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Journal articles on the topic 'Composition Data Analysis'

Author: Grafiati

Published: 10 March 2023

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1

Greenacre, Michael. "Compositional Data Analysis." Annual Review of Statistics and Its Application 8, no. 1 (March 7, 2021): 271–99. http://dx.doi.org/10.1146/annurev-statistics-042720-124436.

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Compositional data are nonnegative data carrying relative, rather than absolute, information—these are often data with a constant-sum constraint on the sample values, for example, proportions or percentages summing to 1% or 100%, respectively. Ratios between components of a composition are important since they are unaffected by the particular set of components chosen. Logarithms of ratios (logratios) are the fundamental transformation in the ratio approach to compositional data analysis—all data thus need to be strictly positive, so that zero values present a major problem. Components that group together based on domain knowledge can be amalgamated (i.e., summed) to create new components, and this can alleviate the problem of data zeros. Once compositional data are transformed to logratios, regular univariate and multivariate statistical analysis can be performed, such as dimension reduction and clustering, as well as modeling. Alternative methodologies that come close to the ideals of the logratio approach are also considered, especially those that avoid the problem of data zeros, which is particularly acute in large bioinformatic data sets.

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Hopke, P. K., Y. Xie, P. Paatero, L. A. Barrie, and S. M. Li. "Multiway analysis of airborne particle composition data." Journal of Aerosol Science 29 (September 1998): S515—S516. http://dx.doi.org/10.1016/s0021-8502(98)00182-7.

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3

Rand, William M. "Experimental design for food composition data analysis." Journal of Food Composition and Analysis 5, no. 4 (December 1992): 267–68. http://dx.doi.org/10.1016/0889-1575(92)90060-w.

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Takahashi, Akira, Miwa Misawa, and Saizo Yanaura. "Pharmacological generalization of DATA-assessment based on data-composition analysis." Japanese Journal of Pharmacology 55 (1991): 290. http://dx.doi.org/10.1016/s0021-5198(19)39257-1.

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Muldowney, D., J. Connolly, and M. G. Keane. "Compositional data analysis in the study of carcass composition of beef cattle." Livestock Production Science 67, no. 3 (January 2001): 241–51. http://dx.doi.org/10.1016/s0301-6226(00)00200-1.

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LEDERMANN, THOMAS, MYRIAM RUDAZ, and ALEXANDER GROB. "Analysis of group composition in multimember multigroup data." Personal Relationships 24, no. 2 (February 14, 2017): 242–64. http://dx.doi.org/10.1111/pere.12176.

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7

Shishkova, Aна, P. Petrova, Anton Tonev, G. Iliev, P. Bahlova, O. Softov, and E. Kalchev. "Analysis of body composition using bioimpedance (BIA) data." Scripta Scientifica Medica 40, no. 2 (December 20, 2008): 187. http://dx.doi.org/10.14748/ssm.v40i2.541.

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Hopke, Philip K., Yulong Xie, and Pentti Paatero. "Mixed multiway analysis of airborne particle composition data." Journal of Chemometrics 13, no. 3-4 (May 1999): 343–52. http://dx.doi.org/10.1002/(sici)1099-128x(199905/08)13:3/4<343::aid-cem550>3.0.co;2-p.

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Soundarya, P., M. Vanitha, and I. Sumaiya Thaseen. "Dynamic user data analysis and web composition technique using big data." IOP Conference Series: Materials Science and Engineering 263 (November 2017): 042064. http://dx.doi.org/10.1088/1757-899x/263/4/042064.

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Liu, Ying, Belle R. Upadhyaya, and Masoud Naghedolfeizi. "Chemometric Data Analysis Using Artificial Neural Networks." Applied Spectroscopy 47, no. 1 (January 1993): 12–23. http://dx.doi.org/10.1366/0003702934048406.

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The on-line measurement of chemical composition under different operating conditions is an important problem in many industries. An approach based on hybrid signal preprocessing and artificial neural network paradigms for estimating composition from chemometric data has been developed. The performance of this methodology was tested with the use of near-infrared (NIR) and Raman spectra from both laboratory and industrial samples. The sensitivity-of-composition estimation as a function of spectral errors, spectral preprocessing, and choice of parameter vector was studied. The optimal architecture of multilayer neural networks and the guidelines for achieving them were also studied. The results of applications to FT-Raman data and NIR data demonstrate that this methodology is highly effective in establishing a generalized mapping between spectral information and sample composition, and that the parameters can be estimated with high accuracy.

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Faes, Christel, Geert Molenberghs, Niel Hens, Arno Muller, Herman Goossens, and Samuel Coenen. "Analysing the composition of outpatient antibiotic use: a tutorial on compositional data analysis." Journal of Antimicrobial Chemotherapy 66, suppl_6 (December 1, 2011): vi89—vi94. http://dx.doi.org/10.1093/jac/dkr461.

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12

Hosein, Raffie, and William D. McCain. "Extended Analysis for Gas Condensate Systems." SPE Reservoir Evaluation & Engineering 12, no. 01 (February 26, 2009): 159–66. http://dx.doi.org/10.2118/110152-pa.

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Summary Equation of State (EOS) predictions for gas condensate systems require extended analysis beyond the heptanes plus (C7+) fraction. In the absence of experimental data, several schemes have been proposed to extend these compositional data based on the observation that a single straightline relationship exists between log of mole percent and molecular weight for these pseudocomponents or single carbon number (SCN) fractions. An examination of compositional analysis for gas condensate systems showed a discontinuity in the relationship between mole percent and molecular weight at C8 and C13. As a result, two straight lines are needed for a more accurate description of SCN composition; one from C8 to C12, and the other from C13 and beyond. When applied, this new universal observation gives an improved prediction of SCN composition. An average absolute deviation of less than 6.0% between the predicted and experimental composition was obtained using parameters from two straight lines. From a single straightline relationship, this difference was as high as 36.0%. This new observation provides the basis for defining the partial experimental analysis required for applying extended models for a more accurate description of SCN composition. For the logarithmic distribution, a partial analysis to C20+ is required to define the change in slope at C13 and beyond. For the three parameter gamma distribution function, a partial analysis is required up to C14 and splitting can be applied from C14+ and beyond. These widely used models are not suitable for extending the C7+ fraction. Introduction With the increasing emphasis on liquid natural gas (LNG), natural gas liquids (NGLs) and liquid condensates during the last 15 years, gas condensate reservoirs became increasingly important. A combination of laboratory studies, such as Chromatographic; true boiling point (TBP); and pressure, volume, temperature (PVT) analyses became necessary for characterizing these reservoir fluids and evaluating their volumetric performance at various pressure depletion stages. An accurate description of pseudocomponent compositions is an integral part of the reservoir fluids characterization process. For gas condensate systems, these data are applied with Equations of State (EOS) to evaluate gas and condensate reserves and production for field development and surface facility design. The evaluations rely on a tuned EOS formulated from adjustment of SCN compositions. Good quality compositional data require minimal adjustment for obtaining the best match between predicted and experimental phase behavior data. Very often the required extended compositional data are unavailable experimentally and are generated from mathematical relationships. Literature (Ahmed 1989; Danesh 1998; Pedersen et el. 1989) has shown that a plot of SCN composition against molecular weight produces a continuous exponential relationship for gas condensate systems. This observation also led to a generally accepted representation of a single straightline relationship between log of mole percent and molecular weight for these SCN fractions. Based on this observation, very useful functional approaches called "splitting" schemes (Whitson 1983; Pedersen et al. 1984) were devised to describe the composition of these SCN fractions in the absence of experimental data. Although splitting schemes are applied from the C7+ or last available plus fraction, a review by Danesh (1998) stated that a partial analysis is first required followed by the application of these schemes. To date, literature has not specified the SCN or last plus fraction for terminating a partial analysis. From an examination of compositional analysis for gas condensate systems, this paper describes a different universal trend from the single straightline relationship between log of mole percent and molecular weight. Also, the last plus fraction is defined for terminating a partial analysis. A total of 22 compositional data sets to C20+ were examined. Six of these were generated experimentally from separator samples taken in Trinidad (Hosein 2004) and 16 were taken from PVT lab reports generated from samples taken worldwide.

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Bulska, E., M. N. Drozdov, G. Mana, A. Pramann, O. Rienitz, P. Sennikov, and S. Valkiers. "The isotopic composition of enriched Si: a data analysis." Metrologia 48, no. 2 (March 22, 2011): S32—S36. http://dx.doi.org/10.1088/0026-1394/48/2/s05.

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14

Church, S. M. "The importance of food composition data in recipe analysis." Nutrition Bulletin 40, no. 1 (February 12, 2015): 40–44. http://dx.doi.org/10.1111/nbu.12125.

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15

Addison, Joanne. "Data Analysis and Subject Representation in Empowering Composition Research." Written Communication 14, no. 1 (January 1997): 106–28. http://dx.doi.org/10.1177/0741088397014001003.

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16

Huisman, Mark, and Tom A. B. Snijders. "Statistical Analysis of Longitudinal Network Data With Changing Composition." Sociological Methods & Research 32, no. 2 (November 2003): 253–87. http://dx.doi.org/10.1177/0049124103256096.

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17

Kylefors, K. "Evaluation of leachate composition by multivariate data analysis (MVDA)." Journal of Environmental Management 68, no. 4 (August 2003): 367–76. http://dx.doi.org/10.1016/s0301-4797(03)00103-8.

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18

Apruzzese, Francesca, Ramin Reshadat, and Stephen T. Balke. "In-Line Monitoring of Polymer Processing. II: Spectral Data Analysis." Applied Spectroscopy 56, no. 10 (October 2002): 1268–74. http://dx.doi.org/10.1366/000370202760354713.

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The objective of this work was to examine the application of various multivariate methods to determine the composition of a flowing, molten, immiscible, polyethylene–polypropylene blend from near-infrared spectra. These spectra were acquired during processing by monitoring the melt with a fiber-optic-assisted in-line spectrometer. Undesired differences in spectra obtained from identical compositions were attributed to additive and multiplicative light scattering effects. Duplicate blend composition data were obtained over a range of 0 to 100% polyethylene. On the basis of previously published approaches, three data preprocessing methods were investigated: second derivative of absorbance with respect to wavelength (d2), multiplicative scatter correction (MSC), and a combination consisting of MSC followed by d2. The latter method was shown to substantially improve superposition of spectra and principal component analysis (PCA) scores. Also, fewer latent variables were required. The continuum regression (CR) approach, a method that encompasses ordinary least squares (OLS), partial least squares (PLS), and principle component regression (PCR) models, was then implemented and provided the best prediction model as one based on characteristics between those of PLS and OLS models.

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19

Ferrer-Rosell, Berta, Germà Coenders, and Esther Martínez-Garcia. "Segmentation by Tourist Expenditure Composition: An Approach with Compositional Data Analysis and Latent Classes." Tourism Analysis 21, no. 6 (November 18, 2016): 589–602. http://dx.doi.org/10.3727/108354216x14713487283075.

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20

Rahman, Md, and Mehedi Masud. "Mapping Composition Combining Schema and Data Level Heterogeneity in Peer Data Sharing Systems." Applied Mathematics & Information Sciences 7, no. 1 (January 1, 2013): 99–112. http://dx.doi.org/10.12785/amis/070111.

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21

Jingwei Lv, Jingwei Lv, Chunnan Li Chunnan Li, Nanxi Zhang Nanxi Zhang, Kaiyue Z. Kaiyue Z, Xiaochen Gao Xiaochen Gao, Na Li Na Li, Lingwen M. Lingwen M, Yinping Y. Yinping Y, and Hui Zhang and Jiaming Sun Hui Zhang and Jiaming Sun. "Metabolomic Profiling of Different Maca Color Types Using Nuclear Magnetic Resonance Spectroscopy and Multivariate Data Analysis." Journal of the chemical society of pakistan 44, no. 4 (2022): 356. http://dx.doi.org/10.52568/001071/jcsp/44.04.2022.

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This study aimed to explore significant differences in chemical composition among maca (Lepidium meyenii Walp.) types with different colors in Yunnan province, China. 1H-NMR spectroscopy, in combination with principal component analysis and partial least squares discriminant analysis, was used to investigate the compounds responsible for compositional differences. Different maca color types in Yunnan were clearly distinguished by 11 differential metabolites. Furthermore, network pharmacology results showed that 30 active components were related to Alzheimer’s disease. Nine intersecting compounds among the 11 differential metabolites and 30 active components, namely, lysine, isoleucine, phenylalanine, β-hydroxybutyrate, tryptophan, pyroglutamate, proline, glutamine, and fructose, were used as bioactive markers to identify different maca color types. The results showed the bioactive markers among different maca color types holistically, providing a scientific basis for assessing the quality of commercial products derived from different maca color types.

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22

Sampasa-Kanyinga, Hugues, Ian Colman, Dorothea Dumuid, Ian Janssen, Gary S. Goldfield, Jian Li Wang, Karen A. Patte, Scott T. Leatherdale, and Jean-Philippe Chaput. "Longitudinal association between movement behaviours and depressive symptoms among adolescents using compositional data analysis." PLOS ONE 16, no. 9 (September 1, 2021): e0256867. http://dx.doi.org/10.1371/journal.pone.0256867.

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Background Research examining the associations between movement behaviours and mental health indicators within a compositional framework are sparse and limited by their cross-sectional study design. This study has three objectives. First, to describe the change in movement behaviour composition over time. Second, to explore the association between change in movement behaviour composition and change in depressive symptoms. Third, to explore how reallocations of time between movement behaviours are associated with changes in depressive symptoms. Methods Longitudinal data of 14,620 students in grades 9–12 (mean age: 14.9 years) attending secondary schools in Canada (Ontario, British Columbia, Alberta, Quebec) were obtained from two waves (2017/18, 2018/19) of the COMPASS study. Moderate-to-vigorous physical activity (MVPA), recreational screen time, and sleep duration were self-reported. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (Revised)−10 (CESD-R-10). Compositional data analyses using pivot coordinates and compositional isotemporal substitution for longitudinal data were used to analyse the data. Analyses accounted for school clustering, were stratified by gender and age (< or ≥ 15 years), and were adjusted for race/ethnicity, body mass index z-score, baseline movement behaviour composition, and baseline depressive symptoms. Results There were significant differences in movement behaviour composition over time across all subgroups. For example, the relative contributions of MVPA and sleep duration to the movement behaviour composition decreased over time while screen time increased among younger boys and girls and older girls. Increasing sleep duration relative to the remaining behaviours (i.e. screen time and MVPA) was associated with lower depressive symptoms among all subgroups. Increasing screen time relative to the remaining behaviours (i.e. MVPA and sleep duration) was associated with higher depressive symptoms among all subgroups. Increasing MVPA relative to the remaining behaviours (i.e. screen time and sleep duration) was associated with lower depressive symptoms in older girls only. Isotemporal substitution estimates indicated that decreasing screen time by 60 minutes/day and replacing that time with 60 minutes of additional sleep is associated with the largest change in depressive symptoms across all subgroups. Conclusions Findings from this prospective analysis suggest that increased sleep duration and reduced screen time are important determinants of lower depressive symptoms among adolescents.

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23

Niebuhr, Annekatrin, and Jan Cornelius Peters. "Workforce Composition and Individual Wages — An Employer–Employee Data Analysis." British Journal of Industrial Relations 58, no. 3 (January 22, 2020): 719–42. http://dx.doi.org/10.1111/bjir.12519.

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24

Frishberg, Amit, Naama Peshes-Yaloz, Ofir Cohn, Diana Rosentul, Yael Steuerman, Liran Valadarsky, Gal Yankovitz, et al. "Cell composition analysis of bulk genomics using single-cell data." Nature Methods 16, no. 4 (March 18, 2019): 327–32. http://dx.doi.org/10.1038/s41592-019-0355-5.

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25

Šimkovic, Ivan, Michal Raab, Raniero Mendichi, Alena Manová, Alberto Giacometti Schieroni, and Miloš Hricovíni. "Heparin composition: calculation based on elemental analysis and NMR data." Chemical Papers 74, no. 1 (October 15, 2019): 349–55. http://dx.doi.org/10.1007/s11696-019-00957-w.

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26

Brodziak, Jon. "An Extension of Stock Composition Analysis to include Marking Data." Canadian Journal of Fisheries and Aquatic Sciences 50, no. 2 (February 1, 1993): 251–57. http://dx.doi.org/10.1139/f93-029.

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Population characteristics and man-made marks have been applied separately to gather information on the stock composition of mixed-stock fisheries. This paper develops an extension of the finite mixture problem with learning samples model of Fournier et al. (1984. Can. J. Fish. Aquat. Sci. 41: 400–408) to include marking data as well as morphometric, meristic, or biochemical genetic data for stock composition analyses. Simulation studies are used to test the model on a previously described "problem cluster" that consists of stocks that exhibit high levels of genetic affinity. The test results show that the inclusion of marking data can improve the accuracy of stock composition estimates.

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Zhou, Liming, Philip K. Hopke, Pentti Paatero, John M. Ondov, J. Patrick Pancras, Natalie J. Pekney, and Cliff I. Davidson. "Advanced factor analysis for multiple time resolution aerosol composition data." Atmospheric Environment 38, no. 29 (September 2004): 4909–20. http://dx.doi.org/10.1016/j.atmosenv.2004.05.040.

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Perović, Slobodan, Sandro Radovanović, Vlasta Sikimić, and Andrea Berber. "Optimal research team composition: data envelopment analysis of Fermilab experiments." Scientometrics 108, no. 1 (April 27, 2016): 83–111. http://dx.doi.org/10.1007/s11192-016-1947-9.

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Xie, Juele, Jiahui Ye, and Jun Zhou. "Category identification and composition analysis of ancient glass products based on GA-BP neural network and factor analysis." Highlights in Science, Engineering and Technology 21 (December 4, 2022): 237–45. http://dx.doi.org/10.54097/hset.v21i.3163.

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Ancient glass is a witness of early trade exchanges, and its study has profound historical significance. In this paper, we analyze the nature and chemical composition data of a batch of ancient glass products to investigate the compositional differences and identification characteristics of different types of glass products. In this paper, we use relevant information to achieve the effect of identifying cultural relics based on chemical composition data by mathematical modeling for a batch of ancient glass products in China (divided into high potassium glass and lead-barium glass), and analyze the correlation between the chemical composition of different categories of glass to compare the differences between classes.

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Gloor, Gregory B., and Gregor Reid. "Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data." Canadian Journal of Microbiology 62, no. 8 (August 2016): 692–703. http://dx.doi.org/10.1139/cjm-2015-0821.

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A workshop held at the 2015 annual meeting of the Canadian Society of Microbiologists highlighted compositional data analysis methods and the importance of exploratory data analysis for the analysis of microbiome data sets generated by high-throughput DNA sequencing. A summary of the content of that workshop, a review of new methods of analysis, and information on the importance of careful analyses are presented herein. The workshop focussed on explaining the rationale behind the use of compositional data analysis, and a demonstration of these methods for the examination of 2 microbiome data sets. A clear understanding of bioinformatics methodologies and the type of data being analyzed is essential, given the growing number of studies uncovering the critical role of the microbiome in health and disease and the need to understand alterations to its composition and function following intervention with fecal transplant, probiotics, diet, and pharmaceutical agents.

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Bayati, Shahab, Ardeshir Bahreininejad, Ali Farahmand Nejad, and Ssdegh Kharazmi. "Improving Semantic Web Services Composition Performance, Using Data Mining Techniques." Journal of Algorithms & Computational Technology 4, no. 4 (December 2010): 409–23. http://dx.doi.org/10.1260/1748-3018.4.4.409.

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Koizumi, Hiroyasu. "Analysis of Food Waste using Waste Composition Data in Japan and Issues for Conducting Composition Surveys." Journal of the Japan Society of Material Cycles and Waste Management 31 (2020): 47–54. http://dx.doi.org/10.3985/jjsmcwm.31.47.

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Titova, Оlga, Zoia Hrushak, Tetiana Kravchuk, Valerii Yefymenko, and Mariia Maksumiuk. "GENERATION OF SYNTHETICAL MEDICAL DATA BY MDR-ANALYSIS." Proceedings of the National Aviation University 87, no. 2 (July 27, 2021): 37–47. http://dx.doi.org/10.18372/2306-1472.87.15686.

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Purpose: The purpose of this article is to determine the dependence of the rate of gel formation on the type of reagent-inhibitor of the gel formation process, and to study the kinetic dependences of gel formation of silicate compositions in the presence of compounds that regulate the rate of gel formation. Research methods: Direct and reverse titration, potentiometric titration, pH-metry, determination of the dynamic viscosity on a rotational viscometer, axial compression method to determine the elastic modulus were used to study the possibility of using chemical reagents as gelation inhibitors. Results: The possibility of using inorganic and organic additives to slow down the gel formation process and the effect of these additives on the strength of the gel, their thermodynamic parameters have been researched. Discussion: The adding of organic acids into the silicate composition as "crosslinking agents" slows down the gel formation process, while replacing hydrochloric acid with other gel acids that slow down the gel formation process does not lead to a decrease in the strength characteristics of these systems compared to the standard. It was found that with an increase in the gelation time, the strength of the gels decreases, and the syneresis increases; the adding of inhibitor additives insignificantly worsens the filterability of silicate compositions, which in general have bad filterability, leading to an increase in resistance during filtration at the inlet section of the sample. Observations have shown that the addition of additives-inhibitors does not affect the thermal stability of gels, regardless of the additives' nature.

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S. Siriweera, T. H. Akila, Incheon Paik, Banage T. G. S. Kumara, and C. K. Koswatta. "Architecture for Intelligent Big Data Analysis based on Automatic Service Composition." Services Transactions on Big Data 2, no. 2 (October 2015): 1–14. http://dx.doi.org/10.29268/stbd.2015.2.2.1.

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Piironen, V., and T. Koivu. "Quality of vitamin K analysis and food composition data in Finland." Food Chemistry 68, no. 2 (February 2000): 223–26. http://dx.doi.org/10.1016/s0308-8146(99)00159-4.

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Abdel-Halim, R. E., and R. E. Abdel-Aal. "Classification of urinary stones by cluster analysis of ionic composition data." Computer Methods and Programs in Biomedicine 58, no. 1 (January 1998): 69–81. http://dx.doi.org/10.1016/s0169-2607(98)00075-3.

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Kadiatmaja, Agung Pranoto. "Error Analysis in Students’ Writing Composition." Jurnal Al-Fawa'id : Jurnal Agama dan Bahasa 11, no. 1 (October 4, 2021): 59–71. http://dx.doi.org/10.54214/alfawaid.vol11.iss1.151.

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To recognize the students’ difficulties, this research analyzes errors in Students’ writing composition of Passive Voice. This study of error (error analysis) is part of an investigation of the process of English language learning. This research is to find the types of error analysis in students’ writing composition and to find the most dominant types of error analysis in students’ writing composition of passive voice. This research is qualitatively accomplished to look into errors in the second language acquisition. This research focused on the qualitative research by using document analysis to collect empirical data from the students’ writing. This method is applied to get the accurate data collection. The object of the research is students’ writing composition randomly taken from sixteen ESP students of Strata 1 – Arabic Department of STAI Ali bin Abi Thalib Surabaya. The researcher investigates more details by using surface strategy taxonomy to get the result of this error analysis. It is limited on the students’ passive sentences error in writing composition consisting of four error types: omission, addition, misformation, and misordering. Finally, after identifying the entire data and analyzing the research findings, it can be summarized that the types of passive voice errors produced by the students are (1) omission that occurred 24 times or 51,06% of all, (2) addition that occurred 15 times or 31,96% of all, (3) misordering that occurred 5 times or 10,64% of all, and (4) misformation that occurred 3 times or 6,38% of all. According to the research findings, the most dominant type of passive voice error is omission.

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Thorson, James T., and Melissa A. Haltuch. "Spatiotemporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment." Canadian Journal of Fisheries and Aquatic Sciences 76, no. 3 (March 2019): 401–14. http://dx.doi.org/10.1139/cjfas-2018-0015.

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Stock assessment models are fitted to abundance-index, fishery catch, and age–length–sex composition data that are estimated from survey and fishery records. Research has developed spatiotemporal methods to estimate abundance indices, but there is little research regarding model-based methods to generate age–length–sex composition data. We demonstrate a spatiotemporal approach to generate composition data and a multinomial sample size that approximates the estimated imprecision. A simulation experiment comparing spatiotemporal and design-based methods demonstrates a 32% increase in input sample size for the spatiotemporal estimator. A Stock Synthesis assessment used to manage lingcod (Ophiodon elongatus) in the California Current also shows a 17% increase in sample size and better model fit using the spatiotemporal estimator, resulting in smaller standard errors when estimating spawning biomass. We conclude that spatiotemporal approaches are feasible for estimating both abundance-index and compositional data, thereby providing a unified approach for generating inputs for stock assessments. We hypothesize that spatiotemporal methods will improve statistical efficiency for composition data in many stock assessments and recommend that future research explore the impact of including additional habitat or sampling covariates.

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Vives-Mestres, Marina, and Josep A. Martín-Fernández. "Some Comments on Compositional Analysis in Management and Production Engineering." Management and Production Engineering Review 6, no. 2 (June 1, 2015): 63–72. http://dx.doi.org/10.1515/mper-2015-0018.

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Abstract This paper introduces the most basic concepts of the compositional analysis of data with a simple but real example form the Management and Production Engineering (MPE) field. Compositional Data (CoDa) are vectors of positive elements that represent parts of a whole and are widely found in MPE, i.e. production times, resource composition, percentage utilization of work stands, waste components. . . The need for an analysis based on ratios of components (or better log-ratios of components) is illustrated step by step, and findings are compared to the corresponding standard methods applied to raw compositions. The paper also exposes the principles of CoDa analysis and presents two basic descriptive tools suitable for CoDa: the clr-biplot and the CoDa dendrogram. The example is a time series, from 1994 to 2013, of motor vehicle production in 8 countries and regions.

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Qin, C. G., Y. Li, W. N. Niu, Y. Ding, X. Y. Shang, and C. L. Xu. "Composition analysis and structural identification of anthocyanins in fruit of waxberry." Czech Journal of Food Sciences 29, No. 2 (March 25, 2011): 171–80. http://dx.doi.org/10.17221/177/2010-cjfs.

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Anthocyanin pigments in the fruit of waxberry (Myrica rubra Sieb. et Zucc.), were extracted with 0.1% HCl in ethanol, and the crude anthocyanin extract was purified by C18 Sep-Pak cartridge open-column chromatography. High-performance liquid chromatography (HPLC) with photodiode array detection (PAD) and matrix-assisted laser desorption/ionisation-time of flight-mass spectrometry (MALDI-TOF-MS) was applied for the separation and identification of anthocyanins in the fruit of waxberry and their aglycones resulting from acid hydrolysis. Three anthocyanins were found in the fruit of waxberry and identified as Cyanidin 3-O-β-galacopyranoside (14.8%), Cyanidin 3-O-β-gluco-<br />pyranoside (60.5%), and petunidin 3-O-β-glucopyranoside (24.7%), respectively, using spectroscopic methods (UV-Vis<br />and MS). The three anthocyanins were isolated and purified by preparative HPLC, and their chemical structures were further characterised by H<sup>1</sup> NMR. On the basis of chromatographic data, the total anthocyanin content was 286 mg/g in fresh fruit of waxberry.  

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Burns, Ryan Donald, Timothy A. Brusseau, Yang Bai, and Wonwoo Byun. "Segmented School Physical Activity and Weight Status in Children: Application of Compositional Data Analysis." International Journal of Environmental Research and Public Health 18, no. 6 (March 21, 2021): 3243. http://dx.doi.org/10.3390/ijerph18063243.

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The purpose of this study was to apply compositional data analysis (CoDA) for the analysis of segmented school step counts and associate the school step count composition to body mass index (BMI) z-scores in a sample of children. Participants were 855 (51.8% female) children recruited from the fourth and fifth grades from four schools following a 7-h school schedule. Using piezoelectric pedometers, step count data were collected during physical education, recess, lunch, and during academic class time. A multi-level mixed effects model associated the step count composition with BMI z-scores. Compositional isotemporal substitution determined changes in BMI z-scores per reallocation of steps between pairs of school segments. A higher percentage of steps accrued during physical education (b = −0.34, 95%CI: −0.65–−0.03, p = 0.036) and recess (b = −0.47, 95%CI: −0.83–−0.11, p = 0.012), relative to other segments, was associated with lower BMI z-scores. Specifically, a 5% to 15% reallocation of steps accrued during lunchtime to either physical education or recess was associated with lower BMI z-scores, ranging from −0.07 to −0.25 standard deviation units. Focusing school-based promotion of physical activity during physical education and recess may have greater relative importance if targeted outcomes are weight-related.

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Zhang, Liu, Hongjuan Li, Yimin Zhang, Zhenxing Kong, Ting Zhang, and Zhaohua Zhang. "Association of Body Compositions and Bone Mineral Density in Chinese Children and Adolescents: Compositional Data Analysis." BioMed Research International 2021 (November 1, 2021): 1–9. http://dx.doi.org/10.1155/2021/1904343.

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The purpose of this study was to investigate the relationship between body compositions and bone mineral density (BMD) and the effect of composition substitution among Chinese children and adolescents without the influence of multicollinearity. A dual-energy X-ray absorptiometry scan was used to determine the amount of truncal fat (TF), nontruncal fat (NTF), fat-free mass (FFM), and BMD. The compositional data analysis and the compositional proportional substitution analysis were conducted to determine the effect of each part of body compositions on BMD and its substitution effects. Four hundred sixty-six (466) (boys: 51.9%) participants completed this cross-sectional study. For girls, in the overweight group, the relationship between TF and the BMD was positive ( β = 2.943 e − 01 , p = 0.006 ) while the NTF showed the opposite trend ( β = − 2.358 e − 01 , p = 0.009 ). When 4% NTF or FFM was substituted by TF, the BMD increased by about 0.1 and 0.05 units ( p < 0.05 ), respectively. For boys, the association between FFM and BMD was statistically positive ( β = 4.091 e − 02 , p = 0.0001 ). There was a positive correlation between TF and BMD ( β = 7.963 e − 02 , p = 0.036 ). But with the increase of BMI, this correlation shifted in the opposite direction. In conclusion, compared to TF and NTF, FFM had a better protective effect on BMD, especially for boys. The risk of NTF accumulation on BMD was greater than that of TF accumulation. Compared with girls, boys were more sensitive to the amount of TF.

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Kusumawardhani, Paramita. "Error Analysis in Writing an English Narrative Composition." Lingua Cultura 9, no. 2 (November 30, 2015): 132. http://dx.doi.org/10.21512/lc.v9i2.824.

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The purpose of the research were to know the errors which have been made by the learners in their English narrative composition and it is also to know why they made the errors, to identify the errors made by the learners, to correct the errors, to improve the teaching quality which is appropriate with the curriculum and the syllabus and to get the data and the explanation about the difficulties in studying English especially in writing the English narrative composition. The method used in this research is descriptive method. Descriptive method means the method which tries to give thedata and the explanation about the recent symptoms. The data was taken from 20 students and it was taken by asking the learners to make the English narrative composition, classifying then analyzing the errors found in their composition.The errors that have been found in the compositions are 30 items or 15% for errors of selection, 25 items or 12, 5% for errors of ordering, 115 items or 57, 5% for errors of omission and 30 items or 15% for errors of addition. Some identifying, classifying, analyzing and correcting the errors have been done to correct the errors that have been found. It was done in order to minimize and avoid the same errors happen again in the learners’ English narrative composition.

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Toraya, Hideo. "Quantitative phase analysis using observed integrated intensities and chemical composition data of individual crystalline phases: quantification of materials with indefinite chemical compositions." Journal of Applied Crystallography 50, no. 3 (May 8, 2017): 820–29. http://dx.doi.org/10.1107/s1600576717005052.

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In a previous report, a new method for quantitative phase analysis (QPA) of multi-component mixtures using a conventional X-ray powder diffractometer was proposed. The formula for deriving weight fractions of individual crystalline phases presented therein includes sets of observed integrated intensities measured in a wide 2\theta range, chemical formula weights and sums of squared numbers of electrons belonging to atoms in respective chemical formula units [Toraya (2016).J. Appl. Cryst.49, 1508–1516]. The latter two parameters required to perform QPA could be calculated from only the information of chemical formulae of individual phases. In the present study, these two parameters are replaced with a single parameter in the form new parameter = (chemical formula weight)/(sum of squared numbers of electrons). As will be expected from this definition, the parameter has nearly equal values for groups of materials consisting of similar kinds of atoms, and its value becomes identical for polytypes or polymorphs having the same chemical composition. That characteristic of this parameter makes it possible to estimate the parameter value not only directly from the chemical composition of the target material itself but also from database-stored chemical analysis data sorted on the basis of mineral or chemical composition. The parameter value is also hardly changed as a result of small compositional variations of the target component material. Therefore, the present method can be applied to QPA of materials not only of definite chemical compositions but also of indefinite chemical compositions without degrading the accuracy of the analysis. This is expected to widen the application to QPA of, for example, natural products containing many kinds of trace elements, industrial materials with complex substitutional replacement of atoms, nonstoichiometric compoundsetc. The theory and some examples of applications are presented. A procedure for quantifying unknown material is also proposed.

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Dumuid, Dorothea, Tyman E. Stanford, Josep-Antoni Martin-Fernández, Željko Pedišić, Carol A. Maher, Lucy K. Lewis, Karel Hron, et al. "Compositional data analysis for physical activity, sedentary time and sleep research." Statistical Methods in Medical Research 27, no. 12 (May 30, 2017): 3726–38. http://dx.doi.org/10.1177/0962280217710835.

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The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children’s daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.

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Nur Fitria, Tira. "SPELLING ERROR ANALYSIS IN STUDENTS’ WRITING ENGLISH COMPOSITION." Getsempena English Education Journal 7, no. 2 (November 19, 2020): 240–54. http://dx.doi.org/10.46244/geej.v7i2.988.

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This research has objective to find out the kind of spelling errors written by the students and to determine the most dominant kind of spelling error written by the students of STIE AAS Surakarta in the academic year 2017/2018. This study used a descriptive qualitative approach. In this study, researchers revealed conclusions by collecting data. The object of the research were taken from 24 students of STIE AAS Surakarta, especially in the 2017/2018 academic year. In collecting data, the researcher used the documentation method. In analyzing errors, the steps were the identification of errors, classification of errors, description of errors, explaining errors, and tabulation of data. The data were tabulated to determine the frequency of errors found in the students’ writing composition. From the results of the study, it was found that there are some errors found in the results of student essays in the aspect of spelling as 50 data which consist of some errors such as in omission as 20 data or 40 %, in substitution as 14 data or 28 %, in insertion/addition as 10 data or 20 % and transposition as 6 data or 12 %. While the most dominant spelling error occurs in the aspect of omission as 20 data or 40 %.

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47

Hanlon, William. "Telescope Array Hybrid Composition and Auger-TA Composition Comparison." EPJ Web of Conferences 208 (2019): 02001. http://dx.doi.org/10.1051/epjconf/201920802001.

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Telescope Array (TA) has completed analysis of nearly nine years of data measuring the atmospheric depth of air shower maximum (Xmax) utilizing the TA surface detector array and the Black Rock Mesa and Long Ridge fluorescence detector stations. By using both the surface array and the fluorescence detector, the geometry and arrival time of air showers can be measured very precisely providing good resolution in determining Xmax. Xmax is directly related to the air shower primary particle mass and is therefore important for understanding the composition of ultra high energy cosmic rays (UHECRs). UHECR composition will help answer questions such as the distance and location of their sources. We discuss the experimental apparatus, analysis method, and Xmax data collected. We compare the energy dependent distributions of the observed data to detailed Monte Carlo simulations of four chemical species, then test which individual species are not compatible with the data through an analysis of the shapes of the distributions. We also discuss the present state of composition analysis and interpretation between the Auger and TA experiments. These are the two largest UHECR observatories in the world with large exposures and should shed light on UHECR composition.

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Legendre, Pierre, and Olivier Gauthier. "Statistical methods for temporal and space–time analysis of community composition data." Proceedings of the Royal Society B: Biological Sciences 281, no. 1778 (March 7, 2014): 20132728. http://dx.doi.org/10.1098/rspb.2013.2728.

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This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran's eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.

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Karagiannis, Tanya, Todd Dowrey, Carlos Villacorta-Martin, George Murphy, Stefano Monti, and Paola Sebastiani. "Analysis of single cell data as it relates to aging and longevity." Innovation in Aging 5, Supplement_1 (December 1, 2021): 674. http://dx.doi.org/10.1093/geroni/igab046.2539.

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Abstract Age-related disability and diseases are known to be delayed in people living to 100 years or more. Changes in the immune system with age are known, including in cell type composition and gene expression differences. To further explore changes in extreme longevity subjects, we investigated peripheral blood immune system cell subpopulations across age and extreme longevity at a single cell resolution. We performed an integrative analysis of public scRNA-seq datasets to define consensus cell types of longevity and age, and classified cell types in our novel New England Centenarian Study dataset. We integrated these datasets together to investigate cell type specific differences at a composition and gene expression level. Our findings identified higher cell type diversity in extreme longevity subjects compared to younger age groups, but no significant difference among younger age groups demonstrating that overall composition differences are unique to longevity. We identified novel differences in myeloid and lymphocyte populations; Extreme longevity subjects have higher composition of CD14+ Monocytes, Natural Killer cells, and T gamma delta populations and lower composition of CD16+ Monocytes and dendritic populations. We characterized gene expression differences between extreme longevity and younger age groups and differences in aging across younger age groups. We found that extreme longevity cell type specific signatures overlapped with the aging signatures by at least 50%. We identified unique genes to extreme longevity that are enriched for pathways specific to immune activation and inflammation, suggesting a protective mechanism for centenarians through efficient activation and regulation of immune subpopulations in peripheral blood.

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Sant'Anna, Annibal P. "Data envelopment analysis of randomized ranks." Pesquisa Operacional 22, no. 2 (December 2002): 203–15. http://dx.doi.org/10.1590/s0101-74382002000200007.

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Probabilities and odds, derived from vectors of ranks, are here compared as measures of efficiency of decision-making units (DMUs). These measures are computed with the goal of providing preliminary information before starting a Data Envelopment Analysis (DEA) or the application of any other evaluation or composition of preferences methodology. Preferences, quality and productivity evaluations are usually measured with errors or subject to influence of other random disturbances. Reducing evaluations to ranks and treating the ranks as estimates of location parameters of random variables, we are able to compute the probability of each DMU being classified as the best according to the consumption of each input and the production of each output. Employing the probabilities of being the best as efficiency measures, we stretch distances between the most efficient units. We combine these partial probabilities in a global efficiency score determined in terms of proximity to the efficiency frontier.

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