Previous studies in Euro-American countries have shown that patients with chronic hepatitis C virus infection have increased levels of neuropsychiatric symptoms. While chronic hepatitis C virus infection has been reported in Arab countries such as Iraq, there is little studies about the neuropsychological burden associated with chronic hepatitis C among patients in the Arab region. The aim of the current study was to measure the prevalence and level of severity of depression, anxiety and stress among a sample of chronic hepatitis C patients in AL-Najaf province /Iraq. The current study was cross-sectional study carried out on (110) already diagnosed chronic viral hepatitis C patients who attended the Gastroenterology and Hepatology

Introduction: Breast cancer is the most common cancer and the major cause of cancer related deaths among Iraqi women. Due to the relatively late detection of breast cancer, the majority of the patients are still treated by modified radicle mastectomy. Aim: To assess the time lag between diagnosis of breast cancer and mastectomy among Iraqi patients; correlating the findings with other clinicopathological characteristics of the disease. Patients and methods: This retrospective study enrolled 226 Iraqi female patients who were diagnosed with breast cancer. Data were registered on the exact time period between signing the histopathological report and the surgical treatment. Other recorded variables included the age of the patients, their level

Objective: To find out the prevalence of anxiety and depression among Iraqi repatriated prisoners of Iran-Iraq war
(IRPOWs), and the relationship with some variables.
Methodology: A descriptive study was carried out from Oct. 18th, 2009 through Jan. 10th, 2010. A Snowball
sampling as a non-probability sampling technique was used to recruit 92 repatriates who had visited Ministry of
Human Rights. An instrument was constructed for this purpose. The constructed instrument consisted of six
demographic characteristics, and fourteen items to measure the level of anxiety and depression in prisoners of
war (POWs). Data were collected with using the constructed instrument and the process of the interview as means
for data col

                  In light of the corona pandemic, educational institutions have moved to learning and teaching via the Internet and e-learning ,and this is considered a turning  point in course of higher education in Iraq in particular and education in general, which generated a great challenge for educational institutions to achieve the highest possible levels in practices and processes to reach the highest quality of their outputs from graduate students to the labor market that auditing performance by adopting e-learning standards is one of the effective tools that help the management of educational institutions by providing information on the ex

Manual fruit picking is labor-intensive and can damage fruit. Fully mechanized picking is efficient, but it also risks fruit damage. Therefore, semi-automated tools are needed to improve bitter orange picking. This paper presents a smart manual picker designed to facilitate picking while predicting fruit maturity based on picking force as well as various chemical and physical parameters using machine learning (ML). The study methodology consists of five stages: (1) manufacturing the smart picker, (2) picking 50 bitter orange samples, (3) measuring the characteristics of the bitter oranges in the laboratory, (4) training different ML models, and (5) identifying the most accurate model for predicting fruit maturity. The results indicate that

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Background: Schneiderian first rank symptoms are
considered highly valuable in the diagnosis of
schneideria.
They are more evident in the acute phase of the
disorder and fading gradually with time. Many studies
have shown that the rate of these symptoms are
variable in different countries and are colored by
cultural beliefs and values.
Objectives: To find out the rate of Schneiderian first
rank symptoms among newly diagnosed schizophrenic
patients, to assess which symptom(s) might
predominate in those patients, and to find out if there
is/are any correlation(s) between the occurrence of
these symptoms and the sex of the patients.
Methods: Out of twenty-four patients with no past
psychiatric hi