According to the current situation of peroxidase (POD), the relevant studies on this enzyme indicated its importance as a tool in clinical biochemistry and different industrial fields. Most of these studies used the fruits and vegetables as source of this enzyme. So that in order to couple the growing requirements for POD with the recent demands for reduc-ing disposal volume by recycling the plant waste, the aim of the present study was to extract POD through management of municipal bio-waste of Iraqi maize species. A simple, green and economical method was used to extract this enzyme. Our results revealed that maize cobs are rich sources of POD, where the activity of this enzyme was found to be 7035.54 U/g of cobs. In pilot experiments this enzyme was extracted from the cobs using an efficient extraction buffer with either Cetyl Trimethyl Ammonium Bromide (CTAB), or sonication. To purify the extracted enzyme the previous step was followed by aqueous two phase extraction (ATPE) using 20% (w/v) polyethylene glycol (PEG) and 9% (w/v) ammonium sulfate. The obtained results indicated that POD was partially purified with 2.36 fold of purification and 81.78% recovery. The optimum temperature and pH of the extracted POD activity as well as the enzyme thermal stabil-ity were determined and found to be 20°C, pH 6, and stable at 60°C for 10 minutes respectively. Out of the present study findings, it can be concluded that maize cobs are rich source for POD and the applied protocol could be poten-tially used for POD extraction with high level. Meantime, this study suggested a new strategy by which the environ-mental pollution results from accumulation of plant waste can be reduced.
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 MoreElectronic Health Record (EHR) systems are used as an efficient and effective method of exchanging patients’ health information with doctors and other key stakeholders in the health sector to obtain improved patient treatment decisions and diagnoses. As a result, questions regarding the security of sensitive user data are highlighted. To encourage people to move their sensitive health records to cloud networks, a secure authentication and access control mechanism that protects users’ data should be established. Furthermore, authentication and access control schemes are essential in the protection of health data, as numerous responsibilities exist to ensure security and privacy in a network. So, the main goal of our s
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