The current study aims at studying the effect of a program based on Curiosity
in developing Attention, Visual perception and Critical thinking for pupils of the
preparatory stage. Sample of the study consisted of (30) pupils of the second year
of the prep. Stage as a Control group, and (30) pupils as an Experimental group.
The current study reached differences between the pre-measuring and the post
measuring for the experimental group in (Attention, Visual perception and Critical
thinking) variables in favor of the post measuring, also The study reached
differences in the post measuring between the Experimental and Control groups in
favor of Experimental group in (Attention, Visual perception and Critical thinking)
variables.

The study of longitudinal data plays a significant role in medicine,
epidemiology and social sciences. Typically, the interest is in the dependence of
the outcome variable on the covariates. The generalized linear models (GLMs)
were proposed to unify the regression approach for a wide variety of discrete and
continuous longitudinal data. The responses (outcomes) in longitudinal data are
usually correlated since repeated observations are taken from the same subject.
Ignoring this type of correlation may affect the validity of the likelihood inference.
Hence, an extension of the GLMs, that account for such correlation, is needed.
This can be done by inclusion of random effects in the linear predictor; that is the
generalized linear mixed models (GLMMs) (also called the random effects
models). The objective of this thesis is to calculate the maximum likelihood
estimates (MLEs) for the regression parameters of the logit model, when the
traditional assumption of normal random effects is relaxed. In this case, a more
convenient skewed distribution, such as the lognormal distribution, is used instead.
However, adding non-normal random effects to the GLMM complicates the
likelihood estimation considerably because the likelihood function can no longer
be expressed in a closed form. So, the direct numerical evaluation techniques
(such as Newton-Raphson) become analytically and computationally tedious. To
overcome such problems, the present study proposes and develops a Monte Carlo
EM (MCEM) algorithm, to obtain the maximum likelihood estimates for a logistic
regression model when the lognormal distribution is assumed as the random
effects density.

This study was conducted to determine the effect of Laser acupuncture on anthropometric parameters, C-reactive protein (CRP) and lipid profile in obese premenopausal women. Thirty obese premenopausal women participated in this study. They were divided into two equal groups: control group received low caloric diet (1200 Cal) for 12 weeks, and the study group received the same diet regime and the laser acupuncture 3 sessions /week for 12 weeks. Evaluation was done through measuring body mass index (BMI), waist/hip ratio (WHR), CRP and lipid profile at the start and after the end of the treatment course. Results revealed that both control and study groups showed a statistically highly significant decrease in BMI (P=0.001), WHR (P=0.001), CRP (P=0.001), low density lipoprotein cholesterol (LDL-C) (P=0.001), triglycerides (P=0.001) and cholesterol (P=0.001), while there was a non-significant increase in high density lipoprotein cholesterol (HDL-C) (P=0.316) post treatment. Compared to the control group, the study group showed a statistically highly significant decrease in BMI (P=0.001), WHR (P=0.001), CRP (P=0.003), LDL-C (P=0.001), triglycerides (P=0.001) and cholesterol (P=0.001) and non significant increase in HDL-C (P=0.316). Thus, laser acupuncture is an effective modality for reducing abdominal adiposity, CRP and improving lipid profile in obese premenopausal women.