Ublished. Nonetheless, towards the best of our know-how, we achieved the most beneficial identification rate of COVID-19 amongst other sorts of pneumonia working with segmented CXR pictures within a much less biased configuration. As future operate, we aim to maintain improving our database to raise our classification overall performance and deliver far more robust Tasisulam Epigenetic Reader Domain estimates by using more CNN architectures for segmentation and classification. Additionally, we wish to apply more sophisticated segmentation procedures to isolate particular lung opacities brought on by COVID-19. Likewise, we also want to explore additional approaches to evaluate the model predictions, for example SHAP [48].Author Contributions: Conceptualization, L.O.T. and Y.M.G.C.; methodology, L.O.T., L.N. and Y.M.G.C.; validation, D.B., L.S.O. and G.D.C.C.; investigation, L.O.T. and R.M.P.; writing–original draft preparation, L.O.T.; writing–review and editing, R.M.P., D.B., L.S.O., L.N. and Y.M.G.C.; supervision, L.S.O., G.D.C.C. and Y.M.G.C.; project administration, Y.M.G.C.; All authors have read and agreed towards the published version with the manuscript. Funding: This research has been partly supported by the National Council for Scientific and Technological Improvement (CNPq) and Coordena o de Aperfei amento de Pessoal de N el SuperiorBrasil (CAPES). Institutional Review Board GLPG-3221 custom synthesis Statement: Not applicable. Informed Consent Statement: Not applicable. Information Availability Statement: The information presented in this study is openly available on GitHub at https://github.com/lucasxteixeira/covid19-segmentation-paper (accessed on 19 August 2021). Acknowledgments: We appreciate the effort of Joseph Paul Cohen in the University of Montreal for sustaining a repository of COVID-19 images for the analysis community. Conflicts of Interest: The authors declare no conflict of interest.
sensorsArticleA Versatile Multiple-Pass Raman Program for Industrial Trace Gas DetectionChunlei Shen, Chengwei Wen, Xin Huang and Xinggui Lengthy Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang 621900, China; [email protected] (C.S.); [email protected] (C.W.); [email protected] (X.H.) Correspondence: [email protected]: Shen, C.; Wen, C.; Huang, X.; Lengthy, X. A Versatile Multiple-Pass Raman Technique for Industrial Trace Gas Detection. Sensors 2021, 21, 7173. https://doi.org/10.3390/s21217173 Academic Editor: Anna Chiara De Luca Received: 28 September 2021 Accepted: 26 October 2021 Published: 28 OctoberAbstract: The speedy and in-line multigas detection is critical to get a assortment of industrial applications. Inside the present operate, we demonstrate the utility of multiple-pass-enhanced Raman spectroscopy as a one of a kind tool for sensitive industrial multigas detection. As opposed to working with spherical mirrors, D-shaped mirrors are chosen as cavity mirrors in our style, and 26 total passes are accomplished within a basic and compact multiple-pass optical system. Due to the big number of passes achieved inside the multiple-pass cavity, experiments with ambient air show that the noise equivalent detection limit (3) of 7.six Pa (N2 ), 8.four Pa (O2 ) and two.eight Pa (H2 O), which correspond to relative abundance by volume at 1 bar total pressure of 76 ppm, 84 ppm and 28 ppm, may be achieved in one second using a 1.five W red laser. Additionally, this multiple-pass Raman program may be effortlessly upgraded to a multiple-channel detection program, and a two-channel detection method is demonstrated and characterized. Higher utilization ratio of laser power (defined as the ratio of laser.