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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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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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 ide
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This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur
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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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The interplay of species in a polluted environment is one of the most critical aspects of the ecosystem. This paper explores the dynamics of the two-species Lokta–Volterra competition model. According to the type I functional response, one species is affected by environmental pollution. Whilst the other degrades the toxin according to the type II functional response. All equilibrium points of the system are located, with their local and global stability being assessed. A numerical simulation examination is carried out to confirm the theoretical results. These results illustrate that competition and pollution can significantly change the coexistence and extinction of each species.
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Human Cytomegalovirus (HCMV) is an enveloped ubiquitous ds-DNA virus that has been implicated in several types of malignancies. The current work was conducted in the period extending from (November 2018 to the end of October 2019) and aimed to assess the frequency of glycoprotein N (gN) genotypes of HCMV. A total number of 91serum and plasma specimens were collected to fulfill this purpose from females (71 breast cancer patients, and a control group of 20 females) attending Al-Amal hospital for cancer management and Baghdad teaching hospital. The molecular part of this data was achieved through both PCR and Multiplex PCR for detection of HCMV gN (UL73) entire gene as well as for genotyping. gN was detected in 36/71 (50.7%) of breast cancer
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This paper represents an experimental study on the application of smart control represented by the use of the fuzzy logic controller. Two-link flexible manipulators that are used in airspace and military applications are made of flexible materials characterized by low frequency and damping ratio. To solve this problem, this paper proposes the use of smart materials (piezoelectric transducers), where each link is bonded with a pair of piezoelectric transducers that act as a sensor and another as an actuator. As the arm vibrates because of the movement generated by the motor, this voltage is controlled by a regulator inside the LABVIEW® 2020 software and sends the output control voltage to the piezoelectric actuator. Experimental results
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