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

tA novel synthesis procedure is presented for preparing triethanolamine-treated graphene nanoplatelets(TEA-GNPs) with different specific areas (SSAs). Using ultrasonication, the covalently functionalizedTEA-GNPs with different weight concentrations and SSAs were dispersed in distilled water to prepareTEA-GNPs nanofluids. A simple direct coupling of GNPs with TEA molecules is implemented to synthesizestable water-based nanofluids. The effectiveness of the functionalization procedure was validated by thecharacterization and morphology tests, i.e., FTIR, Raman spectroscopy, EDS, and TEM. Thermal conduc-tivity, dispersion stability, and rheological properties were investigated. Using UV–vis spectrometer, ahighest dispersion stability of 0.876

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

Iraqi conventional gasoline characterized by its low octane number not exceed 82 and high lead and sulfur content. In this paper tri-component or ternary, blends of gasoline, ethanol, and methanol presented as an alternative fuel for Iraqi conventional gasoline. The study conducted by using GEM blend that equals E85 blend in octane rating. The used GEM selected from Turner, 2010 collection. G37 E20 M43 (37% gasoline + 20% ethanol+ 43% methanol) was chosen as GEM in present study. This blend used in multi-cylinder Mercedes engine, and the engine performance, and emitted emissions compared with that produced by a gasoline engine.

The results show that this blend can formulate with available Iraqi pro

Biosorption of lead, chromium, and cadmium ions from aqueous solution by dead anaerobic biomass (DAB) was studied in single, binary, and ternary systems with initial concentration of 50 mg/l. The metal-DAB affinity was the same for all systems. The main biosorption mechanisms were complexation and physical adsorption of metallic cations onto natural active functional groups on the cell wall matrix of the DAB. It was found that biosorption of the metallic cations onto DAB cell wall component was a surface process. The main functional groups involved in the metallic cation biosorption were apparently carboxyl, amino, hydroxyle, sulfhydryl, and sulfonate. These groups were part of the DAB cell wall structural polymers. Hydroxyle groups (–O

The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

A 3D geological model is an essential step to reveal reservoir heterogeneity and reservoir properties distribution. In the present study, a three-dimensional geological model for the Mishrif reservoir was built based on data obtained from seven wells and core data. The methodology includes building a 3D grid and populating it with petrophysical properties such as (facies, porosity, water saturation, and net to gross ratio). The structural model was built based on a base contour map obtained from 2D seismic interpretation along with well tops from seven wells. A simple grid method was used to build the structural framework with 234x278x91 grid cells in the X, Y, and Z directions, respectively, with lengths equal to 150 meters. The to

Abstract: The aim of the current research is to identify (the relationship between deep understanding skills and mathematical modeling among fifth grade students) the research sample consisted of (411) male and female students of the fifth grade of biology distributed over the General Directorates of Education in Baghdad / Al-Rusafa / 2 / and Al-Karkh / 1 /, and two research tools were built: 1- A test of deep understanding skills, consisting of (20) test items and a scale for two skills. 2- The second test consists of (24) test items distributed among (18) essay items and (6) objective items. The psychometric properties of validity, stability, discriminatory strength, and effectiveness of alternatives were verified for the two tests fo

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c