Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

CUPPER(||)AND MERCURY (||)Complexes WITH SCHIFF BASE LIGAND FROM BENZIDIN WITH ISATIN AND BENZOIN:SYNTHESIS,SPECTRAL CHARACTERIZATION, THERMAL STUDIES AND BIOLOGICAL ACTIVITIES

Background: Alum has been used as a treatment medication in cases of oral and gingival ulcers, and also as antiseptic mouthwash. This study aimed to examine the effects of different concentrations of Alum on inhibition zone, viability counts and adherence ability of Mutans streptococci compared with deionized water and chlorhexidine gluconate in vitro. Materials and methods: The study dealt with an in vitro study to establish a concentration of Alum mouthrinse that would have the minimum inhibitory concentration and minimum bacteriocidal concentration. The second part evaluated the anti-adherence ability of the experimental agents. Results: This study found that the antibacterial effect of Alum increases with its concentration from 50 to 1

Soil is considered one of the main factors of subsidence phenomena which
became continually happen in Baghdad (Ghazalia, Ameria, and Hay al-Amyl)
causing bad effects as shortage of drinking water, traffic jam and formation
swamps.
This thesis depends on soil study to a depth 15 meters, due to its
importance in subsidence. This done through specifying its chemical physical
properties.
Soil within Iraq climate, in case of water stopping for any reason it contract
and shrink away especially when it exposed to high pressure these factors
finally caused subsidence. In case of leakage underground water or that of
damaged water pipes this will contribute to chemical reactions which damage soil
structure and incr

Vibration is a source of energy that can be beneficial or harmful based on the application. Vibration can affect the function of any structure; however, Ceramic matrix composite (CMC) is one of these structures. Whereby less studies have been concentrated on study its function specially when electromagnetic wave (microwave) exposed on its surface to perform its designed function. To address this concept, SiC composite has been fabricated which is designed to have a transparent characteristics to microwave. External vibration had been applied on its surface to monitor how much influence could nanoscale amplitude vibration damage the microwave interaction. The source of vibration was applied from piezoelectric and the vibration was monitored

Acinetobacter baumannii ability to form biofilm makes it to be opportunistic pathogen causing of nosocomial infections and to be good survivor in adverse environmental conditions including medical devices and hospital environments. Six isolates of A. baumannii were isolated from drinking water and tested to investigate biofilm formation capacity on three different type of abiotic surface, also several factors were examined such as hydrophobicity, PH and temperature. All A. baumannii isolates displayed a positive biofilm on congored aga test CRA (pigmented colonies with black color) and Christensen's test (adhesive layer of stained material to the inside surface of the tube).The obtained data of microbial adhesion to hydrocarbons assay (MATH

Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This