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Parviainen T, Goerlandt F, Helle I, Haapasaari P, Kuikka S. Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 278:111520. [PMID: 33166738 DOI: 10.1016/j.jenvman.2020.111520] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 09/15/2020] [Accepted: 10/13/2020] [Indexed: 06/11/2023]
Abstract
The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.
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Affiliation(s)
- Tuuli Parviainen
- University of Helsinki, Marine Risk Governance Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; University of Helsinki, Fisheries and Environmental Management Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Helsinki Institute of Sustainability Science (HELSUS), Porthania (2nd Floor), Yliopistonkatu 3, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland.
| | - Floris Goerlandt
- Aalto University, Department of Mechanical Engineering, Marine Technology, P.O. Box 15300, FI-00076, Aalto, Finland; Dalhousie University, Department of Industrial Engineering, Halifax, Nova Scotia, B3H 4R2, Canada
| | - Inari Helle
- Helsinki Institute of Sustainability Science (HELSUS), Porthania (2nd Floor), Yliopistonkatu 3, FI-00014, University of Helsinki, Finland; University of Helsinki, Environmental and Ecological Statistics Group, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland.
| | - Päivi Haapasaari
- University of Helsinki, Marine Risk Governance Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland
| | - Sakari Kuikka
- University of Helsinki, Fisheries and Environmental Management Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland
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Chang KH, Marcotte G, Pestieau P, Legault-Ouellet É, Pelletier Y. Non-linear source term and scenario for an operational oil spill model. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-03808-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
AbstractThis study presents time-varying oil spill discharge functions and scenarios for operational oil spill models. This study prescribes non-linear models based on experimental measurements (Tavakoli et al. in Ocean Eng 38(17–18):1894–1907, 2011) and then upscaled to the spill duration and discharge quantity for actual oil spill incidents. Scenarios consist in collision and grounding incidents for the instantaneous spill mode; light, medium, and severe incidents for the continuous spill mode; spilt, containment, and retention practices for the spill management mode. A performance analysis of deterministic simulations indicates that the non-linear source terms and scenarios present realistic and reasonable results, showing the detailed spill patterns on the surface ocean, tail-off oil sheens along the areas swept by the dispersion and significantly different results when oil spill management and mitigation practices are activated. For oil spill modelling in support of field operations, responders and decision makers should be made aware of the variability of oil sheen spatial patterns induced by the oil spill source term to better interpret simulation results and assess the impact of source uncertainty on the clean-up, mitigation, ecological and socio-economic risk.
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Helle I, Mäkinen J, Nevalainen M, Afenyo M, Vanhatalo J. Impacts of Oil Spills on Arctic Marine Ecosystems: A Quantitative and Probabilistic Risk Assessment Perspective. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:2112-2121. [PMID: 31971780 PMCID: PMC7145341 DOI: 10.1021/acs.est.9b07086] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Oil spills resulting from maritime accidents pose a poorly understood risk to the Arctic environment. We propose a novel probabilistic method to quantitatively assess these risks. Our method accounts for spatiotemporally varying population distributions, the spreading of oil, and seasonally varying species-specific exposure potential and sensitivity to oil. It quantifies risk with explicit uncertainty estimates, enables one to compare risks over large geographic areas, and produces information on a meaningful scale for decision-making. We demonstrate the method by assessing the short-term risks oil spills pose to polar bears, ringed seals, and walrus in the Kara Sea, the western part of the Northern Sea Route. The risks differ considerably between species, spatial locations, and seasons. Our results support current aspirations to ban heavy fuel oil in the Arctic but show that we should not underestimate the risks of lighter oils either, as these oils can pollute larger areas than heavier ones. Our results also highlight the importance of spatially explicit season-specific oil spill risk assessment in the Arctic and that environmental variability and the lack of data are a major source of uncertainty related to the oil spill impacts.
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Affiliation(s)
- Inari Helle
- Ecosystems
and Environment Research Programme, Faculty of Biological and Environmental
Sciences, University of Helsinki, P.O. Box 65, University of Helsinki FI-00014, Finland
- Helsinki
Institute of Sustainability Science (HELSUS), University of Helsinki, Helsinki, Finland
| | - Jussi Mäkinen
- Organismal
and Evolutionary Biology Research Programme, Faculty of Biological
and Environmental Sciences, University of
Helsinki, P.O. Box 65, University of Helsinki FI-00014, Finland
| | - Maisa Nevalainen
- Organismal
and Evolutionary Biology Research Programme, Faculty of Biological
and Environmental Sciences, University of
Helsinki, P.O. Box 65, University of Helsinki FI-00014, Finland
| | - Mawuli Afenyo
- Transport
Institute, University of Manitoba, 181 Freedman Crescent, Winnipeg, Manitoba R3T 5V4, Canada
| | - Jarno Vanhatalo
- Organismal
and Evolutionary Biology Research Programme, Faculty of Biological
and Environmental Sciences, University of
Helsinki, P.O. Box 65, University of Helsinki FI-00014, Finland
- Department
of Mathematics and Statistics, Faculty of Science, University of Helsinki, Helsinki, Finland
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Zhang G, Thai VV, Law AWK, Yuen KF, Loh HS, Zhou Q. Quantitative Risk Assessment of Seafarers' Nonfatal Injuries Due to Occupational Accidents Based on Bayesian Network Modeling. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2020; 40:8-23. [PMID: 31313353 DOI: 10.1111/risa.13374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 07/19/2018] [Accepted: 05/22/2019] [Indexed: 06/10/2023]
Abstract
Reducing the incidence of seafarers' workplace injuries is of great importance to shipping and ship management companies. The objective of this study is to identify the important influencing factors and to build a quantitative model for the injury risk analysis aboard ships, so as to provide a decision support framework for effective injury prevention and management. Most of the previous research on seafarers' occupational accidents either adopts a qualitative approach or applies simple descriptive statistics for analyses. In this study, the advanced method of a Bayesian network (BN) is used for the predictive modeling of seafarer injuries for its interpretative power as well as predictive capacity. The modeling is data driven and based on an extensive empirical survey to collect data on seafarers' working practice and their injury records during the latest tour of duty, which could overcome the limitation of historical injury databases that mostly contain only data about the injured group instead of the entire population. Using the survey data, a BN model was developed consisting of nine major variables, including "PPE availability," "Age," and "Experience" of the seafarers, which were identified to be the most influential risk factors. The model was validated further with several tests through sensitivity analyses and logical axiom test. Finally, implementation of the result toward decision support for safety management in the global shipping industry was discussed.
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Affiliation(s)
- Guizhen Zhang
- Interdisciplinary Graduate School, Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore
| | - Vinh V Thai
- School of Business IT & Logistics, RMIT University, Melbourne, Australia
| | - Adrian Wing-Keung Law
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Kum Fai Yuen
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Hui Shan Loh
- Logistics and Supply Chain Management Program, School of Business, Singapore University of Social Sciences, Singapore
| | - Qingji Zhou
- School of Civil and Environmental Engineering, Transport Research Centre, Nanyang Technological University, Singapore
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Tabri K, Heinvee M, Laanearu J, Kollo M, Goerlandt F. An online platform for rapid oil outflow assessment from grounded tankers for pollution response. MARINE POLLUTION BULLETIN 2018; 135:963-976. [PMID: 30301122 DOI: 10.1016/j.marpolbul.2018.06.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 06/01/2018] [Accepted: 06/12/2018] [Indexed: 06/08/2023]
Abstract
The risk of oil spills is an ongoing societal concern. Whereas several decision support systems exist for predicting the fate and drift of spilled oil, there is a lack of accurate models for assessing the amount of oil spilled and its temporal evolution. In order to close this gap, this paper presents an online platform for the fast assessment of tanker grounding accidents in terms of structural damage and time-dependent amount of spilled cargo oil. The simulation platform consists of the definition of accidental scenarios; the assessment of the grounding damage and the prediction of the time-dependent oil spill size. The performance of this integrated online simulation environment is exemplified through illustrative case studies representing two plausible accidental grounding scenarios in the Gulf of Finland: one resulting in oil spill of about 50 t, while in the other the inner hull remained intact and no spill occurred.
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Affiliation(s)
- Kristjan Tabri
- Tallinn University of Technology, School of Engineering, Tallinn, Estonia.
| | - Martin Heinvee
- Tallinn University of Technology, School of Engineering, Tallinn, Estonia
| | - Janek Laanearu
- Tallinn University of Technology, School of Engineering, Tallinn, Estonia
| | - Monika Kollo
- Tallinn University of Technology, School of Engineering, Tallinn, Estonia
| | - Floris Goerlandt
- Dalhousie University, Department of Industrial Engineering, Halifax, Nova Scotia B3H 4R2, Canada
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Valdez Banda OA, Goerlandt F, Kuzmin V, Kujala P, Montewka J. Risk management model of winter navigation operations. MARINE POLLUTION BULLETIN 2016; 108:242-262. [PMID: 27207023 DOI: 10.1016/j.marpolbul.2016.03.071] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 03/27/2016] [Indexed: 06/05/2023]
Abstract
The wintertime maritime traffic operations in the Gulf of Finland are managed through the Finnish-Swedish Winter Navigation System. This establishes the requirements and limitations for the vessels navigating when ice covers this area. During winter navigation in the Gulf of Finland, the largest risk stems from accidental ship collisions which may also trigger oil spills. In this article, a model for managing the risk of winter navigation operations is presented. The model analyses the probability of oil spills derived from collisions involving oil tanker vessels and other vessel types. The model structure is based on the steps provided in the Formal Safety Assessment (FSA) by the International Maritime Organization (IMO) and adapted into a Bayesian Network model. The results indicate that ship independent navigation and convoys are the operations with higher probability of oil spills. Minor spills are most probable, while major oil spills found very unlikely but possible.
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Affiliation(s)
- Osiris A Valdez Banda
- Aalto University, Department of Mechanical Engineering (Marine Technology), Research Group on Maritime Risk and Safety, Kotka Maritime Research Centre, Heikinkatu 7, FI-48100 Kotka, Finland.
| | - Floris Goerlandt
- Aalto University, Department of Mechanical Engineering (Marine Technology), Research Group on Maritime Risk and Safety, Kotka Maritime Research Centre, Heikinkatu 7, FI-48100 Kotka, Finland
| | - Vladimir Kuzmin
- Admiral Makarov State University of Maritime and Inland Shipping, Makarov Training Centre, P.O. Box 22, 195112 Saint Petersburg, Russia
| | - Pentti Kujala
- Aalto University, Department of Mechanical Engineering (Marine Technology), Research Group on Maritime Risk and Safety, Kotka Maritime Research Centre, Heikinkatu 7, FI-48100 Kotka, Finland
| | - Jakub Montewka
- Aalto University, Department of Mechanical Engineering (Marine Technology), Research Group on Maritime Risk and Safety, Kotka Maritime Research Centre, Heikinkatu 7, FI-48100 Kotka, Finland; Finnish Geospatial Research Institute, Geodeetinrinne 2, 02430 Masala, Finland; Gdynia Maritime University, Faculty of Navigation, Department of Transport and Logistics, 81-225 Gdynia, Poland
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Helle I, Ahtiainen H, Luoma E, Hänninen M, Kuikka S. A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: A Bayesian network model for the Gulf of Finland. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2015; 158:122-32. [PMID: 25983196 DOI: 10.1016/j.jenvman.2015.04.042] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Revised: 03/27/2015] [Accepted: 04/28/2015] [Indexed: 05/23/2023]
Abstract
Large-scale oil accidents can inflict substantial costs to the society, as they typically result in expensive oil combating and waste treatment operations and have negative impacts on recreational and environmental values. Cost-benefit analysis (CBA) offers a way to assess the economic efficiency of management measures capable of mitigating the adverse effects. However, the irregular occurrence of spills combined with uncertainties related to the possible effects makes the analysis a challenging task. We develop a probabilistic modeling approach for a CBA of oil spill management and apply it in the Gulf of Finland, the Baltic Sea. The model has a causal structure, and it covers a large number of factors relevant to the realistic description of oil spills, as well as the costs of oil combating operations at open sea, shoreline clean-up, and waste treatment activities. Further, to describe the effects on environmental benefits, we use data from a contingent valuation survey. The results encourage seeking for cost-effective preventive measures, and emphasize the importance of the inclusion of the costs related to waste treatment and environmental values in the analysis. Although the model is developed for a specific area, the methodology is applicable also to other areas facing the risk of oil spills as well as to other fields that need to cope with the challenging combination of low probabilities, high losses and major uncertainties.
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Affiliation(s)
- Inari Helle
- Fisheries and Environmental Management Group (FEM), Department of Environmental Sciences, P.O. Box 65, FI-00014, University of Helsinki, Finland.
| | - Heini Ahtiainen
- Natural Resources Institute Finland (Luke), Economics and Society, Latokartanonkaari 9, FI-00790, Helsinki, Finland
| | - Emilia Luoma
- Fisheries and Environmental Management Group (FEM), Department of Environmental Sciences, P.O. Box 65, FI-00014, University of Helsinki, Finland
| | - Maria Hänninen
- Aalto University, Department of Applied Mechanics, Research Group on Maritime Risk and Safety, P.O. Box 12200, FI-00076, Aalto, Finland
| | - Sakari Kuikka
- Fisheries and Environmental Management Group (FEM), Department of Environmental Sciences, P.O. Box 65, FI-00014, University of Helsinki, Finland
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Lehikoinen A, Hänninen M, Storgård J, Luoma E, Mäntyniemi S, Kuikka S. A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015; 49:5301-9. [PMID: 25780862 DOI: 10.1021/es501777g] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
The growth of maritime oil transportation in the Gulf of Finland (GoF), North-Eastern Baltic Sea, increases environmental risks by increasing the probability of oil accidents. By integrating the work of a multidisciplinary research team and information from several sources, we have developed a probabilistic risk assessment application that considers the likely future development of maritime traffic and oil transportation in the area and the resulting risk of environmental pollution. This metamodel is used to compare the effects of two preventative management actions on the tanker collision probabilities and the consequent risk. The resulting risk is evaluated from four different perspectives. Bayesian networks enable large amounts of information about causalities to be integrated and utilized in probabilistic inference. Compared with the baseline period of 2007-2008, the worst-case scenario is that the risk level increases 4-fold by the year 2015. The management measures are evaluated and found to decrease the risk by 4-13%, but the utility gained by their joint implementation would be less than the sum of their independent effects. In addition to the results concerning the varying risk levels, the application provides interesting information about the relationships between the different elements of the system.
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Affiliation(s)
- Annukka Lehikoinen
- †Department of Environmental Sciences, Fisheries and Environmental Management Group, Kotka Maritime Research Center, University of Helsinki, Keskuskatu 10, FI-48100 Kotka, Finland
| | - Maria Hänninen
- ‡School of Engineering, Department of Applied Mechanics, Kotka Maritime Research Centre, Aalto University, Keskuskatu 10, FI-48100 Kotka, Finland
| | - Jenni Storgård
- §Centre for Maritime Studies, Kotka Maritime Research Centre, University of Turku, Keskuskatu 10, FI-48100 Kotka, Finland
| | - Emilia Luoma
- ∥Department of Environmental Sciences, Fisheries and Environmental Management Group, University of Helsinki , P.O. Box 65, Helsinki FI-00014, Finland
| | - Samu Mäntyniemi
- ∥Department of Environmental Sciences, Fisheries and Environmental Management Group, University of Helsinki , P.O. Box 65, Helsinki FI-00014, Finland
| | - Sakari Kuikka
- ∥Department of Environmental Sciences, Fisheries and Environmental Management Group, University of Helsinki , P.O. Box 65, Helsinki FI-00014, Finland
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Hänninen M. Bayesian networks for maritime traffic accident prevention: benefits and challenges. ACCIDENT; ANALYSIS AND PREVENTION 2014; 73:305-312. [PMID: 25269098 DOI: 10.1016/j.aap.2014.09.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Revised: 09/03/2014] [Accepted: 09/13/2014] [Indexed: 06/03/2023]
Abstract
Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making.
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Affiliation(s)
- Maria Hänninen
- Aalto University, School of Engineering, Department of Applied Mechanics, Research Group on Maritime Risk and Safety, P.O. Box 12200, FI-00076 Aalto, Finland.
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Simsir U, Amasyalı MF, Bal M, Çelebi UB, Ertugrul S. Decision support system for collision avoidance of vessels. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.08.067] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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