In this study, concentrations of radon and uranium were measured for twenty six samples of soil. The radon concentrations in soil samples measured by registrant alpha-emitting radon (²²²Rn) by using CR-39 track detector. The uranium concentrations in soil samples measured by using registrar fission fragments tracks in CR-39 track detector that caused by the bombardment of U with thermal neutrons from ²⁴¹ Am-Be neutron source that has flux of 5 ×10³n cm^-2 s^-1.
The concentrations values were calculated by a comparison with standard samples The results show that the radon concentrations are between (91.931-30.645Bq/m³).
The results show that also the uranium concentrat

This study was conducted according to contract with the North Refineries Company-Baiji and deals with the hydrodesulphurization of vacuum gas oil of Kirkuk crude oil, boiling range 611-833 K. A trickle bed reactor packed with a commercial cobalt-molybdenum on alumina catalyst was used. The operating conditions were: temperature range 583-643 K, liquid hourly space velocity range 1.50-3.75 1/h, hydrogen to oil ratio about 250 l/l and pressure kept constant at 3.5MPa.
The results showed that the aromatic content decreased and sulfur removal increased with increasing temperature and decreasing space velocity. The properties (viscosity, density, flash point and carbon residue) of the products decrease with temperature increasing, but the

Background: Insufficient sleep due to excessive media use is linked to decrease physical activity, poor nutrition, obesity, and decreased overall health-related quality of life.

Objectives: To assess the effect of using the internet and social media on the sleep of 4th-stage secondary school students.

Subjects and Methods: Cross-sectional study with the analytic element; for 500 secondary school students, obtained by choosing two schools randomly from each of the six educational directorates, by using a structured questionnaire.

Result: Secondary scho

In this work, polyvinylpyrrolidone (PVP), multi-walled carbon nanotubes (MWCNTs) nanocomposite was prepared and hybridized with Graphene (Gr) by the solution casting method.  The morphological and electrical properties were investigated by field effect scanning electron microscopy (FESEM) images, portraying a uniform dispersion of graphene within the PVP-MWCNT nanocomposite. The AC conductivity increased from (1.45552) to (2.34812) (Ω cm)^-1 with the use of nanocomposite. The increasing continues for the AC conductivity after hybridized with graphene up to (7.20641) (Ω cm)^-1. In addition, the performances of the prepared samples for gas sensor application have been investigated.

Gas and downhole water sink-assisted gravity drainage (GDWS-AGD) is a new process of enhanced oil recovery (EOR) in oil reservoirs underlain by large bottom aquifers. The process is capital intensive as it requires the construction of dual-completed wells for oil production and water drainage and additional multiple vertical gas-injection wells. The costs could be substantially reduced by eliminating the gas-injection wells and using triple-completed multi-functional wells. These wells are dubbed triple-completion-GDWS-AGD (TC-GDWS-AGD). In this work, we design and optimize the TC-GDWS-AGD oil recovery process in a fictitious oil reservoir (Punq-S3) that emulates a real North Sea oil field. The design aims at maximum oil recovery us

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte