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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Ronalter EnviroDevSust22 660.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>
'''"[[Journal:Quality and environmental management systems as business tools to enhance ESG performance: A cross-regional empirical study|Quality and environmental management systems as business tools to enhance ESG performance: A cross-regional empirical study]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The growing societal and political focus on sustainability at the global level is pressuring companies to enhance their [[wikipedia:Environmental, social, and corporate governance|environmental, social, and governance]] (ESG) performance to satisfy respective stakeholder needs and ensure sustained business success. With a data sample of 4,292 companies from Europe, East Asia, and North America, this work aims to prove through a cross-regional empirical study that [[quality management system]]s (QMSs) and [[environmental management system]]s (EMSs) represent powerful business tools to achieve this enhanced ESG performance. Descriptive and cluster analyses reveal that firms with QMSs and/or EMSs accomplish statistically significant higher ESG scores than companies without such management systems. Furthermore, the results indicate that operating both types of management systems simultaneously increases performance in the environmental and social pillar even further, while the governance dimension appears to be affected mainly by the adoption of EMSs alone ... ('''[[Journal:Quality and environmental management systems as business tools to enhance ESG performance: A cross-regional empirical study|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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