Twelve species of Tubuliferous thrips, of the family Phlaeothripidae had been reported from Iraq. Two of these were reported previously, Haplothrips cerealis Priesner, by El-Haidari and Daoud 1971 and Haplothrips tritici kurdjumov by Al-Ali 1977 and the rest were recorded for the first time: these are Haplothrips hukkineni Priesner; Haplothrips subtilissimus (Haliday); Haplothrips reuteri Karny; Haplothrips jasonis Priesner; Haplothrips sallloumensis Priesner; Haplothrips pharao Priesner; Phlaeothrips sycomri Priesner; Karnyothrips flavipus (Jones); Karnyothrips melaleucus (Bagnall); Dolicholepta micrurus (Bagnall). Number of insec

         Saccharomyces cerevisiae filtrate showed inhibitory effect against Fusarium spp. when grow in a liquid medium (Sabouraud) with different concentrations (1, 3, 5) %. The higher inhibitory effect against fungus growth was (24.5) mm at (5%) in PDA medium compared with control (36.5) mm during the seventh day propagation. The filtrate of Lactobacillus plantarum isolate was mixed with the PDA medium ,which showed inhibitory effect against Fusarium spp. The concentrated filtrate( one fold) appcarcd a  higher effect against the same fungus compared with un concentrated filtrate one. Saceharomyces cerevisiae and Lactobacillus plantarum

Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection

Drag has long been identified as the main reason for the loss of energy in fluid transmission like pipelines and other similar transportation channels. The main contributor to this drag is the viscosity as well as friction against the pipe walls, which will results in more pumping power consumption.

   The aim in this study was first to understand the role of additives in the viscosity reduction and secondly to evaluate the drag reduction efficiency when blending with different solvents.

   This research investigated flow increase (%FI) in heavy oil at different flow rates (2 to 10 m3/hr) in two pipes (0.0381 m & 0.0508 m) ID By using different additives (toluene and naphtha) with different concent

The bioequivalence of a single dose tablet containing 5 mg amlodipine as a test product in comparison to Norvasc^® 5 mg tablet (Pfizer USA) as the reference product was studied. Both products were administered to twenty eight healthy male adult subjects applying  a fasting, single-dose, two-treatment, two-period, two-sequence, randomized crossover design with two weeks washout period between dosing. Twenty blood samples were withdrawn from each subject over 144 hours period. Amlodipine concentrations were determined in plasma by a validated HPLC-MS/MS method. From the plasma concentration-time data of each individual, the pharmacokinetic parameters; C_max, T_max, AUC_0-t, AUC_0-

The aim of this research is to use robust technique by trimming, as the analysis of maximum likelihood (ML) often fails in the case of outliers in the studied phenomenon. Where the (MLE) will lose its advantages because of the bad influence caused by the Outliers. In order to address this problem, new statistical methods have been developed so as not to be affected by the outliers. These methods have robustness or resistance. Therefore, maximum trimmed likelihood: (MTL) is a good alternative to achieve more results. Acceptability and analogies, but weights can be used to increase the efficiency of the resulting capacities and to increase the strength of the estimate using the maximum weighted trimmed likelihood (MWTL). In order to perform t