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Journal articles on the topic 'Multivariate analysis technique'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Imai, Kosuke. "Multivariate Regression Analysis for the Item Count Technique." Journal of the American Statistical Association 106, no. 494 (2011): 407–16. http://dx.doi.org/10.1198/jasa.2011.ap10415.

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Silén, J., H. Cottin, M. Hilchenbach, et al. "COSIMA data analysis using multivariate techniques." Geoscientific Instrumentation, Methods and Data Systems Discussions 4, no. 2 (2014): 455–89. http://dx.doi.org/10.5194/gid-4-455-2014.

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Abstract. We describe how to use multivariate analysis of complex TOF-SIMS spectra introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a crossvalidation Cohen κ for classification of typically about 80%. We intend to use the method as a fast tool to deduce a qualitative simil

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Silén, J., H. Cottin, M. Hilchenbach, et al. "COSIMA data analysis using multivariate techniques." Geoscientific Instrumentation, Methods and Data Systems 4, no. 1 (2015): 45–56. http://dx.doi.org/10.5194/gi-4-45-2015.

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Abstract. We describe how to use multivariate analysis of complex TOF-SIMS (time-of-flight secondary ion mass spectrometry) spectra by introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a cross-validation Cohen κ for classification of typically about 80%. We intend to use th

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Aaid, Djamel, and Özen Özer. "New technique for solving multivariate global optimization." Journal of Numerical Analysis and Approximation Theory 52, no. 1 (2023): 3–16. http://dx.doi.org/10.33993/jnaat521-1287.

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In this paper, we propose an algorithm based on branch and bound method to underestimate the objective function and reductive transformation which is transformed the all multivariable functions on univariable functions. We also demonstrate several quadratic lower bound functions are proposed which they are better/preferable than the others well-known in literature. We obtain that our experimental results are more effective when we face different nonconvex functions.

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Librando, V., G. Magazzù, and A. Puglisi. "Multivariate micropollutants analysis in marine waters." Water Science and Technology 32, no. 9-10 (1995): 341–48. http://dx.doi.org/10.2166/wst.1995.0701.

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The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Mul

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Artyushkova, K., S. Pylypenko, J. Fenton, K. Archuleta, L. Williams, and J. Fulghum. "Multi-technique, Multivariate Analysis Methods for Enhanced Sample Characterization." Microscopy and Microanalysis 12, S02 (2006): 1402–3. http://dx.doi.org/10.1017/s1431927606069492.

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Maeder, Marcel, and Arne Zilian. "Evolving factor analysis, a new multivariate technique in chromatography." Chemometrics and Intelligent Laboratory Systems 3, no. 3 (1988): 205–13. http://dx.doi.org/10.1016/0169-7439(88)80051-0.

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LIPOVETSKY, STAN. "DUAL PLS ANALYSIS." International Journal of Information Technology & Decision Making 11, no. 05 (2012): 879–91. http://dx.doi.org/10.1142/s0219622012500241.

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A new multivariate statistical technique is obtained for comparing and combining two or more data sets each of which has a different number of respondents but the same variables. This approach can be considered as dual to such techniques as partial least squares, also known as inter-battery factor analysis and robust canonical correlation analysis for two data sets. It is shown that the problem can be reduced to the eigenproblem of the product of correlation matrices of each data set. The technique is generalized to three or more data sets in an eigenproblem of block-matrices of the correlatio

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Barbosa, Josino José, Anderson Ribeiro Duarte, and Helgem Souza Ribeiro Martins. "A performance evaluation in multivariate outliers identification methods." Ciência e Natura 42 (May 15, 2020): e16. http://dx.doi.org/10.5902/2179460x41662.

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Methodologies for identifying multivariate outliers are extremely important in statistical analysis. Outliers may reveal relevant information to variables under investigation. Statistical applications without prior identification of possible extreme values may yield controversial results and induce mistaken decision making. In many contexts, outliers are points of great practical interest. Given this, this paper seeks to discuss methodologies for the detection of multivariate outliers through a fair and adequate comparative technique in their simulation procedure. The comparison considers dete

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ter Braak, Cajo J. F. "Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis." Ecology 67, no. 5 (1986): 1167–79. http://dx.doi.org/10.2307/1938672.

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SHIMIZU, Toshiyuki, Yusuke HIGASHI, Kumiko TANIGUCHI, and Kiyoshi YAMADA. "A CONSIDERATION OF DOMESTIC WATER FORECASTING WITH MULTIVARIATE ANALYSIS TECHNIQUE." Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 68, no. 1 (2012): 72–83. http://dx.doi.org/10.2208/jscejer.68.72.

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Astephen, J. L., and K. J. Deluzio. "A multivariate gait data analysis technique: Application to knee osteoarthritis." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 218, no. 4 (2004): 271–79. http://dx.doi.org/10.1243/0954411041560983.

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Kweon, Soon Wan, Yong Ju Lee, Na Young Kang, A. Yeon Lee, and Hyoung Jin Kim. "Identification of Mulberry Bast Fiber Using a Multivariate Analysis Technique." Journal of Korea Technical Association of The Pulp and Paper Industry 55, no. 6 (2023): 59–69. http://dx.doi.org/10.7584/jktappi.2023.12.55.6.59.

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S., Yuvasri* B. Dhatchinamoorthy G. Dhivagar N. Dravidvishal A. Ganesh N. Gokula Krishnan. "Validated Uv Spectrophotometric Quantification of Dutasteride In Pharmaceutical Formulations by Using Multivariate Technique." International Journal of Scientific Research and Technology 2, no. 3 (2025): 167–74. https://doi.org/10.5281/zenodo.14983796.

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The Present work focuses an accurate, simple and precise UV method with multivariate calibration technique for the estimation of Dutasteride in pharmaceutical dosage form. Multivariate calibration method uses the linear regression equations by correlating the relation between concentration and absorbance at 5 different wavelengths. The λmaxof Dutasteride shows at 269 nm and obeyed Beers law in the range of 1-5μg/ml. The percentage recovery of tablet formulation was found to be in the range of 98.50 to 99.88%. The limit of quantification and limit of detection were found to be 1.272 a

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Mohsen, Mohamed Y. M., Tarek F. Nagla, Mohamed Elsamahy, and Mohamed A. E. Abdel-Rahman. "Multivariate Image Analysis for Core Monitoring in PWRs." Journal of Physics: Conference Series 2616, no. 1 (2023): 012059. http://dx.doi.org/10.1088/1742-6596/2616/1/012059.

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Abstract In pressurized water reactor (PWR) it is crucial for the operator to monitor the reactor parameters at the same time, such as temperature, pressure, boron concentration, control rod position, coolant density, etc., in order to make proper decision. However, the huge size of data reading from the different instrumentations, in addition to the limited human ability to visually detect, interpret, assess add a lot of uncertainty to the operator qualitative and quantitative analysis of the reactor performance. Therefore, this paper proposes the utilization of radial thermal flux maps (Neut

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S, Luna. "Identification of the Remediation of Contaminated Soil with Oil through the Flotation with Surfactant by Molecular Fluorescence Related to the Analysis of Multivariate Data." Petroleum & Petrochemical Engineering Journal 4, no. 2 (2020): 1–7. http://dx.doi.org/10.23880/ppej-16000221.

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The countless cases of environmental contamination by oil activities have grown significantly over time. This implies the need to develop new techniques for recovery of the affected areas. The flotation technique has been highlighted for this purpose and associated with other techniques, either chemical, physical or biological, improves their efficiency, and consequently, the recovery of location will be effective. This work aims to evaluate the remediation of two horizons of Typic Quartzipisamment originating in Recôncavo, which were contaminated artificially with oil and recovered using flot

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Shultz, E. K. "Multivariate receiver-operating characteristic curve analysis: prostate cancer screening as an example." Clinical Chemistry 41, no. 8 (1995): 1248–55. http://dx.doi.org/10.1093/clinchem/41.8.1248.

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Abstract The evolution of test performance analysis should include the long-term costs and benefits associated with testing. Evolutionary laboratory techniques to achieve this include introduction of a new methodological technique, a multivariate extension to a current analytical technique, receiver-operating characteristic (ROC) curve analysis (MultiROC analysis). This extension to ROC methodology allows the comparison of composite test rules in a format similar to that of ROC curves. Statistical properties, guidelines for use, and a detailed example are described. MultiROC is used in the out

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Serra, Kassio Felipe, Alamzeb Khan, Raquel Maria Fernandes, Pedro Antonio Vazquez, and Alamgir Khan. "Multivariate statistical analysis approach to investigate the thermodynamic quantities of the benign alternative fuel." Journal of the Serbian Chemical Society, no. 00 (2023): 90. http://dx.doi.org/10.2298/jsc230530090s.

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In order to extract meaningful interpretation from the large data and provide their value to the application areas, chemical data analysis has become a serious challenge in the development and applications of new protocols, technique and methodologies for the mathematical modelling communities and other data science societies. Therefore, in the present work a rapid and robust box-and-whisker plot and multivariate principal component statistical techniques (PCA) are being proposed for the evaluations of the thermodynamic molecular properties data of the benign fuel structures. We observed that,

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19

B. Hemel, Jan, Hilko van der Voet, Frans R. Hindriks, and Willem van der Slik. "Stepwise deletion: a technique for missing-data handling in multivariate analysis." Analytica Chimica Acta 193 (1987): 255–68. http://dx.doi.org/10.1016/s0003-2670(00)86157-7.

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Rahman, S., MAK Miah, and H. Rahman. "Genetic diversity of muskmelon using multivariate technique." Bangladesh Journal of Agricultural Research 41, no. 2 (2016): 273–86. http://dx.doi.org/10.3329/bjar.v41i2.28230.

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An experiment was conducted at the experimental farm of Plant Genetic Resources Centre (PGRC), Bangladesh Agricultural Research Institute (BARI), Gazipur in 2011 to estimate genetic diversity through multivariate technique. Based on multivariate analysis and application of covariance matrix for nonhierarchical clustering, 64 genotypes of muskmelon were grouped into six clusters to indicate the existence of considerable diversity among the genotypes. The cluster IV was consisted of single genotypes (BD2303). The highest number of genotypes possessed in Cluster I. The first principal axis largel

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Haddi, Zouhair, Bouchra Ananou, Miquel Alfaras, et al. "Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data." Algorithms 2022, no. 15 (2022): 231. https://doi.org/10.3390/a15070231.

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Atrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate d

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22

He, Shu-Guang, G. Alan Wang, and Deborah F. Cook. "Multivariate measurement system analysis in multisite testing: An online technique using principal component analysis." Expert Systems with Applications 38, no. 12 (2011): 14602–8. http://dx.doi.org/10.1016/j.eswa.2011.05.022.

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23

Ebdali, Mohammad, Ardeshir Hezarkhani, Adel Shirazy, and Amin Beiranvnd Pour. "Machine learning-driven mineral prospectivity mapping: A predictive performance analysis in Janja, Iran." Nafta-Gaz 81, no. 5 (2025): 295–317. https://doi.org/10.18668/ng.2025.05.01.

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Geochemical analysis is an effective technique for detecting mineral deposits by examining element concentrations. Various statistical techniques have been developed to differentiate abnormal values from background values. A more accurate analysis can be obtained by employing multivariate statistical methods. The use of these methods enables the simultaneous analysis of changes in multiple variables. This research utilized correlation coefficients, cluster analysis, and factor analysis to demonstrate the genetic connections among various elements. The factor analysis method was additionally ap

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De Ruiter, Naomi M. P., Steffie Van Der Steen, Ruud J. R. Den Hartigh, and Paul L. C. Van Geert. "Capturing moment-to-moment changes in multivariate human experience." International Journal of Behavioral Development 41, no. 5 (2016): 611–20. http://dx.doi.org/10.1177/0165025416651736.

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In this article, we aim to shed light on a technique to study intra-individual variability that spans the time frame of seconds and minutes, i.e., micro-level development. This form of variability is omnipresent in behavioural development and processes of human experience, yet is often ignored in empirical studies, given a lack of proper analysis tools. The current article illustrates that a clustering technique called Kohonen’s Self-Organizing Maps (SOM), which is widely used in fields outside of psychology, is an accessible technique that can be used to capture intra-individual variability o

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25

Erten, Oktay, Fábio P. L. Pereira, and Clayton V. Deutsch. "Projection Pursuit Multivariate Sampling of Parameter Uncertainty." Applied Sciences 12, no. 19 (2022): 9668. http://dx.doi.org/10.3390/app12199668.

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The efficiency of sampling is a critical concern in Monte Carlo analysis, which is frequently used to assess the effect of the uncertainty of the input variables on the uncertainty of the model outputs. The projection pursuit multivariate transform is proposed as an easily applicable tool for improving the efficiency and quality of a sampling design in Monte Carlo analysis. The superiority of the projection pursuit multivariate transform, as a sampling technique, is demonstrated in two synthetic case studies, where the random variables are considered to be uncorrelated and correlated in low (b

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Guccione, Pietro, Mattia Lopresti, Marco Milanesio, and Rocco Caliandro. "Multivariate Analysis Applications in X-ray Diffraction." Crystals 11, no. 1 (2020): 12. http://dx.doi.org/10.3390/cryst11010012.

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Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden i

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Hlavka, Christine A., David L. Peterson, Lee F. Johnson, and Barry Ganapol. "Analysis of Forest Foliage Spectra Using a Multivariate Mixture Model." Journal of Near Infrared Spectroscopy 5, no. 3 (1997): 167–73. http://dx.doi.org/10.1255/jnirs.110.

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Wet chemical measurements and near infrared spectra of dry ground leaf samples were analysed to test a multivariate regression technique for estimating component spectra. The technique is based on a linear mixture model for log(1/ R) pseudoabsorbance derived from diffuse reflectance measurements. The resulting unmixed spectra for carbohydrates, lignin and protein resemble the spectra of extracted plant carbohydrates, lignin and protein. The unmixed protein spectrum has prominent absorption peaks at wavelengths that have been associated with nitrogen bonds. It therefore appears feasible to inco

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Chernoded, Andrei, Lev Dudko, Georgi Vorotnikov, et al. "Optimization of the input space for deep learning data analysis in HEP." EPJ Web of Conferences 222 (2019): 02016. http://dx.doi.org/10.1051/epjconf/201922202016.

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Deep learning neural network technique is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of such analysis is the optimization of the input space for multivariate technique. In the article we propose the general recipe how to find the general set of low-level observables sensitive for the differences in the collider hard processes.

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Resnick, Sidney. "On the foundations of multivariate heavy-tail analysis." Journal of Applied Probability 41, A (2004): 191–212. http://dx.doi.org/10.1239/jap/1082552199.

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Univariate heavy-tailed analysis rests on the analytic notion of regularly varying functions. For multivariate heavy-tailed analysis, reliance on functions is awkward because multivariate distribution functions are not natural objects for many purposes and are difficult to manipulate. An approach based on vague convergence of measures makes the differences between univariate and multivariate analysis evaporate. We survey the foundations of the subject and discuss statistical attempts to assess dependence of large values. An exploratory technique is applied to exchange rate return data and show

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Resnick, Sidney. "On the foundations of multivariate heavy-tail analysis." Journal of Applied Probability 41, A (2004): 191–212. http://dx.doi.org/10.1017/s002190020011229x.

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Univariate heavy-tailed analysis rests on the analytic notion of regularly varying functions. For multivariate heavy-tailed analysis, reliance on functions is awkward because multivariate distribution functions are not natural objects for many purposes and are difficult to manipulate. An approach based on vague convergence of measures makes the differences between univariate and multivariate analysis evaporate. We survey the foundations of the subject and discuss statistical attempts to assess dependence of large values. An exploratory technique is applied to exchange rate return data and show

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Selvi, Huseyin Zahit, and Burak Caglar. "Using cluster analysis methods for multivariate mapping of traffic accidents." Open Geosciences 10, no. 1 (2018): 772–81. http://dx.doi.org/10.1515/geo-2018-0060.

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Abstract Many factors affect the occurrence of traffic accidents. The classification and mapping of the different attributes of the resulting accident are important for the prevention of accidents. Multivariate mapping is the visual exploration of multiple attributes using a map or data reduction technique. More than one attribute can be visually explored and symbolized using numerous statistical classification systems or data reduction techniques. In this sense, clustering analysis methods can be used for multivariate mapping. This study aims to compare the multivariate maps produced by the K

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Andrew, Jeremy J., and Thomas M. Hancewicz. "Rapid Analysis of Raman Image Data Using Two-Way Multivariate Curve Resolution." Applied Spectroscopy 52, no. 6 (1998): 797–807. http://dx.doi.org/10.1366/0003702981944526.

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The application of standard two-way curve resolution methods is reported for analysis of three-way Raman image data. Two current curve resolution methods are described: principal factor multivariate curve resolution (PF-MCR), which uses principal factor analysis (PFA) combined with varimax rotation and alternating least-squares optimization (ALS), and orthogonal projection multivariate curve resolution (OP-MCR), which uses a Gram–Schmidt modified orthogonal projection approach (OPA) followed by ALS. The OP-MCR technique is shown to be an extremely rapid method of analysis producing results equ

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TAKINO, Masahiko. "Multivariate Analysis and Other Data Minding Technique to Visualize LC/MS Data." BUNSEKI KAGAKU 63, no. 6 (2014): 497–513. http://dx.doi.org/10.2116/bunsekikagaku.63.497.

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Park, Jang, Byong-Ho Jun, and Suk-Hwan Jang. "A Study on the Regionalization of Point Rainfall by Multivariate Analysis Technique." Journal of Korea Water Resources Association 36, no. 5 (2003): 879–92. http://dx.doi.org/10.3741/jkwra.2003.36.5.879.

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Bergholt, Mads Sylvest, Shiyamala Duraipandian, Wei Zheng, and Zhiwei Huang. "Multivariate Reference Technique for Quantitative Analysis of Fiber-Optic Tissue Raman Spectroscopy." Analytical Chemistry 85, no. 23 (2013): 11297–303. http://dx.doi.org/10.1021/ac402059v.

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van den Brink, Paul J., Piet J. den Besten, Abraham bij de Vaate, and Cajo J. F. ter Braak. "Principal response curves technique for the analysis of multivariate biomonitoring time series." Environmental Monitoring and Assessment 152, no. 1-4 (2008): 271–81. http://dx.doi.org/10.1007/s10661-008-0314-6.

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Wang, Pan, Haihe Li, Shiwang Tan, and Xiaoyu Huang. "Multivariate Global Sensitivity Analysis for Casing String Using Neural Network." International Journal of Computational Methods 17, no. 05 (2019): 1940015. http://dx.doi.org/10.1142/s0219876219400152.

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To evaluate the safety of casing string is an important task in the oil exploitation. In this paper, the casing string with complex environment is investigated and the global sensitivity analysis (SA) technique is employed to identify the influential factors on the safety. Since the damage of casing string is of different kinds, three failure modes are mainly considered in the analysis. Then, the multivariate global SA technique is employed to identify the influential factors for the three failure modes simultaneously. Due to the full-size FE analysis of casing string which involves contact an

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Clarke, Stella M., Jan H. Griebsch, and Timothy W. Simpson. "Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses." Journal of Mechanical Design 127, no. 6 (2004): 1077–87. http://dx.doi.org/10.1115/1.1897403.

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A variety of metamodeling techniques have been developed in the past decade to reduce the computational expense of computer-based analysis and simulation codes. Metamodeling is the process of building a “model of a model” to provide a fast surrogate for a computationally expensive computer code. Common metamodeling techniques include response surface methodology, kriging, radial basis functions, and multivariate adaptive regression splines. In this paper, we investigate support vector regression (SVR) as an alternative technique for approximating complex engineering analyses. The computational

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Coletti, Christiane, Roberto Testezlaf, Túlio A. P. Ribeiro, Renata T. G. de Souza, and Daniela de A. Pereira. "Water quality index using multivariate factorial analysis." Revista Brasileira de Engenharia Agrícola e Ambiental 14, no. 5 (2010): 517–22. http://dx.doi.org/10.1590/s1415-43662010000500009.

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The evaluation of environmental effects generated by agricultural production on water quality became essential in Brazil after the creation of policies for the use and conservation of water resources. For such, water quality indices have been considered with the purpose of showing the spatial and temporal variation of water quality in a watershed. The objective of this study was to develop a water quality index (WQI) applying the Multivariate Factorial Analysis (MFA) statistical technique, which could indicate the influence of agricultural activities in the quality of water resources. Water in

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Wustqa, Dhoriva Urwatul, Endang Listyani, Retno Subekti, Rosita Kusumawati, Mathilda Susanti, and Kismiantini Kismiantini. "Analisis Data Multivariat Dengan Program R." Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA 2, no. 2 (2018): 83–86. http://dx.doi.org/10.21831/jpmmp.v2i2.21913.

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Analisis multivariat adalah salah satu teknik dalam statistika yang digunakan untuk menganalisis secara simultan variabel lebih dari satu. Perhitungan dalam analisis data multivariat lebih kompleks dibandingkan dengan analisis univariat, sehingga penggunaan program statistika akan mempermudah dalam analisis. Salah satu program statistika yang dapat diperoleh secara gratis (tanpa lisensi) adalah program R. Workshop program R untuk analisis data multivariat bagi para lulusan S1 Pendidikan Matematika/Matematika dan mahasiswa program pasca sarjana Pendidikan Matematika secara umum bertujuan untuk

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Bounoua, Wahiba, Amina B. Benkara, Abdelmalek Kouadri, and Azzeddine Bakdi. "Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection." Transactions of the Institute of Measurement and Control 42, no. 6 (2019): 1225–38. http://dx.doi.org/10.1177/0142331219888370.

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Principal component analysis (PCA) is a common tool in the literature and widely used for process monitoring and fault detection. Traditional PCA is associated with the two well-known control charts, the Hotelling’s T2 and the squared prediction error (SPE), as monitoring statistics. This paper develops the use of new measures based on a distribution dissimilarity technique named Kullback-Leibler divergence (KLD) through PCA by measuring the difference between online estimated and offline reference density functions. For processes with PCA scores following a multivariate Gaussian distribution,

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Pham, Luan, Huong Ha, and Hongyu Zhang. "BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection." Proceedings of the ACM on Software Engineering 1, FSE (2024): 2214–37. http://dx.doi.org/10.1145/3660805.

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Detecting failures and identifying their root causes promptly and accurately is crucial for ensuring the availability of microservice systems. A typical failure troubleshooting pipeline for microservices consists of two phases: anomaly detection and root cause analysis. While various existing works on root cause analysis require accurate anomaly detection, there is no guarantee of accurate estimation with anomaly detection techniques. Inaccurate anomaly detection results can significantly affect the root cause localization results. To address this challenge, we propose BARO, an end-to-end appr

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Mittelstädt, Anke, Tobias von Loeffelholz, Klaus Weber, et al. "Influence of interrupted versus continuous suture technique on intestinal anastomotic leakage rate in patients with Crohn’s disease — a propensity score matched analysis." International Journal of Colorectal Disease 37, no. 10 (2022): 2245–53. http://dx.doi.org/10.1007/s00384-022-04252-1.

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Abstract Purpose Intestinal anastomosis is a crucial step in most intestinal resections, as anastomotic leakage is often associated with severe consequences for affected patients. There are especially two different techniques for hand-sewn intestinal anastomosis: the interrupted suture technique (IST) and the continuous suture technique (CST). This study investigated whether one of these two suture techniques is associated with a lower rate of anastomotic leakage. Methods A retrospective review of 332 patients with Crohn’s disease who received at least one hand-sewn colonic anastomosis at our

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Haddi, Zouhair, Bouchra Ananou, Miquel Alfaras, et al. "Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data." Algorithms 15, no. 7 (2022): 231. http://dx.doi.org/10.3390/a15070231.

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Atrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate d

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K. Bagui, Olivier, Kenneth A. Kaduki, Edouard Berrocal, and Jeremie T. Zoueu. "Structured Laser Illumination Planar Imaging Based Classification of Ground Coffee Using Multivariate Chemometric Analysis." Applied Physics Research 8, no. 3 (2016): 32. http://dx.doi.org/10.5539/apr.v8n3p32.

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<p class="1Body">Most commercially available ground coffees are processed from Robusta or Arabica coffee beans. In this work, we report on the potential of Structured Laser Illumination Planar Imaging (SLIPI) technique for the classification of five types of Robusta and Arabica commercial ground coffee samples (Familial, Belier, Brazil, Colombia and Malaga). This classification is made, here, from the measurement of the extinction coefficient µ<sub>e</sub> and of the optical depth OD by means of SLIPI. The proposed technique offers the advantage of eliminating the light inten

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Hartanto, Priyo, Rachmat Fajar Lubis, Boy Yoseph C. S. S. Syah Alam, et al. "MULTIVARIATE DATA ANALYSIS TO ASSESS GROUNDWATER HYDROCHEMICAL CHARACTERIZATION IN RAWADANAU BASIN, BANTEN INDONESIA." Rudarsko-geološko-naftni zbornik 39, no. 1 (2024): 141–54. http://dx.doi.org/10.17794/rgn.2024.1.12.

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A multivariate statistical technique of principal component analysis (PCA) and hierarchical cluster analysis (HCA) has been applied to identify and classify the various water sources that comprise the Rawadanau Basin. The data collection includes 60 samples taken during the dry (29 samples) and the rainy season (31 samples) in tropical regions. Sources of sampled water include dug wells, rivers, cold springs, and hot springs. Water chemistry measurable variables include field data (T, pH, EC), major ions (Na+, K+, Ca2+, Mg2+, Cl-, HCO3 -, SO4 2-), SiO2 , Fetotal, Mn, and stable isotopes of wat

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Rodriguez, Mark A., Michael R. Keenan, and Ganesan Nagasubramanian. "In situX-ray diffraction analysis of (CFx)nbatteries: signal extraction by multivariate analysis." Journal of Applied Crystallography 40, no. 6 (2007): 1097–104. http://dx.doi.org/10.1107/s0021889807042045.

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(CFx)ncathode reaction during discharge has been investigated usingin situX-ray diffraction (XRD). Mathematical treatment of thein situXRD data set was performed using multivariate curve resolution with alternating least squares (MCR–ALS), a technique of multivariate analysis. MCR–ALS analysis successfully separated the relatively weak XRD signal intensity due to the chemical reaction from the other inert cell component signals. The resulting dynamic reaction component revealed the loss of (CFx)ncathode signal together with the simultaneous appearance of LiF by-product intensity. Careful exami

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Souza, Isis Gomes de Brito, Valdomiro Aurélio Barbosa de Souza, Kaesel Jackson Damasceno e. Silva, and Paulo Sarmanho da Costa Lima. "Multivariate analysis of ‘bacuri’ reproductive and vegetative morphology." Comunicata Scientiae 7, no. 2 (2016): 232. http://dx.doi.org/10.14295/cs.v7i2.779.

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The objective of this study was to characterize sixteen genotypes of P. insignis available in the Embrapa Meio-Norte germplasm collection (Teresina, Piauí, Brazil) with respect to 33 morphological traits relating to leaves, flowers, branches, fruits and seeds. Phenotypic variance among genotypes was estimated using the Mahalanobis distance technique and the unweighted pair group method with arithmetic mean analysis (UPGMA). The method of Singh (1981) was used to determine which of the traits contributed most to diversity within genotypes. The occurrence of phenotypic variability among P. insig

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Suh, Changwon, Slobodan Gadzuric, Marcelle Gaune-Escard, and Krishna Rajan. "Multivariate Analysis for Chemistry-Property Relationships in Molten Salts." Zeitschrift für Naturforschung A 64, no. 7-8 (2009): 467–76. http://dx.doi.org/10.1515/zna-2009-7-809.

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AbstractWe systematically analyze the molten salt database of Janz to gain a better understanding of the relationship between molten salts and their properties. Due to the multivariate nature of the database, the intercorrelations amongst the molten salts and their properties are often hidden and defining them is challenging. Using principal component analysis (PCA), a data dimensionality reduction technique, we have effectively identified chemistry-property relationships. From the various patterns in the PCA maps, it has been demonstrated that information extracted with PCA not only contains

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Fayydh, Adnan. "EVALUATION OF GROUNDWATER QUALITY IN AL-WAFFA AND KUBAYSA AREAS USING MULTIVARIATE STATISTICAL ANALYSIS, AL-ANBAR, WESTERN IRAQ." Iraqi Geological Journal 53, no. 2D (2020): 107–27. http://dx.doi.org/10.46717/igj.53.2d.8ms-2020.10-30.

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The groundwater is a substantial source of fresh water and has been used for various anthropogenic uses. The aim of this work is to investigate groundwater quality and type in kubaysa and AL-Waffa areas, Anbar, Iraq using multivariate statistics approach. The groundwater was sampled from ten wells for each region during the period from October 2018 to March 2019. The levels of T, TUR, pH, EC, TDS, DO, COD, TH, Na+, K+, Ca2+, Mg2+, NH4+, Cl-, F-, SO42-,, NO3-, HCO3-, S-2 and SiO2, were measured. The majority of the physicochemical parameters exceed the permissible guidelines. Pearson’s Correlat

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