Study of the Texture Parameters Effects on the Anti-fingerprint Function

The textural properties of butter are influenced by its fat content and implicitly by the fatty acids composition. The impact of butter’s chemical composition variation was studied in accordance with texture and color properties. From 37 fatty acids examined, only 18 were quantified in the analyzed butter fat samples, and approximately 69.120% were saturated, 25.482% were monounsaturated, and 5.301% were polyunsaturated. The butter samples’ viscosity ranged between 0.24 and 2.12 N, while the adhesiveness ranged between 0.286 to 18.19 N·mm. The principal component analysis (PCA) separated the butter samples based on texture parameters, fatty acids concentration, and fat content, which were in contrast with water content. Of the measured color parameters, the yellowness b* color parameter is a relevant indicator that differentiated the analyzed sample into seven statistical groups; the ANOVA statistics highlighted this difference at a level of p < 0.001.

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Characterization of the surface of formworks – Influence of the surface energy and surface texture parameters on the demolding forces

Construction and Building Materials ◽

10.1016/j.conbuildmat.2020.121947 ◽

2021 ◽

Vol 272 ◽

pp. 121947

Author(s):

Calypso Chadfeau ◽

Safiullah Omary ◽

Essia Belhaj ◽

Christophe Fond ◽

Françoise Feugeas

Keyword(s):

Surface Energy ◽

Surface Texture ◽

Texture Parameters

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Analysis of High-Friction Surface Texture with Respect to Friction and Wear

Coatings ◽

10.3390/coatings11070758 ◽

2021 ◽

Vol 11 (7) ◽

pp. 758

Author(s):

Cibi Pranav ◽

Minh-Tan Do ◽

Yi-Chang Tsai

Keyword(s):

Friction Coefficient ◽

Surface Texture ◽

Safety Concern ◽

Future Research ◽

Scanning System ◽

Novel Approach ◽

Texture Parameters ◽

Aggregate Loss ◽

Friction Surfaces ◽

The Impact

High Friction Surfaces (HFS) are applied to increase friction capacity on critical roadway sections, such as horizontal curves. HFS friction deterioration on these sections is a safety concern. This study deals with characterization of the aggregate loss, one of the main failure mechanisms of HFS, using texture parameters to study its relationship with friction. Tests are conducted on selected HFS spots with different aggregate loss severity levels at the National Center for Asphalt Technology (NCAT) Test Track. Friction tests are performed using a Dynamic Friction Tester (DFT). The surface texture is measured by means of a high-resolution 3D pavement scanning system (0.025 mm vertical resolution). Texture data are processed and analyzed by means of the MountainsMap software. The correlations between the DFT friction coefficient and the texture parameters confirm the impact of change in aggregates’ characteristics (including height, shape, and material volume) on friction. A novel approach to detect the HFS friction coefficient transition based on aggregate loss, inspired by previous works on the tribology of coatings, is proposed. Using the proposed approach, preliminary outcomes show it is possible to observe the rapid friction coefficient transition, similar to observations at NCAT. Perspectives for future research are presented and discussed.

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Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases

Cancers ◽

10.3390/cancers13030453 ◽

2021 ◽

Vol 13 (3) ◽

pp. 453

Author(s):

Vincenza Granata ◽

Roberta Fusco ◽

Antonio Avallone ◽

Alfonso De Stefano ◽

Alessandro Ottaiano ◽

...

Keyword(s):

Multivariate Analysis ◽

Magnetic Resonance ◽

Liver Metastases ◽

Univariate Analysis ◽

Features Selection ◽

Ras Mutation ◽

Mutation Status ◽

Contrast Enhanced ◽

Texture Parameters ◽

Derived Data

Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36–80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). Conclusions: Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.

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