The title takes its realistic connotations with its symbols, slogans and historical extensions in Iraq, also, the repercussions of the organic crisis that afflicted it for almost half a century, especially after the abortion of the July 14 Revolution and the spread of the culture of weapons, violence and death that we are living in Iraq nowadays.

The topic is suitable for critical analytical studies carried out by specialists in scientific and academic research centers and cultural institutions because they have an important feasibility at the strategic cultural level in Iraq, the Middle East and the world.

Research in Iraq has fallen to the extreme and is no longer effective, to the extent that it represents a severe cri

Abstract

Biosimilars are non-innovative copy versions of biologic medicines which are proven to be clinically equivalent to, as effective and as safe as their reference biologics. Biosimilars creates opportunities for cost savings for payers, governments, and patients compared with the reference products. Pharmacist plays an essential role in developing biosimilar medicines from manufacturing to post-marketing pharmacovigilance monitoring. The aim of the current study was to explore  the level of  knowledge, behaviors and practices of  a sample of Iraqi pharmacists towards biosimilar medicines.  The current study was a cross sectional, carried out during May 2020. A total of 2

Background: The presence of cancer has a profound psychological impact on the quality of life of patients and their families, on family and social relationships, and on role functioning.

Aim of the study: Assess the impact of childhood cancer on patients and their families.

Subjects and methods: A Prospective questionnaire-based study, for 151 patients, had malignancy identified by tumor registry of Children Welfare Teaching Hospital. The information was taken from the parent(s) in the presence of the patient who sometimes answered some questions during the interview.

Result: There was an interview with 151 families of children with cancer in t

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

Mixed ligand metal complexes of CrIII, FeIII,II, NiII and CuII have been synthesized using 5-chlorosalicylic acid (5-CSA) as a primary ligand and L-Valine (L-Val) as secondary ligand. The metal complexes have been characterized by elemental analysis, electrical conductance, magnetic susceptibility measurements and spectral studies. The electrical conductance studies of the complexes indicate their electrolytic nature. Magnetic susceptibility measurements revealed paramagnetic nature of the all complexes. Bonding of the metal ion through –OHand –COOgroups of bidentate to the 5-chlorosalicylic acid and through –NH2 and –COOgroups of bidentate to the L-valine by FT-IR studies . The agar diffusion method has been used to study the antib

Аннотация

    в статье рассматриваются проблемы преподавания русской литературы в иракской аудитории.. Использование литературы в преподавании иностранного языка, как правило, имеет две цели. Первая-чисто лингвистическая .. Вторая цель, однако, ассоциируется больше с экстралингвистикой  и представляет собой ознакомление студентов с различными аспектами русской жизни, культуры,

A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.

Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D