An Analytical Algorithm for Pi-Network Impedance Tuners

A three dimensional treatment planning system has been installed in the Oncology Center, Bangladesh. This system is based on the Anisotropic Analytical Algorithm (AAA). The aim of this study is to verify the validity of photon dose distribution which is calculated by this treatment planning system by comparing it with measured photon beam data in real water phantom. To do this verification, a quality assurance program, consisting of six tests, was performed. In this program, both the calculated output factors and dose at different conditions were compared with the measurement. As a result of that comparison, we found that the calculated output factor was in excellent agreement with the measured factors. Doses at depths beyond the depth of maximum dose calculated on-axis or off-axis in both the fields or penumbra region were found in good agreement with the measured dose under all conditions of energy, SSD and field size, for open and wedged fields. In the build up region, calculated and measured doses only agree (with a difference 2.0%) for field sizes > 5 × 5 cm2 up to 25 × 25 cm2. For smaller fields, the difference was higher than 2.0% because of the difficulty in dosimetry in that region. Dose calculation using treatment planning system based on the Anisotropic Analytical Algorithm (AAA) is accurate enough for clinical use except when calculating dose at depths above maximum dose for small field size.DOI: http://dx.doi.org/10.3329/bjmp.v4i1.14686 Bangladesh Journal of Medical Physics Vol.4 No.1 2011 43-49

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An analytical algorithm to predict weldability of precipitation-strengthened nickel-base superalloys

Journal of Materials Processing Technology ◽

10.1016/j.jmatprotec.2012.06.010 ◽

2012 ◽

Vol 212 (11) ◽

pp. 2210-2218 ◽

Cited By ~ 18

Author(s):

Homam Naffakh Moosavy ◽

Mohammad-Reza Aboutalebi ◽

Seyed Hossein Seyedein

Keyword(s):

Analytical Algorithm ◽

Nickel Base Superalloys ◽

Nickel Base

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Machine learning identifies abnormal Ca2+ transients in human induced pluripotent stem cell-derived cardiomyocytes

Scientific Reports ◽

10.1038/s41598-020-73801-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Hyun Hwang ◽

Rui Liu ◽

Joshua T. Maxwell ◽

Jingjing Yang ◽

Chunhui Xu

Keyword(s):

Machine Learning ◽

Stem Cell ◽

Pluripotent Stem Cell ◽

Induced Pluripotent Stem Cell ◽

Expert Assessment ◽

Svm Classifier ◽

Cell Level ◽

Human Expert ◽

Analytical Algorithm ◽

Induced Pluripotent

Abstract Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca2+ transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca2+ transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca2+ transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca2+ transient signals. By applying this pipeline to our Ca2+ transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca2+ analysis.

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