Analysis of Management Costs of Waste Organization and Identification of Factors Affecting Them with a Factor Analysis Model in One of the Northern Cities of Iran

2022 ◽  
Vol 26 (2) ◽  
Author(s):  
Zahra Kazemi ◽  
Ahmad Jonidi Jafari ◽  
Ehsan Kazemnezhad Leyli ◽  
Zohre Kazemi
Keyword(s):  
Factor Analysis ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Factors Affecting ◽  
Northern Cities

Analysis of Factors Affecting Safety Management of Construction Site Based on Fuzzy Factor Analysis Model

2013 ◽  
Vol 753-755 ◽  
pp. 1862-1867 ◽  
Author(s):  
Long Jiang Shen ◽  
Suo Shi
Keyword(s):  
Factor Analysis ◽  
Safety Management ◽  
Common Factors ◽  
Correlation Coefficients ◽  
Construction Safety ◽  
Influential Factors ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Factors Affecting ◽  
Fuzzy Correlation

Thirty-nine influential factors of construction safety are identified in this study, and then five categories of respondents estimate their influential degrees through a questionnaire survey. In order to analyze these factors more accurately, a fuzzy factor analysis model (FFAM) is proposed. After calculating fuzzy eigenvalue, fuzzy correlation coefficients and factor loadings matrix in the model, seven different common factors are extracted. Finally, the author put forward several effective measures for improving construction safety based on these common factors.

Analysis of Factors Affecting Green Residential Building Costs Based on Fuzzy Factor Analysis Model

2012 ◽  
Vol 178-181 ◽  
pp. 12-19
Author(s):  
Lian Fa Ruan ◽  
Chang Quan Gu
Keyword(s):  
Factor Analysis ◽  
Questionnaire Survey ◽  
Common Factors ◽  
Residential Building ◽  
Influential Factors ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Factors Affecting ◽  
Fuzzy Correlation ◽  
Fuzzy Eigenvalues

Forty-seven influential factors of green residential costs were identified in this study, and then four categories of respondents estimated their influential degrees through a questionnaire survey. In order to analyze these factors more accurately, a fuzzy factor analysis model (FFAM) was proposed while the classical one has often been affected by interference information. After calculating fuzzy eigenvalues, fuzzy correlation cofficients and factor loadings matrix in the model, eight different common factors were extracted. Finally, the author put forward several effective measures for controlling green residential costs based on these common factors.

Noniterative Estimation for the Multiple Battery Factor Analysis Model

1997 ◽  
Vol 24 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Michael W. Browne ◽  
Krishna Tateneni
Keyword(s):  
Factor Analysis ◽  
Analysis Model ◽  
Factor Analysis Model

A Factor Analysis model of the instrumented Timed Up and Go test for physical capability assessment

2018 ◽  
Vol 66 ◽  
pp. S11-S12 ◽  
Author(s):  
A. Coni ◽  
S. Mellone ◽  
M. Colpo ◽  
S. Bandinelli ◽  
L. Chiari
Keyword(s):  
Factor Analysis ◽  
Analysis Model ◽  
Timed Up And Go ◽  
Factor Analysis Model ◽  
Physical Capability

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]

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