European Journal of Engineering Science and Technology <p>European Journal of Engineering Science and Technology (EJEST) is a peer-reviewed, open-access journal that provides rapid publication of articles in all areas of Engineering Science and Technology. EJEST is an international, scholarly and peer-reviewed journal (online) published Quarterly by Diamond Scientific Publication.</p> en-US (If you have any questions, please feel free to contact us:) Mon, 15 Mar 2021 08:38:34 +0000 OJS 60 Design and Experimental Verification of a Single-Phase Asymmetric Hybrid Multi-level Inverter <p>In recent years, multi-level inverters have emerged as a feasible power conversion solution for medium and high power applications due to better harmonic performance and ability to operate at high voltage/power when compared to traditional two-level inverters. Since the output level of the multi-level inverters depends on the number of the switching elements, as more levels are required, more switching elements are used. This situation makes the circuit and the control design complex and the losses to upsurge. To overcome these limitations and produce low harmonic content at the output, reduced switch count topologies are popular. In this study, a single-phase asymmetric hybrid multi-level inverter is proposed by combining diode clamped and cascaded H-bridge topologies. The inputs of the proposed inverter are selected as two unequal DC voltage sources. In this regard, fewer switching elements are used to obtain the same number of voltage levels at the output when compared to traditional multi-level inverters. The efficiency and the harmonic performance of the proposed topology is both verified by simulation and experimental studies. The gating signals of the semiconductor switches are produced by phase disposition pulse width modulation with carriers’ frequency of 4 kHz. It is shown by the experiments that a maximum efficiency of 94 % and a total harmonic distortion of 29 % are attained in the case studies.</p> Ahmet Mete VURAL, Ali Osman ARSLAN, Mustafa DENİZ Copyright (c) 2021 European Journal of Engineering Science and Technology Wed, 30 Dec 2020 00:00:00 +0000 Influence of Administration Route and Dose on Biodistribution Profile and Effects of PEG-PLA Nanoparticles in Mice <p>Polyethylene glycol-polylactic acid nanoparticles (PEG-PLA NPs) represent a new generation of parenteral therapeutics systems. Following administration, these NPs possess the potential to interact with biological machinery. Therefore, it is essential to get a systematic understanding of the biological fate of these NPs to evaluate their safety. In the present study, two doses (20 and 40 mg/kg) of technetium-99m labeled PEG-PLA NPs were administered intravenous (i.v.) and oral into mice and the distribution was assessed at 1, 2, 4 and 24 h post administration. Biodistribution and blood kinetic profiles revealed the extended systemic circulation of the NPs. Dose-dependent presence of NPs (<em>p</em>&lt;0.05) was detected in the blood, liver, lung, spleen, and kidney of i.v. injected mice, and also in the blood, lung, spleen, stomach, and intestine of oral administered mice. The consequences of NP interaction with the biological components were studied by measurement of hematology, oxidative stress, genotoxic and histological parameters. Significantly increased levels of oxidative stress markers such as glutathione were observed in the liver, and spleen of i.v. injected mice and liver, stomach, and intestine of orally treated mice. Decreased lipid peroxidation levels (<em>p</em>&lt;0.05) were observed in the liver of orally treated mice versus untreated mice. Even though PEG-PLA NPs have been shown to induce oxidative DNA damage, interestingly no histological lesions were observed in selected organs except lung of i.v. treated mice, which showed moderate vascular congestion. Such insights on in vivo distribution and understanding of nano-bio interactions at molecular and genetic levels are considered fundamental for the designing of safer nanoparticles for biomedical applications.</p> Sangeetha Aula, Samyuktha Lakkireddy, Atya Kapley, Rakesh Kumar Sharma, Shantveer G Uppin, Kaiser Jamil Copyright (c) 2021 European Journal of Engineering Science and Technology Wed, 30 Dec 2020 00:00:00 +0000 Influence of Ni (II) oxime complex coupled with the combination of diverse sized ZnO nanoparticles on Photovoltaic Performance <p>The one side selective synthesis of quinoline carboxylic acid oxime complex was carried out successfully. The as-prepared quinoline carboxylic acid oxime complex was complexed with nickel (II) salts to form nickel (II) oxime complex. These complexes were further adsorbed onto ZnO films containing ZnO nanoparticles of various sizes. ZnO films containing a diverse proportion of ZnO nanoparticles were investigated to enhance the photovoltaic efficiency of the dye-sensitized solar cell. The as-synthesized complex was characterized by scanning electron microscopy (SEM), Ultra violet visible spectroscopy (UV-vis), Fourier Transform Infrared Spectroscopy (FT-IR) spectroscopy, 1Hydrogen Nuclear magnetic resonance spectroscopy (1HNMR), Liquid chromatography coupled with mass spectrometry (LC-MR), Brunauer–Emmett–Teller (BET), and Attenuated total reflection Infra-red spectroscopy (ATR-IR). The combination of large and small ZnO nanoparticles has significantly improves the photovoltaic efficiency. The optimum mixing ratio for the best performance (0.127%) of a dye-sensitized solar cell is achieved by mixing the small: large ZnO particles in a ratio 60:40. The increased efficiency is due to the harvesting of light caused by scattering effect from larger sized ZnO particles. The ZnO layer consisting of smaller particles which are very next to the ZnO bigger particles makes a good electronic contact between film electrode and the Indium-doped tin oxide glass substrate resulting in the increases in the dye molecules adsorption. The over-layered, large-sized ZnO particles enhance the light-harvesting by light scattering effect. Compared to the other mixtures of ZnO films, there is a decrease in the photovoltaic performance of the solar cell when ZnO particles (small and large in a ratio 1:1) were adsorbed onto the Ni (II) oxime complex, which are caused due to the decrease in the surface area and dye aggregation.<br /><br /></p> Prashanth Kumar P.N., Sajan Ponnappa, Ravi Hethegowdanahally Rajegowda, Amol Naik, Maxwell Selase Akple Copyright (c) 2021 European Journal of Engineering Science and Technology Wed, 30 Dec 2020 00:00:00 +0000 Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease <p>Alzheimer's disease (AD) is a significant regular type of dementia that causes damage in brain cells. Early detection of AD acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. Our proposed model in the first stage, apply the medical dataset to a composite hybrid feature selection (CHFS) to extract new features for select the best features to improve the performance of the classification process due to eliminating obscures. In the second stage, we applied a dataset to a stacked hybrid classification system to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. All experiments conducted on a laptop with an Intel Core i7- 8750H CPU at 2.2 GHz and 16 G of ram running on windows 10 (64 bits). The dataset evaluated using an explorer set of WEKA data mining software for the analysis purpose. The experimental show that the proposed model of (CHFS) feature extraction performs better than proncipal component analysis (PCA), and lead to effectively reduced the false-negative rate with a relatively high overall accuracy with support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% which is considerably better than the previous state-of-the-art result. The receiver operating characteristic (ROC) curve was equal to 95.5%. Also, the experiment on MRI images Kaggle dataset of CNN classification process with 80.21% accuracy result. The results of the proposed model show an accurate classify Alzheimer's clinical samples against MRI neuroimaging for diagnoses AD at a low cost.</p> Ahmed Abdullah Farid, Gamal Ibrahim Selim, Hatem Awad A. Khater Copyright (c) 2021 European Journal of Engineering Science and Technology Wed, 30 Dec 2020 00:00:00 +0000