Dielectric, elastic and optical properties of 80TeO2-(20-x) MnO2-xFe2O3 and 30Li2O-4MoO3-(66-x)TeO2-xV2O5 mixed oxide tellurite glasses in the conductivity anomaly region / Rosdiyana Hasham @ Hisam

In this study, two series of mixed oxide tellurite based glasses with
composition 80TeO2-(20-x)MnO2-xFe2O3 (x = 5 mol% to 20 mol%) and
30Li2O-4MoO3-(66-x)TeO2-xV2O5 (x = 0.2–1.2 mol%) were prepared
using melt-quenching method to investigate their dielectric, AC
conductivity, elastic and optical properties. For the 80TeO2-(20-x)MnO2-
xFe2O3 glass samples, the dielectric constant showed strong variation
with Fe2O3 at a frequency ≥ 10 kHz, where ε′ decreased to a minimum
value at x = 10 mol% before increasing for x >10%. The decrease in ε′
may be attributed to some form of hindrance effect on heavy dipoles
caused by the mixed transition-ion effect (MTE). Meanwhile, variation of
AC conductivity with Fe2O3 showed non-linear increase for x ≤ 10 mol%
before dropping to a minimum at 15 mol% Fe2O3. This result is attributed
to Anderson localization because of the disorder in the glass system.
On the other hand, DC conductivity for the same glass system showed
a strong increase for x ≤ 10 mol% Fe2O3 before reaching a saddle-like
behavior between 10 mol % ≤ x ≤ 15 mol%, followed by a large increase
for x > 15 mol%. Independent longitudinal modulus (CL), shear modulus
(μ) and bulk modulus (Ke) showed increased values for x ≤ 10 mol% with
an anomalous drop at x = 15 mol% Fe2O3, followed by a large increase
at x > 15 mol%. The anomalous region between 10 mol% ≤ x ≤ 15 mol%
coincided with DC conductivity saddle-like region and is suggested to be
related to the MTE. Meanwhile, in the same region, optical band gap (Eopt)
exhibited a maxima, whereas refractive index showed a minima, thereby
indicating a variation in polarizability due to changes in concentration
of bridging and non-bridging oxygens. For the 30Li2O-4MoO3-(66-x)
TeO2-xV2O5 glasses, the variation of AC conductivity with V2O5 showed
This thesis presents a simulation study on parameter estimation for binary
and multinomial logistic regression, and the extension of the clustering
partitioning strategy for goodness-of-fit test to multinomial logistic
regression model. The motivation behind this study is influenced by two
main factors. Firstly, parameter estimation is often sensitive to sample
size and types of data. Simulation studies are useful to assess and confirm
the effects of parameter estimation for binary and multinomial logistic
regression under various conditions. The first phase of this study covers
the effect of different types of covariate, distributions and sample size
on parameter estimation for binary and multinomial logistic regression
model. Data were simulated for different sample sizes, types of covariate
(continuous, count, categorical) and distributions (normal or skewed for
continuous variable). The simulation results show that the effect of skewed
and categorical covariate reduces as sample size increases. The parameter
estimates for normal distribution covariate apparently are less affected
by sample size. For multinomial logistic regression model with a single
covariate, a sample size of at least 300 is required to obtain unbiased
estimates when the covariate is positively skewed or is a categorical
covariate. A much larger sample size is required when covariates are
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a non-linear increase for x ≤ 0.6 mol% before decreasing to a minimum
at 0.8 mol% V2O5.