Digital Twins offer vast potential, yet many companies, particularly small and medium-sized enterprises, hesitate to implement them. This hesitation stems partly from the challenges posed by the interdisciplinary nature of creating Digital Twins. To address these challenges, this paper explores systematic approaches for the development and creation of Digital Twins, drawing on relevant methods and approaches presented in the literature. Conducting a systematic literature review, we delve into the development of Digital Twins while also considering analogous concepts, such as Cyber-Physical Systems and Product-Service Systems. The compiled literature is categorised into three main sections: holistic approaches, architecture, and models. Each category encompasses various subcategories, all of which are detailed in this paper. Through this comprehensive review, we discuss the findings and identify research gaps, shedding light on the current state of knowledge in the field of Digital Twin development. This paper aims to provide valuable insights for practitioners and researchers alike, guiding them in navigating the complexities associated with the implementation of Digital Twins.
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http://dx.doi.org/10.3390/s23249786 | DOI Listing |
Water Res
December 2024
School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Marine Pollution, City University of Hong Kong, Hong Kong SAR, China. Electronic address:
Airflow models are powerful tools for ventilation design to achieve odour and corrosion mitigation in sewer networks. Currently, there lacks a model able to efficiently predict in-sewer dynamic airflows, as all available dynamic models with an acceptable accuracy are computationally demanding. In this study, a swift dynamic airflow model based on an ordinary differential equation (ODE) is derived by simplifying the one-dimensional Navier Stokes Equations (NSE), supported by the observation that the NSE solutions always display negligible spatial variations in air velocity when applied to a sewer conduit.
View Article and Find Full Text PDFPLoS One
January 2025
ESQlabs Gmbh, Saterland, Germany.
Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Aitia, Somerville, MA, USA
Background: Amyloid, Tau and neurodegeneration (ATN), the hallmark pathologies of Alzheimer’s Disease (AD) translating to measurable biomarkers are important for disease modifying therapeutics.
Method: AD Digital‐Twins were built using AITIA’s patented A.I.
Alzheimers Dement
December 2024
Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
Background: Randomized placebo‐controlled trials (RCTs) are the gold standard to evaluate efficacy of new drug treatments for Alzheimer’s disease. For example, the United States FDA approved the brain amyloid‐targeting drug lecanemab following CLARITY AD, Biogen and Eisai’s Phase 3 RCT. However, recruiting enough participants for a high‐powered and demographically representative trial is difficult and expensive.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
AbbVie Inc., North Chicago, IL, USA
Background: In Alzheimer’s Disease (AD) trials, clinical scales are used to assess treatment effect in patients. Minimizing statistical uncertainty of trial outcomes is an important consideration to increase statistical power. Machine learning models can leverage baseline data to create AI‐generated digital twins – individualized predictions (or prognostic scores) of how each patient’s clinical outcomes may change during a trial assuming they received placebo.
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