IEEE Systems, Man and Cybernetics Magazine - April 2023 - 3

quantification (UQ). For model benchmarking,
the datasets are divided
into train, validation, and test, each
of which is accompanied by detailed
equations and descriptions. In addition,
the authors train shallow neural
networks and random vector functional
link networks on the proposed
datasets and offer publicly available
codes that contain training details to
support researchers in comprehending
the datasets.
Another interesting article, " Pointof-Care
Testing in the Diagnosis of
Deep Vein Thrombosis: A Review "
[A5], is contributed by a group of researchers
from Northeastern University,
China. A clinical diagnostic
approach to deep vein thrombosis
(DVT) and the history of diagnostic
procedures are discussed in this article.
The authors present an analysis
of the advantages and drawbacks of
using ultrasound (US) and D-dimer
for diagnosing traditional DVT. They
also analyze the current clinical diagnostic
needs for DVT. They then illustrate
the benefits and limitations of
point-of-care (POC) testing diagnostic
equipment, such as POC US, POC
D-dimer, and photoplethysmography,
for early diagnosis and nonmedical
application. Furthermore, the performance
measures derived from the
research methods mentioned in the
preceding, including sensitivity and
specificity, are analyzed. Finally, the
authors outline the emerging trends
in DVT detection methods and emphasize
the considerable potential
of POC testing in the early diagnosis
and routine assessment of patients
with DVT.
I hope these articles provide inspiration
for the research work of our
readers. Enjoy the reading!
About the Author
Tingwen Huang (tingwen.huang@
qatar.tamu.edu) earned his Ph.D.
degree from Texas A&M University in
College Station, Texas, USA. Currently,
he is a professor at Texas A&M University
at Qatar. His research interests
include intelligent control, computational
intelligence, and multi-agent
systems. He is a Fellow of IEEE, the
IAPR, and TWAS.
Appendix: Related Articles
[A1] H. Ma, J. Li, Y. Tang, H. Zhu, Z. Huang, and
W. Tang, " Universal optimization framework, " IEEE
Syst., Man, Cybern. Syst. Mag., vol. 9, no. 2, pp. 6-17,
Apr. 2023, doi: 10.1109/MSMC.2022.3231698.
[A2] K. Karunanithi, S. Ramesh, S. P. Raja, and S.
Saravanan, " Optimum sizing and modeling of standalone
DC microgrid with hybrid energy storage system
for domestic applications, " IEEE Syst., Man, Cybern.
Syst. Mag., vol. 9, no. 2, pp. 18-24, Apr. 2023, doi:
10.1109/MSMC.2022.3229838.
[A3] F. B. Tesema et al., " Addressee detection using
facial and audio features in mixed human-human and
human-robot settings, " IEEE Syst., Man, Cybern. Syst.
Mag., vol. 9, no. 2, pp. 25-38, Apr. 2023, doi: 10.1109/
MSMC.2022.3224843.
[A4] H. M. Dipu Kabir et al., " Synthetic datasets for
numeric uncertainty quantification, " IEEE Syst., Man,
Cybern. Syst. Mag., vol. 9, no. 2, pp. 39-48, Apr. 2023,
doi: 10.1109/MSMC.2022.3218423.
[A5] Z. Zhang, J. Wu, W. Cheng, X. Zhang, L. Xu, and Y. Yao,
" Point-of-care testing in the diagnosis of deep vein thrombosis, "
IEEE Syst., Man, Cybern. Syst. Mag., vol. 9, no. 2,
pp. 49-56, Apr. 2023, doi: 10.1109/MSMC.2022.3224595.
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April 2023 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE
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IEEE Systems, Man and Cybernetics Magazine - April 2023

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - April 2023
