","author":"S Kats","year":"2009","unstructured":"Kats, S., et al.: Anti-inflammatory effects of alkaline phosphatase in coronary artery bypass surgery with cardiopulmonary bypass. Recent Pat. Inflamm. Allergy Drug Discov. 3, 214\u2013220 (2009). IADD-01 [pii]","journal-title":"Recent Pat. Inflamm. Allergy Drug Discov."},{"key":"27_CR12","doi-asserted-by":"publisher","unstructured":"Cohen, J.: The immunopathogenesis of sepsis (2002). https:\/\/doi.org\/10.1038\/nature01326","DOI":"10.1038\/nature01326"},{"key":"27_CR13","doi-asserted-by":"publisher","unstructured":"Schulte, W., Bernhagen, J., Bucala, R.: Cytokines in sepsis: potent immunoregulators and potential therapeutic targets\u2014an updated view. Mediat. Inflamm. (2013). https:\/\/doi.org\/10.1155\/2013\/165974","DOI":"10.1155\/2013\/165974"},{"key":"27_CR14","doi-asserted-by":"publisher","unstructured":"Trefois, C., Antony, P.M.A., Goncalves, J., Skupin, A., Balling, R.: Critical transitions in chronic disease: transferring concepts from ecology to systems medicine (2015). https:\/\/doi.org\/10.1016\/j.copbio.2014.11.020","DOI":"10.1016\/j.copbio.2014.11.020"},{"key":"27_CR15","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1093\/bioinformatics\/btu084","volume":"30","author":"R Liu","year":"2014","unstructured":"Liu, R., Yu, X., Liu, X., Xu, D., Aihara, K., Chen, L.: Identifying critical transitions of complex diseases based on a single sample. Bioinformatics 30, 1579\u20131586 (2014). https:\/\/doi.org\/10.1093\/bioinformatics\/btu084","journal-title":"Bioinformatics"},{"key":"27_CR16","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1073\/pnas.1312114110","volume":"111","author":"IA van de Leemput","year":"2014","unstructured":"van de Leemput, I.A., et al.: Critical slowing down as early warning for the onset and termination of depression. Proc. Natl. Acad. Sci. U. S. A. 111, 87\u201392 (2014). https:\/\/doi.org\/10.1073\/pnas.1312114110","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"27_CR17","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1038\/451893a","volume":"451","author":"RM May","year":"2008","unstructured":"May, R.M., Levin, S.A., Sugihara, G.: Complex systems: ecology for bankers. Nature 451, 893\u2013895 (2008). https:\/\/doi.org\/10.1038\/451893a","journal-title":"Nature"},{"key":"27_CR18","doi-asserted-by":"publisher","first-page":"1898","DOI":"10.1038\/srep01898","volume":"3","author":"R Quax","year":"2013","unstructured":"Quax, R., Kandhai, D., Sloot, P.M.A.: Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series. Sci. Rep. 3, 1898 (2013). https:\/\/doi.org\/10.1038\/srep01898","journal-title":"Sci. Rep."},{"key":"27_CR19","doi-asserted-by":"publisher","first-page":"14308","DOI":"10.1073\/pnas.0802430105","volume":"105","author":"V Dakos","year":"2008","unstructured":"Dakos, V., Scheffer, M., van Nes, E.H., Brovkin, V., Petoukhov, V., Held, H.: Slowing down as an early warning signal for abrupt climate change. Proc. Natl. Acad. Sci. U. S. A. 105, 14308\u201314312 (2008). https:\/\/doi.org\/10.1073\/pnas.0802430105","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"27_CR20","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1098\/rsta.2011.0304","volume":"370","author":"TM Lenton","year":"2012","unstructured":"Lenton, T.M., Livina, V.N., Dakos, V., van Nes, E.H., Scheffer, M.: Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 370, 1185\u20131204 (2012). https:\/\/doi.org\/10.1098\/rsta.2011.0304","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"27_CR21","doi-asserted-by":"publisher","first-page":"10984","DOI":"10.1038\/ncomms10984","volume":"7","author":"CF Clements","year":"2016","unstructured":"Clements, C.F., Ozgul, A.: Including trait-based early warning signals helps predict population collapse. Nat. Commun. 7, 10984 (2016). https:\/\/doi.org\/10.1038\/ncomms10984","journal-title":"Nat. Commun."},{"key":"27_CR22","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1038\/nature09389","volume":"467","author":"JM Drake","year":"2010","unstructured":"Drake, J.M., Griffen, B.D.: Early warning signals of extinction in deteriorating environments. Nature 467, 456\u2013459 (2010). https:\/\/doi.org\/10.1038\/nature09389","journal-title":"Nature"},{"key":"27_CR23","doi-asserted-by":"publisher","unstructured":"Presbitero, A., Quax, R., Krzhizhanovskaya, V., Sloot, P.: Anomaly detection in clinical data of patients undergoing heart surgery. Procedia Comput. Sci. (2017). https:\/\/doi.org\/10.1016\/j.procs.2017.05.002","DOI":"10.1016\/j.procs.2017.05.002"},{"key":"27_CR24","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1016\/j.procs.2015.05.404","volume":"51","author":"AL Pyayt","year":"2015","unstructured":"Pyayt, A.L.: Combining data-driven methods with finite element analysis for flood early warning systems. Procedia Comput. Sci. 51, 2347\u20132356 (2015). https:\/\/doi.org\/10.1016\/j.procs.2015.05.404","journal-title":"Procedia Comput. Sci."},{"key":"27_CR25","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.procs.2016.05.339","volume":"80","author":"WD Fisher","year":"2016","unstructured":"Fisher, W.D., Camp, T.K., Krzhizhanovskaya, V.V.: Crack detection in earth dam and levee passive seismic data using support vector machines. Procedia Comput. Sci. 80, 577\u2013586 (2016). https:\/\/doi.org\/10.1016\/j.procs.2016.05.339","journal-title":"Procedia Comput. Sci."},{"key":"27_CR26","doi-asserted-by":"publisher","first-page":"764","DOI":"10.2307\/1936746","volume":"61","author":"DL DeAngelis","year":"1980","unstructured":"DeAngelis, D.L.: Energy flow, nutrient cycling, and ecosystem resilience. Ecology 61, 764\u2013771 (1980). https:\/\/doi.org\/10.2307\/1936746","journal-title":"Ecology"},{"key":"27_CR27","doi-asserted-by":"publisher","unstructured":"Dakos, V., et al.: Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS One 7 (2012). https:\/\/doi.org\/10.1371\/journal.pone.0041010","DOI":"10.1371\/journal.pone.0041010"},{"key":"27_CR28","doi-asserted-by":"publisher","unstructured":"Wissel, C.: A universal law of the characteristic return time near thresholds. Oecologia (1984). https:\/\/doi.org\/10.1007\/BF00384470","DOI":"10.1007\/BF00384470"},{"key":"27_CR29","doi-asserted-by":"publisher","unstructured":"Van Nes, E.H., Scheffer, M.: Slow recovery from perturbations as a generic indicator of a nearby catastrophic shift. Am. Nat. (2007). https:\/\/doi.org\/10.1086\/516845","DOI":"10.1086\/516845"},{"key":"27_CR30","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1038\/nature08227","volume":"461","author":"M Scheffer","year":"2009","unstructured":"Scheffer, M., et al.: Early-warning signals for critical transitions. Nature 461, 53\u201359 (2009). https:\/\/doi.org\/10.1038\/nature08227","journal-title":"Nature"},{"key":"27_CR31","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1890\/11-0889.1","volume":"93","author":"V Dakos","year":"2012","unstructured":"Dakos, V., van Nes, E.H., D\u2019Odorico, P., Scheffer, M.: Robustness of variance and autocorrelation as indicators of critical slowing down. Ecology 93, 264\u2013271 (2012)","journal-title":"Ecology"},{"key":"27_CR32","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1111\/j.1461-0248.2008.01160.x","volume":"11","author":"V Guttal","year":"2008","unstructured":"Guttal, V., Jayaprakash, C.: Changing skewness: an early warning signal of regime shifts in ecosystems. Ecol. Lett. 11, 450\u2013460 (2008). https:\/\/doi.org\/10.1111\/j.1461-0248.2008.01160.x","journal-title":"Ecol. Lett."},{"key":"27_CR33","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1073\/pnas.0811729106","volume":"106","author":"R Biggs","year":"2009","unstructured":"Biggs, R., Carpenter, S.R., Brock, W.A.: Turning back from the brink: detecting an impending regime shift in time to avert it. Proc. Natl. Acad. Sci. U. S. A. 106, 826\u2013831 (2009). https:\/\/doi.org\/10.1073\/pnas.0811729106","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"27_CR34","doi-asserted-by":"publisher","unstructured":"Held, H., Kleinen, T.: Detection of climate system bifurcations by degenerate fingerprinting. Geophys. Res. Lett. (2004). https:\/\/doi.org\/10.1029\/2004GL020972","DOI":"10.1029\/2004GL020972"},{"key":"27_CR35","doi-asserted-by":"publisher","unstructured":"Ives, A.R., Dakos, V.: Detecting dynamical changes in nonlinear time series using locally linear state-space models. Ecosphere 3 (2012). https:\/\/doi.org\/10.1890\/ES11-00347.1. art58","DOI":"10.1890\/ES11-00347.1"},{"key":"27_CR36","doi-asserted-by":"publisher","unstructured":"Nguyen, D.M., Mulder, D.S., Shennib, H.: Effect of cardiopulmonary bypass on circulating lymphocyte function. Ann. Thorac. Surg. (1992). https:\/\/doi.org\/10.1016\/0003-4975(92)90319-Y","DOI":"10.1016\/0003-4975(92)90319-Y"},{"key":"27_CR37","doi-asserted-by":"publisher","unstructured":"Peretto, G., Durante, A., Limite, L.R., Cianflone, D.: Postoperative arrhythmias after cardiac surgery: incidence, risk factors, and therapeutic management. Cardiol. Res. Pract. (2014). https:\/\/doi.org\/10.1155\/2014\/615987","DOI":"10.1155\/2014\/615987"},{"key":"27_CR38","doi-asserted-by":"publisher","unstructured":"Hashemzadeh, K., Dehdilani, M., Dehdilani, M.: Postoperative atrial fibrillation following open cardiac surgery: predisposing factors and complications. J. Cardiovasc. Thorac. Res. (2013). https:\/\/doi.org\/10.5681\/jcvtr.2013.022","DOI":"10.5681\/jcvtr.2013.022"},{"key":"27_CR39","doi-asserted-by":"publisher","unstructured":"Diegeler, A., et al.: Humoral immune response during coronary artery bypass grafting\u202f: a comparison of limited approach, \u201cOff-Pump\u201d technique, and conventional cardiopulmonary bypass. Circulation (2000). https:\/\/doi.org\/10.1161\/01.cir.102.suppl_3.iii-95","DOI":"10.1161\/01.cir.102.suppl_3.iii-95"},{"key":"27_CR40","doi-asserted-by":"publisher","unstructured":"Presbitero, A., Mancini, E., Castiglione, F., Krzhizhanovskaya, V.V., Quax, R.: Evolutionary game theory can explain the choice between apoptotic and necrotic pathways in neutrophils. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1401\u20131405. IEEE (2018). https:\/\/doi.org\/10.1109\/BIBM.2018.8621127","DOI":"10.1109\/BIBM.2018.8621127"},{"key":"27_CR41","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1186\/s12859-019-3044-6","volume":"20","author":"A Presbitero","year":"2019","unstructured":"Presbitero, A., Mancini, E., Castiglione, F., Krzhizhanovskaya, V.V., Quax, R.: Game of neutrophils: modeling the balance between apoptosis and necrosis. BMC Bioinformatics 20, 475 (2019). https:\/\/doi.org\/10.1186\/s12859-019-3044-6","journal-title":"BMC Bioinformatics"},{"key":"27_CR42","doi-asserted-by":"publisher","unstructured":"Honda, T., Uehara, T., Matsumoto, G., Arai, S., Sugano, M.: Neutrophil left shift and white blood cell count as markers of bacterial infection (2016). https:\/\/doi.org\/10.1016\/j.cca.2016.03.017","DOI":"10.1016\/j.cca.2016.03.017"},{"key":"27_CR43","doi-asserted-by":"publisher","unstructured":"Athens, J.W.: Blood: leukocytes. Annu. Rev. Physiol. (2003). https:\/\/doi.org\/10.1146\/annurev.ph.25.030163.001211","DOI":"10.1146\/annurev.ph.25.030163.001211"},{"key":"27_CR44","doi-asserted-by":"publisher","unstructured":"Summers, C., Rankin, S.M., Condliffe, A.M., Singh, N., Peters, A.M., Chilvers, E.R.: Neutrophil kinetics in health and disease (2010). https:\/\/doi.org\/10.1016\/j.it.2010.05.006","DOI":"10.1016\/j.it.2010.05.006"},{"key":"27_CR45","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1097\/00000658-199204000-00009","volume":"215","author":"P Damas","year":"1992","unstructured":"Damas, P., et al.: Cytokine serum level during severe sepsis in human IL-6 as a marker of severity. Ann. Surg. 215, 356\u2013362 (1992). https:\/\/doi.org\/10.1097\/00000658-199204000-00009","journal-title":"Ann. Surg."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50371-0_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T23:18:25Z","timestamp":1718666305000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-50371-0_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030503703","9783030503710"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50371-0_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"15 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"230","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"98","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"248 workshop papers were selected from 489 submissions to the thematic tracks. The conference was canceled due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}