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)
(58 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:Fig3 Ronalter EnviroDevSust22 660.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Quality and environmental management systems as business tools to enhance ESG performance: A cross-regional empirical study|Quality and environmental management systems as business tools to enhance ESG performance: A cross-regional empirical study]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The growing societal and political focus on sustainability at the global level is pressuring companies to enhance their [[wikipedia:Environmental, social, and corporate governance|environmental, social, and governance]] (ESG) performance to satisfy respective stakeholder needs and ensure sustained business success. With a data sample of 4,292 companies from Europe, East Asia, and North America, this work aims to prove through a cross-regional empirical study that [[quality management system]]s (QMSs) and [[environmental management system]]s (EMSs) represent powerful business tools to achieve this enhanced ESG performance. Descriptive and cluster analyses reveal that firms with QMSs and/or EMSs accomplish statistically significant higher ESG scores than companies without such management systems. Furthermore, the results indicate that operating both types of management systems simultaneously increases performance in the environmental and social pillar even further, while the governance dimension appears to be affected mainly by the adoption of EMSs alone ... ('''[[Journal:Quality and environmental management systems as business tools to enhance ESG performance: A cross-regional empirical study|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 />
<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:PIKAChU: A Python-based informatics kit for analyzing chemical units|PIKAChU: A Python-based informatics kit for analyzing chemical units]]
* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
* [[Journal:Development of Biosearch System for biobank management and storage of disease-associated genetic information|Development of Biosearch System for biobank management and storage of disease-associated genetic information]]
* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
* [[Journal:Establishing a common nutritional vocabulary: From food production to diet|Establishing a common nutritional vocabulary: From food production to diet]]
* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]
}}
}}

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...)
Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=63298"