Ublished. Nevertheless, for the greatest of our know-how, we accomplished the top identification rate of COVID-19 amongst other varieties of pneumonia making use of segmented CXR photos within a much less biased configuration. As future perform, we aim to keep improving our database to raise our classification functionality and offer additional robust estimates by using additional CNN architectures for segmentation and classification. Furthermore, we desire to apply additional sophisticated segmentation tactics to isolate certain lung opacities brought on by COVID-19. Likewise, we also would like to discover 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 read and agreed to the published version of the manuscript. Funding: This study 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 Overview Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study is openly readily 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 maintaining a repository of COVID-19 photos for the investigation community. Conflicts of Interest: The authors declare no conflict of interest.
sensorsArticleA Versatile Multiple-Pass Raman Method for Industrial Trace Gas DetectionChunlei Shen, Chengwei Wen, Xin Huang and Xinggui Long 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.; Extended, 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 rapid and in-line multigas detection is essential for any assortment of industrial applications. Within the present perform, we demonstrate the utility of multiple-pass-enhanced Raman spectroscopy as a distinctive tool for sensitive industrial multigas detection. As an alternative to making use of spherical mirrors, D-shaped GYKI 52466 Antagonist mirrors are selected as cavity mirrors in our design and style, and 26 total passes are accomplished within a simple and compact multiple-pass optical program. Due to the large variety of passes accomplished inside the multiple-pass cavity, experiments with ambient air show that the noise equivalent detection limit (3) of 7.six Pa (N2 ), eight.4 Pa (O2 ) and 2.eight Pa (H2 O), which correspond to relative abundance by volume at 1 bar total stress of 76 ppm, 84 ppm and 28 ppm, might be achieved in one particular second with a 1.five W red laser. Additionally, this multiple-pass Raman method is often IQP-0528 Reverse Transcriptase simply upgraded to a multiple-channel detection program, and a two-channel detection technique is demonstrated and characterized. Higher utilization ratio of laser power (defined as the ratio of laser.
