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In vitro investigation of phytochemical, antioxidant and antimicrobial activities of Harpagophytum procumbens seeds extracts
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Medicinal plants contain bioactive substances that are highly bioavailable in extracts or pure molecules, making them promising for therapeutic applications and precursors for chemo-pharmaceutical semi-synthesis. Harpagophytum procumbens (Devil’s Claw) is widely recognized as one of the most potent therapeutic herbs. This study aimed to extract seeds from H. procumbens using two types of solvents and to assess both qualitative and quantitative aspects of the extracts. The two extracts were evaluated for antibacterial and anti-biofilm activities using agar well diffusion assays against four bacterial isolates and two yeast isolates. Qualitative analysis identified the presence of alkaloids, flavonoids, tannins, saponins, and terpenoids. The active components detected were: alkaloids (12.69%), flavonoids (3.25%), total phenolic compounds (24.58%), total terpenoids (8.55%), and total steroids (1.25% for methanolic and 4.55% for petroleum ether). Both methanolic and petroleum ether extracts exhibited antioxidant activities of approximately 85.33% and 74.19%, respectively, compared to ascorbic acid, which had an antioxidant effect of 67.99% at a concentration of 200 µg/ml. The extracts demonstrated a broad spectrum of activity against all tested bacteria (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pyogenes) and fungi (Candida albicans, C. tropicalis, C. parapsilosis). At a concentration of 1000 µg/ml, the seed extracts showed the highest bactericidal activity, with inhibition zones ranging from 10 to 22 mm. Moreover, both extracts exhibited greater anti-biofilm activity at 1000 µg/ml compared to lower concentrations. Our study found that seed extracts of H. procumbens possess significant antibacterial and antioxidant activities, particularly at a concentration of 1000 µg/ml.

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Arabic Sentiment Analysis (ASA) Using Deep Learning Approach
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Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

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Publication Date
Tue May 16 2023
Journal Name
Journal Of Engineering
Load Distribution Factors For Horizontally Curved Composite Concrete-Steel Girder Bridges
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This paper focuses on Load distribution factors for horizontally curved composite concrete-steel girder bridges. The finite-element analysis software“SAP2000” is used to examine the key parameters that can influence the distribution factors for horizontally curved composite steel
girders. A parametric study is conducted to study the load distribution characteristics of such bridge system due to dead loading and AASHTO truck loading using finite elements method. The key parameters considered in this study are: span-to-radius of curvature ratio, span length, number of girders, girders spacing, number of lanes, and truck loading conditions. The results have shown that the curvature is the most critical factor which plays an important

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Publication Date
Sat Dec 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
The Cluster Analysis by Using Nonparametric Cubic B-Spline Modeling for Longitudinal Data
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Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.

In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.

The longitudinal balanced data profile was compiled into subgroup

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