RNA Sequencing (RNA-Seq) is the sequencing and analysis of transcriptomes. The main purpose of RNA-Seq analysis is to find out the presence and quantity of RNA in an experimental sample under a specific condition. Essentially, RNA raw sequence data was massive. It can be as big as hundreds of Gigabytes (GB). This massive data always makes the processing time become longer and take several days. A multicore processor can speed up a program by separating the tasks and running the tasks’ errands concurrently. Hence, a multicore processor will be a suitable choice to overcome this problem. Therefore, this study aims to use an Intel multicore processor to improve the RNA-Seq speed and analyze RNA-Seq analysis's performance with a multiproce

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 notion of a Tˉ-pure sub-act and so Tˉ-pure sub-act relative to sub-act are introduced. Some properties of these concepts have been studied.

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

Background: spontaneous abortion constitutes one of the most important adverse pregnancy outcomes affecting human reproduction, and its risk factors are not only affected by biological, demographic factors such as age, gravidity, and previous history of miscarriage,but also by individual women’s personal social characteristics, and by the larger social environment. Objective:To identifyEnvironmental effects on Women's with Spontaneous Abortion. Methodology:Non-probability(purposive sample)of(200) women, who were suffering from spontaneous abortion in maternity unitfrom four hospitals at Baghdad City which include Al-ElwiaMaternity Teaching Hospital, and Baghdad Teaching Hospital at Al-Russafa sector. Al–karckhMaternityHospita

Equilibrium adsorption isotherm for the removal of trifluralin from aqueous solutions using ? –alumina clay has been studied. The result shows that the isotherms were S3 according Giels classification. The effects of various experimental parameters such as contact time, adsorbent dosage, effect of pH and temperature of trifluralin on the adsorption capacities have been investigated. The adsorption isotherms were obtained by obeying freundlich adsorption isotherm with (R2 = 0.91249-0.8149). The thermodynamic parameters have been calculated by using the adsorption process at five different temperature, the values of ?H, ?G and ?S were (_1.0625) kj. mol-1, (7.628 - 7.831) kj.mol-1 and (_2.7966 - _2.9162) kg.