Laboratory experience in Iraq with cold asphalt concrete mixtures is very limited. The design and use of cold mixed asphalt concrete had no technical requirements. In this study, two asphalt concrete mixtures used for the base course were prepared in the laboratory using conventional cold-mixing techniques to test cold asphalt mixture (CAM) against aging and moisture susceptibility. Cold asphalt mixtures specimens have been prepared in the lab with cutback and emulsion binders, different fillers, and curing times. Based on the Marshal test result, the cutback proportion was selected with the filler, also based on the Marshal test emulsion. The first mixture was medium setting cationic emulsion (MSCE) as a binder, hydrate

Concrete is widely used in construction materials since early 1800's. It has been known that concrete is weak in tension, so it requires some addition materials to have ductile behavior and enhance its tensile strength and strain capacity to improve their uses. In this study reactive powder concrete (RPC) was used with steel fiber by using different types of cement; (Ordinary Portland cement (OPC) and/or Portland- Limestone cement (PLC)) with three types of mixtures (OPC at the first mix, 50 % OPC and 50 % PLC at the second mix and PLC at the third mix). The behavior of RPC with steel fibers on compressive strength and tensile strength of concrete with different ages of curing (7, 14, 28 and 60) days and shrinkage have been studied. The clo

ABSTRACT Background: Dental caries is a most common social and intractable infectious disease in human. Saliva is critical for preserving and maintaining oral health and salivary elements had many effects on caries experience. Aim of study: This study was conducted to assess dental caries severity by age and gender and their relation to salivary zinc and copper among a group of adults aged (19-22) years. Materials and methods: After examination eighty persons aged 19-22 years of both gender. Caries severity was documented according to DMFS index. Stimulated salivary samples were collected and chemically analyzed under standardized condition to detect salivary elements zinc and copper. Concentrations of Zinc and copper were measured by using

Artemisia is a perennial wild shrub with large branches and compound leaves. Artemisia contains about 400 types, and its medical importance is due to the presence of many active substances and compounds such as volatile oils, alkaloids and flavonoids, glycosides, saponins, tannins, and coumarins. This study was designed to study the effect of the aqueous extract of the fruit of the Artemisia plant on the organs of the body, as well as to know its ability to activate the hepatic enzyme alanine transaminase (ALT/GPT). The fruit of this shrub was extracted using the measurement technique gas chromatography-mass spectrometry (GC/MASS) and organic solvent hexane and ethyl acetate in one to one ratio. It contained 21 compounds, a high percentage

Abstract

 A surface fitting model is developed based on calorimeter data for two famous brands of household compressors. Correlation equations of ten coefficient polynomials were found as a function of refrigerant saturating and evaporating temperatures in range of (-35℃ to -10℃) using Matlab software for cooling capacity, power consumption, and refrigerant mass flow rate.

Additional correlations equations for these variables as a quick choice selection for a proper compressor use at ASHRAE standard that cover a range of swept volume range (2.24-11.15) cm³.

The result indicated that these surface fitting models are accurate with in ± 15% for 72 compressors model of cooling cap

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

BACKGROUND: Preterm labour is a major cause of perinatal morbidity and mortality, so it is important to predict preterm delivery using the clinical examination of the cervix and uterine contraction frequency. New markers for the prediction of preterm birth have been developed such as transvaginal ultrasound measurement of cervical length as this method is widely available. OBJECTIVE: To determine, whether transvaginal cervical length measurement predicts imminent preterm delivery better than digital cervical length measurement in women presented with preterm labour and intact membranes. PATIENTS AND METHODS: Two hundred women presented with preterm labour between 24 and 36+6 weeks of gestation were included in this study. All women subjecte

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp