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Fully Automated Transcranial Doppler Ultrasound for Middle Cerebral Artery Insonation
Michael J. O’Brien,Amber Y. Dorn,Mina Ranjbaran,Zhaojun Nie,Mateo Scheidt,Nasim Mirnateghi,Shankar Radhakrishnan,Robert B. Hamilton 대한신경초음파학회 2022 대한신경초음파학회지 (JNN) Vol.14 No.1
Background: Transcranial Doppler ultrasound (TCD) is utilized in the assessment of neurological conditions in clinical environments such as the intensive care unit and emergency department. However, obstacles for widespread use of TCD include a lack of trained registered vascular technologists (RVT) and operator variability. We present a study comparing RVT and a fully automated robotic TCD system (NovaGuide rTCD) for insonation of the middle cerebral artery (MCA). Methods: A trained RVT and rTCD sequentially collected bilateral MCA cerebral blood flow velocity (CBFV) from 86 healthy subjects. Mean CBFV (mCBFV) and the signal quality assessment (SQA) acquired manually by RVT and autonomously via rTCD were compared. Comparison metrics evaluated include mean accuracy ratio (MAR), and Bland-Altman mean-difference (MD) between rTCD and RVT with paired t-Test for significance. Bootstrapping was used in the accuracy ratio and mean-time to best signal computations to establish 95% confidence intervals. Results: The mCBFVs and SQAs found by rTCD compared to RVT had MAR of 99.7% (97.7-101.7%) and 102.7% (101.1-104.8%), respectively. The rTCD mean-time to best-quality signal was 0.87 min (0.71-1.05) (RVT was not timed). The mean-difference scores for mCBFV and SQA were MD=-0.43cm/s (p=0.053) and MD=-0.36 (p=0.61), respectively. The rTCD had a 3.5% no-window failure rate compared to RVT no-window rate of 4.1%. Conclusion: Comparison of bilateral TCD signals collected by rTCD and RVT demonstrated equivalence in mCBFV and signal quality, suggesting rTCD’s potential to expand utility of TCD in clinical settings that are resource-limited.
Cosmological constraints from the SDSS luminous red galaxies
Tegmark, Max,Eisenstein, Daniel J.,Strauss, Michael A.,Weinberg, David H.,Blanton, Michael R.,Frieman, Joshua A.,Fukugita, Masataka,Gunn, James E.,Hamilton, Andrew J. S.,Knapp, Gillian R.,Nichol, Robe American Physical Society 2006 PHYSICAL REVIEW D - Vol.74 No.12
Niamat Ullah Ibne Hossain,Morteza Nagahi,Raed Jaradat,Chiranjibi Shah,Randy Buchanan,Michael Hamilton 한국CDE학회 2020 Journal of computational design and engineering Vol.7 No.3
Due to the widespread of new technologies, the modern electric power system has become much more complex and uncertain. The Integration of technologies in the electric power system has increased the exposure of cyber threats and correlative susceptibilities from malicious cyber-attacks. To better address these cyber risks and minimize the effects of the power system outage, this research identifies the potential causes and mitigation techniques for the smart grid (SG) and assesses the overall cyber resilience of smart grid systems using a Bayesian network approach. Bayesian network is a powerful analytical tool predominantly used in risk, reliability, and resilience assessment under uncertainty. The quantification of the model is examined, and the results are analyzed through different advanced techniques such as predictive inference reasoning and sensitivity analysis. Different scenarios have been developed and analyzed to identify critical variables that are susceptible to the cyber resilience of a smart grid system of systems. Insight drawn from these analyses suggests that overall cyber resilience of the SG system of systems is dependent upon the status of identified factors, and more attention should be directed towards developing the countermeasures against access domain vulnerability. The research also shows the efficacy of a Bayesian network to assess and enhance the overall cyber resilience of the smart grid system of systems.