Objective: To assess of Science Teachers' Awareness towards Communicable Diseases Control in Baghdad City
Primary Schools
Methodology: A descriptive study was conducted, included (100) primary school, (50) in Al-Rassafa sector, and
(50) in Al-Karkh sector, from March 5th 2012 to March 15th 2013, to assess of science teachers' awareness
towards communicable diseases control. A cluster sample of (100) Science teachers (males and females) were
selected, as one teacher from each school. A questionnaire format was used for data collection. The validity of
questionnaire was estimated through a penal of experts related to the field of study, and its reliability was
estimated through a pilot study conducted in (20) schools (excluded from the original sample) which included (20)
teachers from April 8th 2012 to April 14th 2012. Data were analyzed through the application of descriptive and
inferential statistical analysis.
Results: The results of the study indicated that (78%) of sample were female, and (39%) of them their ages were
between (40-49) years old, (65%) of them were married, while (21%) of them were single, and (50%) of them were
institute graduates. Also the study indicated that (26%) of teachers had (15–19) years of employment in teaching
sector. Furthermore, (58 %) of sample had no opportunity to be involved in training courses concerning
communicable diseases control. The results of this study indicate that, teachers' awareness was poor (in adequate
awareness) towards communicable diseases control, immunity and immunization, and also concerning health
problems in school.
Recommendations: The study recommends that there is a need for cooperation between the Ministry of Health
and the Ministry of Education to prepare a textbook for primary schools students about the nature of the
communicable diseases and how to control them with adequate explanation of the most important diseases in
school provided with photos and illustrations, and emphasis on continuous sessions concerning communicable
diseases control for teachers in all primary schools
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Background: The world is in front of two emerging problems being scarceness of virgin re-sources for bioactive materials and the gathering of waste production. Employment of the surplus waste in the mainstream production can resolve these problems. The current study aimed to prepare and characterize a natural composite CaO-SiO2 based bioactive material derived from naturally sustained raw materials. Then deposit this innovative novel bioactive coating composite materials overlying Yttria-stabilized tetragonal zirconia substrate. Mate-rials and method; Hen eggshell-derived calcium carbonate and rice husk-derived silica were extracted from natural resources to prepare the composite coating material. The manufac-tured powder was characterized
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Ag-doped NiO thin films were fabricated by dip coating and subsequently annealed at 373, 473, 573, and 673 K to elucidate the influence of annealing temperature on their structural, morphological, optical, and NO2-sensing characteristics. X-ray diffraction confirmed the formation of polycrystalline Ag/NiO films, with progressively improved crystallinity and larger crystallite size at elevated annealing temperatures. Atomic force microscopy revealed a homogeneous surface morphology with grain growth, with the average grain diameter increasing from 26.40 to 34.2 nm. Optical analysis showed high visible-region transmittance (65–70%) and a gradual reduction in the optical band gap from 2.75 to 2.07 eV with increasing annealing temperature. Ga
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To evaluate the shear bond strength and interfacial morphology of sound and caries-affected dentin (CAD) bonded to two resin-modified glass ionomer cements (RMGICs) after 24 hours and two months of storage in simulated body fluid at 37°C.
Sixty-four permanent human mandibular first molars (32 sound and 32 with occlusal caries, following the International Caries Detection and Assessment System) were selected. Each prepared substrate (sound and CAD) was co
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Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
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