3 resultados para Industry 4.0,Hot-Dip Galvanizing Process,Air-knife process,Neural Networks,Deep Learning

em Aberdeen University


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The authors would like to thank the participants to the Respiratory Effectiveness Group Adherence symposium for their comments on the model overview presented during this meeting, members of the ASTRO-LAB consortium for collaborative work on reviewing literature and performing qualitative interviews, and patients and clinicians that shared valuable insights into asthma management during the telephone interviews.

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The authors would like to thank the leadership of the Deep Ocean Stewardship Initiative (DOSI), including Lisa Levin, Maria Baker, and Kristina Gjerde, for their support in developing this review. This work evolved from a meeting of the DOSI Oil and Gas working group supported by the J.M. Kaplan Fund, and associated with the Deep-Sea Biology Symposium in Aveiro, Portugal in September 2015. The members of the Oil and Gas working group that contributed to our discussions at that meeting or through the listserve are acknowledged for their contributions to this work. We would also like to thank the three reviewers and the editor who provided valuable comments and insight into the work presented here. DJ and AD were supported by funding from the European Union's Horizon 2020 research and innovation programme under the MERCES (Marine Ecosystem Restoration in Changing European Seas) project, grant agreement No 689518. AB was supported by CNPq grants 301412/2013-8 and 200504/2015-0. LH acknowledges funding provided by a Natural Environment Research Council grant (NE/L008181/1). This output reflects only the authors' views and the funders cannot be held responsible for any use that may be made of the information contained therein.