paper

In this paper the specific activity of 238U, 232Th and 40K
radionuclides were determined by sodium iodide enhanced by
thallium NaI(Tl) detector and assessment the annual effective dose
in samples of powder milk from different companies such as
Almunaish, Seven Caw, Molty and Altunsa for adult available in
Baghdad markets. The specific activity of 40K has the greater value in
all the samples which is in the range of allowed levels globally that
suggested by UNSCEAR. The mean value of annual effective doses
were 0.121, 0.314775 and 0.305 mSv/y for 238U, 232Th and 40K
respectively.

The specific activities of the natural radionuclides U-238 and Th-
232 and K-40 in 14 soil samples collected from different sites from
AL-Mustansiriyah university at two depths (topsoil "surface" and
20cm depth) were be investigated using gamma ray spectrometer
3"x3" NaI(Tl) scintillation detector.
The analysis of the energy spectra of the soil samples show that
these samples have specific activities ranging with (16.08-51.11)
Bq/kg for U-238, (14.79-52.29) Bq/kg for Th-232 and (191.08-
377.64) Bq/kg for K-40, with an average values of 29.37, 34.14 and
289.62 Bq/kg for U-238, Th-232, k-40 respectively. The radiation
hazard parameters of the natural radionuclides; radium equivalent
activity (Raeq), gamma a

Objectives: To determine the impact of an educational program on nurses’ knowledge
and practices concerning neurogenic bladder rehabilitation for spinal cord injured persons
through a follow-up approach each two months post program implementation for six
months.
Methodology: "Follow-up" longitudinal design by using time series approach of data
analysis and the application of pre-post tests approach for the study and the control
groups. The study was carried out at Ibn Al-Kuff hospital for (SCI) in Baghdad governorate
from 5th of July 2010 to 15th of October 2011. To achieve the objectives of the study, a
non-probability (purposive) sample of (60) nurses (males and females) were working in SCI
units were selec

Industrial development has recently increased, including that of plastic industries. Since plastic has a very long analytical life, it will cause environmental pollution, so studies have resorted to reusing recycled waste plastic (sustainable plastic) to produce environmentally friendly concrete (green concrete). In this research, producing environmentally friendly load-bearing concrete masonry units (blocks) was considered where five concrete mixtures were compressed at the blocks producing machine. The cement content reduced from 400 kg/m³ (B-400) to 300 kg/m³ (B-300) then to 200 kg/m³ (B-200). While (B-380) was produced using 380 kg/m³ cement and 20 kg/m³ nano-sil

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