scholarly journals Application of Factorisation Methods to Analysis of Elemental Distribution Maps Acquired with a Full-Field XRF Imaging Spectrometer

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7965
Author(s):  
Bartłomiej Łach ◽  
Tomasz Fiutowski ◽  
Stefan Koperny ◽  
Paulina Krupska-Wolas ◽  
Marek Lankosz ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Energy Resolution ◽  
Principal Component ◽  
Elemental Distribution ◽  
Imaging Data ◽  
Imaging Spectrometer ◽  
Electron Multiplier ◽  
Full Field ◽  
Limited Energy ◽  
Analysis Methods

The goal of the work was to investigate the possible application of factor analysis methods for processing X-ray Fluorescence (XRF) data acquired with a full-field XRF spectrometer employing a position-sensitive and energy-dispersive Gas Electron Multiplier (GEM) detector, which provides only limited energy resolution at a level of 18% Full Width at Half Maximum (FWHM) at 5.9 keV. In this article, we present the design and performance of the full-field imaging spectrometer and the results of case studies performed using the developed instrument. The XRF imaging data collected for two historical paintings are presented along with the procedures applied to data calibration and analysis. The maps of elemental distributions were built using three different analysis methods: Region of Interest (ROI), Non-Negative Matrix Factorisation (NMF), and Principal Component Analysis (PCA). The results obtained for these paintings show that the factor analysis methods NMF and PCA provide significant enhancement of selectivity of the elemental analysis in case of limited energy resolution of the spectrometer.

Molecular Imaging: Design of the PMP Imaging Spectrometer, Resolution Limits, and Radiation Sensitivity

1996 ◽  
Vol 54 ◽  
pp. 424-425
Author(s):  
M.M.G. Barfels ◽  
Y. Heng ◽  
F.P. Ottensmeyer
Keyword(s):  
Fine Structure ◽  
Boundary Element Method ◽  
Energy Resolution ◽  
Radiation Sensitivity ◽  
Low Energy ◽  
Elemental Distribution ◽  
Electron Spectroscopic Imaging ◽  
Imaging Spectrometer ◽  
Resolution Limits ◽  
Fringing Fields

One of the main applications of an energy filter is to obtain elemental distribution images by electron spectroscopic imaging. This technique has been pushed to the imaging of fine structure such as the sp2 and sp3 states of carbon at a sub-nanometer spatial resolution. However, low energy loss imaging in the region of the molecular excitations has largely remained unexplored due to the limitation in the energy resolution obtainable in the energy filters. Molecular absorptions in the visible and UV regions of the spectrum occur at 1.5 eV to 10 eV. Thus it would be necessary to have an energy resolution of about 1 eV or better.An optimized Prism-Mirror-Prism energy filter has been built by altering the straight faced pole pieces to circular curvatures. See Figure 1. The optimum curvatures for each of the faces were determined iteratively. In order to simulate the electron path accurately through the Prism-Mirror-Prism filter, it was necessary to calculate the magnetic fringing fields using the Boundary Element Method as outlined by Kasper. The ray-tracing procedure indicated a theoretical energy resolution of 1.3 eV for the optimized magnetic prism. This has been tested on our prototype spectrometer currently installed in the Siemens EM 102. An energy resolution of 1.1 eV was obtained.

scholarly journals Time lapse synchrotron IR chemical imaging for observing the acclimation of a single algal cell to CO2 treatment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ghazal Azarfar ◽  
Ebrahim Aboualizadeh ◽  
Simona Ratti ◽  
Camilla Olivieri ◽  
Alessandra Norici ◽  
...  
Keyword(s):  
Algal Cell ◽  
Principal Component ◽  
External Perturbation ◽  
Time Lapse ◽  
Chemical Imaging ◽  
Hyperspectral Data ◽  
Aqueous Environment ◽  
Ir Absorption ◽  
Imaging Data ◽  
Mid Infrared

AbstractAlgae are the main primary producers in aquatic environments and therefore of fundamental importance for the global ecosystem. Mid-infrared (IR) microspectroscopy is a non-invasive tool that allows in principle studying chemical composition on a single-cell level. For a long time, however, mid-infrared (IR) imaging of living algal cells in an aqueous environment has been a challenge due to the strong IR absorption of water. In this study, we employed multi-beam synchrotron radiation to measure time-resolved IR hyperspectral images of individual Thalassiosira weissflogii cells in water in the course of acclimation to an abrupt change of CO2 availability (from 390 to 5000 ppm and vice versa) over 75 min. We used a previously developed algorithm to correct sinusoidal interference fringes from IR hyperspectral imaging data. After preprocessing and fringe correction of the hyperspectral data, principal component analysis (PCA) was performed to assess the spatial distribution of organic pools within the algal cells. Through the analysis of 200,000 spectra, we were able to identify compositional modifications associated with CO2 treatment. PCA revealed changes in the carbohydrate pool (1200–950 cm$$^{-1}$$ - 1 ), lipids (1740, 2852, 2922 cm$$^{-1}$$ - 1 ), and nucleic acid (1160 and 1201 cm$$^{-1}$$ - 1 ) as the major response of exposure to elevated CO2 concentrations. Our results show a local metabolism response to this external perturbation.

scholarly journals Metacognitive Rating Scale: A Study Applying a Korean Translation to Individuals with Schizophrenia

2021 ◽  
Vol 18 (13) ◽  
pp. 6853
Author(s):  
Mihwa Han ◽  
Kyunghee Lee ◽  
Mijung Kim ◽  
Youngjin Heo ◽  
Hyunseok Choi
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Mental Status ◽  
Rating Scale ◽  
Anger Expression ◽  
Principal Component ◽  
Negative Beliefs ◽  
Five Factors ◽  
Metacognitive Beliefs

Metacognition is a higher-level cognition of identifying one’s own mental status, beliefs, and intentions. This research comprised a survey of 184 people with schizophrenia to verify the reliability of the metacognitive rating scale (MCRS) with the revised and supplemented metacognitions questionnaire (MCQ) to measure the dysfunctional metacognitive beliefs of people with schizophrenia by adding the concepts of anger and anxiety. This study analyzed the data using principal component analysis and the varimax method for exploratory factor analysis. To examine the reliability of the extracted factors, Cronbach’s α was used. According to the results, reliability was ensured for five factors: positive beliefs about worry, negative beliefs about uncontrollability and danger of worry, cognitive confidence, need for control, and cognitive self-consciousness. The negative beliefs about uncontrollability and danger of worry and the need for control on anger expression, which were both added in this research, exhibited the highest correlation (r = 0.727). The results suggest that the MCRS is a reliable tool to measure the metacognition of people with schizophrenia.

Use of the Perceived Physical Competence Scale with Adolescents with Disabilities

1995 ◽  
Vol 80 (2) ◽  
pp. 571-577 ◽  
Author(s):  
Taru Lintunen ◽  
Pilvikki Heikinaro-Johansson ◽  
Claudine Sherrill
Keyword(s):  
Gender Differences ◽  
Factor Analysis ◽  
Principal Component ◽  
Principal Component Factor Analysis ◽  
Validity And Reliability ◽  
Varimax Rotation ◽  
Physical Competence ◽  
Related Literature ◽  
Competence Scale ◽  
Two Factors

The construct validity and reliability of the 1987 Perceived Physical Competence Scale of Lintunen were examined to assess the applicability of the instrument for use with adolescents with disabilities. Subjects were 51 girls and 34 boys ( M age = 15.1 yr.) from several schools in central Finland. Principal component factor analysis with varimax rotation yielded the same two factors for adolescents with disabilities as reported for nondisabled adolescents in the related literature. Cronbach alphas for the two factors were .89 and .56. It was concluded that the scale is an appropriate measure for adolescents with disabilities. Statistical analysis indicated no gender differences for adolescents with disabilities. When compared with nondisabled groups in the related literature, these adolescents had perceived fitness similar to nondisabled peers but significantly lower than that of athletes without disabilities.

Developing the Organisational Justice Scale and Examining Teachers' and School Administrators' Views about Organisational Justice in Primary Schools

2021 ◽  
pp. 33-50
Author(s):  
Hasan Basri Memduhoðlu ◽  
Ali Ýhsan Yildiz
Keyword(s):  
Factor Analysis ◽  
Primary Schools ◽  
School Administrators ◽  
Data Gathering ◽  
Principal Component ◽  
Measurement Tool ◽  
Principal Component Factor Analysis ◽  
Reliable Measurement ◽  
Organisational Justice ◽  
Confirmatory Factor

The purpose of this study is to develop a reliable and valid measurement tool to explore views about organisational justice in schools and to examine teachers' and school administrators' views about organisational justice in primary schools. The sample of the study consisted of a total of 455 participants, 176 school administrators and 279 teachers from the primary schools in the Centre of Van. The Organisational Justice Scale, developed by the authors, was employed as data gathering tool. Principal Component Factor Analysis was used to determine the content and construct validities of the scale and Confirmatory Factor Analysis was employed to evaluate the obtained results. As a result of the study, the developed Organisational Justice Scale (OJS) was found to be a valid and reliable measurement tool for school applications.

scholarly journals Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
David Cárdenas-Peña ◽  
Diego Collazos-Huertas ◽  
German Castellanos-Dominguez
Keyword(s):  
Neural Network ◽  
Characteristic Curve ◽  
Principal Component ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Dementia Diagnosis ◽  
Target Matrix ◽  
Computer Aided ◽  
Artificial Neural Network Ann ◽  
Aided Diagnosis

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014CADDementiachallenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing.

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