Objective: To evaluate two kinds of extraction (aqueous and ethanolic) for coriander using seeds, leaves and stems and
studying their antibacterial activity against nine different microorganisms.
Methodology: Coriander was selected to carry out this study. Seeds, leaves and stems were collected from local markets in
Baghdad then dried in shade for at least 10 days and grinded to fine powder. Aqueous hot extracts for 1hr. at (50
c) and
cold extracts for 24 hrs at (4
c) were performed by using seeds, leaves and stems then studied antibacterial effect against
nine different microorganisms by using well diffusion technique. Cold aqueous extracts of coriander seeds for 48 hrs. and
72 hrs and ethanolic extraction for 48hrs of seed, leaves and stems also performed.
Results: This study showed that hot aqueous extracts for 1hr. to all parts of coriander indicated no antibacterial activity,
while cold aqueous extract for 24hrs of coriander seeds had inhibitory effect for some tested bacteria, but leaves and stems
had not. Cold aqueous extract of seeds for 48hrs showed antibacterial activity for all tested bacteria but in 72hrs there was
no inhibitory effect. On the other hand, ethanolic extracts of seeds, leaves and stems for 48hrs had antibacterial activity and
the highest values for inhibition zone shown in Klebsiella pneumoniae and Proteus mirabilis.
Recommendations: The study recommends using coriander seeds extract as alternative medical therapy for
microorganisms which may resist conventional treatment. This study is a first step for further studies. It is necessary to use
various extraction methods to give active materials with high percentage, although different organic solvents to be used
with coriander plant to obtain extracts used for testing different kinds of microorganisms which have highly resistance to
conventional treatment.
The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com
... Show MoreAdverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD
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