Double hydrothermal method was used to prepare nano gamma alumina using aluminum nitrate nano hydrate and sodium aluminate as an aluminum source, CTAB (cetyltrimethylammonium bromide) as surfactant, and variable acids: weak acids like; citric, and acitic acids, and strong acids like; hydrochloric and nitric acids as a bridge between aluminum salts and surfactant. Different crystallization times 12, 24, 48, and 72 hrs were applied. All the batches were prepared at pH equals to 9. XRD diffraction technique was used to investigate the crystalline nano gamma alumina pure from surfactant. N2 adsorption-desorption (BET) was used to measure the surface area and pore volume of the prepared nano alumina, the average particle size and the morphology of the surface of nano gamma alumina were estimated using AFM and SEM techniques, respectively. The sharpness of the peaks increased with increasing of crystallization time. The surface area, pore volume, and average particle size were decreased with increasing crystallization time. The best result of surface area was 383 m2/gm obtained using citric acid at 12 hr crystallization time, while the best results of pore volume and average particle size were 0.54cm3/gm and 72.37nm obtained using hydrochloric acid at12 hr crystallization time. Low agglomeration with hexagonal structure obtained using weak acids, while agglomeration occurred and clusters formed using strong acids.
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Cystic fibrosis (CF) is an autosomal recessive multisystem disease that results from mutation(s) of the cystic fibrosis transmembrane conductance regulator (
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The need to overcome the failure of orthodontic micro-implants which might reach to 30% has led to the development of different methods, one of which is nanoparticle deposition.
To evaluate the anti-microbial efficiency of TiO2 and ZnO nanoparticles (NP) when used as a coating for orthodontic micro-implants.
Thirty titanium alloy micro-implants were used in the presented study. They were divided into three
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Diabetes distress (DD), an emotional burden that does not meet the diagnostic criteria for major depressive disorder, has gained attention in the diabetes literature. It was to evaluate the impact of diabetes education among psychological distress in type 2 diabetes patients.
A cross-sectional study was conducted between November 1, 2024, and March 1, 2025, at the Endocrine and diabetes center in Baghdad City, Iraq, tar
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Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an
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In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve
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Titanium dioxide nanotubes were synthesized by anodizing Ti sheets in the ethylene glycol solution and were covered in Pt nanoparticles onto the surface of TiO2NTs using electrodeposition method from using five derivatives of Mannich base Pt complexes which have been used as precursor of platinum. The mean size, shape, elemental composition of the titanium dioxide nanotubes and platinum deposited on the template were evaluated by different techniques such as field emission scanning electron microscope (FE-SEM), transmission electron microscopy (TEM), X-ray diffraction pattern (XRD), and energy dispersive X-ray (EDX) technique. From all these analyses, the TiO2NTs prepared and Ptnanoparticles deposited on it were identified. The diagnoses pr
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Conjugate heat transfer has significant implications on heat transfer characteristics, particularly in thick wall applications and small diameter pipes. In this study, a three-dimensional numerical investigation was carried out using commercial CFD software “ANSYS FLUENT” to study the influence of conjugate heat transfer of laminar flow in mini channels at constant heat flux wall conditions. Two parameters were studied and analyzed: the wall thickness and thermal conductivity and their effect on heat transfer characteristics such as temperature profile and Nusselt number. Thermal conductivity of (0.25, 10, 202, and 387) W/m2C and wall thickness of (1, 5, and 50) mm were used for a channel of (1*2) mm cross
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The influence of Cr3+ doping on the ground state properties of SrTiO3 perovskite was evaluated using GGA-PBE approximation. Computational modeling results infered an agreement with the previously published literature. The modification of electronic structure and optical properties due to Cr3+ introducing into SrTiO3 were investigated. Structural parameters assumed that Cr3+ doping alters the electronic structures of SrTiO3 by shifting the conduction band through lower energies for the Sr and Ti sites. Besides, results showed that the band gap was reduced by approximately 50% when presenting one Cr3+ atom into the SrTiO3 system and particularly positioned at Sr sites. Interestingly, substituting Ti site by Cr3+ led to eliminating the band ga
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