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

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'''"[[Journal:Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing|Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Point-of-care testing (POCT) is one of the fastest growing disciplines in [[clinical laboratory]] medicine. POCT [[Medical device|devices]] are widely used in both acute and chronic patient management in the [[hospital]] and [[Physician office laboratory|primary care physician office]] settings. As demands for POCT in various healthcare settings increase, managing POCT testing quality and [[regulatory compliance]] are continually challenging. Despite technological advances in applying automatic system checks and built-in [[quality control]] to prevent analytical and operator errors, poor planning for POCT [[Interface (computing)|connectivity]] and [[Informatics (academic field)|informatics]] can limit [[Data sharing|data accessibility]] and [[Information management|management]] efficiency which impedes the utilization of POCT to its full potential. This article will summarize how connectivity and data management systems can improve timely access to POCT results, effective management of POCT programs, and ensure regulatory compliance. ('''[[Journal:Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing|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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