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.
The research aims to shed light on the role of artificial intelligence in achieving Ambidexterity performance, as banks work to take advantage of modern technologies, artificial intelligence is an innovation that is expected to have a long-term impact, as well as banks can improve the quality of their services and analyze data to ensure that customers' future needs are understood. . The Bank of Baghdad and the Middle East Bank were chosen as a community for the study because they had a role in the economic development of the country as well as their active role in the banking market. A sample of department managers was highlighted in collecting data and extracting results based on the checklist, which is the main tool for the stu
... Show More Background: Congenital malformations are responsible for a considerable proportion of perinatal, neonatal and infant mortality in many Eastern Mediterranean countries. So this study
aims to find out the incidence, types and probable risk factors of these malformations in Baghdad.
Patients and methods: one hundred cases of congenital malformations were studied out of 8090 neonates born in 4 hospitals in Baghdad province over 5 months in 2002 (preterm, term) .The incidence , types and risk factors were analysed.
Results: the incidence of congenital malformations was 12.36/ 1000 live births, with the central nervous system malformations being the commonest. There is increased risk in consanguinous mar
تعد صناعة السمنت في العراق من اقدم الصناعات الحديثة واكثرها تطورا وتقدما ومن اقواها تاثيرا في الاقتصاد القومي. واذ توفر في صناعة السمنت العراقي كافة المستلزمات الناجحة من حيث توفر المواد الاولية والخبرات الفنية والتقنية واسواق ثابتة وراسخة محليا وعالميا فقد كان من المفروض ان يتم التوسع في هذه الصناعة، وان التخطيط لهذه الصناعة امرا ضروريا خاصة وان مادة السمنت هي احدى اهم المواد الرئيسة التي يؤثر توفره
... Show MoreA model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs lengths and their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy. The optimization carried out is subjected to constraints that ensure a safe structure aga
... Show MoreThe prostheses sockets use normally composite materials which means that their applications may be related with the human body. Therefore, it was very necessary to improve the mechanical properties of these materials. The prosthetic sockets are subjected to varying stresses in gait cycle scenario which may cause a fatigue damage. Therefore, it is necessary or this work to modify the fatigue behavior of the materials used for manufacturing the prostheses sockets. In this work, different Nano particle materials are used to modify the mechanical properties of the composite materials, and increase the fatigue strength. By using an experimental technique, the effect of using different volu
Background: The emergence of multiple drug resistant (MDR) Mycobacterium tuberculosis (M.TB) and extensive drug resistant (XDR) M.TB lay huge burden on TB endemic countries such as Iraq.
Objectives: Bacteriophage (phage) therapy can be used as alternative approach to tackle this problem. Patients and methods: Forty isolates of M.TB were cultured from TB-positive sputum specimens with three ATCC strains. Phage passaging and biokinetic based techniques were used to optimize wild anti-M.TB phages. Three chemical, non genetic- designing techniques, tween-80, mycobacterial lysis buffer, and xyelen, were used to change the specificity of wild anti-TB phages towards phage-resistant target M.TB.
Results: Five wild anti-M.TB phages were iso
This research includes the study of dual data models with mixed random parameters, which contain two types of parameters, the first is random and the other is fixed. For the random parameter, it is obtained as a result of differences in the marginal tendencies of the cross sections, and for the fixed parameter, it is obtained as a result of differences in fixed limits, and random errors for each section. Accidental bearing the characteristic of heterogeneity of variance in addition to the presence of serial correlation of the first degree, and the main objective in this research is the use of efficient methods commensurate with the paired data in the case of small samples, and to achieve this goal, the feasible general least squa
... Show MoreIn this work, the dynamic behavior of discrete models is analyzed with Beverton- Holt function growth . All equilibria are found . The existence and local stability are investigated of all its equilibria.. The optimal harvest strategy is done for the system by using Pontryagin’s maximum principle to solve the optimality problem. Finally numerical simulations are used to solve the optimality problem and to enhance the results of mathematical analysis
A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m
... Show MoreThe issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of
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