Computational methods in fluid research have been progressing during the past few years, driven by the incorporation of massive amounts of data, either in textual or graphical form, generated from multi-scale simulations, laboratory experiments, and real data from the field. Artificial Intelligence (AI) and its adjacent field, Machine Learning (ML), are about to reach standardization in most fields of computational science and engineering, as they provide multiple ways for extracting information from data that turn into knowledge, with the aid of portable software implementations that are easy to adopt. There is ample information on the historical and mathematical background of all aspects of AI/ML in the literature. Thus, this review article focuses mainly on their impact on fluid research at present, highlighting advances and opportunities, recognizing techniques and methods having been proposed, tabulating, and testing some of the most popular algorithms that have shown significant accuracy and performance on fluid applications. We also investigate algorithmic accuracy on several fluid datasets that correspond to simulation results for the transport properties of fluids and suggest that non-linear, decision tree-based methods have shown remarkable performance on reproducing fluid properties.

Cardiac Rehabilitation (CR) is a secondary prevention intervention proven to improve quality of life, yet with low participation. The Cardiac Rehabilitation Barriers Scale (CRBS) was developed to assess multi-level barriers to participation. This study aimed at the translation, and cross-cultural adaptation of the CRBS into the Greek language (CRBS-GR), followed by psychometric validation. Some 110 post-angioplasty patients with coronary artery disease (88.2% men, age 65.3 ± 10.2 years) answered the CRBS-GR. Factor analysis was performed to obtain the CRBS-GR subscales/factors. The internal consistency and 3-week test–retest reliability was evaluated using Cronbach’s alpha (α) and intraclass correlation coefficient (ICC), respectively. Construct validity was tested via convergent and divergent validity. Concurrent validity was assessed with the Hospital Anxiety and Depression Scale (HADS). Translation and adaptation resulted in 21 items similar to the original version. Face validity and acceptability were supported. Construct validity assessment revealed four subscales/factors, with acceptable overall reliability (α = 0.70) and subscale internal consistency for all but one factor (α range = 0.56–0.74). The 3-week test-retest reliability was 0.96. Concurrent validity assessment demonstrated a small to moderate correlation of the CRBS-GR with the HADS. The greatest barriers were the distance from the rehabilitation center, the costs, the lack of information about CR, and already exercising at home. The CRBS-GR is a reliable and valid tool for identifying CR barriers among Greek-speaking patients.