An experimental and computational study is conducted to analyze the thermal performance of heat sinks and to pick up more profound information in this imperative field in the electronic cooling. One important approach to improve the heat transfer on the air-side of the heat exchanger is to adjust the fin geometry. Experiments are conducted to explore the impact of the changing of diverse operational and geometrical parameters on the heat sink thermal
performance. The working fluid used is air. Operational parameters includes: air Reynolds number (from 23597 to 3848.9) and heat flux (from 3954 to 38357 W/m
2 ). Conformational parameters includes: change the direction of air flow and the area of conduct

In the hybrid coolingsolar systems , a solar collectoris used to convertsolar energy intoheat sourcein order to super heat therefrigerant leave thecompressor,andthisprocess helpsin the transformation ofrefrigerant state from gaseous statetothe liquid statein upper two-thirdsof thecondenserinstead of the lower two-thirdssuchas in thetraditional air-conditioning systems and this willreduce theenergyneeded torun the process ofcooling.In this research two hybrid air-conditioning system with an evacuated tube solar collector were used, therefrigerant was R22 and the capacity was 2 tons each.The tilt angle of the evacuated tube solar collector was changed and the solar collector fluid was replaced into oil instead of water.A comparison wasi

Ninety eight mobile samples, (54) galaxy phone and (44) I phone, were swabbed for bacterial culture determination by culturing on MacConky agar , Blood agar , Mannitol salt agar , Muller Hinton agar .Staphylococcuswas the highest frequent isolated bacteria from Galaxy phone (33%) and I phone (37%). This study revealed that galaxy phone appears less contaminated with bacteria, the ratio of non-contaminated devices is (44%) when compared with I phone (9%). Sensitivity test showed that Ogmintin have the lowest effect on Staphylococcusisolated from both type of devices while cefitriaxone have the highest effect. DNA of isolate from galaxy 31 that exhibit highest resistance against antibiotics was extracted and 16S rRNA gene was polymerized by P

This experiment was carried out at the Grdarash field belongs to the Agricultural researches directorate/general Agriculture directorate-Erbil in September (2007)، 27 Arabic lambs aged (5-6 months) With average initial weight (39.178 Kg/lamb) were used. Lambs were divided in to (3) groups (9 lambs/group). Control group was fed on (85% barely، 10% bran and 5% straw)، and second and third groups were fed on various by-product in replacement with barely in loss and block shape respectively.

The total gain of three groups were (392708.32، 634826.52 & 445613.72 ID resp

Background: Strangles is a highly contagious equine respiratory disease caused by Streptococcus equi subsp. equi. It is a globally significant pathogen and one of the most common infectious agents in horses. In Iraq, no sequencing data on this pathogen are available, and only two molecular studies have been published to date. This study provides preliminary insights into strain diversity and provides a foundation for future large-scale investigations. Aim: This study aimed to investigate the molecular characteristics, identify SeM gene alleles, and perform a phylogenetic analysis of S. equi isolates from horses in Baghdad, Iraq. Methods: We analyzed 59 Streptococcus spp. isolates previously obtained from equine clinical sample

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a