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

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text.)
(Updated article of the week text)
 
(443 intermediate revisions by the same user not shown)
Line 1: Line 1:
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:NCDN - CDN.png|280px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
A '''[[content delivery network]]''' or '''content distribution network''' ('''CDN''') is a large distributed system of servers deployed in multiple data centers or "nodes" across the Internet. The goal of a CDN is to serve content to end-users with the intended benefit of reducing bandwidth costs, improving page load times, and/or increasing global availability of content. This is done by hosting the content on several servers, and when a user makes a request to CDN-hosted content, the domain name server (DNS) will resolve to an optimized server based on location, availability, cost, and other metrics.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Content providers such as media companies and e-commerce vendors pay CDN operators to deliver their content to their audience of end-users. In turn, a CDN pays Internet service providers (ISPs), carriers, and network operators for hosting its servers in their data centers. Besides better performance and availability, CDNs also offload the traffic served directly from the content provider's origin infrastructure, resulting in possible cost savings for the content provider. In addition, CDNs provide the content provider a degree of protection from denial-of-service (DoS) attacks by using their large distributed server infrastructure to absorb the traffic.  
[[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 />
 
''Recently featured'':
An increasing number of ISPs have built their own CDNs to improve on-net content delivery, reduce demand on their own infrastructure, and generate revenues from content customers. Additionally, some companies such as Microsoft, Amazon, and Netflix have built their own CDNs to tie in with their own products. ('''[[Content delivery network |Full article...]]''')<br />
{{flowlist |
<br />
* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
''Recently featured'': [[Federally qualified health center]], [[Home health agency]], [[ISO 9000]]
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
}}

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...)
Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=63551"