scholarly journals Moroccan residents’ perceptions of the hospital learning environment measured with the French version of the Postgraduate Hospital Educational Environment Measure

2020 ◽  
Vol 17 ◽  
pp. 4
Author(s):  
Hajar Berrani ◽  
Redouane Abouqal ◽  
Amal Thimou Izgua
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Educational Environment ◽  
Internal Reliability ◽  
French Version ◽  
The Mean ◽  
High Level ◽  
Poor Infrastructure

Purpose: This study aimed to assess the educational environment of residents in Morocco and to compare residents’ perceptions depending on their specialty.Methods: We applied the French version of the Postgraduate Hospital Educational Environment Measure (PHEEM) to measure the educational environment at 6 hospitals in Rabat from January to June 2017. The internal reliability of the questionnaire was assessed using Cronbach’s α coefficient. Principal component analysis was conducted to assess the construct validity of the 3 subscales of the PHEEM questionnaire. Analysis of variance was performed to compare the mean scores of the overall PHEEM, its subscales, and each item among the 6 specialties.Results: Responses from 255 residents were included. The 40-item PHEEM questionnaire showed a high level of reliability, with a Cronbach’s α of 0.91. Principal component analysis of all 40 items suggested that 3 factors explained 48% of the variance, with better results for the teaching subscale. Moroccan residents perceived their educational environment as more positive than negative. There were significant differences in the overall and subscale scores among the 6 specialties.Conclusion: The French version of the PHEEM was confirmed to be a valid and reliable instrument in Morocco. Moroccan residents perceived their educational environment as more positive than negative, but room for improvement remained, with challenges including the poor infrastructure, the suboptimal quality of supervision, and inadequate teaching and work regulations.

scholarly journals Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2342
Author(s):  
Corentin Martens ◽  
Olivier Debeir ◽  
Christine Decaestecker ◽  
Thierry Metens ◽  
Laetitia Lebrun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Added Value ◽  
Time Activity ◽  
Positron Emission ◽  
Activity Curve ◽  
The Mean ◽  
Mean Time ◽  
Met Pet

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

scholarly journals A Principal Component Analysis of Acoustic Emission Signals from a Landing Gear Component

2008 ◽  
Vol 13-14 ◽  
pp. 41-47 ◽  
Author(s):  
Rhys Pullin ◽  
Mark J. Eaton ◽  
James J. Hensman ◽  
Karen M. Holford ◽  
Keith Worden ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Acoustic Emission ◽  
Background Noise ◽  
Principal Component ◽  
Component Analysis ◽  
Landing Gear ◽  
Fracture Detection ◽  
Artificial Fracture ◽  
High Level ◽  
Ae Signals

This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

scholarly journals Hyperspectral data compression based upon the principal component analysis

2021 ◽  
Vol 45 (2) ◽  
pp. 235-244
Author(s):  
A.S. Minkin ◽  
O.V. Nikolaeva ◽  
A.A. Russkov
Keyword(s):  
Principal Component Analysis ◽  
Data Compression ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Gas Absorption ◽  
Absorption Bands ◽  
Spectral Bands ◽  
The Mean ◽  
Retrieval Errors

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

scholarly journals Discrimination of Natural Mature Acacia Honey Based on Multi-Physicochemical Parameters Combined with Chemometric Analysis

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2674 ◽  
Author(s):  
Tianchen Ma ◽  
Haoan Zhao ◽  
Caiyun Liu ◽  
Min Zhu ◽  
Hui Gao ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Physicochemical Parameters ◽  
Phenolic Content ◽  
Principal Component ◽  
Component Analysis ◽  
Chemometric Analysis ◽  
Total Phenolic ◽  
Total Protein Content ◽  
Acacia Honey

Honey maturity is an important factor in evaluating the quality of honey. We established a method for the identification of natural mature acacia honey with eighteen physicochemical parameters combined with chemometric analysis. The analysis of variance showed significant differences between mature and immature acacia honey in physicochemical parameters. The principal component analysis explained 82.64% of the variance among samples, and indicated that total phenolic content, total protein content, and total sugar (glucose, fructose, sucrose) were the major variables. The cluster analysis and orthogonal partial least squares-discriminant analysis demonstrated that samples were grouped in relation to the maturity coinciding with the results of the principal component analysis. Meanwhile, the 35 test samples were classified with 100% accuracy with the method of multi-physicochemical parameters combined with chemometric analysis. All the results presented above proved the possibility of identifying mature acacia honey and immature acacia honey according to the chemometric analysis based on the multi-physicochemical parameters.

Tactile evaluation of down jacket fabric by the comprehensive handle evaluation system for fabrics and yarns

2020 ◽  
pp. 004051752097720
Author(s):  
Yuan Tian ◽  
Yi Sun ◽  
Zhaoqun Du ◽  
Dongming Zheng ◽  
Haochen Zou ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Principal Component Analysis ◽  
Evaluation System ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Quality Level ◽  
Fabric Handle ◽  
Good Agreement

Down jacket fabric is greatly important in determining the quality of a down jacket. In order to enrich the research on fabric handle, subjective and objective evaluations were made for down jacket fabrics that were less studied. The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) can be used to evaluate the tactile handle of the fabric by accurately and efficiently measuring the basic mechanical properties of the fabric. Therefore, the CHES-FY was used to link the objective evaluation with the subjective handle, so as to effectively estimate the total handle value of the down jacket fabric. Fifty-two kinds of down jacket fabrics were objectively tested through measuring 17 extracted parameters, and principal component analysis was adopted to establish the five main handle characteristics of fullness, softness, stiffness, smoothness, looseness and tightness to characterize basic style of the down jacket fabrics. The results showed that the subjective and objective results were in good agreement. These characteristics can be used as indicators to characterize fabric performance, and the principal component expression to characterize fabric handle can better predict the handle characteristics of down jacket fabrics. This also proves that the CHES-FY can quickly and accurately obtain the fabric handle value, and can also evaluate the fabric quality level.

The Establishment of Principal Component Analysis Assessment Model for Drinking Water Quality of City Resource

2014 ◽  
Vol 675-677 ◽  
pp. 960-963
Author(s):  
Li Feng Sun ◽  
Qing Jie Qi ◽  
Xiao Liang Zhao ◽  
Rui Feng Li
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Water Resources ◽  
Principal Component ◽  
Water Source ◽  
Component Analysis ◽  
Assessment Model ◽  
Affecting Factors

In order to effectively control pollution of sources of drinking water, improve the environmental quality of drinking water and guarantee the sanitation of drinking water, it is very important to assess water source quality. Main factors of drinking water were identified. Then principal component analysis was used to establish assessment model of drinking water, which could ensure that under the condition that the primitive data information was in the smallest loss, a small number of variables were used to replace the integrated multi-dimensional variables to simplify the data structure. The weightings of principal component were determinated as theirs pollution ratios. This paper was based on the theoretical study of principal component analysis, used the monitoring data on water quality of the main water resources in 2013 to evaluate and analyze the water quality of water resources. Analysis content included the main affecting factors, cause of pollution and the degree of pollution.The resulted showed that: the main affecting factors on water quality of Fo Si water source was CODMn, TP, fluoride.

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