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

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'''"[[Journal:Strategies for laboratory professionals to drive laboratory stewardship|Strategies for laboratory professionals to drive laboratory stewardship]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Appropriate [[laboratory]] [[Medical test|testing]] is critical in today's healthcare environment that aims to improve patient care while reducing cost. In recent years, laboratory stewardship has emerged as a strategy for assuring [[Quality (business)|quality]] in laboratory medicine with the goal of providing the right test for the right patient at the right time. Implementing a laboratory stewardship program now presents a valuable opportunity for laboratory professionals to exercise leadership within health systems and to drive change toward realizing aims in healthcare. The proposed framework for program implementation includes five key elements: 1) a clear vision and organizational alignment; 2) appropriate skills for program execution and management; 3) resources to support the program; 4) incentives to motivate participation; and, 5) a plan of action that articulates program objectives and metrics. This framework builds upon principles of [[change management]], with emphasis on engagement with clinical and administrative stakeholders and the use of clinical data as the basis for change. These strategies enable laboratory professionals to cultivate organizational support for improving laboratory use and take a leading role in providing high-quality patient care. ('''[[Journal:Strategies for laboratory professionals to drive laboratory stewardship|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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