Ublished. Nonetheless, for the greatest of our information, we accomplished the ideal identification rate of COVID-19 amongst other kinds of pneumonia utilizing segmented CXR photos within a less biased configuration. As future perform, we aim to keep enhancing our database to increase our classification overall performance and deliver a lot more robust estimates by utilizing far more CNN architectures for segmentation and classification. Additionally, we desire to apply more sophisticated segmentation approaches to isolate precise lung opacities caused by COVID-19. Likewise, we also choose to explore far more approaches to evaluate the model predictions, for instance 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 study and agreed for the published version of the manuscript. Funding: This analysis 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 Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The information presented within this study is openly accessible on GitHub at https://github.com/lucasxteixeira/covid19-segmentation-paper (accessed on 19 August 2021). Acknowledgments: We appreciate the effort of Joseph Paul Cohen from the University of Montreal for keeping a repository of COVID-19 photos for the analysis neighborhood. Conflicts of Interest: The authors declare no conflict of interest.
sensorsArticleA Versatile Multiple-Pass Raman Technique 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 fast and in-line multigas detection is vital to get a wide variety of industrial applications. Inside the present perform, we demonstrate the RP101988 In stock utility of multiple-pass-enhanced Raman spectroscopy as a exclusive tool for sensitive industrial multigas detection. Instead of employing spherical mirrors, AAPK-25 supplier D-shaped mirrors are chosen as cavity mirrors in our design, and 26 total passes are accomplished inside a very simple and compact multiple-pass optical technique. Because of the significant number of passes accomplished inside the multiple-pass cavity, experiments with ambient air show that the noise equivalent detection limit (three) of 7.six Pa (N2 ), eight.4 Pa (O2 ) and 2.8 Pa (H2 O), which correspond to relative abundance by volume at 1 bar total stress of 76 ppm, 84 ppm and 28 ppm, could be accomplished in 1 second using a 1.5 W red laser. Additionally, this multiple-pass Raman program might be conveniently upgraded to a multiple-channel detection technique, as well as a two-channel detection system is demonstrated and characterized. High utilization ratio of laser energy (defined as the ratio of laser.