Virtual Infrastructure Twin for Computing-Instrument Ecosystems: Software and Measurements

Al-Najjar, Anees; Rao, Nageswara S. V.

doi:10.1109/access.2023.3246954

Cited by 3 publications

(1 citation statement)

References 20 publications

(41 reference statements)

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“…The complexity of current systems: The complexity of the SDLC requires simulation capabilities for the automation efficiency that is needed for the continuous improvement of aspects of the value stream and the product. Al-Najjar et al [190] use virtual infrastructure DTs to help with the complexity of complex workflow ecosystems. In contrast, Oliveira Antonino et al [114] highlight the need for appropriate methods and tools to enable continuous and accurate assessments of the quality of system architectures so that it is not a siloed territory based on the expertise of a few engineers.…”

Section: Rq2mentioning

confidence: 99%

Digital Twins in Software Engineering—A Systematic Literature Review and Vision

Guinea-Cabrera,

Holgado-Terriza

2024

Applied Sciences

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Digital twins are a powerful consequence of digital transformation. In fact, they have been applied to many industries to enhance operations, predict needs, improve decision making, or optimize performance, even though the definition of digital twins is still evolving. However, their impact on the software industry is still limited. Thus, this work aims to analyze the current adoption of digital twins in the software industry as a potential path to integrate them into application lifecycle management. To achieve this objective, first, the significant characteristics of current digital twins are analyzed in their application to manufacturing to understand how the knowledge and the lessons learned can be transferred to the software industry. Second, a systematic literature review was conducted on Scopus, the Web of Science, and the ScienceDirect database. The literature review revealed 93 documents after data screening and cleaning 251 initial documents. Our main findings are that digital twins are already influencing and will significantly affect the software industry, revolutionizing various aspects of the software development lifecycle. This study tackles what identifies a digital twin in the software industry, the specific domains and areas where they can be applied in the software lifecycle, and the proposed approaches explored to build digital twins for developing, deploying, and maintaining software systems. Finally, this study proposes some guidelines for building digital twins in the context of application lifecycle management. Determining an appropriate roadmap shortly is essential to achieve a widespread applicability to building suitable digital twins and preparing organizations for the software industry.

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Section: Rq2mentioning

confidence: 99%

Digital Twins in Software Engineering—A Systematic Literature Review and Vision

Guinea-Cabrera,

Holgado-Terriza

2024

Applied Sciences

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Machine learning for automated experimentation in scanning transmission electron microscopy

Kalinin,

Mukherjee,

Roccapriore

et al. 2023

npj Comput Mater

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Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

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