Objectives: To determine the effectiveness of the educational program on nursing staff knowledge about infection control measures at the Intensive Care Unit in Al-Diwaniya Teaching Hospital.

Methodology: A pre-experimental design (one group design: pre-test and post-test) was used. This study was conducted in Al-Diwaniya Teaching Hospital for the period from ( 20^th February to 5^th March, 2020) on a non-probability (purposive) sample consisting of (25 nurses) working in ICU. A questionnaire was built as a data collection tool and consisted of two parts:

First part: The demographic characteristics of the nursing staff (age, gender, level of education, years of experien

             Hospitals are part of the service organizations and most importantly at the level of individuals because they are tied to the people health and their daily lives , the nursing service is one of the important services provided by hospitals, and nurses are the human resource that offers this service, from this standpoint the idea of  ​​research came to prepare work Scheduling for nurses in a scientific way to improve performance operational for their services and provide efficient service available 24 hours a day, the research use one of the modern and scientific rules of scheduling its “schedule of

Aims: This study aimed to assess complications of caesarean section among pregnant women attending AL Diwaniyah Maternity and Pediatric Hospital; and find out relationship between complications of caesarean section and socio-demographic data of women. Methods: A descriptive cross-sectional study design is conducted for the period of December 26th 2020 to June 1st 2021. The validity of the questionnaire is determine through a panel of experts and reliability was achieved through a pilot study. By a purposive sample is selected among pregnant women who undergo caesarean section, data was collected through the use of questionnaire and interview techniques; and analyzed through the descriptive and inferential statistic. Results: Findings indica

This study aimed to evaluate the effect of the COVID-19 outbreak on emergencies and pain among orthodontic patients attending a teaching hospital. The study was conducted among orthodontic patients receiving active orthodontic treatment or in a retention period at the College of Dentistry, University of Baghdad, Iraq. Their participation was voluntary, and they filled out an Arabic-translated questionnaire. The survey included general information, orthodontic problems, and a numerical rating scale for pain assessment. We used descriptive and inferential statistics (frequencies and intersecting frequencies), chi-square test and linear regression. Out of 75 orthodontic patients, only 54 (15 males and 39 females) were included in the s

Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

Heart sound is an electric signal affected by some factors during the signal's recording process, which adds unwanted information to the signal. Recently, many studies have been interested in noise removal and signal recovery problems. The first step in signal processing is noise removal; many filters are used and proposed for treating this problem. Here, the Hankel matrix is implemented from a given signal and tries to clean the signal by overcoming unwanted information from the Hankel matrix. The first step is detecting unwanted information by defining a binary operator. This operator is defined under some threshold. The unwanted information replaces by zero, and the wanted information keeping in the estimated matrix. The resulting matrix