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Related Concept Videos

Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Chemical Formulas02:52

Chemical Formulas

A chemical formula presents information about the proportions of atoms constituting a particular chemical compound or molecule, mainly using symbols of elements and numbers. At times other symbols, such as dashes, parentheses, brackets, commas, plus, and minus signs, are also used. A chemical formula can be one of three types – molecular, empirical, and structural.
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Euler's Formula to Columns with Other End Conditions01:15

Euler's Formula to Columns with Other End Conditions

Euler's formula is very important in the field of structural engineering, providing a foundation for understanding the critical loading conditions of pin-ended columns. This formula links the modulus of elasticity, the moment of inertia of the cross-section, and the column's length, offering a precise calculation of the critical load at which a column is prone to buckling.
Stereotypes, Prejudice, and Discrimination02:55

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Euler's Formula to Columns: Problem Solving01:23

Euler's Formula to Columns: Problem Solving

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Related Experiment Video

Updated: Jun 15, 2026

Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

Whiteness formulas: a selection.

E Ganz

    Applied Optics
    |March 9, 2010
    PubMed
    Summary

    This study proposes new whiteness formulas for industrial applications, considering fluorescent samples and color preferences. A standard formula with neutral, green, and red hue preferences offers comprehensive white sample characterization.

    Area of Science:

    • Color Science
    • Materials Science
    • Industrial Metrology

    Background:

    • Assessing whiteness is crucial for industrial applications.
    • Existing whiteness formulas face challenges with fluorescent samples and specific hue preferences.

    Purpose of the Study:

    • To develop and evaluate new whiteness formulas tailored for industrial requirements.
    • To address the complexities of measuring fluorescent samples.
    • To propose a comprehensive characterization method for white samples.

    Main Methods:

    • Consideration of industrial requirements and experience in whiteness assessment.
    • Analysis of photometric challenges with fluorescent samples.
    • Development of standard, green-preference, and red-preference whiteness formulas.

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    A Simple Behavioral Assay for Testing Visual Function in Xenopus laevis
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    Last Updated: Jun 15, 2026

    Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
    09:16

    Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

    Published on: May 12, 2023

    A Simple Behavioral Assay for Testing Visual Function in Xenopus laevis
    08:34

    A Simple Behavioral Assay for Testing Visual Function in Xenopus laevis

    Published on: June 12, 2014

    Main Results:

    • A standard whiteness formula with neutral hue preference is proposed.
    • Two supplementary formulas, one for green hue preference and one for red hue preference, are introduced.
    • The feasibility of characterizing white samples using whiteness, tint, and luminance factor is discussed.

    Conclusions:

    • The proposed whiteness formulas provide a more nuanced assessment for industrial needs.
    • A combination of whiteness, tint, and luminance factor enables complete characterization of white samples.
    • The study offers improved methods for evaluating whiteness in diverse industrial contexts.