Background: This in vitro study compares a novel calcium-phosphate etchant paste to conventional 37% phosphoric acid gel for bonding metal and ceramic brackets by evaluating the shear bond strength, remnant adhesive and enamel damage following water storage, acid challenge and fatigue loading. Material and Methods: Metal and ceramic brackets were bonded to 240 extracted human premolars using two enamel conditioning protocols: conventional 37% phosphoric acid (PA) gel (control), and an acidic calcium-phosphate (CaP) paste. The CaP paste was prepared from β-tricalcium phosphate and monocalcium phosphate monohydrate powders mixed with 37% phosphoric acid solution, and the resulting phase was confirmed using FTIR. The bonded premolars were exposed to four artificial ageing models to examine the shear bond strength (SBS), adhesive remnant index (ARI score), with stereomicroscopic evaluation of enamel damage. Results: Metal and ceramic control subgroups yielded significantly higher (p ˂ 0.05) SBS (17.1-31.8 MPa) than the CaP subgroups (11.4-23.8 MPa) post all artificial ageing protocols, coupled with higher ARI scores and evidence of enamel damage. In contrast, the CaP subgroups survived all artificial ageing tests by maintaining adequate SBS for clinical performance, with the advantages of leaving unblemished enamel surface and bracket failures at the enamel-adhesive interface. Conclusions: Enamel conditioning with acidic CaP pastes attained adequate bond strengths with no or minimal adhesive residue and enamel damage, suggesting a suitable alternative to the conventional PA gel for orthodontic bonding.
In the age of information and communication revolution, education as one of life
aspects has influenced with that revolution by integrating technology in education, which
have become as an important learning tools of the whole educational process . Technology,
when used appropriately, can help make science classroom a site of active learning and
critical thinking, furthering student inquiry and connections with different materials. It is
necessary to develop human rights education programs and materials for discretionary and
extracurricular activities as it provide them with the skills and tools so that they are
empowered to take action to realize their rights. Human rights education is a critical means of
instill
A mathematical model was created to study the influences of Hall current and Joule heating with wall slip conditions on peristaltic motion of Rabinowitsch fluid model through a tapered symmetric channel with Permeable Walls. The governing equations are simplified under low Reynolds number and the long-wavelength approximations. The perturbation method is used to solve the momentum equation. The physiological phenomena are studied for a certain set of pertinent parameters. The effects offered here show that the presence of the hall parameter, coefficient of pseudo-plasticity, and Hartman number impact the flow of the fluid model. Additional, study reveals that a height in the Hall parameter and the velocity slip parameter incre
... Show MoreThe main objectives of this study are to study the enhancement of the load-carrying capacity of Asymmetrical castellated beams with encasement the beams by Reactive Powder Concrete (RPC) and lacing reinforcement, the effect of the gap between top and bottom parts of Asymmetrical castellated steel beam at web post, and serviceability of the confined Asymmetrical castellated steel. This study presents two concentrated loads test results for four specimens Asymmetrical castellated beams section encasement by Reactive powder concrete (RPC) with laced reinforcement. The encasement of the Asymmetrical castellated steel beam consists of, flanges unstiffened element height was filled with RPC for each side and laced reinforced which are use
... Show MoreThis paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time [Formula: see text]. The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integ
... Show MoreMultiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of
... Show MoreDifferent formula of bioagents (Rhizobium cicceri cp-93, Azospirillum sp.,
Pseudomonas fluorescence, Trichoderma harzianum ) used in this study as a
biofertilizer on wheat crop with two level of chemical fertilizer (0 and 12.5
kg/donm Dap) compared to 50kg/donm Dap (standard amount).the study carried out
in Iraq/Diyala –Alkhales during November 2014,results showed significant increase
in no. of spikes, no. of spikelet’s, length of spike ,Weight of 1000 seed and yield of
one m2 when adding (Rhizobium cicceri cp-93,Azospirillumsp+ Trichoderma
harzianum +12.5 kg/donm Dap) in comparison with the 50kg/donm Dap. Other
formulas recorded same results with the treatment 50kg/Donm Dap with not
significant differences
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for