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Assessment of Levels of premenstrual psychological and physical Problems (Premenstrual Syndrome-PMS) of Students of the Colleges of Bab Al-Mua’dham Complex/ University of Baghdad
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Objectives: To assess levels of premenstrual psychological disorders of the students in Bab Al-Mua’dham Complex and to find out the relationship between the levels of premenstrual psychological and physical disorders and some demographic characteristics of the students. Methodology: A descriptive study was accomplished throughout the period from the 1st of October, 2015 to the 8th of July, 2016 to assess the psychological and physical problems. A purposive sample of 313 students distributed among different colleges of Bab Al-Mua’dam complex distributed as following: 82 students are from college of Arts; 79 students are from College of Languages; 48 students are from college of Islamic Sciences: and 104 are from College of Nursing. For the purposes of the study a questionnaire was constructed which represents the premenstrual symptoms screening tool (PSST) by Steiner et al.(2003)(1). This questionnaire consists of three parts: first, the demographic characteristics of students agreed to participate in the present study; Second part contains 16 items represents the psychological problems and the third part is supposed to measure the physical problems and contains 11 items. Levels of severity of problems were determined by using the quartiles step. The data of the study were analysed by applying descriptive data analysis: frequencies, percentages and tables of distribution; and inferential data analysis: Pearson coefficient correlation and Chi square, using the statistical analysis program of SPSS 19th version. Results: The present study reveals that more than half of the students are 20 and 21 years old; the majority of them are unmarried; one third of the sample are from the first class; more than three quarters have different levels of severity of psychological and physical problems distributed among slight level to the levels which interrupt the daily activities and/ or change the life style. The study has not found any differences between married and unmarried students. Also, the study does not find significant association between demographic characteristics and psychological problems but there is a significant relationship between the study stage and physical problems and age and psycho-physical problems. Recommendations: The present study recommends that medical and health follow up for students with levels which interrupt the daily activities and/ or change the life style of those students. Further studies concerning the impact of psychological and physical problems upon the academic achievement and also upon social relationship and the way the student deals with her family and society. Further studies aimed to discover the mood fluctuations which might result to suicidal ideation.

 
 

 

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Publication Date
Mon Jan 01 2024
Journal Name
Explainable Artificial Intelligence In The Digital Sustainability Administration
Harnessing Technological Innovation and Artificial Intelligence in Iraqi Commercial Banks to Achieve Sustainability
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Publication Date
Wed Aug 28 2024
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Mesopotamian Journal Of Cybersecurity
A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

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Publication Date
Wed Aug 28 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

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Publication Date
Fri Jan 01 2021
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Response surface methodology: A review on its applications and challenges in microbial cultures
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Publication Date
Sun Aug 08 2021
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Proceedings Of International Conference On Emerging Technologies And Intelligent Systems
Drone Altitude Control Using Proportional Integral Derivative Technique and Recycled Carbon Fiber Structure
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Thu Jun 01 2023
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2023 Ieee 36th International Symposium On Computer-based Medical Systems (cbms)
Toward a Minimal sEMG Setup for Knee and Ankle Kinematic Estimation during Gait
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Publication Date
Fri Aug 12 2022
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Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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Publication Date
Sat Nov 01 2025
Journal Name
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Chitosan salt as a dual-function agent for CO₂ sequestration and acidizing enhancement
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The utilization of carbon dioxide (CO₂) to enhance wellbore injectivity presents a cost-effective and sustainable strategy for mitigating greenhouse gas emissions while improving reservoir performance. This study introduces an environmentally friendly method employing a water-soluble chitosan salt (CS) that generates a carbonated-rich acid solution upon contact with dry CO₂ at 25 °C and 508 psi. CS solutions (100–2000 ppm) were prepared and evaluated for CO₂ uptake, acid generation, and rheological behavior. Results show that 1000 ppm achieves an optimal CO2 uptake (2612 mg/l), with moderate viscosity increase (from 1.52 to 3.37 cp), while higher concentrations exhibit a sharp rise due to polymer-like network formation. Core floodi

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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Publication Date
Sun Dec 21 2025
Journal Name
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Motivation-based Human Needs and their Disclosure Prompts in Chris Cleave’s Little Bee
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The present work folds two qualitative objectives; the first focuses on investigating the multiplicity of motivation-based human needs in Little Bee. The second objective involves examining the linguistic forms adopted to disclose such needs. Consequently, the researchers are to adapt eclectically Alderfer's Existence, Relatedness, and Growth Theory (1969) and Langacker’s theory of Domains (1987) together with his Active Zone Operation (1991). Such a study helps to embody the connectivity between the social and psychological aspects, and the way these two aspects are disclosed using particular linguistic The study has concluded that Bee needed Alderfer’s basic human needs: existence, relatedness, and growth. Besides, satisfying

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