Space Vector Pulse Width Modulation Technique Applied to Two Level Voltage Source Inverter

Space vector pulse width modulation SVPWM provides a better technique compared to the other pulse width modulation techniques. This paper presents simulation and implementation of SVPWM signal generation for driving three phase two level voltage source inverter VSI, also proposes and analyzes a new switching sequence for generating an SVPWM. Simulation results are obtained using the simulation package PSIM. and the inverter performance is evaluated in terms of total harmonic distortion (THD). The model is experimentally implemented and verified on Arduino Mega Atmega2560 microcontroller

A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets

In this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering using Euclidean distance. It is noted that performance of clustering algorithm is highly dependent on the similarity measure. Moreover, as the number of clusters gets reasonably increased, the clustering algorithms’ performance gets higher

Phytochemical Investigation and B Ioactivity Screening of Vitex (Verbenaceae) and Ficus (Moraceae) Species

The study on Vitex longisepala involves extraction, various chromatographic methods and structural determination by spectroscopic techniques such as IR, compounds were also elucidated by comparison with the previous works. works on leaves and bark of the plant yielded cholesterol (51),para-hydroxybenzoic acid (52), terpene. Crude extracts and isolated compounds from two parts of this plant were screened for antimicrobial activity using disc diffusion method and cytotoxic activity by using microtitration method. compouds exhibited antimicrobial activity against Gram-possitive and Gram-negative bacteria Cholesterol exhibited significant cytotoxic activity against T-lymphoblastic leukemic cell line with IC₅₀ 10 µg/ml. not show any antimicrobial activity against fungi. The methanol crude extract of the bark failed to show any significant antimicrobial activity, while the petroleum ether and the chloroform crude extracts of the bark exhibited weak antimicrobial activity against Bacillus cereus. The study on Vitex quinata involved the same procedure adopted above. Isolation works on the leaves and bark of the plant yielded cholesterol (51), 13- sitosterol (57), para-hydroxybenzoic acid (52), fructose (53), glucose (58), catechin (55), quercetin (59) and quercitrin (60). However, the bark of the plant yielded a mixture of long-chain compound, fatty acid and unidentified terpene. The crude extracts and isolated compounds of this plant were tested for antimicrobial and cytotoxic activity using disc diffusion and microtitration methods respectively. The crude extracts and pure isolated compounds exhibited positive antimicrobial results against two bacteria organisms and negative results against four fungi. Cholesterol and β-sitosterol gave cytotoxic activity against T -lymphoblastic leukemic cell line with IC₅₀ 10 and 25 µg/ml respectively. Detail investigation on the leaves, bark and fruits of Ficus benjamina has resulted in the isolation of seven compounds. The structure of these compounds were elucidated by means of spectroscopic methods including by the extensive use of various NMR techniques and also comparison with previous studies. The use of High Field NMR is essential in structural determination of these complex molecules. With the aids of various NMR experimental techniques and oth er spectroscopic methods such as IR, UV and MS, the correct structures of the pure isolated compounds were established. (63), The presence of bioactive compounds in th is plant was detected by the use of antimicrobial organism. crude plant extracts or pure isolated compounds could be determined. of chloroform and methanol extracts of the leaves of Ficus benjamina gave no significant activity while caffeic acid gave IC₅₀ value of 25 µg/ml. Phytochemical studies on leaves and bark of Ficus elastica have resulted in the isolation of emodin (66), (70) together with long-chain fatty acids. were established based on spectral studies using different spectroscopic methods and on comparison with published data

Pharmacological Investigations and LD50 Determination of New Amide Prodrug of Ibuprofen

Abstract is present with attachment

On the Integration of Similarity Measures with Machine Learning Models to Enhance Text Classification Performance

Several techniques have long been proposed to enhance text classification performance, such as: classifier ensembles, feature selection, the integration of similarity measures with classifiers, and meta-heuristic algorithms. The integration of similarity measures with machine learning models (ML), however, has not yet received thorough analysis for text classification. As a result, in an effort to thoroughly investigate the impact of similarity measures integration with ML models, this work makes three major contributions: (1) proposing newly-integrated models and presenting benchmarking studies for integration methodology over balanced/imbalanced datasets; (2) offering detailed analysis for dozens of integrated models that are established, and experimentally proven, to significantly outperform state-of-the-art performance. The models\u27 construction used fourteen similarity measures, three knowledge representations (BoW, TFIDF, and Word embedding), and five models (Support Vector Machine, N-Centroid-based Classifier, Multinomial Naïve Bayesian, Convolutional Neural Network, and Artificial Neural Network); and (3) introducing significantly-effective and highly-efficient variations of these five models. The evaluation study has been conducted internally for integrated models against their baselines, and externally against the state-of-the-art models. While the internal evaluation constantly showed a total enhancement rate of 49.3% and 59% over the balanced and imbalanced datasets, respectively, the external evaluation attested to the superiority of the integrated models