As one type of resistance furnace, the electrical tube furnace (ETF) typically experiences input noise, measurement noise, system uncertainties, unmodeled dynamics and external disturbances, which significantly degrade its temperature control performance. To provide precise, and robust temperature tracking performance for the ETF, a robust composite control (RCC) method is proposed in this paper. The overall RCC method consists of four elements: First, the mathematical model of the ETF system is deduced, then a state feedback control (SFC) is constructed. Third, a novel disturbance observer (DO) is designed to estimate the lumped disturbance with one observer parameter. Moreover, the stability of the closed loop system including controller

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The reactive yellow azo dye (λmax = 420 nm) is widely utilized for textile coloring due to its low-cost stability and tolerance properties. Treatment of dye-containing wastewater by traditional methods is usually inadequate because of its resistance to biological and chemical degradation. From this research, the continuous reactor of an advanced oxidation method supported the use of H₂O₂/TiO₂/UV to remove the coloration of the reactive yellow dye from the discharge. At constant best conditions obtained from the batch reactor tests pH=7, H₂O₂ dosage = 400 mg/l and TiO₂=25mg/l , the aqueous solutions were tested in the continuous reactor at different dye concentration and d

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Alumina thin films have significant applications in the areas of optoelectronics, optics, electrical insulators, sensors and tribology. The novel aspect of this work is that the homogeneous alumina thin films were prepared in several stages to generate a plasma jet. In this paper, aluminium nanoparticles suspended in vinyl alcohol were prepared using exploding wire plasma. TEM analysis was used to determine the size and shape of particles in aluminium and vinyl alcohol suspensions; the TEM images showed that the particle size is 17.2 nm. Aluminium/poly vinyl alcohol (Al/PVA) thin films were prepared using this suspension on quartz substrate by plasma jet technique at room temperature with an argon gas flow rate of 1 L/min. The Al/PV

The present study aims to study the correlation between visfatin levels and metabolic syndrome in Iraqi obese adolescence (with and without metabolic syndrome) and its relation with other studied biochemical parameters. Sixty obese adolescences were depended in this study (with and without metabolic syndrome), compared with (30) non-obese children as control group. This study was done in the period from April 2020 until the end of December 2020, in the National Diabetes Centre/Mustansiriya University, Baghdad/Iraq. There were no significant differences in age, height, waist circumferences (WC), and diastolic blood pressure (DBP) in the patients' groups. In contrast, a significant increase differs (p<0.05) was recorded in the values of

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Seepage through earth dams is one of the most popular causes for earth dam collapse due to internal granule movement and seepage transfer. In earthen dams, the core plays a vital function in decreasing seepage through the dam body and lowering the phreatic line. In this research, an alternative soil to the clay soil used in the dam core has been proposed by conducting multiple experiments to test the permeability of silty and sandy soil with different additives materials. Then the selected sandy soil model was used to represent the dam experimentally, employing a permeability device to measure the amount of water that seeps through the dam's body and to represent the seepage line. A numerical model was adopted using Geo-Studio software i