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

This study examined >140 relevant publications from the last few years (2018–2021). In this study, classification was reviewed depending on the operation's progress. Electrocoagulation (EC), electrooxidation (EO), electroflotation (EF), electrodialysis (ED), and electro-Fenton (EFN) processes have received considerable attention. The type of action (individual or hybrid) for each electrochemical procedure was evaluated, and statistical analysis was performed to compare them as a new manner of reviewing cited papers providing a massive amount of information efficiently to the readers. Individual or hybrid operation progress of the electrochemical techniques is critical issues. Their design, operation, and maintenance costs vary depending o

Increased the need to promote the profession of auditing, and improving the performance of the audit process, which comes through the effective implementation of its tasks, where the auditor collects the necessary data about the nature of client activity, and any other information it deems necessary to carry out the planning and development of the strategy for how to determine the procedures for implementing the audit process and the scope and timing for evidence clues in order to form an opinion technician neutral about the fairness of the financial statements, as required by the standards of the field work of the planning and supervision of the internal control and evidence to prove, but the failure of the auditor whether in th

Bacterial meningitis is a leading cause of illness and death worldwide. It is crucial for clinical and public health care, as well as disease control, to identify the meningitis-causing agent promptly. Between June 2021-February 2022, a total of 100 cerebrospinal fluid (CSF) and blood samples were collected from suspected cases of meningitis admitted to Raparin Paediatric Teaching Hospital, Erbil city-Iraq. Cytochemical, cultural, and biochemical tests were conducted, and confirmed by molecular techniques. Bacterial culture findings were positive in 7% of CSF samples and just one positive among blood samples. The most common pathogens found by cultural characteristics and VITEK 2 Compact System were Staphylococcus sciuri in two

Adverse 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

The anatomical features of Agave americana L. leaf have been described, transverse sections of the leaf have been examined, the epidermis is single-layered on both surfaces, the stomata are sunken and mesophyll is (2-3) layers of parenchyma cells, vascular bundles are collateral type. The pollen of A. americana was studied. The observation was made with L.M. (Light microscope) and S.E.M. (Scanning electron microscope) to determine the significance of pollen features as taxonomic characters. The pollen was monades, homopolar, monosulcate, and with large size, subprolate in shape from P/E ratio (Polar axis/ Equatorial diameter) and furrow length and width, exine thickness and ornamentation.

Data generated from modern applications and the internet in healthcare is extensive and rapidly expanding. Therefore, one of the significant success factors for any application is understanding and extracting meaningful information using digital analytics tools. These tools will positively impact the application's performance and handle the challenges that can be faced to create highly consistent, logical, and information-rich summaries. This paper contains three main objectives: First, it provides several analytics methodologies that help to analyze datasets and extract useful information from them as preprocessing steps in any classification model to determine the dataset characteristics. Also, this paper provides a comparative st