Microfibers released by synthetic clothes have a significant negative effect on the environment. Several solutions have been proposed and evaluated for their effectiveness, but studies have failed to address the human-centered aspects of these products. In this research, the possibilities and needs from a consumer perspective for a new filtering system for domestic washing machines were examined. First, a quantitative (questionnaire) and a qualitative (interviews and observations) exploration were done to understand the desired requirements from a user perspective. Next, the acceptance of various existing solutions for microfiber catching was investigated. To verify these requirements, a new concept was designed and evaluated with a

Objectives: To determine the effectiveness of physical education program on the domains of the university
students attitudes of physical activity and health, physical activity and mental health, physical activity and nutrition
toward physical fitness.
Methodology: A quasi-experimental design is carried out throughout the present study with the application of
test-retest approach through the period from February 3rd 2013 to June 30th 2013. The study is conducted on
purposive sample of(40) Undergraduate Students at the College of Science University of Baghdad . The sample is
Consisted of (20) males and (20) females. Questionnaire of two main parts, Personal and demographic
information and students' attitudes about phys

<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

<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

Objective(s): The study aimed to assess the level of nursing performance and practices in terms of approaching or
distancing itself from the optimal performance criteria universally adopted within the variable dressing surgical
wounds of patients admitted to the surgical wards, and determine the relationship between the level of nurse's
performance and socio-demographic characteristics of them in those wards.
Methodology: A descriptive assessing design was adopted from November the 10th, 2010 until June the 1st, 2011 to
assess the nursing care provided practices for the postoperative period within the variable dressing surgical wounds in
the complex of Medical City. Whereas the study was conducted in three hospitals; Ba

The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T