scholarly journals Host Selection in Tomicus piniperda L.: Composition of Monoterpene Hydrocarbons in Relation to Attack Frequency in the Shoot Feeding Phase

2006 ◽  
Vol 61 (5-6) ◽  
pp. 439-444 ◽  
Author(s):  
Ann-Charlotte Almquist ◽  
Jenny Fäldt ◽  
Annie Yart ◽  
Yohann Chevet ◽  
Daniel Sauvard ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Host Selection ◽  
Principal Component ◽  
Tomicus Piniperda ◽  
Host Volatiles ◽  
Principal Component Analysis Plot ◽  
Attack Frequency ◽  
Pine Shoot Beetle ◽  
Monoterpene Hydrocarbons ◽  
The One

The aim of this study was to investigate the host selection capacity of the pine shoot beetle, Tomicus piniperda, in the shoot-feeding phase and analyze the chiral and non-chiral host volatiles by means of GC-MS and 2D-GC in five Pinus species originating from France (Pinus sylvestris, P. halepensis, P. nigra laricio, P. pinaster maritima, P. pinaster mesogeensis). Dominating monoterpenes were (-)-α-pinene, (+)-α-pinene, (-)-β-pinene and (+)-3-carene. The amounts of the enantiomers varied considerably within and among the species. In a principal component analysis-plot, based on the absolute amounts of 18 monoterpene hydrocarbons, separation of the pine species into two groups was obtained. P. halepensis and P. sylvestris were grouped according to the amount of (+)-α-pinene and (+)-3-carene, while P. nigra laricio, P. pinaster maritima and P. pinaster mesogeensis were grouped according to (-)-α-pinene and (D)-β-pinene. P. nigra laricio was the species most attacked and P. halepensis the one least attacked by T. piniperda.

The Ethics of Tax Evasion: Relevant Demographic Variables within the scope of Higher Education Students and Professors.

2020 ◽  
Vol 8 (10) ◽  
pp. 24-37
Author(s):  
César Ribeiro ◽  
Carlos Santos Pinho
Keyword(s):  
Higher Education ◽  
Principal Component Analysis ◽  
Tax Evasion ◽  
Principal Component ◽  
Older Men ◽  
Demographic Variables ◽  
Education Students ◽  
Input Matrix ◽  
The One ◽  
Official Information

The purpose of our study is to determine the depth of various arguments that have emerged to justify tax evasion as an ethical procedure considering several demographic variables. Data collection was done using a questionnaire addressed to professors and students of higher management and non-management courses. This instrument was based on the 18 statements reflecting the three views of tax evasion ethics used by McGee and Benk [1]. Using a 5-point Likert scale, it is intended to evaluate whether the arguments contained in the statements have an effect on the perception of tax evasion as an ethical procedure and whether the previous effect varies according to age, sex, bachelor degree and income level. A universe of 406,980 individuals was determined using official information (sample: 384 individuals). Principal Component Analysis was used, as well as the Kaiser-Meyer-Olkin Statistics in order to measure the adequacy of the input matrix. After the extraction of the components three variables were identified: “Always Ethical”, “Waste, Corruption and Injustice” and “Discrimination and Oppressive Regimes” (Cronbach's Alpha results: 0.887, 0.85 and 0.862). “Discrimination and Oppressive Regimes” is the one that has values ​​closest to “totally agree” that tax evasion is ethical. In general, older men with higher incomes tend to disagree about the ethics of tax evasion. The originality of the study is reflected in the controversial relationship between Ethics and Evasion and the source of the data collected. Interacting with professors and students allows the business and academic components to be combined.

Psychotic Symptoms Predicting Evidence Integration in Schizophrenia

2018 ◽  
Vol 226 (3) ◽  
pp. 174-181 ◽  
Author(s):  
Jessica Luk ◽  
Kendra Underhill ◽  
Todd S. Woodward
Keyword(s):  
Principal Component Analysis ◽  
Cognitive Bias ◽  
Principal Component ◽  
Thought Disorder ◽  
Psychotic Symptoms ◽  
Component Structure ◽  
Single Measure ◽  
Patient Groups ◽  
The One ◽  
Evidence Integration

Abstract. A bias against disconfirmatory evidence (BADE) is a cognitive bias associated with delusions in schizophrenia. Previous studies reporting an association between reduced evidence integration and delusions used a single measure of delusion severity, typically to form patient groups. In the current study we perform an exploratory analysis to investigate whether BADE is specific to delusions or extends to other symptoms of psychosis. To address this, we used constrained principal component analysis (CPCA) on four merged BADE studies on schizophrenia to explore the component structure in the BADE task measures that is predictable from symptoms. A component reflecting evidence integration emerged, and was predicted by delusions as expected, but also by thought disorder. This provides novel methodology for cognitive neuropsychiatric investigations into the underpinnings of the symptoms of schizophrenia by enabling investigators to consider a range of symptoms alongside the one that is the target of their investigation.

scholarly journals Typology of Medium-Sized Cities in Spain Using PCA (Principal Component Analysis)

2021 ◽  
Vol 13 (5) ◽  
pp. 2456
Author(s):  
Francisco Cebrián-Abellán ◽  
María-Jesús González-González ◽  
María-Eva Vallejo-Pascual
Keyword(s):  
Principal Component Analysis ◽  
Economic Activity ◽  
Statistical Data ◽  
Principal Component ◽  
Statistical Analyses ◽  
The Other ◽  
Processes Of Change ◽  
Cultural Contexts ◽  
The One ◽  
Different Sources

This article analyses processes of change undergone by Spanish medium-sized cities during 1981–2011 on the one hand, and 2000–2018 on the other, as they are different sources. We established a classification to show the importance of this type of city starting from the hypothesis that the process is a generalised one in which they behave according to their position in the territory. The dynamics of change are predominantly associated with contexts of economic expansion. The typology was generated based on population and housing variables, which synthesise the role played by economic activity in each city. Additional methodologies were used: firstly, the bibliography on medium-sized cities in different social and cultural contexts was reviewed; secondly, statistical data were selected, compiled and processed using multivariant statistical analyses, and the results mapped. A study of 133 cities was carried out and absolute values and variation rates used to understand the processes of change. As a result, seven representative groups were obtained. The conclusions of the study can be corroborated by different sources.

scholarly journals Principal component analysis for human gait recognition system

2019 ◽  
Vol 8 (2) ◽  
pp. 569-576
Author(s):  
Othman O. Khalifa ◽  
Bilal Jawed ◽  
Sharif Shah Newaj Bhuiyn
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Gait Recognition ◽  
Principal Component ◽  
Initial Step ◽  
Component Analysis ◽  
Recognition System ◽  
Human Gait ◽  
The One ◽  
Main Components

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject.

scholarly journals How Do Movement Patterns in Weightlifting (Clean) Change When Using Lighter or Heavier Barbell Loads?—A Comparison of Two Principal Component Analysis-Based Approaches to Studying Technique

2021 ◽  
Vol 11 ◽  
Author(s):  
Inge Werner ◽  
Nicolai Szelenczy ◽  
Felix Wachholz ◽  
Peter Federolf
Keyword(s):  
Principal Component Analysis ◽  
Repeated Measures ◽  
Principal Component ◽  
Movement Pattern ◽  
Component Analysis ◽  
Whole Body ◽  
Load Effects ◽  
The One ◽  
Coordination Patterns ◽  
Initial Elevation

This study compared whole body kinematics of the clean movement when lifting three different loads, implementing two data analysis approaches based on principal component analysis (PCA). Nine weightlifters were equipped with 39 markers and their motion captured with 8 Vicon cameras at 100 Hz. Lifts of 60, 85, and 95% of the one repetition maximum were analyzed. The first PCA (PCAtrial) analyzed variance among time-normed waveforms compiled from subjects and trials; the second PCA (PCAposture) analyzed postural positions compiled over time, subjects and trials. Load effects were identified through repeated measures ANOVAs with Bonferroni-corrected post-hocs and through Cousineau-Morey confidence intervals. PCAtrial scores differed in the first (p < 0.016, ηp2 = 0.694) and fifth (p < 0.006, ηp2 = 0.768) principal component, suggesting that increased barbell load produced higher initial elevation, lower squat position, wider feet position after squatting, and less inclined arms. PCAposture revealed significant timing differences in all components. We conclude, first, barbell load affects specific aspects of the movement pattern of the clean; second, the PCAtrial approach is better suited for detecting deviations from a mean motion trajectory and its results are easier to interpret; the PCAposture approach reveals coordination patterns and facilitates comparisons of postural speeds and accelerations.

Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets

2020 ◽  
Author(s):  
Santiago Codesido ◽  
Mohamed Hanafi ◽  
Yoric Gagnebin ◽  
Víctor González-Ruiz ◽  
Serge Rudaz ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Model Parameters ◽  
Complex Data ◽  
Metabolomic Data ◽  
Objective Interpretation ◽  
Versatile Tool ◽  
Data Tables ◽  
The One

Abstract Motivation Complex data structures composed of different groups of observations and blocks of variables are increasingly collected in many domains, including metabolomics. Analysing these high-dimensional data constitutes a challenge, and the objective of this article is to present an original multivariate method capable of explicitly taking into account links between data tables when they involve the same observations and/or variables. For that purpose, an extension of standard principal component analysis called NetPCA was developed. Results The proposed algorithm was illustrated as an efficient solution for addressing complex multigroup and multiblock datasets. A case study involving the analysis of metabolomic data with different annotation levels and originating from a chronic kidney disease (CKD) study was used to highlight the different aspects and the additional outputs of the method compared to standard PCA. On the one hand, the model parameters allowed an efficient evaluation of each group’s influence to be performed. On the other hand, the relative relevance of each block of variables to the model provided decisive information for an objective interpretation of the different metabolic annotation levels. Availability and implementation NetPCA is available as a Python package with NumPy dependencies.

Prediction of tRNA Based on LS-SVM Algorithm with Principal Component Analysis

2012 ◽  
Vol 461 ◽  
pp. 753-756
Author(s):  
Chong Xing ◽  
Yao Wang ◽  
You Zhou ◽  
Yan Chun Liang
Keyword(s):  
Principal Component Analysis ◽  
Prediction Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Single Nucleotide ◽  
Non Coding Rna ◽  
Svm Algorithm ◽  
The One ◽  
Prediction Strategy

Recently, non-coding RNA prediction is the one of the most important researches in bioinformatics. In this paper, on the basis of principal component analysis, we present a tRNA prediction strategy by using least squares support vector machine (LS-SVM). Appearance frequencies of single nucleotide, 2 – nucleotides and (G-C) %, (A-T) % were chosen as characteristics inputs. Results from tests showed that the prediction accuracy was 90.51% on prokaryotic tRNA dataset. Experimental results indicate that the method is effective for prokaryotic ncRNA prediction.

Incremental Tensor Principal Component Analysis for Image Recognition

2013 ◽  
Vol 710 ◽  
pp. 584-588
Author(s):  
Wei Dong Zhao ◽  
Chang Liu ◽  
Tao Yan
Keyword(s):  
Principal Component Analysis ◽  
Learning Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Training Problem ◽  
Structure Information ◽  
Handwritten Numeral Recognition ◽  
The One ◽  
Tensor Data ◽  
Numeral Recognition

Aiming at the disadvantages of the traditional off-line vector-based learning algorithm, this paper proposes a kind of Incremental Tensor Principal Component Analysis (ITPCA) algorithm. It represents an image as a tensor data and processes incremental principal component analysis learning based on update-SVD technique. On the one hand, the proposed algorithm is helpful to preserve the structure information of the image. On the other hand, it solves the training problem for new samples. The experiments on handwritten numeral recognition have demonstrated that the algorithm has achieved better performance than traditional vector-based Incremental Principal Component Analysis (IPCA) and Multi-linear Principal Component Analysis (MPCA) algorithms.

scholarly journals Towards a 3D articulatory model of velum based on MRI and CT images

2005 ◽  
Vol 40 ◽  
pp. 195-211
Author(s):  
Antoine Serrurier ◽  
Pierre Badin
Keyword(s):  
Principal Component Analysis ◽  
Coordinate System ◽  
Elastic Deformation ◽  
Degrees Of Freedom ◽  
Principal Component ◽  
Component Analysis ◽  
Ct Images ◽  
The One ◽  
Articulatory Model ◽  
3D Shapes

This paper describes the processing of MRI and CT images needed for developing a 3D linear articulatory model of velum. The 3D surface that defines each organ constitutive of the vocal and nasal tracts is extracted from MRI and CT images recorded on a subject uttering a corpus of artificially sustained French vowels and consonants. First, the 2D contours of the organs have been manually extracted from the corresponding images, expanded into 3D contours, and aligned in a common 3D coordinate system. Then, for each organ, a generic mesh has been chosen and fitted by elastic deformation to each of the 46 3D shapes of the corpus. This has finally resulted in a set of organ surfaces sampled with the same number of 3D vertices for each articulation, which is appropriate for Principal Component Analysis or linear decomposition. The analysis of these data has uncovered two main uncorrelated articulatory degrees of freedom for the velum's movement. The associated parameters are used to control the model. We have in particular investigated the question of a possible correlation between jaw / tongue and velum's movement and have not find more correlation than the one found in the corpus.  

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