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Medicine and Pharmaceuticals Biomanufacturing – Industry 5.0
Published in Pau Loke Show, Kit Wayne Chew, Tau Chuan Ling, The Prospect of Industry 5.0 in Biomanufacturing, 2021
Zahra Nashath, Doris Ying Ying Tang, Kit Wayne Chew, Pau Loke Show
Despite the impact and benefits of Industry 5.0 to the pharmaceutical industry, this evolution brings with it several challenges, primarily ethical issues, as technology becomes more intertwined with human lives. Additionally, the use of AI technology in data processing requires further development into synchronized storage systems, data security and automated data scrubbing systems. In short, while there are several challenges and issues still to be resolved before the transition to Industry 5.0, it is clear that it will lead to the advancement of the medical and pharmaceutical industry, particularly in the manufacturing stages, bringing more benefits to humans through machine interactions and AI and in the creation of optimized production lines.
Time-Triggered Protocol
Published in Cary R. Spitzer, Uma Ferrell, Thomas Ferrell, Digital Avionics Handbook, 2017
MEDL stores information such as clock setup data for global timing and communication rate; communication schedules with slots, rounds, and cycles; transmission delays taking into account distances between nodes; bus guardian parameters; startup parameters; and various service and identification parameters. The configuration data also contain CRCs used for continuous scrubbing and self-checking of all configuration data structures.
Identifying Valuable Candidates for Project Lessons Learned
Published in Mel Bost, Project Management Lessons Learned, 2018
These were as follows: Old data cleanupData scrubbingData translationData compatibility with new application
DNS rule-based schema to botnet detection
Published in Enterprise Information Systems, 2021
Kamal Alieyan, Ammar Almomani, Mohammed Anbar, Mohammad Alauthman, Rosni Abdullah, B. B. Gupta
Data Cleansing: This process is also called data scrubbing. It may be also called data cleaning. This stage aims at detecting and removing the errors lying within the datasets. It also aims at discarding the conflicting instances inside the datasets.