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:NIH Master Logo Vertical 2Color.png|160px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
The '''[[National Institutes of Health]]''' ('''NIH''') is a biomedical research facility primarily located in Bethesda, Maryland, USA, operating as an agency of the [[United States Department of Health and Human Services]]. The NIH is the U.S. agency most responsible for biomedical and health-related research, primarily through its Intramural Research Program (IRP), which claims to be "the largest institution for biomedical science on earth." In addition to conducting its own research, the agency provides major biomedical research funding to non-NIH research facilities through its Extramural Research Program (ERP). For example, in 2003 the NIH and its extramural arm provided 28% of biomedical research funding spent annually in the U.S., or about $26.4 billion.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The NIH comprises 27 separate institutes and centers that conduct research in different disciplines of biomedical science. The IRP is responsible for many scientific accomplishments, including the discovery of fluoride to prevent tooth decay, the use of lithium to manage bipolar disorder, and the creation of vaccines against hepatitis, ''Haemophilus influenzae'' (HIB), and human papillomavirus. The funding of NIH has at times been a source of contention in Congress, serving as a proxy for the political currents of the time. In fiscal year 2010, NIH spent $10.7 billion (not including temporary funding from the ARRA) on clinical research, $7.4 billion on genetics-related research, $6.0 billion on prevention research, $5.8 billion on cancer, and $5.7 billion on [[biotechnology]]. ('''[[National Institutes of Health]]''')<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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