The phenomenon of negative behavior has studied as a social and psychological phenomenon that effect on the performance and life of workers inside and outside the organization. The adoption of this phenomenon is studied in terms of the role of the internal environment of the organization in addressing this behavior, being the variables belong to the field of organizational behavior to see the results of those variables on the Iraqi organizations, since the specificities of it differ from the rest of the Arab and foreign environments. Therefore, this study focused on testing the relationship of the internal environment of the organization and its role in addressing the negative behavior of the workers.

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Abstract
Disturbs the social system in any society specially in Iraq other than
other Islamic and Arabic countries. That is according to the abnormal
conditions that Iraq has past through as wars and blockage, and lastly the
invasion. Therefore it has been necessary to put this phenomenon under
study and analysis to discover Juvenile Delinquency is one of the most
prominent social phenomenon that the important reasons behind it, and trying
to treat what can be treated of the effects of it upon society.
This study is mainly concerned with the explaining the social factors
leading toward juvenile delinquency trying to crystallize the problem of the
study in the following question: (What are the psychological,

The present study was designed to investigate the effect of different concentrations of Maxxthor on some hematological and oxidative stress parameters in male albino rats.Twenty male rats with age of 6-8 weeks and weight of 200-250gm were equally divided into four groups as follow:G1 treated with normal saline as control group,while G2,G3andG4groups were treated with(0.01,0.1,1)mg\kg body weight of Maxxthor respectively for 40 days.The following hematological parameters were measured: red blood cell(RBC),hemoglobin (Hb),white blood cell(WBC), platelets(PLT),malondialdehyde(MDA),glutathione(GSH),catalase and vitamin E. The hematological parameters results revealed highly significant(p<0.01)decreasein RBC and H,while a highly significant(p<0.

Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings

       In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes