Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.
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http://dx.doi.org/10.3390/s22114153 | DOI Listing |
PLoS One
January 2025
Department of Construction and Quality Management, School of Science and Technology, Hong Kong Metropolitan University, Homantin Kowloon, Hong Kong SAR, China.
Industry 4.0 has transformed manufacturing with the integration of cutting-edge technology, posing crucial issues in the efficient task assignment to multi-tasking robots within smart factories. The paper outlines a unique method of decentralizing auctions to handle basic tasks.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
Importance: The integration of patient-reported outcome (PRO) assessments in cardiovascular care has encountered considerable obstacles despite their established clinical relevance.
Objective: To assess the impact of a physician- and patient-friendly electronic PRO (ePRO) monitoring system on the quality of cardiovascular care in clinical practice.
Design, Setting, And Participants: This open-label, multicenter, pilot randomized clinical trial was phase 2 of a multiphase study that was conducted from October 2022 to October 2023 and focused on the implementation and evaluation of an ePRO monitoring system in outpatient clinics in Japan.
Microb Cell Fact
January 2025
Chair of Biochemistry of Microorganisms, Faculty of Life Sciences: Food, Nutrition and Health, University of Bayreuth, 95326, Kulmbach, Germany.
Background: During the last decades, the advancements in synthetic biology opened the doors for a profusion of cost-effective, fast, and ecologically friendly medical applications priorly unimaginable. Following the trend, the genetic engineering of the baker's yeast, Saccharomyces cerevisiae, propelled its status from an instrumental ally in the food industry to a therapy and prophylaxis aid.
Main Text: In this review, we scrutinize the main applications of engineered S.
J Bacteriol
January 2025
Department of Environment and Energy Systems, Graduate School of Science and Technology, Shizuoka University, Shizuoka, Japan.
Nitrification by heterotrophic microorganisms is an important part of the nitrogen cycle in the environment. The enzyme responsible for the core function of heterotrophic nitrification is pyruvic oxime dioxygenase (POD). POD is a non-heme, Fe(II)-dependent enzyme that catalyzes the dioxygenation of pyruvic oxime to produce pyruvate and nitrite.
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