Hysteresis-based Voltage and Current Control Techniques for Grid Connected Solar Photovoltaic Systems: Comparative Study

Solar PV system development and integration with existing grid is very fast in recent years all over the world, as they require limited maintenance, pollution free and simple structure. When observing the factors affecting the performance of the grid connected solar photovoltaic system, the inverter output voltage with harmonics add with the harmonics generated due to the non-linear loads, retain a bigger challenge to maintain power quality in the grid. To maintain grid power quality, better inverter control technique should be developed. This paper presents the two control techniques for grid-tied inverters. This study developed the hysteresis controller for the inverter. Hysteresis controller used in this work two way (i) Voltage control mode (ii) Current control mode. Matlab/Simulink model is developed for the proposed system. Further the study presents the comparative evaluation of the performance of both control techniques based on the percentage of total harmonic distortion (THD) with the limits specified by the standards such as IEEE 1547 and IEC 61727 and IEEE Std 519-201

Molecular characterization and prevalence of antibiotic resistance in Helicobacter pylori isolates in Kuala Lumpur, Malaysia

Acknowledgment We would like to thank the Universiti Kebangsaan Malaysia for providing both the permission and the facilities to conduct and publish this research. The research was funded by a grant from Universiti Kebangsaan Malaysia under Economic Transformation Programme Research Fund Scheme (grant no. ETP-2013-042).Peer reviewedPublisher PD

Structural Analysis of URL For Malicious URL Detection Using Machine Learning

Malicious websites are intentionally created websites that aid online criminals in carrying out illicit actions. They commit crimes like installing malware on the victim's computer, stealing private data from the victim's system, and exposing the victim online. Malicious codes can also be found on legitimate websites. Therefore, locating such a website in cyberspace is a difficult operation that demands the utilization of an automated detection tool. Currently, machine learning/deep learning technologies are employed to detect such malicious websites. However, the problem persists since the attack vector is constantly changing. Most research solutions use a limited number of URL lexical features, DNS information, global ranking information, and webpage content features. Combining several derived features involves computation time and security risk. Additionally, the dataset's minimal features don't maximize its potential. This paper exclusively uses URLs to address this problem and blends linguistic and vectorized URL features. Complete potential of the URL is utilized through vectorization. Six machine learning algorithms are examined. The results indicate that the proposed approach performs better for the count vectorizer with random forest algorith

Diabetes Prediction using Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression Classifiers

One of the world's deadliest diseases is diabetes. It is an additional creator of different assortments of problems. Ex: Coronary disappointment, Visual impairment, Urinary organ illnesses, and so forth. In such cases, the patients are expected to visit a hospital to get a consultation with doctors and their reports. They must contribute their time and cash every time they visit the hospital. Yet, with the development of AI techniques, we have the adaptability to search out a response to the present problem. We have progressed an advanced framework for handling data that can figure regardless of whether the patient has polygenic sickness. In addition, being able to foresee the onset of the disease is crucial for patients. Data withdrawal has the adaptability to eliminate concealed information from an enormous amount of diabetes-related data. The most important outcomes of this research are the establishment of a theoretical framework that can reliably predict a patient's level of risk for developing diabetes. We have utilized the existing categorization methods such as DT (Decision Tree), RF (Random Forest), SVM (Support vector Machine), LR (Logistic Regression) as well as K-NN (K-Nearest Neighbors) for predicting the severity of Type-II Diabetes patients. We got an accuracy of 99% for the Random Forest, 98.40% for the Decision Tree, 78.54% for Logistic Regression, 77.94% for SVM (Using RBF Kernal SVM), and 77.64% for KNN

Antimicrobial Applications of Transition Metal Complexes of Benzothiazole Based Terpolymer: Synthesis, Characterization, and Effect on Bacterial and Fungal Strains

Terpolymer of 2-amino-6-nitro-benzothiazole-ethylenediamine-formaldehyde (BEF) has been synthesized and characterized by elemental analysis and various spectral techniques like FTIR, UV-Visible, and 1H and 13C-NMR. The terpolymer metal complexes were prepared with Cu2+, Ni2+, and Zn2+ metal ions using BEF terpolymer as a ligand. The complexes have been characterized by elemental analysis and IR, UV-Visible, ESR, 1H-NMR, and 13C-NMR spectral studies. Gel permeation chromatography was used to determine the molecular weight of the ligand. The surface features and crystalline behavior of the ligand and its complexes were analyzed by scanning electron microscope and X-ray diffraction methods. Thermogravimetric analysis was used to analyze the thermal stability of the ligand and its metal complexes. Kinetic parameters such as activation energy (Ea) and order of reaction (n) and thermodynamic parameters, namely, ΔS, ΔF, S*, and Z, were calculated using Freeman-Carroll (FC), Sharp-Wentworth (SW), and Phadnis-Deshpande (PD) methods. Thermal degradation model of the terpolymer and its metal complexes was also proposed using PD method. Biological activities of the ligand and its complexes were tested against Shigella sonnei, Escherichia coli, Klebsiella species, Staphylococcus aureus, Bacillus subtilis, and Salmonella typhimurium bacteria and Aspergillus flavus, Aspergillus niger, Penicillium species, Candida albicans, Cryptococcus neoformans, Mucor species fungi

Analysis of electric field behaviour for wind turbine blades under the influence of various gas

Wind turbines are one of the most important natural sources of energy. The components of atmosphere gases in the surrounding wind turbines that are installed may significantly affect the increasing of electrical field resulting from lighting strikes. Here, we use the initiation and spread of electrical field in various gases O2, N2, Ar, Ne and SO2 to examine the behaviour of electrical field on blade. This study uses the Finite Element Method to investigate the influence of gases on the lightning strike carbon fibre wind turbine blade. We use 3D modelling geometry i n this study to get accurate results for all sides of the blade. The generation of an impulse wave uses three stages with time varying from 0 to 60 µs. It was observed that N2 and Air give the same reading because Nitrogen represents 72% of the air contents. Thus, our study elucidates that applying various gases can affect the electric field strength

Thyrotoxic goiter and asymptomatic thyroid nodule as an initial presentation of clear cell renal cell carcinoma: a report of two cases

Thyroid nodule as a metastasis to renal cell carcinoma (RCC) is rarely found. We present two cases – presented with thyroid nodules and diagnosed as metastatic RCC; one patient had thyrotoxic goiter, whereas the second patient presented with asymptomatic thyroid nodule. Subsequently, hemithyroidectomy and total thyroidectomy were performed, respectively. Then, both patients underwent radical nephrectomy for the primary tumor. At present, patients are under regular oncology follow-up, with no evidence of disease recurrence

The effect of tobacco, XPC, ERCC2 and ERCC5 genetic variants in bladder cancer development

<p>Abstract</p> <p>Background</p> <p>In this work, we have conducted a case-control study in order to assess the effect of tobacco and three genetic polymorphisms in <it>XPC, ERCC2 and ERCC5 </it>genes (rs2228001, rs13181 and rs17655) in bladder cancer development in Tunisia. We have also tried to evaluate whether these variants affect the bladder tumor stage and grade.</p> <p>Methods</p> <p>The patients group was constituted of 193 newly diagnosed cases of bladder tumors. The controls group was constituted of non-related healthy subjects. The rs2228001, rs13181 and rs17655 polymorphisms were genotyped using a polymerase chain reaction-restriction fragment length polymorphism technique.</p> <p>Results</p> <p>Our data have reported that non smoker and light smoker patients (1-19PY) are protected against bladder cancer development. Moreover, light smokers have less risk for developing advanced tumors stage. When we investigated the effect of genetic polymorphisms in bladder cancer development we have found that ERCC2 and ERCC5 variants were not implicated in the bladder cancer occurrence. However, the mutated homozygous genotype for XPC gene was associated with 2.09-fold increased risk of developing bladder cancer compared to the control carrying the wild genotype (p = 0.03, OR = 2.09, CI 95% 1.09-3.99). Finally, we have found that the XPC, ERCC2 and ERCC5 variants don't affect the tumors stage and grade.</p> <p>Conclusion</p> <p>These results suggest that the mutated homozygous genotype for XPC gene was associated with increased risk of developing bladder. However we have found no association between rs2228001, rs13181 and rs17655 polymorphisms and tumors stage and grade.</p