Background: The development of orthodontic biomaterials that attract less biofilm has been a goal for decades. Adhesion and colonization of cariogenic streptococci are considered to play key roles in the development of enamel demineralization related to orthodontic materials. The aim of this study was to quantitatively evaluate the Mutans streptococci adhesion to coated orthodontic archwires (Epoxy and Teflon) and uncoated archwires (stainless steel and nickel-titanium) with respect to incubation time in the presence and absence of saliva. Material and Method: Six types of archwires stainless steel and nickel titanium with two type of coating (Epoxy, Teflon) were used in this study. Twelve specimens of each archwire were incubated in sterilized unstimulated whole saliva (for the study group) and phosphate-buffered saline (for control group) for 2 hours, then incubated with suspension of Mutans streptococci allowed to adhere for (5,90,180 minutes). Adhesion was quantitated by a microbial culture technique by treating the archwires with adhering bacteria with trypsin and enumerating the colony forming unit (CFU) counts of bacteria recovered after cultivation by using Dentocult SM kit. Results: There was significant difference among the tested archwire types in each time interval with the highest bacterial adhesion on the NiTi archwires in the absence of saliva. In the presence of saliva, the results revealed non-significant difference at 5 min. while there was significant difference at 90 min and highly significant difference at 180 min. Conclusion: The adherence of Mutans streptococci was decreased in the presence of saliva on different archwires and the extended incubation time was significantly related to increase colony forming unit of Mutans Streptococci.
The region-based association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease. Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls. To tackle the problem of the sparse distribution, a two-stage approach was proposed in literature: In the first stage, haplotypes are computationally inferred from genotypes, followed by a haplotype coclassification. In the second stage, the association analysis is performed on the inferred haplotype groups. If a haplotype is unevenly distributed between the case and control samples, this haplotype is labeled
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Trip generation is the first phase in the travel forecasting process. It involves the estimation of the
total number of trips entering or leaving a parcel of land per time period (usually on a daily basis);
as a function of the socioeconomic, locational, and land-use characteristics of the parcel.
The objective of this study is to develop statistical models to predict trips production volumes for a
proper target year. Non-motorized trips are considered in the modeling process. Traditional method
to forecast the trip generation volume according to trip rate, based on family type is proposed in
this study. Families are classified by three characteristics of population social class, income, and
number of vehicle ownersh
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Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p
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The UN plans to achieve several development objectives by 2030. These objectives address global warming, a major issue. This method aims to improve sustainable accounting performance (AP). In this circumstance, AI is being applied in various fields, notably in economic, social, and environmental (ESE) domains. This research investigates how sustainable development (SD) influences AI methodologies and AP improvement. The research examined a sample of Iraqi banks listed on the Iraq Stock Exchange from 2014 to 2022. AI was measured by ATM and POS prevalence. A three-dimensional approach examined economic, social, and environmental (ESE) sustainability. Meanwhile, the performance of sustainable accounting was measured through the return on asse
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In this paper a prey - predator model with harvesting on predator species with infectious disease in prey population only has been proposed and analyzed. Further, in this model, Holling type-IV functional response for the predation of susceptible prey and Lotka-Volterra functional response for the predation of infected prey as well as linear incidence rate for describing the transition of disease are used. Our aim is to study the effect of harvesting and disease on the dynamics of this model.
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In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to simulate values of the variable coefficients as random sampling instead being limited as real values with respect to time. The mean of the n final solutions via this integrated technique, named in short as mean Monte Carlo finite difference (MMCFD) method, represents the final solution of the system. This method is proposed for the first time to calculate the numerical solution obtained fo
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