Mixed effects modelling for glass category estimation from glass refractive indices

2011 ◽  
Author(s):  
David Lucy ◽  
Grzegorz Zadora
Keyword(s):  
Mixed Effects ◽  
Refractive Indices ◽  
Category Estimation

The spindle stage: A powerful optical tool

1988 ◽  
Vol 46 ◽  
pp. 52-53
Author(s):  
Mickey E. Gunter ◽  
F. Donald Bloss
Keyword(s):  
Significant Error ◽  
Optical Data ◽  
Refractive Indices ◽  
Axis Of Rotation ◽  
Polarizing Microscope ◽  
Microscope Stage

A single, reasonably homogeneous, nonopaque 30-to-300 μm crystal, mounted on a spindle stage and studied by immersion methods under a polarizing microscope, yields optical data frequently sufficient to identify and characterize a substance unequivocally. The data obtainable include (1) the orientation of the crystal's principal vibration axes and (2) its principal refractive indices, to within 0.0002 if desired, for light vibrating along these principal vibration axes. Spindle stages tend to be simple and relatively inexpensive, some costing less than $50. They permit rotation of the crystal about a single axis which is parallel to the microscope stage. This spindle or S-axis is thus perpendicular to the M-axis, namely the microscope stage's axis of rotation.A spindle stage excels when studying anisotropic crystals. It orients uniaxial crystals within minutes and biaxial crystals almost as quickly so that their principal refractive indices - ɛ and ω (uniaxial); α, β and γ (biaxial) - can be determined without significant error from crystal misorientation.

Light microscopy of ceramics

1993 ◽  
Vol 51 ◽  
pp. 908-909
Author(s):  
Walter C. McCrone
Keyword(s):  
Refractive Index ◽  
Light Microscopy ◽  
Light Microscope ◽  
Polarized Light ◽  
Refractive Indices ◽  
Complete Coverage ◽  
The Subject ◽  
Lower Refractive Index

An excellent chapter on this subject by V.D. Fréchette appeared in a book edited by L.L. Hench and R.W. Gould in 1971 (1). That chapter with the references cited there provides a very complete coverage of the subject. I will add a more complete coverage of an important polarized light microscope (PLM) technique developed more recently (2). Dispersion staining is based on refractive index and its variation with wavelength (dispersion of index). A particle of, say almandite, a garnet, has refractive indices of nF = 1.789 nm, nD = 1.780 nm and nC = 1.775 nm. A Cargille refractive index liquid having nD = 1.780 nm will have nF = 1.810 and nC = 1.768 nm. Almandite grains will disappear in that liquid when observed with a beam of 589 nm light (D-line), but it will have a lower refractive index than that liquid with 486 nm light (F-line), and a higher index than that liquid with 656 nm light (C-line).

On electronic polarizabilities and refractive indices in SbSI crystal

1983 ◽  
Vol 44 (12) ◽  
pp. 349-359
Author(s):  
Wataru Kinase ◽  
Tadataka Morishita ◽  
Yutaka Hiyama ◽  
Tomoo Maeda
Keyword(s):  
Refractive Indices

Modeling Intraindividual Variability in Three-Level Multilevel Models

Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 95-108 ◽  
Author(s):  
Steffen Nestler ◽  
Katharina Geukes ◽  
Mitja D. Back
Keyword(s):  
Multilevel Model ◽  
Multilevel Models ◽  
Mixed Effects ◽  
Repeated Measurements ◽  
Scale Model ◽  
Suggested Approach ◽  
Psychological Study ◽  
Level Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

Extremism Leads to Ostracism

2020 ◽  
Vol 51 (3) ◽  
pp. 149-156
Author(s):  
Andrew H. Hales ◽  
Kipling D. Williams
Keyword(s):  
Mixed Effects ◽  
Social Connection ◽  
Mixed Effects Modeling ◽  
Activist Groups

Abstract. Ostracism has been shown to increase openness to extreme ideologies and groups. We investigated the consequences of this openness-to-extremity from the perspective of potential ostracizers. Does openness-to-extremity increase one’s prospects of being ostracized by others who are not affiliated with the extreme group? Participants rated willingness to ostracize 40 targets who belong to activist groups that vary in the type of goals/cause they support (prosocial vs. antisocial), and the extremity of their actions (moderate vs. extreme). Mixed-effects modeling showed that people are more willing to ostracize targets whose group engages in extreme actions. This effect was unexpectedly stronger for groups pursuing prosocial causes. It appears openness-to-extremity entails interpersonal cost, and could increase reliance on the extreme group for social connection.

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