Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

The purpose of our work is to report a theoretical study of electrons tunneling through semiconductor superlattice (SSL). The (SSL) that we have considered is (GaN/AlGaN) system within the energy range of ε < Vo, ε = Vo and ε > Vo, where Vo is the potential barrier height. The transmission coefficient (TN) was determined using the transfer matrix method. The resonant energies are obtained from the T (E) relation. From such system, we obtained two allowed quasi-levels energy bands for ε < VO and one band for ε  VO.

Background: War represents a major human crisis; it destroys communities and results in ingrained consequences for public health and well-being

Objective: We set this study to shed light on the public health status in Iraq after the successive wars, sanctions, sectarian conflicts, and terrorism, in light of certain health indicators.

Design: The primary source of data for this analysis comes from the Iraqi Ministry of Health, and The World Health Organization disease surveillance.

Results: Most of the morbidity indicators are high, even those that are relatively declining recently, are still higher than those repor

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

Background:-The Modified Alvarado Scoring System (MASS) has been reported to be a cheap and quick diagnostic tool in patients with acute appendicitis. However, differences in diagnostic accuracy have been observed if the scores were applied to various populations and clinical settings.
Objectives:- The purpose of this study was to evaluate the diagnostic value of Modified Alvarado Scoring System in patients with acute appendicitis in our setting.
Methods:-one hundre twenty eight patients ,were included in this study, admitted to Al-Kindy teaching hospital from June 2009 to June 2010. Patients’ age ranged from 8 to 56 years (21±10) they were divided into three groups; paediatrics, child bearing age females & adult males,. MAS

The banking industry, as a result of the great challenges it faced, required continuous development of the principles of management, control and mechanisms used. The Basel Committee on Banking Supervision has played a leading role in legalizing many of these developments and has been able to contribute significantly to establishing a common framework for banking supervision, The central role in the various countries of the world is based on coordination between the various regulatory authorities and thinking about finding mechanisms to confront the risks faced by banks, recognizing the importance of the banking sector in the stability of domestic and international banking systems or the danger of this sector in the emergence of F

In this work the production of activated carbon (AC) from Imperata is done by microwave assisted Potassium hydroxide (KOH) activation and using this activated carbon for the purpose of the uptake of amoxicillin (AMX) by adsorption process from aqueous solution. The effects for irradiation power (450-800W), irradiation time (6-12min) as well as impregnation ratio (0.5-1 g/g) on the AMX uptake and yield AMX uptake at an initial concentration of AMX (150 mg/g). The optimum conditions were 700 W irradiation power, 10 min time of irradiation, as well as 0.8 g/g impregnation ratio with 14.821% yield and 12.456 mg/g AMX uptake. Total volume of hole and the area of the surface (BET) are 0.3027 m³/g, and 552.7638 m²/g respectively. The properti