The aim of this study is to detect the level of psychological stress among unemployed individuals and the level of their wellbeing by finding the correlation between these two variables.

          The research sample consisted of (99) people who are currently unemployed and registered at the Ministry of Labor Affairs.

Schafer (1996) scale for psychological stress was used and alongside Ziout's (2012) scale for wellbeing.

          The results of the research showed an inverse relation between being unemployed and having wellbeing.

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Background: Iron homeostasis is crucial to many physiological functions in the human body, such as cellular activity, erythropoiesis, and the innate immune response. Iron deficiency anemia may occur from obesity's ability to disturb iron homeostasis. Obesity may be seen as a pre-inflammatory condition with mild, ongoing systemic inflammation. Additionally, an increase in hepcidin levels by chronic inflammation causes iron insufficiency in obese people. For this reason, this current experiment is designed to investigate the iron profile and some hematological and inflammatory parameters in obese adults in the Kurdistan region-Iraq.

Subjects and Methods: The cross-sectional study w

Imam Ibn Abi Shaybah counted in his book classified twenty-five and a hundred recent and impact claimed that Abu Hanifa has violated the provisions required by apparent, and these conversations and effects including with regard to worship and transactions, including with regard to personal status, including with regard to borders and felonies, and collected by Sheikh Muhammad Zahid Al-Kothari in his book funny jokes in talking about the responses of Ibn Abu Shaybah on Abu Hanifa, and these issues we will study in our research.

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p