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This research uses Principal Component Analysis (PCA) to investigate global ionospheric integrated electron content map (GIM) anomalies corresponding to Japan’s Iwate-Miyagi Nairiku earthquake on 13 June 2008 (UT) (M j = 7.2, JMA scale). The PCA transform is applied to GIMs for 20:00 to 22:00 on June 08, 11 and 12, 2008 (UT). To perform the transform, image processing is used to subdivide the GIMs into 100 (36° long. and 18° lat.) smaller maps to form transform matrices of dimensions 2 × 1. The transform allows for principal eigenvalues to be assigned to ionospheric integrated electron content anomalies. Anomalies are represented by large principal eigenvalues (i.e., >0.5 in a normalized set). The possibility of geomagnetic storms and solar flare activity affecting the results is done through examining the D st index for corresponding days. The study shows that for the Iwate-Miyagi Nairiku earthquake, PCA possibly determined earthquake related ionospheric disturbances for the whole region, including the epicenter.

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The goal of this study is to determine whether principal component analysis (PCA) can be used to process latitude-time ionospheric TEC data on a monthly basis to identify earthquake associated TEC anomalies earlier than 5 days before a large (M ≥ 6) earthquake. PCA is applied to latitude-time (mean-of-a-month) ionospheric total electron content (TEC) records collected from the Japan GEONET system to detect TEC anomalies associated with 26 earthquakes in Japan (M ≥ 6.0) from 2004 to 2005. According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 26 earthquakes occurred. After reviewing months when no M ≥ 6.0 earthquakes occurred but geomagnetic storm activity was present, it is possible that the maximal principal eigenvalues PCA returned for these 26 earthquakes indicate earthquake associated TEC anomalies. Previously, PCA has been used to discriminate earthquake-associated TEC anomalies recognized by other researchers who found that statistical association between large earthquakes and TEC anomalies could be established in the 5 days before earthquake nucleation; however, since PCA uses the characteristics of principal eigenvalues to determine earthquake related TEC anomalies, it is possible to show that such anomalies existed earlier than this 5-day statistical window. In this paper, this is shown through the application of PCA to one-dimensional TEC data relating to the Kyushu earthquake of 20 March 2005 (M = 6.6). The analysis is applied to daily TEC and reveals a large principal eigenvalue (representative of an earthquake associated anomaly) for March 9, 11 days before the March 20 earthquake.

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Principal Component Analysis (PCA) is a risk management technique which is, due to the consequences of multicollinearity, particularly suitable to describe the yield curve. Its final results in this segment are presented through three main factors: shift, slope and curvature. They express predictive trajectories and explain over 95% of variability under normal market conditions. The main goal of this paper is to assess whether the established behavioural patterns are observable in the presence of negative interest rates. The EU bond market was used as an empirical basis with respect to the reactions of the European Central Bank and the establishment of negative reference interest rates in the assessed period. The algebraic properties of the principal components in the presence of negative interest rates correspond to the determined directions of movement, except that the slope and curvature have different signs given their diametrically opposite trends. The percentage of variability explained with the help of PCA is lower compared to the normal market conditions and if an equivalent level of approximation is required, it is necessary to include a fourth factor in PCA. This factor is, due to its properties, aptly named oscillatority. An implicit conclusion of our research is that the duration in the conditions of negative interest rates has less useful power in managing the interest rate risk of individual instruments.

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the other indices (see text) Finally, Principal Component Analysis (PCA) was also performed collecting together all the data of the four data sets. Then, the analyzed data matrix is constituted by 47 authors

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, and after it the principal component analysis (PCA) method ( Wold et al., 1987 ; Martens and Næs, 1991 ), the cluster analysis (CA) method ( Heise and Winzen, 2002 ), and the polar qualification system (PQS) method ( Kaffka and Seregély, 2002 ) were

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shells preparations. The chemical fingerprints of Moringa seed shells from various regions were established and investigated by similarity analysis (SA), principal component analysis (PCA) and hierarchical clustering analysis (HCA). The combination of

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solid dosage forms, in the evaluation of properties of pharmaceutical powders and tablet [ 30 – 32 ]. Principal component analysis (PCA) is a simple method for receipting relevant information from multivariate data sets, identifying trends and clusters

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approximation. At the same time there is the possibility to standardize based on the longest radius and make manual definition of the starting point. PrinComp software performs the Principal Component Analysis (PCA) of the normalized EFDs based on the variance

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the changes of VOCs in different grades of oil by GC-MS technology combined with solid-phase micro-extraction (SPME). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to build a non-linear discriminant model, which

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Hierarchical Clustering Analysis (HCA), Principal Component Analysis (PCA), and Partial Least Square Discriminate Analysis (PLS-DA), were conducted to screen the characteristic components as chemical indicators for assessing the quality of fermented trifoliate

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