Combining different treatment strategies successively or simultaneously has become recommended to achieve high purification standards for the treated discharged water. The current work focused on combining electrocoagulation, ion-exchange, and ultrasonication treatment approaches for the simultaneous removal of copper, nickel, and zinc ions from water. The removal of the three studied ions was significantly enhanced by increasing the power density (4–10 mA/cm2) and NaCl salt concentration (0.5–1.5 g/L) at a natural solution pH. The simultaneous removal of these metal ions at 4 mA/cm2 and 1 g NaCl/L was highly improved by introducing 1 g/L of mordenite zeolite as an ion-exchanger. A remarkable removal of heavy metals was reported

This study analyzes the stylistic features of euphemisms in feminist discourse, based on material from the contemporary Russian press of the period (2015–2025). The central issue lies in how the linguistic devices used to soften the harshness and confrontation in discourse contribute to shaping a distinct feminine communicative style within media discourse, where the context necessitates combining the expression of the author\\'s position with maintaining a harmonious interaction with the wider audience. This study aims to uncover and classify the stylistic mechanisms of euphemism at the lexical-grammatical, syntactic, and pragmatic levels, as well as to determine its role in constructing gendered linguistic behavior. The research finding

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

The purpose of this research shed light on the analysis of the relationship between the knowledge gap and the strategic performance  gap and diagnose the level of impact this relationship in building a learning organization, and sought search to achieve a number of goals, cognitive and Applied been tested nature of the relationship and effect between variables in a sample size (62) of the managers of banks civil in Baghdad (Baghdad, Gulf, Assyria, Union, Elaf) and focused research problem in question is bold is whether the analysis of the relationship between the knowledge gap and the performance gap strategic leads to recognize organizations need to shift to organizations educated, either in the side of the field was the pr

Background: Toxin-producing Shiga Escherichia coli has been identified as a new foodborne pathogen that poses a significant health risk to humans. Shiga toxin-producing Escherichia coli can be found in raw cow milk and its derivatives. A small number of Escherichia coli strains that produce shiga toxin are pathogenic. Aim of study: The study aimed to see if there were any virulence genes in 50 milk samples that were typical of Entero-haemorrhagic E. coli and evaluate the Myrtus communis effects on these bacteria. Materials and Method: Milk samples were used to isolate E. coli bacteria (n= 27), biochemically analyzed, and genetically screened for virulence genes using a multiplex (PCR). The hydro-alcoholic extraction of Myrtus communis leave

Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical for embedded deployments. This work introduces M2RU, a mixed-signal architecture that implements the minion recurrent unit for efficient temporal processing with on-chip continual learning. The architecture integrates weighted-bit streaming, which enables multi-bit digital inputs to be processed in crossbars without high-resolution conversion, and an experience replay mechanism that stabilizes learning under domain shifts. M2RU achieves ∼13 GOPS at 16.76 mW, corresponding to 776 GOPS per watt, and maintains accuracy within 5 percent of software baselines on seque

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

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