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Emerging Enabling Technologies for Industry 4.0 and Beyond
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“While the I4.0 paradigm is still gaining momentum, further evolution leveraging on additional set of emerging enabling technologies [5] and enhancing solutions by combining current enabling technologies goes beyond technology-driven production efficiency accomplishing merely economic goals, and extends to broader societal significance in an attempt to achieve resilient, sustainable and human-or”
From SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0 (2022) · cites this paper as [5]
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Metadata
- Authors
- Sigov, A., Ratkin, L., Ivanov, L. A., & Xu, L. D.
- Affiliations
- Old Dominion University (US)
- Russian Academy of Sciences (RU)
- Russian Engineering Academy (RU)
- Corresponding author
- Sigov, Alexander
- Ratkin, Leonid
- Ivanov, Leonid A.
- Venue
- Information Systems Frontiers
- Published
- 2022-01-10
- Volume
- 26
- Issue
- 5
- Pages
- 1585-1595
- ISSN
- 1387-3326 (ISSN-L)
- 1572-9419
- OpenAlex
- W4205584165
- Cited by
- 234
- References
- 98
- Citations by year
- 2026: 32 · 2025: 41 · 2024: 74 · 2023: 54 · 2022: 33
- FWCI
- 23.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Systems Frontiers (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
- Keywords
- Industry 4.0, Industry 5.0, enabling technologies, artificial intelligence, 5G/6G networks, quantum computing, cyber-physical systems, Internet of Things, manufacturing industry, technological evolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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