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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Liscouski PlanDisruptLabOper2022.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[LII:Planning for Disruptions in Laboratory Operations|Planning for Disruptions in Laboratory Operations]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


A high-level of productivity is something laboratory management wants and those working for them strive to achieve. However, what happens when reality trips us up? We found out when [[COVID-19]] appeared. This work from laboratory informatics veteran Joe Liscouski examines how [[laboratory]] operations can be organized to meet that disruption, as well as other disruptions we may have to face. Many of these changes, including the introduction of new technologies and changing attitudes about work, were in the making already but at a much slower pace. Over the years, productivity has had many measures, from 40 to 60 hour work weeks and piece-work to pounds of material processed to samples run, all of which comes from a manufacturing mind set. People went to work in an office, lab, or production site, did their work, put in their time, and went home. That was in the timeframe leading up to the 1950s and '60s. Today, in 2022, things have changed ... ('''[[LII:Planning for Disruptions in Laboratory Operations|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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