Eleven new 2,6-di-tert-butyl-4-(5-aryl-1,3,4-oxadiazol-2-yl)phenols 5a–k were synthesized by reacting aryl hydrazides with 3,5-di-tert butyl 4-hydroxybenzoic acid in the presence of phosphorus oxychloride. The resulting compounds were characterized based on their IR, 1H-NMR, 13C-NMR, and HRMS data. 2,2-Diphenyl-1-picrylhydrazide (DPPH) and ferric reducing antioxidant power (FRAP) assays were used to test the antioxidant properties of the compounds. Compounds 5f and 5j exhibited significant free-radical scavenging ability in both assays.

    Годы, принадлежащие концу XIX – началу XX столетия, вошли в русскую литературу под красивым названием Серебряный век. В это время в стране появился целый ряд выдающихся поэтов, имена которых навсегда останутся в русской и мировой литературе. Это Ахматова, Гумилев, Блок, Маяковский и многие другие. Все эти люди творили в нелегкое для России время. Началом Серебряного века условно считают 189

   The present study introduced a new description of the last larval instar of the oak tree borer, Latipalpis johanidesi Niehuis, 2002 (Coleoptera, Buprestidae). The larval specimens were collected from the oak trees within the mountainous areas, Erbil governorate, Iraqi Kurdistan Region, during the beginning of April till the end of May 2019.

   Schematic sketches were provided to illustrate unclear morphological features, and the results presented importance morphological evidence for confirming the identification of this species in the larval stage precisely.

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

As we live in the era of the fourth technological revolution, it has become necessary to use artificial intelligence to generate electric power through sustainable solar energy, especially in Iraq and what it has gone through in terms of crises and what it suffers from a severe shortage of electric power because of the wars and calamities it went through. During that period of time, its impact is still evident in all aspects of daily life experienced by Iraqis because of the remnants of wars, siege, terrorism, wrong policies ruling before and later, regional interventions and their consequences, such as the destruction of electric power stations and the population increase, which must be followed by an increase in electric power stations,

The penalized least square method is a popular method to deal with high dimensional data ,where  the number of explanatory variables is large than the sample size . The properties of  penalized least square method are given high prediction accuracy and making estimation and variables selection

 At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and

Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

Shadow detection and removal is an important task when dealing with color outdoor images. Shadows are generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. Most shadow detection and segmentation methods are based on image analysis. However, some factors will affect the detection result due to the complexity of the circumstances. In this paper a method of segmentation test present to detect shadows from an image and a function concept is used to remove the shadow from an image.