A model-based design for coating materials including perlite-peanut shell-volcanic lava stone ternary system

Cellulose in the fibrous structure, which is the main structural component in the cell wall of plants, was formed by the combination of three thousand or more glucose molecules and was a natural polymer synthesized by many living microorganisms. In this study, it was aimed to observe the performance of carrot pulp physically added to water-based coating to increase the viscosity of the coatings. In addition, volcanic lava stone (red pumice stone) and perlite stone were added to the coating to improve its properties that heat transfer, adhesion, hydrophobicity, corrosion resistance. The effect of cellulose-based material on the viscosity of the coating was determined using the Design Expert Optimization Method. In this experimental design method, the experimental set was created to be “carrot pulp, volcanic lava stone, and perlite stone” and additives was added as 0-6% by weight of the coating. By using this method, the most suitable process parameters were determined, and the effects of the additives added to the coating were examined, comparatively. As a result, it was found that cellulose derived organic additives and other additives improved the various properties of coating and could be evaluated for industrial coatings.

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A modified version of BRDF model based on Kubelka-Munk theory for coating materials

Optik ◽

10.1016/j.ijleo.2019.162982 ◽

2019 ◽

Vol 193 ◽

pp. 162982 ◽

Cited By ~ 2

Author(s):

Min Yang ◽

Wenbin Xu ◽

Junwei Li ◽

Zhiyuan Zhou ◽

Yi Lu

Keyword(s):

Coating Materials ◽

Model Based

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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