Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Yeste AdvLabMed2021 2-3.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Management of post-analytical processes in the clinical laboratory according to ISO 15189:2012: Considerations about the management of clinical samples, ensuring quality of post-analytical processes and laboratory information management|Management of post-analytical processes in the clinical laboratory according to ISO 15189:2012: Considerations about the management of clinical samples, ensuring quality of post-analytical processes and laboratory information management]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


[[ISO 15189|ISO 15189:2012 ''Medical laboratories — Requirements for quality and competence'']] establishes the requirements for clinical specimen management, ensuring the [[Quality (business)|quality]] of processes and [[laboratory]] [[information management]]. ENAC (Entidad Nacional de Acreditación), the sole accreditation authority in Spain, established the requirements for the authorized use of the ISO 15189 accreditation label in reports issued by accredited laboratories. These recommendations are applicable to the lab's post-analytical processes and the professionals involved. The standard requires laboratories to define and document the duration and conditions of specimen [[Retention period|retention]]. Laboratories are also required to design an internal [[quality control]] scheme to verify whether post-analytical activities attain the expected standards. Information management requirements are also established, and laboratories are required to design a contingency plan to ensure the communication of laboratory results  ... ('''[[Journal:Management of post-analytical processes in the clinical laboratory according to ISO 15189:2012: Considerations about the management of clinical samples, ensuring quality of post-analytical processes and laboratory information management|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Latest revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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