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:Fig2 Melanitis MATECWebConf21 349.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Designing a knowledge management system for naval materials failures|Designing a knowledge management system for naval materials failures]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Implemented materials fail from time to time, requiring [[failure analysis]]. This type of scientific analysis expands into [[Forensic science|forensic]] engineering for it aims not only to identify individual and symptomatic reasons for failure, but also to assess and understand repetitive failure patterns, which could be related to underlying material faults, design mistakes, or maintenance omissions. Significant [[information]] can be gained and studied from carefully documenting and managing the data that comes from failure analysis of materials, including in the naval industry. The NAVMAT research project, presented herein, attempts an interdisciplinary approach to [[materials informatics]] by integrating materials engineering and [[Informatics (academic field)|informatics]] under a platform of [[Information management|knowledge management]]. Our approach utilizes a focused, common-cause failure analysis methodology for the naval and marine environment. The platform's design is dedicated to the effective recording, efficient indexing, and easy and accurate retrieval of relevant information, including the associated history of maintenance and secure operation concerning failure incidents of marine materials, components, and systems in an organizational fleet. ... ('''[[Journal:Designing a knowledge management system for naval materials failures|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
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Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
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