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

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.

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No-tillage system participatory quality index

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v24n2p128-133 ◽

2020 ◽

Vol 24 (2) ◽

pp. 128-133 ◽

Cited By ~ 2

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.

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

Journal of Research in Marketing and Entrepreneurship ◽

10.1108/jrme-06-2016-0017 ◽

2017 ◽

Vol 19 (1) ◽

pp. 59-76 ◽

Cited By ~ 1

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.

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

Acta Chimica Slovaca ◽

10.2478/acs-2013-0040 ◽

2013 ◽

Vol 6 (2) ◽

pp. 269-280 ◽

Cited By ~ 11

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.

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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

Frontiers in Psychology ◽

10.3389/fpsyg.2020.606070 ◽

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.

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

E3S Web of Conferences ◽

10.1051/e3sconf/202021403003 ◽

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.

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Dear Program Director: Deciphering Letters of Recommendation

Journal of Graduate Medical Education ◽

10.4300/jgme-d-17-00712.1 ◽

2018 ◽

Vol 10 (3) ◽

pp. 261-266 ◽

Cited By ~ 15

Author(s):

Kris Saudek ◽

David Saudek ◽

Robert Treat ◽

Peter Bartz ◽

Rachel Weigert ◽

...

Keyword(s):

Principal Component Analysis ◽

Repeated Measures ◽

Principal Component ◽

Component Analysis ◽

Program Director ◽

Cross Sectional ◽

Likert Score ◽

Significant Difference ◽

Fellowship Programs ◽

Letters Of Recommendation

ABSTRACT Background Letters of recommendation (LORs) are an important part of applications for residency and fellowship programs. Despite anecdotal use of a “code” in LORs, research on program director (PD) perceptions of the value of these documents is sparse. Objective We analyzed PD interpretations of LOR components and discriminated between perceived levels of applicant recommendations. Methods We conducted a cross-sectional, descriptive study of pediatrics residency and fellowship PDs. We developed a survey asking PDs to rate 3 aspects of LORs: 13 letter features, 10 applicant abilities, and 11 commonly used phrases, using a 5-point Likert scale. The 11 phrases were grouped using principal component analysis. Mean scores of components were analyzed with repeated-measures analysis of variance. Median Likert score differences between groups were analyzed with Mann-Whitney U tests. Results Our survey had a 43% response rate (468 of 1079). “I give my highest recommendation” was rated the most positive phrase, while “showed improvement” was rated the most negative. Principal component analysis generated 3 groups of phrases with moderate to strong correlation with each other. The mean Likert score for each group from the PD rating was calculated. Positive phrases had a mean (SD) of 4.4 (0.4), neutral phrases 3.4 (0.5), and negative phrases 2.6 (0.6). There was a significant difference among all 3 pairs of mean scores (all P < .001). Conclusions Commonly used phrases in LORs were interpreted consistently by PDs and influenced their impressions of candidates. Key elements of LORs include distinct phrases depicting different degrees of endorsement.

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

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060417000257 ◽

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.

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Quality of Eucalyptus Wood Grown in Rio de Janeiro State for Bioenergy

Floresta e Ambiente ◽

10.1590/2179-8087.140315 ◽

2016 ◽

Vol 23 (3) ◽

pp. 435-442 ◽

Cited By ~ 5

Author(s):

Ananias Francisco Dias Júnior ◽

Djailson Silva da Costa Júnior ◽

Azarias Machado de Andrade ◽

Elisabeth de Oliveira ◽

Artur Queiroz Lana ◽

...

Keyword(s):

Principal Component Analysis ◽

Rio De Janeiro ◽

Energy Use ◽

Pearson Correlation ◽

Eucalyptus Grandis ◽

Basic Density ◽

Principal Component ◽

Component Analysis ◽

Eucalyptus Wood ◽

Janeiro State

ABSTRACT This study aimed to evaluate the Eucalyptus grandis and Eucalyptus saligna, from production areas of Rio de Janeiro State, intended for energy use. The selection consisted of six trees per specie, at six years old. The wood samples had its basic density determined, then, was subjected to the pyrolysis process with 500 °C of final temperature. Charcoal, pyroligneous liquid and non-condensable gases yields were determined. In addition, the charcoal had its immediate analysis performed to determine the levels of volatiles matter, fixed carbon and ash content. Data were analyzed using descriptive statistics, Pearson correlation and principal component analysis. The correlation analysis and principal component analysis were effective to predict recommended species. Based on the results, the most recommended specie for energy purposes was the Eucalyptus grandis.

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Evaluation of Water Quality and Benthic Macrointervebrates Fauna Relationship Using Principal Component Analysis (PCA): A Case Study of Cameron Highlands Malaysia

Environmental Management and Sustainable Development ◽

10.5296/emsd.v5i1.9399 ◽

2016 ◽

Vol 5 (1) ◽

pp. 187 ◽

Cited By ~ 1

Author(s):

Kok Weng Tan ◽

Weng Chee Beh

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Dissolved Oxygen ◽

Oxygen Demand ◽

Principal Component ◽

Component Analysis ◽

Ammonia Nitrogen ◽

Average Density ◽

Class Iii ◽

Cameron Highlands

This study applies the Principal Component Analysis (PCA) to evaluate and interpret the relationship between water quality and benthic macro-invertebrates fauna data obtained from Pauh River, Cameron Highlands. Samples were collected once every two months (in February, April, June, August and October 2013) with six chosen sampling stations. Six water quality parameters namely dissolved oxygen (DO), pH, biological oxygen demand (BOD5), chemical oxygen demand (COD), ammonia-nitrogen (NH3-N), total suspended solid (TSS) and heavy metals contents were analyzed according to American Public Health Association (APHA), Standard Methods for Examination of Water and Wastewater (1998). Macro-invertebrates were also sampled using Surber sampler and were identified until their family level. Water Quality Index (WQI) values for all stations were class II except for the station 6 which was recorded as class III. Both the diversity and biotic indices showed decreasing value from the upstream (Station 1) to downstream (Station 6). A total 28 to 31 taxa have been found in Station 1, 2, 3 and 5 (upstream to middle stream). However, only 7 taxa found at station 6 (downstream). Total 31 taxa with an average density 368.28 ind/m2 were found in Station 4 which was highest number of taxa among the monitoring stations. The principal component analysis (PCA) was applied on the dataset, which explained 72.15 % of the total variance of the variables. Three components were extracted in this study. First component was classified as benthic macroinvertebrates which tolerated to low water quality condition and high loading of organic matters. The benthic macro-invertebrates families loaded in second component were sensitive to water environment such as NH3-N, dissolved oxygen (DO), organic matter and stream flow. The benthic macroinvertebrate families loaded in third component were recognized as species which might not tolerate low concentration of dissolved oxygen.

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A modified correlation in principal component analysis for torrential rainfall patterns identification

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i4.pp655-661 ◽

2020 ◽

Vol 9 (4) ◽

pp. 655

Author(s):

Shazlyn Milleana Shaharudin ◽

Norhaiza Ahmad ◽

Siti Mariana Che Mat Nor

Keyword(s):

Principal Component Analysis ◽

High Dimension ◽

Pearson Correlation ◽

Principal Component ◽

Breakdown Point ◽

Distribution Patterns ◽

Component Analysis ◽

Peninsular Malaysia ◽

Torrential Rainfall ◽

Rainfall Patterns

This paper presents a modified correlation in principal component analysis (PCA) for selection number of clusters in identifying rainfall patterns. The approach of a clustering as guided by PCA is extensively employed in data with high dimension especially in identifying the spatial distribution patterns of daily torrential rainfall. Typically, a common method of identifying rainfall patterns for climatological investigation employed T mode-based Pearson correlation matrix to extract the relative variance retained. However, the data of rainfall in Peninsular Malaysia involved skewed observations in the direction of higher values with pure tendencies of values that are positive. Therefore, using Pearson correlation which was basing on PCA on rainfall set of data has the potentioal to influence the partitions of cluster as well as producing exceptionally clusters that are eneven in a space with high dimension. For current research, to resolve the unbalanced clusters challenge regarding the patterns of rainfall caused by the skewed character of the data, a robust dimension reduction method in PCA was employed. Thus, it led to the introduction of a robust measure in PCA with Tukey’s biweight correlation to downweigh observations along with the optimal breakdown point to obtain PCA’s quantity of components. Outcomes of this study displayed a highly substantial progress for the robust PCA, contrasting with the PCA-based Pearson correlation in respects to the average amount of acquired clusters and indicated 70% variance cumulative percentage at the breakdown point of 0.4.

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