Background: (ABO) Blood type have an effect on general health including oral health as salivary physicochemical characteristics differ among different type of blood and as consequence these affect the severity of dental caries. The aim of the present study is an assessment of the prevalence of caries experience among different blood type in relation to salivary physicochemical characteristic. Materials and Methods: Two hundred and fifty females' college students in Al-Qadisyia University aged 18 years old were selected on random basis; they were divided to four groups according to their blood type, Dental experience was diagnosed and recorded according to DMFs (Mülemman, 1976) Index, this allows recording decayed lesion by severity. A su

The aim of the research is to evaluate the response of the researched leaders towards practicing the concept of the lens, which is its dimensions with (stakeholders, resource mobilization, knowledge development, culture management) and the nature of its relationship to tax pioneer performance represented in its dimensions (strategic direction, leadership indicators, growth, renewal and modernization, efficiency, Effectiveness) The questionnaire was approved as a main tool in collecting data and information from the sample members in the General Authority of Taxes, which number (91) Who are on (M. General Manager, Division Director, Deputy Director, Senior Division Director, Deputy Director, Second Division, Division Officer, M. D

  The study aimed to highlight the reality of the functional pressures with its dimensions (role ambiguity, role conflict, role burden, glass ceiling, and discrimination in composition). The researchers also relied on the questionnaire as a essential tool for data collection. The field study was conducted at the University of Mohammed Khiedr - Biskra -, the study was conducted on the basis of the total survey, which included all the workers of the 6 faculties of Biskra University (523 female employees).

     After the analyzing of the data using the version 21 of the statistical program Spss, The study reached a number of results, the most of them is the low level of the functiona

The waste sorting and recycling plant in the district of al- Mahmudiya (Yusufiya) is the only laboratory in Baghdad governorate. It is the only one in Iraq that collects and recycles waste. After sorting, we have packages of raw materials (aluminum cans, glass bottles, plastic bottles, Nylon bags) as well as natural fertilizers (food residues and others) and a sanitary landfill with specifications that meet international standards; However, the absence of a structure for the management of the plant (manager, assistant manager, accountant, etc.) in addition to the non-supply of waste on a daily and continuous basis and the lack of cooperation of the Municipality of Baghdad with the management of the plant led to delay in the work

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

The use of artificial intelligence (AI) technology is rapidly expanding in nursing and society. However, its use in healthcare comes with a number of challenges and concerns. The authors of this article use the sociotechnical model to consider the expanding use of AI in nursing and healthcare from a global perspective. Select references from the literature are used to support this important discussion for nurses and other healthcare professionals. Artificial intelligence is a major innovation that, if used properly, can reduce errors and improve efficiency and healthcare quality. It has also been shown to increase patient support, healthcare access and patient care. Here the authors address some of the limitations and challenges of

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c