Background: Chronic periodontitis is an inflammatory disease that affects the supporting tissues of the teeth and it’s common among adults. Smoking is an important risk factor for periodontitis induces alveolar bone loss. Alkaline phosphatase enzyme is involved in the destruction of the human periodontium. It is produced by many cells such as polymorphonuclear leukocytes, osteoblasts, macrophages and fibroblasts within the area of the periodontium and gingival crevice. Osteocalcin is one of the most abundant matrix proteins found in bones and the only matrix protein synthesized exclusively there. Smaller Osteocalcin fragments are found in areas of bone remodeling and are actually degradation products of the bone matrix.The purpose of this study was to evaluatethe effect of smoking on the salivary alkaline phosphatase and Osteocalcin in subjects with chronic periodontitis compared to control subjects. Materials and Methods: Five ml of unstimulated whole saliva samples and full-mouth clinical periodontal recordings (plaque index, gingival index, bleeding on probing, probing pocket depth and clinical attachment level) were obtained from study groups (25 light smokers and 33 non-smokerssubjects, both with chronic periodontitis) and control groups (8 light smokers and 13 non-smokers subjects, both with healthy periodontium). All subjects were systemically healthy males, with age range (30-50) years. Salivary Alkaline phosphatase and Osteocalcin levels were determined by Colorimetric and Enzyme-linked Immunosorbent Assays, respectively. Results: Smoker chronic periodontitis patients revealed non-significant differences in clinical periodontal parameters with non-smoker counterparts (P˃o.o5) in terms of Plaque index, Probing pocket depth and Clinical attachment loss, with slight increase in plaque index value in smoker chronic periodontitis group(1.42±0.46) than non-smoker chronic periodontitis group, while there were highly significant differences in terms of Gingival index and Bleeding on probing(P ≤ 0.01).Osteocalcin levels were lower in smoker chronic periodontitis group (0.13±0.20) than non-smoker chronic periodontitis group (1.09±2.26) with significant difference (0.05 ≥ P > 0.01). Mean of Alkaline phosphatase level was lower in smoker chronic periodontitis (11.14±4.53) than non-smoker chronic periodontitis (11.45±4.17) with a non-significant difference, while there was a significant difference inAlkaline phosphatase concentrations between smoker and non-smoker control groups.There were non-significant differences between smoker chronic periodontitis and smoker control groups in terms of Osteocalcin and Alkaline phosphatase concentrations. There were non-significant differences between non-smoker chronic periodontitis and non-smoker control groups in terms of Osteocalcin and Alkaline phosphatase concentrations. Conclusion: Within the limits of this study, it may be suggested that suppression of salivary Osteocalcin levels by smoking and slight increase in alkaline phosphatase in smokers groups, may explain the deleterious effects of smoking on periodontal health status.
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The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimati
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This article presents the results of an experimental investigation of using carbon fiber–reinforced polymer sheets to enhance the behavior of reinforced concrete deep beams with large web openings in shear spans. A set of 18 specimens were fabricated and tested up to a failure to evaluate the structural performance in terms of cracking, deformation, and load-carrying capacity. All tested specimens were with 1500-mm length, 500-mm cross-sectional deep, and 150-mm wide. Parameters that studied were opening size, opening location, and the strengthening factor. Two deep beams were implemented as control specimens without opening and without strengthening. Eight deep beams were fabricated with openings but without strengthening, while
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This paper provides an attempt for modeling rate of penetration (ROP) for an Iraqi oil field with aid of mud logging data. Data of Umm Radhuma formation was selected for this modeling. These data include weight on bit, rotary speed, flow rate and mud density. A statistical approach was applied on these data for improving rate of penetration modeling. As result, an empirical linear ROP model has been developed with good fitness when compared with actual data. Also, a nonlinear regression analysis of different forms was attempted, and the results showed that the power model has good predicting capability with respect to other forms.
There is an interesting potential for the use of GFRP-pultruded profiles in hybrid GFRP-concrete structural elements, either for new constructions or for the rehabilitation of existing structures. This paper provides experimental and numerical investigations on the flexural performance of reinforced concrete (RC) specimens composite with encased pultruded GFRP I-sections. Five simply supported composite beams were tested in this experimental program to investigate the static flexural behavior of encased GFRP beams with high-strength concrete. Besides, the effect of using shear studs to improve the composite interaction between the GFRP beam and concrete as well as the effect of web stiffeners of GFRP were explored. Encasing the GFRP
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Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal. The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in
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The paper uses the Direct Synthesis (DS) method for tuning the Proportional Integral Derivative (PID) controller for controlling the DC servo motor. Two algorithms are presented for enhancing the performance of the suggested PID controller. These algorithms are Back-Propagation Neural Network and Particle Swarm Optimization (PSO). The performance and characteristics of DC servo motor are explained. The simulation results that obtained by using Matlab program show that the steady state error is eliminated with shorter adjusted time when using these algorithms with PID controller. A comparative between the two algorithms are described in this paper to show their effectiveness, which is found that the PSO algorithm gives be
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