Congenital distal vaginal obstruction is usually asymptomatic in a newborn female. On rare occasions, it may present as an acute emergency with life threatening complications.This paper is reporting the rare condition of two newborn females presenting urgently with abdominal distension and acute urinary retention as a result of congenital distal vaginal Obstruction. The case history and urgent management shall be presented and both conditions shall be discussed.

This article showcases the development and utilization of a side-polished fiber optic sensor that can identify altered refractive index levels within a glucose solution through the investigation of the surface Plasmon resonance (SPR) effect. The aim was to enhance efficiency by means of the placement of a 50 nm-thick layer of gold at the D-shape fiber sensing area. The detector was fabricated by utilizing a silica optical fiber (SOF), which underwent a cladding stripping process that resulted in three distinct lengths, followed by a polishing method to remove a portion of the fiber diameter and produce a cross-sectional D-shape. During experimentation with glucose solution, the side-polished fiber optic sensor revealed an adept detection

 Background: It is evident that there is a lack of clear consensus on the role of luteal phase serum Progesterone (P) level in the prediction of early pregnancy after controlled ovarian hyperstimulation (COH) protocols in assisted reproductive techniques (ART).
Objective: We conducted this study in order to investigate the potential value of luteal phase serum progesterone measurement, in women undergoing ICSI treatment cycles and receiving progesterone supplements, in relation to pregnancy rate.
Patients: A total of 68 women aged 20-40 years undergoing their first intracytoplasmic sperm injection (ICSI) cycles in fertility and I.V.F center of Kamal Al samrai hospital.
Methods: women consecutively treated by ICSI had Estima

Multiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of

Semi-parametric models analysis is one of the most interesting subjects in recent studies due to give an efficient model estimation. The problem when the response variable has one of two values either 0 ( no response) or one – with response which is called the logistic regression model.

We compare two methods Bayesian and . Then the results were compared using MSe criteria.

A simulation had been used to study the empirical behavior for the Logistic model , with  different sample sizes and variances. The results using represent that the Bayesian method is better than the   at small samples sizes.

Background: The main objective was to compare the outcome of single layer interrupted extra-mucosal sutures with that of double layer suturing in the closure of colostomies.

Subjects and Methods: Sixty-seven patients with closure colostomy were assigned in a prospective randomized fashion into either single layer extra-mucosal anastomosis (Group A) or double layer anastomosis (Group B). Primary outcome measures included mean time taken for anastomosis, immediate postoperative complications, and mean duration of hospital stay. Secondary outcome measures assessed the postoperative return of bowel function, and the overall mean cost. Chi-square test and student t-test did the statistical analysis..

Resu

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall