ISME Research Publications

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    Control limits for monitoring the average of correlated streams
    (Universidade do Minho, 2024) Ribas, Clovis S.; Weheba, Gamal
    Purpose - An increasing number of manufacturing processes involve multiple streams where the same type of item is produced in a parallel fashion. Traditionally, streams need to be monitored using separate control charts. The number of charts becomes unrealistic as the number of streams increases. This research proposes modified limits for individual measurement group charts to control the average of multiple streams and account for the level of correlation between them. Methodology - Results of simulation studies were used to develop a mathematical model representing the relationship between the in-control average run length (ARL0), the number of streams, the level of correlation between them, and the half-width of the control limits. The fitted model was confirmed and used to generate tables of recommended values of the half-width to be used in constructing group control charts to achieve a specified level of the ARL0. A similar approach was used to characterize the shift detection capability of the proposed charts. Findings - The fitted model was confirmed and used to generate tables for modified values of the half-width factor based on the number of streams and the level of correlation between them. Research limitations - Simulated measurements were generated from the normal distribution assuming that the process variability is in-control and that the streams are equally correlated. Value - Research findings offer a solution for implementing group control charts to monitor the average of multiple stream processes. © 2024 Universidade do Minho. All rights reserved.
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    Streamlining science: Recreating systematic literature reviews with AI-Powered decision tools
    (Association for Information Systems, 2023) Manning, Christy; Zhuma, Sophie; Nagrecha, Shivani; Yessoufou, Mouiz; Koutogui, Toko; Gruetzemacher, Ross
    In recent years there has been an explosion of new information technologies that use artificial intelligence (AI) to improve decision-making in scientific research. However, the pace of innovation has far exceeded the capacity of researchers to evaluate such technologies. This project evaluates two new AI-powered research assistant tools for decision-making in literature review: Elicit, which uses GPT, and Research Rabbit, which uses a snowballing algorithm and natural language processing. Using a database search as a control, this project will evaluate overlap of records retrieved, proportion of records missed, time savings, and usability for each tool. The goal is to ascertain the technologies' reliability, efficiency, and acceptance. Such thorough evaluation is necessary to establish trust in these tools' performance and therefore to promote their adoption. This is the first known assessment of AI tools that operate by iteratively employing user's decisions as feedback for retrieving information for literature review. © 2023 29th Annual Americas Conference on Information Systems, AMCIS 2023. All rights reserved.
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    Comparison of multi-task ergonomic assessment methods for risk of upper extremity and low back musculoskeletal disorders
    (Elsevier Ltd, 2024) Jorgensen, Michael J.; Martinez, Andrea; Hakansson, Nils A.
    Work-related musculoskeletal disorder of upper extremity multi-task assessment methods (Revised Strain Index [RSI], Distal Upper Extremity Tool [DUET]) and manual handling multi-task assessment methods (Revised NIOSH Lifting Equation [RNLE], Lifting Fatigue Failure Tool [LiFFT]) were compared. RSI and DUET showed a strong correlation (rs = 0.933, p < 0.001) where increasing risk factor exposure resulted in increasing outputs for both methods. RSI and DUET demonstrated fair agreement (? = 0.299) in how the two methods classified outputs into risk categories (high, moderate or low) when assessing the same tasks. The RNLE and LiFFT showed a strong correlation (rs = 0.903, p = 0.001) where increasing risk factor exposure resulted in increasing outputs, and moderate agreement (? = 0.574) in classifying the outputs into risk categories (high, moderate or low) when assessing the same tasks. The multi-task assessment methods provide consistent output magnitude rankings in terms of increasing exposure, however some differences exist between how different methods classify the outputs into risk categories. © 2024 Elsevier Ltd
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    Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion
    (Elsevier Ltd, 2024) Asgari, Ali; Si, Wujun; Yuan, Liang; Krishnan, Krishna K.; Wei, Wei
    In system prognostics and health management, multivariable degradation models have been widely developed to predict the life of complex systems using degradation data of multiple Performance Characteristics (PCs). Recent studies have detected a Long-Term Memory (LTM) effect among the degradation process of various PCs, implying a strong coupling phenomenon between the future degradation behavior and historical degradation trajectory. Although the LTM has been widely integrated into single-PC-based degradation modeling, it has not been considered in multi-PC-based scenarios. To capture LTM among multiple PCs, this article proposes a novel LTM-integrated Multivariate Degradation Model (MDM) for system life prediction based on multivariate fractional Brownian motion, which simultaneously incorporates the cross-correlation among different PCs. To estimate parameters of the LTM-integrated MDM, a maximum likelihood method is developed. Two likelihood-ratio hypothesis tests are developed to test the existence of the overall and individual LTM effect among multiple PCs. Both simulation studies and physical experiments on the performance degradation of solar energy conversion and storage devices are conducted to validate the proposed model. Results reveal that the proposed LTM-integrated MDM significantly outperforms existing MDMs in life prediction, while the lifetime uncertainty is heavily underestimated by those traditional approaches that neglect the LTM. © 2024 Elsevier Ltd
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    Soft Robot Design, Manufacturing, and Operation Challenges: A Review
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Ambaye, Getachew; Boldsaikhan, Enkhsaikhan; Krishnan, Krishna K.
    Advancements in smart manufacturing have embraced the adoption of soft robots for improved productivity, flexibility, and automation as well as safety in smart factories. Hence, soft robotics is seeing a significant surge in popularity by garnering considerable attention from researchers and practitioners. Bionic soft robots, which are composed of compliant materials like silicones, offer compelling solutions to manipulating delicate objects, operating in unstructured environments, and facilitating safe human-robot interactions. However, despite their numerous advantages, there are some fundamental challenges to overcome, which particularly concern motion precision and stiffness compliance in performing physical tasks that involve external forces. In this regard, enhancing the operation performance of soft robots necessitates intricate, complex structural designs, compliant multifunctional materials, and proper manufacturing methods. The objective of this literature review is to chronicle a comprehensive overview of soft robot design, manufacturing, and operation challenges in conjunction with recent advancements and future research directions for addressing these technical challenges. © 2024 by the authors.