Elliptic Fourier analysis and perceptual matching for the evaluation of bioinspired sketching in conceptual design

2017 ◽  
Vol 32 (1) ◽  
pp. 92-107
Author(s):  
Zhongliang Yang ◽  
Yumiao Chen ◽  
Zheng Liu
Keyword(s):  
Principal Component Analysis ◽  
Conceptual Design ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Fourier Descriptors ◽  
Elliptic Fourier Analysis ◽  
Elliptic Fourier Descriptors ◽  
Matching Degree ◽  
Perceptual Matching

AbstractBiologically inspired design can be used to aid in conceptual design. Sketching is an important ideation process in conceptual design for recording and evaluating flashing moments of inspiration. The present study aims to provide a framework for exploring the effects of biological examples on the sketching contours of products, as well as the perceptual matching degree between design ideas generated via sketching and the desired functions. Elliptic Fourier descriptors with principal component analysis and perceptual matching were used to evaluate and compare the effects of biological examples, no examples, and human-engineered examples from different product categories and within one product category on the sketches in an experiment that involved 28 participants. The application of elliptic Fourier descriptors with principal component analysis shows that there are significant differences in the third and seventh principal components. It is also found that exposure to biological examples can produce more sketches with high perceptual matching degree than the other three conditions, but there are no significant effects of the example exposure on the Pearson correlation coefficients of semantic differential evaluation value vectors between design problems and sketches. These results demonstrate that exposure to biological examples will correlate with Elliptic Fourier descriptors of sketches and will not significantly increase the perceptual matching degree between sketches and the desired function.

scholarly journals Digital Morphometrics: A Tool for Leaf Morpho- Taxonomical Studies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-7
Author(s):  
Kavi K. Oza ◽  
Rinku J. Desai ◽  
Vinay M. Raole*
Keyword(s):  
Principal Component Analysis ◽  
Abiotic Factors ◽  
Leaf Shape ◽  
Principal Component ◽  
Component Analysis ◽  
Herbarium Specimens ◽  
Fourier Descriptors ◽  
Shape Variation ◽  
Quantitative Analysis Method ◽  
Elliptic Fourier Descriptors

Leaves are most important part of the plant and can be used for the identification of a taxon. An appropriate understanding of leaf development in terms of shape and responsible abiotic factors is necessary for improvement in plant. Leaf shape variation could be evaluated successfully, and the symmetrical and asymmetrical elements of the overall shape variation could be detected. The aim of the present study was to establish a quantitative analysis method of leaf shape by elliptic Fourier descriptors and principal component analysis (EF-PCA). EF-PCA describes an overall shape mathematically by transforming coordinate information concerning its contours into elliptic Fourier descriptors (EFDs) and summarizing the EFDs by principal component analysis. We can be able to extract six variables by using leaf specimen images from field and herbarium specimens. In the present study, total leaf area with respect to notch area is more variable within species. Within a species the major source of the symmetrical elements may be governed by genotypic features and the asymmetrical elements are strongly affected by the environment. We could discuss the value of morphometrics to detect subtle morphological variation which may be undetectable by human eye.

PSXIII-26 Supplementation with a polyphenolic pine bark extract reduces ammonia nitrogen production in a RUSITEC system as determined by principal component analysis

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 435-436
Author(s):  
Nelson Vera ◽  
Constanza Gutierrez ◽  
Pamela Williams ◽  
Cecilia Fuentealba ◽  
Rodrigo Allende ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Repeated Measures ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Ammonia Nitrogen ◽  
Pine Bark ◽  
Bark Extract ◽  
Ruminal Fermentation ◽  
Fermentation Parameters

Abstract The aim of the study was to correlate the effects of supplementation with a polyphenolic pine bark extract (PBE) in diets with different forage to concentrate (F:C) ratio on methane (CH4), ammonia nitrogen (NH3–N) production and ruminal fermentation parameters using the Rumen Simulation Technique (RUSITEC). The experimental diets were F:C 70:30 (HF) or F:C 30:70 (HC) with or without 2% PBE on a DM basis. The four diets were isoproteic (15% CP), with similar OM (HF 94% and HC 96%), but different NDF (HF 40% and HC 25%). The treatments, in duplicate, were assigned in an 8 fermenter RUSITEC apparatus. Incubations were run twice, with 5 days of sampling after 10 days adaptation. The experimental design was a 2x2 factorial arrangement in a randomized complete block with repeated measures. Pearson correlation and principal component analysis (PCA) were conducted to elucidate relationships among PBE total polyphenols (TP) and the variables evaluated. The TP was highly correlated with NH3–N (r = –0.98; P < 0.001) and butyrate (r = –0.85; P < 0.001), and had a high correlation with propionate (r = 0.75; P < 0.001) and acetate (r = 0.68; P = 0.001). Correlation with total VFA was moderate (r = –0.59; P = 0.006), and CH4 yield and IVDMD there were not correlated (r ≤ –0.07; P ≥ 0.188). The PCA (KMO = 0.655; BTS < 0.001) shows that 75.2% of the total variation is explained by the first two principal components (PC1 = 46.5% and PC2 = 28.7%). In the score plot, PC1 discriminated between diets with and without PBE, while the PC2 separated based on NDF. The loading plot showed that TP and propionate were clustered, and had inverse directions to NH3–N. In conclusion, the PBE supplementation reduces NH3–N production in a RUSITEC system without decreasing CH4 yield or negatively affecting ruminal fermentation parameters.

scholarly journals No-tillage system participatory quality index

2020 ◽  
Vol 24 (2) ◽  
pp. 128-133 ◽  
Author(s):  
Tiago S. Telles ◽  
Ana J. Righetto ◽  
Marco A. P. Lourenço ◽  
Graziela M. C. Barbosa
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Crop Rotation ◽  
Quality Index ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Tillage ◽  
No Tillage ◽  
Tillage System

ABSTRACT The no-tillage system participatory quality index aims to evaluate the quality and efficiency of soil management under no-tillage systems and consists of a weighted sum of eight indicators: intensity of crop rotation, diversity of crop rotation, persistence of crop residues in the soil surface, frequency of soil tillage, use of agricultural terraces, evaluation of soil conservation, balance of soil fertilization and time of adoption of the no-tillage system. The aim of this study was to assess the extent to which these indicators correlate with the no-tillage system participatory quality index and to characterize the farmers who participated in the research. The data used were provided by ITAIPU Binacional for the indicators of the no-tillage system participatory quality index II. Descriptive analyses were performed, and the Pearson correlation coefficient between the index and each indicator was calculated. To assess the relationship between the indicators and the farmers’ behavior toward the indicators, principal component analysis and cluster analysis were performed. Although all correlations are significant at p-value ≤ 0.05, some correlations are weak, indicating a need for improvement of the index. The principal component analysis identified three principal components, which explained 66% of the variability of the data, and the cluster analysis separated the 121 farmers into five groups. It was verified that the no-tillage system participatory quality index II has some limitations and should therefore be reevaluated to increase its efficiency as an indicator of the quality of the no-tillage system.

Branding efforts and SME performance – an empirical investigation of variations across firm sizes and business sectors

2017 ◽  
Vol 19 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Raphael Odoom ◽  
Priscilla Mensah ◽  
George Asamoah
Keyword(s):  
Principal Component Analysis ◽  
Emerging Market ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Content Type ◽  
Brand Building ◽  
Brand Marketing ◽  
And Performance ◽  
Stage Analysis

Purpose This paper aims to draw on the organizational ecology theory to examine variations in branding efforts and performance of small and medium-sized enterprises (SMEs) across enterprises sizes and business operating sectors. Design/methodology/approach A four-stage analysis involving principal component analysis, Pearson correlation, ANOVA and logistic regressions was used on a sample of 430 SMEs within an emerging market. Findings Principal component analysis identified four brand marketing efforts relevant to the SMEs. These efforts were used in fluctuating extents among small-sized versus medium-sized enterprises, as well as manufacturing versus services SMEs. Additionally, proportionate levels of performance corollaries were found to be accruable across the enterprise sizes and operating sectors. Originality/value The paper first identifies four brand-building efforts germane to SMEs within an emerging market and examines their precise contributions to firm performance within enterprise sizes and business operating sectors. It further reinforces the relevance of brand marketing programs to the growth of SMEs by establishing the likelihood and extent to which brand-building efforts impact on SME performance across enterprise sizes, as well as operating sectors. The study also presents issues of potential research and managerial interest from an emerging market, offering insightful implications to researchers and SME managers.

scholarly journals Analysis of the physical-chemical and sensorial properties of Maria type cookies

2013 ◽  
Vol 6 (2) ◽  
pp. 269-280 ◽  
Author(s):  
Daniela Pereira ◽  
Paula M. R. Correia ◽  
Raquel P. F. Guiné
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Sensory Evaluation ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Volume Density ◽  
Physical Parameters ◽  
Multivariate Statistical ◽  
Main Component

Abstract Given the importance of the cookies of type Maria worldwide, and considering the absence of any scientific study setting out their main features, it becomes important to identify the differentiating characteristics of several commercialized brands, in particular related to the chemical, physical and sensory characteristics. In this way, the aim of this work was to study and compare eight different brands of cookies of type Maria. The elemental chemical analysis (moisture, ash, protein, fat, fibre and carbohydrates contents), determination of physical parameters (volume, density, texture and colour) and sensory evaluation of studied cookies were performed. Multivariate statistical methods (Pearson correlation, principal component analysis and cluster analysis) were applied to estimating relationships in analysed data. The results for the elemental analysis showed that the samples were very similar in terms of some components, like for example ashes, while quite different in terms of other components, such as moisture and fat contents. With respect to texture and colour the samples showed, in general, some important differences. In terms of sensory evaluation, the sample C was the one that in most sensory tests gathered the preference of the panellists. The cluster analysis showed that the sample A was much different from the other samples. The results of principal component analysis showed that the main component explains 32.6 % of the total variance, and is strongly related to variables associated to colour.

scholarly journals Research on Economic Periodicity Based on Principal Component-Weighted Distance and Clustering Analysis

2020 ◽  
Vol 214 ◽  
pp. 03003
Author(s):  
Jiayi Yan ◽  
Qian Pu ◽  
Junfei Liu
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Clustering Analysis ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
The United States ◽  
Economic Situation ◽  
Analysis Model ◽  
Weighted Distance

Based on the knowledge of economics, this paper selects 22 macroeconomic indicators that best reflect the overall economic situation of the United States. After differential, logarithmic and exponential preprocessing of the original data, this paper, based on the power spectral analysis model, adaptively identifies the periodicity of the selected economic indicators, and visualize the results. As a result, it screens out 11 indicators with obvious periodicity. In the process of solving the weighted distance based on principal component analysis, correlation test is first conducted on the selected 11 single indicators of periodicity to obtain Pearson correlation heatmap. Then, the principal components are extracted by selecting the first five principal components as the virtual indicators to represent the monthly economic situation, and calculating the weighted distance value between months for visualization. Finally, we select the results of 36 months’ smoothing for analysis, figure out the time intervals with similar economic situation, and verify the conjecture of economic periodicity. Finally, based on K-MEAN clustering analysis, the economic conditions of 352 months are classified into 3 clusters by using the weighted distance after 36 months’ smoothing. From the visualized results, it is found that there are two complete cycles, i.e. red-yellow-blue and red-yellow-blue, which is consistent with the conclusion of principal component analysis model, and proves the existence of economic cycle again. In conclusion, based on the above PCA weighted distance and clustering analysis, it can be concluded that the economic period is around 176 months, in favor of medium long periodicity theory.

Discrimination of the species of the Crassiphycus corneus/C. usneoides complex (Gracilariaceae, Rhodophyta) through geometric morphometric analysis

Nova Hedwigia ◽  
2019 ◽  
Vol 109 (3) ◽  
pp. 291-301 ◽  
Author(s):  
Martha Isabel Vilchis ◽  
Jiri Neustupa ◽  
Kurt Dreckmann ◽  
Alejandra Quintanar ◽  
Abel Sentíes
Keyword(s):  
Principal Component Analysis ◽  
Morphometric Analysis ◽  
Principal Component ◽  
Fourier Descriptors ◽  
Cortical Cells ◽  
Geometric Morphometric ◽  
Phenotypic Differences ◽  
Geometric Morphometric Analysis ◽  
Elliptic Fourier Descriptors ◽  
Manova Analysis

The results of the discrimination by geometric morphometry of the species within the Crassiphycus corneus/C. usneoides complex is presented, from the shape of its cortical, subcortical and medullary cells in the apical, medium and basal portions of thallus. Principal component analysis, based on the elliptic Fourier descriptors of cellular outline shapes and NP-MANOVA analysis, show that only the cortical cells in the medium portion were significantly wider in C. corneus than in C. usneoides. The isoperimetric quotients (Q) indicated that these cells were significantly more circular in C. corneus than in C. usneoides. We conclude that the outline analyses efficiently recovered phenotypic differences between the species defined by molecular systematics and considered to be cryptic, so far.

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