•Three correlation coefficients of hydration heat and strength are compared.•Linear and nonparametric correlation analysis is used quantitatively.•More variables are considered, the greater of the ...multiple correlation coefficient.
Analysis of linear and nonlinear dependencies of hydration characteristics and strength development is of interest for reliable and cost-effective designs. In this paper, the statistical assessment of linear and nonparametric correlation analysis model is used to investigate the association of these properties quantitatively. The bivariate correlation of early hydration characteristics within 72 h and compressive strength at different curing ages is evaluated by Pearson’s, Spearman’s rho and Kendall’s tau correlation coefficient, respectively. Assessment results of various methods show that early hydration characteristics and compressive strength have a strong correlation coefficient. Furthermore, the coefficient fitted by Spearman’s rho correlation analysis is higher than those by Pearson’s and Kendall’s tau analyses. The calculated correlation coefficients of middle and long curing ages (e.g. 28 d and 56 d) are higher than those in the early curing ages (e.g. 3 d and 7 d). For multiple correlation analysis, the correlation coefficients between heat release characteristics and mechanical properties undergo a fundamental change. The more variables of hydration characteristic are considered, the greater the multiple correlation coefficient of compressive strength, and the coefficients of middle and long curing age strength have a narrower range than early age compressive strength. Hence, the linear and nonparametric correlation model is a useful quantitative evaluation method for assessing the relationship between the early hydration characteristics and compressive strength for multi-composite blends.
Correlation implies association, but not causation. Conversely, causation implies association, but not correlation. Most studies include multiple response variables, and the dependencies among them ...are often of great interest. For example, we may wish to know whether the levels of mRNA and the matching protein vary together in a tissue, or whether increasing levels of one metabolite are associated with changed levels of another. This month we begin a series of columns about relationships between variables (or features of a system), beginning with how pairwise dependencies can be characterized using correlation.
Multicollinearity is a potential problem in all regression analyses. However, the examination of multicollinearity is rarely reported in primary studies. In this article we discuss and show several ...post hoc methods for assessing multicollinearity. One such multicollinearity diagnostic is the variance inflation factor. We outline the post hoc variance inflation factor method, which computes the variance inflation factor from the standardized regression coefficient and semi-partial correlation, both of which can be calculated from commonly reported regression results. Three examples of computing multicollinearity diagnostics using data from published studies are shown. We conclude with a discussion and practical implications.
In this article, a novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are common in ...complex industrial processes, the classic canonical correlation analysis (CCA) always perform poorly. Many scholars have noticed the nonlinear problem of the process and have also proposed some improved schemes, such as the kernel method. However, the selection of suitable parameters in the kernel method is extremely difficult, so most of the kernel learning methods are slightly unsatisfactory. Considering that the artificial neural network (ANN) can well extract the required feature components from the nonlinear data, we combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network is presented. In addition, we have designed two indices to monitor the changes of process variables and performance indicators. Finally, a numerical example, the Tennessee Eastman benchmark, and the Zhoushan thermal power plant process illustrate the superiority of the proposed method.
Abstract
Introduction
To evaluate resting state functional connectivity and topological properties of brain network in narcolepsy compared with healthy controls.
Methods
Resting state fMRI was ...performed in 26 adult narcolepsy patients and 30 matched healthy controls. MRI data was first analyzed by group independent component analysis, then a graph theoretical method was applied to evaluate topological properties within whole brain. Small-world network parameters and nodal topological properties were measured. Altered topological properties in brain areas between groups were selected as ROI-seeds, then functional connectivity among these ROI-seeds were compared between groups. Partial correlation analysis was performed to evaluate the relationship between sleepiness severity and functional connectivity or topological properties in the narcolepsy.
Results
21 independent components out of 48 components were obtained. Compared with healthy controls, narcolepsy exhibited a significant decreased functional connectivity within the executive and salience network, while increased functional connectivity in bilateral frontal lobe within executive network can be detected in narcolepsy. There were no differences in small-world network properties between narcolepsy and healthy controls. The altered brain areas in nodal topological properties were mainly located in inferior frontal cortex, basal ganglia, anterior cingulate, sensory cortex, supplementary motor cortex and visual cortex between groups. In the partial correlation analysis, nodal topological properties in putamen, anterior cingulate and sensory cortex as well as functional connectivity between these brain regions were correlated with the severity of sleepiness (sleep latency, REM sleep latency and ESS) among narcolepsy.
Conclusion
Altered connectivity within executive network and salience network were found in narcolepsy. Functional connection changes between left frontal cortex and left caudate nucleus may be one of the parameters describing the severity of narcolepsy. Nodal topological properties alterations in left putamen and left posterior cingulate, changes in functional connectivity between left supplementary motor area and right occipital as well as changes in functional connectivity between left anterior cingulate gyrus and bilateral postcentral gyrus can be considered to be a specific indicator for evaluating the severity of narcolepsy.
Support
National Natural Science Foundation of China (81700088)National Program on Key Basic Research Project of China (973 Program, 2015CB856405)
PDF
