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 Sun JBiosafetyBiosecurity2021 3-1.jpg|120px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Laboratory information management system for the biosafety laboratory: Safety and efficiency|Laboratory information management system for the biosafety laboratory: Safety and efficiency]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The [[laboratory information management system]] (LIMS) has been widely used to facilitate [[laboratory]] activities. However, the current LIMS does not contain functions to improve the [[Laboratory#Laboratory safety|safety of laboratory work]], which is a major concern of [[biosafety]] laboratories (BSLs). With significant biosafety [[information]] needing to be managed and an increasing number of biosafety-related research projects underway, it is worthy of expanding the current framework of LIMS and building a system that is more suitable for BSL usage. Such a system should carefully trade off between the safety and efficiency of regular lab activities, allowing laboratory staff to conduct their research as freely as possible while also ensuring their and the environment’s safety. In order to achieve this goal, relevant information on the type of research, laboratory personnel, experimental materials, and experimental equipment must be collected and fully utilized by a centralized system and its databases. ('''[[Journal:Laboratory information management system for the biosafety laboratory: Safety and efficiency|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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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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