This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

Complexes of Au (III), Pd (II), Pt (IV ) and Rh(III) with S–propynyle-2- thiobenzimidazole (BENZA) have been prepared and characterized by IR and UV- Visible spectral methods in addition to magnetic and conductivity measurements and micro–elemental analysis (CHN).The probable structures of the new complexes have been suggested.

The study of the distribution of major oxides and heavy metals in some plants collecting and analyzing eighteen plant samples of vegetables including carrot, onion, eggplant, cucumber, and okra obtained from Abu Ghraib land located about 20 km west of Baghdad, Iraq. Eighteen plant samples of vegetables,.Heavy metals can have a severe impact if released into the environment, even in trace quantities. These can enter the food chain from aquatic and agricultural ecosystems and indirectly threaten human health.. Trace elements and oxides of As, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Se, Th, U, V, and Zn were measured in plant samples using an X-Ray Fluorescence Instrument (XRF). TEs analyses of vegetables were performed in the Iraqi German Lab

Contamination of the operating theatre is a major cause of nosocomial infection. This report aimed to show the types of microorganisms and their percentages cultivated from operating theatres of 16 health directorates of 14 Iraqi governorates (3 health directorates in Baghdad and 1 in each other 13 governorates) that was reported to the Pollution Control Section at Ministry of Health in Iraq from first of January to the end of June 2018. The data of all health directorates have included cultivation taken from governmental and private hospitals. Duhok, Erbil, Sulaymaniyah, and Nineveh were not involved in this report due to unavailability of their data during the above period. Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureu

Investigations made and soil samples brought from 14 sites in different areas, including Abu Ghraib and Al Anbar. Tests and measurements made in the Microbiology Laboratory at the College of Agriculture, University of Anbar. Department of Soil Sciences and water Resources, in order to isolate bio-fertilizers and test isolates fixing nitrogen in atmosphere and solvents for phosphorous compounds efficiency. The experiment included isolating and diagnosis of bacteria from rhizosphere soils of different plants that were brought from different agricultural areas, 74 isolates obtained by soils alleviation, and then the bio-chemical morphological and microscopic characteristics of these soils studied. The results showed that the most abundant and

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices