Objective(s): To evaluate teachers’ performance of counseling for pupils with Attention Deficit and Hyperactivity Disorder, to identify the relationship between Teachers’ Performance of Counselling for Pupils with Attention Deficit and Hyperactivity Disorder and their demographic.

Methodology: A quasi-experimental (pre-posttest) design was carried out to evaluate teachers’ performance of counseling for pupils with Attention Deficit and Hyperactivity Disorder, at Al-Firdous mixed primary School and to find out the association between teachers' performance about Attention Deficit and Hyperactivity Disorder and their socio-demographic characteristic. The study was started from 18th September 2

Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

Trip generation is the first phase in the travel forecasting process. It involves the estimation of the
total number of trips entering or leaving a parcel of land per time period (usually on a daily basis);
as a function of the socioeconomic, locational, and land-use characteristics of the parcel.
The objective of this study is to develop statistical models to predict trips production volumes for a
proper target year. Non-motorized trips are considered in the modeling process. Traditional method
to forecast the trip generation volume according to trip rate, based on family type is proposed in
this study. Families are classified by three characteristics of population social class, income, and
number of vehicle ownersh

A   new   complex  of   Cr(UI)   has   been   prepared.  The   kinetics  and eqailibrium study of  the substitution reaction   for  the complex Trans­ KfCr(ox)z(H 0)2].3l--h0 {T I }, with 4-aminoantipyrine {AAP}, bave been per£Qrm d      in  aqueous  media   at  .(pH. = 4.9,   5.6 and 6.0)   (!!?0.4M NaN03).   Activation  pararrieters   for  the  reac(ions are  {Eat= l.89l  kCal

mor 1 ,        l:t=89.29  kCal  mo1"1  &nbsp

Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentati

      Will address this research interaction and coordination between fiscal and monetary policies and the impact of this interaction and coordination on economic stability and growth، and how the financial implications of monetary policy may stimulate action monetary policy and treatment side effects and the nature of responsiveness and bounce between procedures both two policies and their impact on the balance of overall economic and explained in the folds of searchjustifications coordination and the extent necessary in order to address the imbalances in economic activity through twinning actions of monetary and fiscal، has embodied this coordination and interaction between policies and their impact m

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit