Complete 3D brain tumour detection using a two-phase method along with confidence function evaluation

2021 ◽  
Author(s):  
Sushanta Debnath ◽  
Fazal A. Talukdar ◽  
Mohiul Islam
Keyword(s):  
Brain Tumour ◽  
Function Evaluation ◽  
Phase Method ◽  
Two Phase ◽  
Tumour Detection

Peristaltic Carreau-Yasuda nanofluid flow and mixed heat transfer analysis in an asymmetric vertical and tapered wavy wall channel

2020 ◽  
Vol 1 (1) ◽  
pp. 128-140 ◽  
Author(s):  
Mohammad Hatami ◽  
◽  
D Jing ◽  
Keyword(s):  
Heat Transfer ◽  
Least Square Method ◽  
Weissenberg Number ◽  
Least Square ◽  
Heat Transfer Analysis ◽  
Phase Method ◽  
Nanofluid Flow ◽  
Two Phase ◽  
Nanoparticles Concentration ◽  
The Right

In this study, two-phase asymmetric peristaltic Carreau-Yasuda nanofluid flow in a vertical and tapered wavy channel is demonstrated and the mixed heat transfer analysis is considered for it. For the modeling, two-phase method is considered to be able to study the nanoparticles concentration as a separate phase. Also it is assumed that peristaltic waves travel along X-axis at a constant speed, c. Furthermore, constant temperatures and constant nanoparticle concentrations are considered for both, left and right walls. This study aims at an analytical solution of the problem by means of least square method (LSM) using the Maple 15.0 mathematical software. Numerical outcomes will be compared. Finally, the effects of most important parameters (Weissenberg number, Prandtl number, Brownian motion parameter, thermophoresis parameter, local temperature and nanoparticle Grashof numbers) on the velocities, temperature and nanoparticles concentration functions are presented. As an important outcome, on the left side of the channel, increasing the Grashof numbers leads to a reduction in velocity profiles, while on the right side, it is the other way around.

Brain Tumour Detection Using Deep Learning

2021 ◽  
Author(s):  
Avigyan Sinha ◽  
Aneesh R P ◽  
Malavika Suresh ◽  
Nitha Mohan R ◽  
Abinaya D ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumour ◽  
Tumour Detection

The Method of Dynamic Reserves Prediction for a Constant Volume Gas Reservoir

2013 ◽  
Vol 275-277 ◽  
pp. 456-461
Author(s):  
Lei Zhang ◽  
Lai Bing Zhang ◽  
Bin Quan Jiang ◽  
Huan Liu
Keyword(s):  
Pressure Drop ◽  
Material Balance ◽  
Production Method ◽  
Gas Production ◽  
Constant Volume ◽  
Phase Method ◽  
Gas Reservoirs ◽  
Two Phase ◽  
Material Balance Method ◽  
Unit Pressure

The accurate prediction of the dynamic reserves of gas reservoirs is the important research content of the development of dynamic analysis of gas reservoirs. It is of great significance to the stable and safe production and the formulation of scientific and rational development programs of gas reservoirs. The production methods of dynamic reserves of gas reservoirs mainly include material balance method, unit pressure drop of gas production method and elastic two-phase method. To clarify the characteristics of these methods better, in this paper, we took two typeⅠwells of a constant volume gas reservoir as an example, the dynamic reserves of single well controlled were respectively calculated, and the results show that the order of the calculated volume of the dynamic reserves by using different methods is material balance method> unit pressure drop of gas production method >elastic two-phase method. Because the material balance method is a static method, unit pressure drop of gas production method and elastic two-phase method are dynamic methods, therefore, for typeⅠwells of constant volume gas reservoirs, when the gas wells reached the quasi-steady state, the elastic two-phase method is used to calculate the dynamic reserves, and when the gas wells didn’t reach the quasi-steady state, unit pressure drop of gas production method is used to calculate the dynamic reserves. The conclusion has some certain theoretical value for the prediction of dynamic reserves for constant volume gas reservoirs.

scholarly journals Protected Gold Nanoparticles with Thioethers and Amines As Surrogate Ligands

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
M. Rafiq H. Siddiqui
Keyword(s):  
Gold Nanoparticles ◽  
Variable Temperature ◽  
Size Control ◽  
Analytical Techniques ◽  
High Resolution Electron Microscopy ◽  
Phase Method ◽  
Two Phase ◽  
H Nmr ◽  
Resolution Electron ◽  
Vis Spectroscopy

Dodecyl sulfide, dodecyl amine, and hexylamine were shown to act as surrogate ligands (L) via metastable gold nanoparticles. By collating analytical and spectroscopic data obtained simultaneously, empirical formula Au24L was assigned. These impurity-free nanoparticles obtained in near quantitative yields showing exceptional gold assays (up to 98%Au) were prepared by a modification of the two-phase method. Replacement reactions on the Au24L showed that Au:L ratios may be increased (up to Au55:L (L= (H25C12)2S)) or decreased (Au12:L (L= H2NC12H25and H2NC6H13)) as desired. This work encompassing the role of analytical techniques used, that is, elemental analysis, variable temperature1H NMR, FAB mass spectrometry, UV-Vis spectroscopy, thin film X-ray diffraction, and high-resolution electron microscopy (HREM) has implications in the study of size control, purity, stability, and metal assays of gold nanoparticles.

An Efficient Two-phase Method for Prime Compilation of Non-clausal Boolean Formulae

2021 ◽  
Author(s):  
Weilin Luo ◽  
Hai Want ◽  
Hongzhen Zhong ◽  
Ou Wei ◽  
Biqing Fang ◽  
...  
Keyword(s):  
Phase Method ◽  
Two Phase

A New Two-Phase Method for Damage Detection in Skeletal Structures

2018 ◽  
Vol 43 (S1) ◽  
pp. 49-65 ◽  
Author(s):  
A. Kaveh ◽  
S. R. Hosseini Vaez ◽  
P. Hosseini ◽  
M. A. Fathali
Keyword(s):  
Damage Detection ◽  
Phase Method ◽  
Two Phase ◽  
Skeletal Structures

A novel enhanced softmax loss function for brain tumour detection using deep learning

2020 ◽  
Vol 330 ◽  
pp. 108520 ◽  
Author(s):  
Sunil Maharjan ◽  
Abeer Alsadoon ◽  
P.W.C. Prasad ◽  
Thair Al-Dalain ◽  
Omar Hisham Alsadoon
Keyword(s):  
Deep Learning ◽  
Brain Tumour ◽  
Loss Function ◽  
Tumour Detection

scholarly journals Day-time identification of summer hailstorm cells from MSG data

2013 ◽  
Vol 1 (5) ◽  
pp. 5453-5498 ◽  
Author(s):  
A. Merino ◽  
L. López ◽  
J. L. Sánchez ◽  
E. García-Ortega ◽  
E. Cattani ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Deep Convection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Phase Method ◽  
Data Sets ◽  
Liquid Water Path ◽  
Two Phase ◽  
Logistic Regression Models ◽  
Ebro Valley

Abstract. Identifying deep convection is of paramount importance, as it may be associated with extreme weather that has significant impact on the environment, property and the population. A new method, the Hail Detection Tool (HDT), is described for identifying hail-bearing storms using multi-spectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the Convective Mask (CM) algorithm devised for detection of deep convection, and the second a Hail Detection algorithm (HD) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HD are based on logistic regression models trained with multi-spectral MSG data-sets comprised of summer convective events in the middle Ebro Valley between 2006–2010, and detected by the RGB visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HD are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients." Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall Probability of Detection (POD) was 76.9% and False Alarm Ratio 16.7%.

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