Background: Orthodontic mini-implants are increasingly used in orthodontics and the bone density is a very important factor in stabilization and success of mini-implant. The aim of this study was to observe the relationship among maximum bite force (MBF); body mass index (BMI); face width, height and type; and bone density in an attempt to predict bone density from these variables to eliminate the need for CT scan which have a highly hazard on patient. Materials and Methods: Computed tomographic (CT) images were obtained for 70 patients (24 males and 46 females) with age range 18-30 years. The maxillary and mandibular buccal cortical and cancellous bone densities were measured between 2nd premolar and 1st molar at two levels from the alveolar crest (3 and 6 mm). Face height and width were measured from CT. Clinically; Maximum bite force was measured on first molar region unilaterally by a digital device. The sample was divided into two groups according to the body mass index into; normal and overweight. Results: The results obtained showed that there were no statistical significant differences in MBF or bone density in both genders. Only the cortical bone density in maxilla in overweight group tended to be higher than normal BMI group. The face width and height correlated significantly negatively with MBF which correlated significantly positively with cortical bone density. Conclusions: It was concluded that a prediction of cortical bone density of preselected areas can be made from maximum bite force, body mass index and inter-zygomatic width.
Volcaniclastic rocks of Al Muqdadiya Formation (Pliocene) in Injana area, southern Hemrin anticline, NE of Iraq, were studied ( petrographically, physically, mineralogically and geochemically , as well as the engineering properties) to assess the suitability of volcaniclastic rocks to use them in industry as refractories. The results show that the physical and engineering properties change with the temperature change. The bulk density and the specific gravity increase by increasing temperature while the apparent porosity, water sorption and the linear shrinkage decrease. On the other hand the compressive strength increase by increasing temperature. The volcaniclastics have very low thermal conductivi
... Show MorePermeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy
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In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc
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. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a
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Background: The present in-vitro study was undertaken to evaluate and compare fracture resistance of weakened endodontically treated premolars with class II MOD cavities restored with different bulk fill composite restorations (EverX posterior, Alert, Tetric EvoCeram Bulk Fill, and SDR). The type and mode of fracture were also assessed for all the experimental groups. Materials and Method: Forty-eight human adult maxillary premolar teeth were selected for this study. Standardized extensive class II MOD cavities with endodontic treatment were prepared for all teeth, except those that were saved as intact control. The teeth were divided into six groups of eight teeth each (n=8): (Group 1) intact control group, (Group 2) unrestored teeth with
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Background: Periodontal diseases are one of the major dental pathologies that affect human populations worldwide at high prevalence rates The term periodontal disease usually refers only to plaque related inflammatory disease of the dental supporting tissues. Mouth rinses which act as an anti-plaque agents mostly used as adjuncts to oral hygiene. Aims of the study: To Estimate and compare the effects of Aloe vera relative to chlorhexidine on the clinical periodontal parameters (plaque index, gingival index, bleeding on probing). Material and method: A total of 44 subjects with plaque-induced gingivitis, baseline of data were collected for (PLI, GI, and BOP) and underwent oral hygiene instruction, scaling and polishing, then divided into:
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KA Hadi, AH Asma’a, IJONS, 2018 - Cited by 1
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