A new ligand (H4L) and its complexes with ( ZnII, CdII and HgII) were prepared. This ligand was prepared in two steps. In the first step a solution of terephthaldehyde in methanol was reacted under reflux with 1,2-phenylenediamine to give an precursor compound which reacted in the second step with 2,4-dihydroxybenzaldehyde to give the ligand. The complexes were then synthesized by direct reaction of the corresponding metal chloride with the ligand. The ligand and complexes were characterized by spectroscopic methods FT-IR, UV-Vis, 1 HNMR, and atomic absorption, chloride content, HPLC, mole-ratio determination. in addition to conductivity measurement. The data of these measurements suggest a distorted tetrahedral geometry for ZnII, C

     A new ligand (H4L) and its complexes with ( ZnII, CdII and HgII) were prepared. This ligand was prepared in two steps. In the first step a solution of terephthaldehyde in methanol was reacted under reflux with 1,2-phenylenediamine to give an precursor compound which reacted in the second step with 2,4-dihydroxybenzaldehyde to give the ligand. The complexes were then synthesized by direct reaction of the corresponding metal chloride with the ligand. The ligand and complexes were characterized by spectroscopic methods FT-IR, UV-Vis, 1HNMR, and atomic absorption, chloride content, HPLC, mole-ratio determination. in addition to conductivity measurement. The data of these measurements suggest a distorted tetrahedral g

The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this di

Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the l

In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

Tested effective Alttafaria some materials used for different purposes, system a bacterial mutagenesis component of three bacterial isolates belonging to different races and materials tested included drug Briaktin

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

Ammi species belong to the family Umbellifereae that provide a host of bioactive compounds (mainly coumarins and flavonoids) of important biological activities, like prevention and treatment of heart and vascular disease and some types of cancer. Literature survey revealed that there was no study concerning Ammi flavonoids in Iraq. Ammi majus and Ammi visnaga, which are wildly grown in Iraq, were chosen for this study. This study concerned with extraction, identification, isolation, and purification of some biologically important flavonols quercetin and kaempferol from the fruits of Ammi majus and Ammi visnaga. Extraction of these flavonols was carried out using 85% methanol and 90% e