‘‘TITAM’’ thermionic integrated transient analysis model: Load-following of a single-cell TFE

1992 ◽  
Author(s):  
Mohamed S. El-Genk ◽  
Huimin Xue ◽  
Chris Murray ◽  
Shobhik Chaudhuri
Keyword(s):  
Single Cell ◽  
Transient Analysis ◽  
Analysis Model ◽  
Load Following

scholarly journals Non-negative Independent Factor Analysis for single cell RNA-seq

2020 ◽  
Author(s):  
Weiguang Mao ◽  
Maziyar Baran Pouyan ◽  
Dennis Kostka ◽  
Maria Chikina
Keyword(s):  
Factor Analysis ◽  
Single Cell ◽  
Matrix Factorization ◽  
Component Analysis ◽  
Computational Techniques ◽  
Cell Type ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Type Identity ◽  
Wide Range

AbstractMotivationSingle cell RNA sequencing (scRNA-seq) enables transcriptional profiling at the level of individual cells. With the emergence of high-throughput platforms datasets comprising tens of thousands or more cells have become routine, and the technology is having an impact across a wide range of biomedical subject areas. However, scRNA-seq data are high-dimensional and affected by noise, so that scalable and robust computational techniques are needed for meaningful analysis, visualization and interpretation. Specifically, a range of matrix factorization techniques have been employed to aid scRNA-seq data analysis. In this context we note that sources contributing to biological variability between cells can be discrete (or multi-modal, for instance cell-types), or continuous (e.g. pathway activity). However, no current matrix factorization approach is set up to jointly infer such mixed sources of variability.ResultsTo address this shortcoming, we present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that combines features of complementary approaches like Independent Component Analysis (ICA), Principal Component Analysis (PCA), and Non-negative Matrix Factorization (NMF). NIFA simultaneously models uni- and multi-modal latent factors and can so isolate discrete cell-type identity and continuous pathway-level variations into separate components. Similar to NMF, NIFA constrains factor loadings to be non-negative in order to increase biological interpretability. We apply our approach to a range of data sets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA and NMF in terms of cell-type identification and biological interpretability. Studying an immunotherapy dataset in detail, we show that NIFA identifies biomedically meaningful sources of variation, derive an improved expression signature for regulatory T-cells, and identify a novel myeloid cell subtype associated with treatment response. Overall, NIFA is a general approach advancing scRNA-seq analysis capabilities and it allows researchers to better take advantage of their data. NIFA is available at https://github.com/wgmao/[email protected]

A heat pipe transient analysis model

1994 ◽  
Vol 37 (5) ◽  
pp. 753-762 ◽  
Author(s):  
J.-M. Tournier ◽  
M.S. El-Genk
Keyword(s):  
Heat Pipe ◽  
Transient Analysis ◽  
Analysis Model

Pressure Transient Analysis for Fracture-Cavity Carbonate Reservoirs with Large Scale Fractures-Caves in Series Connection

2021 ◽  
pp. 1-20
Author(s):  
Cuiqiao Xing ◽  
Hongjun Yin ◽  
Hongfei Yuan ◽  
Jing Fu ◽  
Guohan Xu
Keyword(s):  
Transient Analysis ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Test Model ◽  
Well Test ◽  
Analysis Model ◽  
Pressure Transient Analysis ◽  
Linear Flow ◽  
Pressure Transient ◽  
Well Test Model

Abstract Fractured vuggy carbonate reservoirs are highly heterogeneous and non-continuous, and contains not only erosion pores and fractures but also the vugs. Unfortunately, the current well test model cannot be used to analyze fractured-vuggy carbonate reservoirs, due to the limitations of actual geological characteristics. To solve the above-mentioned problem, a pressure transient analysis model for fracture-cavity carbonate reservoir with radial composite reservoir that the series multi-sacle fractures and caves exist and dual-porosity medium (fracture and erosion pore) is established in this paper, which is suitable for fractured vuggy reservoirs. Laplace transformation is used to alter and solve the mathematical model. The main fractures' linear flow and the radial flow of caves drainage area are solved by coupling. The pressure-transient curves of the bottomhole have been obtained with the numerical inversion algorithms. The typical curves for well test model which has been established are drawn, and flow periods are analyzed. The sensitivity analysis for different parameters is analyzed. The variation characteristic of typical curves is by the theoretical analysis. With the increasing of fracture length, the time of linear flow is increased. While the cave radius is the bigger, the convex and concave of the curve is the larger. As a field example, actual test data is analyzed by the established model. An efficient well test analysis model is developed, and it can be used to interpret the actual pressure data for fracture-cavity carbonate reservoirs.

scholarly journals An Electromagnetic Transient Analysis Model for DFIG Considering LVRT Hardware Protection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mingyang Liu ◽  
Wenxia Pan ◽  
Yufei Rao ◽  
Chenghao Li ◽  
Tongchui Liu ◽  
...  
Keyword(s):  
Transient Analysis ◽  
Analysis Model ◽  
Electromagnetic Transient ◽  
Electromagnetic Transient Analysis

Fuzzy C-Means based LSED analysis model of single-cell RNA sequencing data for gene expression difference data

2021 ◽  
pp. 1-10
Author(s):  
Min Wu ◽  
Junhua Xu ◽  
Shanshan Zhu ◽  
Jinzhi Lei ◽  
Jie Gao
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Set Enrichment Analysis ◽  
Parameter Analysis ◽  
Analysis Model ◽  
Sequencing Data ◽  
Process Data ◽  
Fuzzy C Means ◽  
Single Cell Rna Sequencing

Analysis of single-cell RNA sequencing (scRNA-seq) data is often complicate due to the sparsity and high data dimensionality. In this work, we proposed Fuzzy C-means based linear stable-exponential distribution (LSED) model for analyzing scRNA-seq count data of chronic myeloid leukemia (CML) patients. We propose pipelines stages for analysis in which noisy and inconsistent data form sequencing is removed during data preprocessing, this process data then form s the cluster of gene feature using fuzzy c-means (FCM) clustering, relevant features are extracted during feature extraction approach. These extracted features are then fed into LSED model in order to difference feature data of gene expression. Finally we evaluate the performance for proposed analysis model based on parameter estimation, distribution comparison and parameter analysis. From the result analysis it was observed that proposed analysis model parameter reflect change in condition of patient more effectively as well as this model fits difference data of gene expression in more better way in comparison to Cauchy and stable distribution. Additional, the results of Gene-set enrichment analysis specify the affinity of proposed model can replicate the distinct enhancement of BCR-ABL+ stem cell as well as BCR-ABL- stem cells. Significantly, Proposed FCM based LSED analysis model studies CML from the perspective of statistical models, which present a new sight for CML scientific research.

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