Maximization Of Correlation Coefficient Research Articles

Handheld versus mounted laser speckle contrast perfusion imaging demonstrated in psoriasis lesions

Enabling handheld perfusion imaging would drastically improve the feasibility of perfusion imaging in clinical practice. Therefore, we examine the performance of handheld laser speckle contrast imaging (LSCI) measurements compared to mounted measurements, demonstrated in psoriatic skin. A pipeline is introduced to process, analyze and compare data of 11 measurement pairs (mounted-handheld LSCI modes) operated on 5 patients and various skin locations. The on-surface speeds (i.e. speed of light beam movements on the surface) are quantified employing mean separation (MS) segmentation and enhanced correlation coefficient maximization (ECC). The average on-surface speeds are found to be 8.5 times greater in handheld mode compared to mounted mode. Frame alignment sharpens temporally averaged perfusion maps, especially in the handheld case. The results show that after proper post-processing, the handheld measurements are in agreement with the corresponding mounted measurements on a visual basis. The absolute movement-induced difference between mounted-handheld pairs after the background correction is 16.4pm 9.3~% (mean ± std, n=11), with an absolute median difference of 23.8%. Realization of handheld LSCI facilitates measurements on a wide range of skin areas bringing more convenience for both patients and medical staff.

The application of new HARD-descriptor available from the CORAL software to building up NOAEL models

Continuous QSAR models have been developed and validated for the prediction of no-observed-adverse-effect (NOAEL) in rats, using training and test sets from the Fraunhofer RepDose® database and EFSA’s Chemical Hazards Database: OpenFoodTox. This paper demonstrates that the HARD index, as an integrated attribute of SMILES, improves the prediction power of NOAEL values using the continuous QSAR models and Monte Carlo simulations. The HARD-index is a line of eleven symbols, which represents the presence, or absence of eight chemical elements (nitrogen, oxygen, sulfur, phosphorus, fluorine, chlorine, bromine, and iodine) and different kinds of chemical bonds (double bond, triple bond, and stereo chemical bond). Optimal molecular descriptors calculated with the Monte Carlo technique (maximization of correlation coefficient between the descriptor and endpoint) give satisfactory predictive models for NOAEL. Optimal molecular descriptors calculated in this way with the Monte Carlo technique (maximization of correlation coefficient between the descriptor and endpoint) give amongst the best results available in the literature. The models are built up in accordance with OECD principles.

Location errors in QPFs over the Calabria region: Does a multi-model poor man's ensemble over-perform each member?

The aim of this work is to evaluate the quality, in terms of location errors, of multi-model poor man's ensemble (PME) forecasts against the single model ones over the Calabria region. Several strategies were adopted to combine precipitation forecasts by three limited area models (LAMs), namely the mean, the median, and a probabilistic matching approach. The Contiguous Rain Area (CRA) analysis was the method selected to detect and quantify the location errors of the forecast precipitation patterns with respect to the corresponding rain gauge-based analyses. Two best-fit criteria, the minimization of mean squared error and the maximization of correlation coefficient, were chosen for matching forecast and observed features. The ability to forecast correctly the precipitation patterns was then quantified by means of a summary measure, the CRA mean shift (CMS). It condenses the outcomes of the twenty-month CRA analyses with a unique value. A bootstrap procedure was applied to test the statistical significance of differences among CMS indices of LAMs and PMEs. Despite the ensemble forecasts display a general improvement, which results in a lower CMS index, with respect to the single LAMs, such improvement was not statistically significant for most ensembles. When the best-fit criterion is the maximization of correlation coefficient, no ensemble was statistically significant better than single models. Instead when the minimization of mean squared error was chosen as best-fit criterion, two out of four PMEs were significantly better than at least a LAM.

Priority estimation in the AHP through maximization of correlation coefficient

This paper proposes a correlation coefficient maximization approach (CCMA) for estimating priorities from a pairwise comparison matrix. The priorities are supposed to be as highly correlated with each column of a pairwise comparison matrix as possible. Such priorities are not unique and can be determined in different ways. Two optimization models are therefore suggested for determining further the priorities, one of which leads to an analytic solution. Theorems about the CCMA are developed and its potential applications are illustrated with two numerical examples.