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)
 
(71 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:Fig2 Melanitis MATECWebConf21 349.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Soto-Perdomo SoftwareX2023 24.jpg|240px]]</div>
'''"[[Journal:Designing a knowledge management system for naval materials failures|Designing a knowledge management system for naval materials failures]]"'''
'''"[[Journal:OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs|OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs]]"'''
 
OptiGUI DataCollector is a Python 3.8-based graphical user interface (GUI) that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including [[spectrometer]]s and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and [[Data analysis|analysis]]. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies ... ('''[[Journal:OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs|Full article...]]''')<br />


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 />
<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:Laboratory information management system for COVID-19 non-clinical efficacy trial data|Laboratory information management system for COVID-19 non-clinical efficacy trial data]]
* [[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]
* [[Journal:Improving data quality in clinical research informatics tools|Improving data quality in clinical research informatics tools]]
* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
* [[Journal:Electronic tools in clinical laboratory diagnostics: Key examples, limitations, and value in laboratory medicine|Electronic tools in clinical laboratory diagnostics: Key examples, limitations, and value in laboratory medicine]]
* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
}}
}}

Latest revision as of 15:05, 17 June 2024

Fig1 Soto-Perdomo SoftwareX2023 24.jpg

"OptiGUI DataCollector: A graphical user interface for automating the data collecting process in optical and photonics labs"

OptiGUI DataCollector is a Python 3.8-based graphical user interface (GUI) that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including spectrometers and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and analysis. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies ... (Full article...)

Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=64487"