The investigation of the inhibiting patterns and variations of 10cm3 leaves extracts of yam, maize and cassava on the corrosion of mild steel in a selected media using weight loss method was carried out. The mild steel samples were pre-weighed, immersed in different concentrations of NaOH, NaCl and H2SO4 solutions with the 10cm3 leaves extracts alongside the control samples immersed in solution of the media without leaves extracts. The arrangements were allowed to stand for 672 hours and a set of samples from each environment withdrawn at intervals of 168 hours for corrosion characterization. The research findings indicate that the corrosion rate decreased as a result of the 10cm3 leaves extracts introduced into the media thereby confirming that the leaves extracts functioned as effective and excellent inhibitors in the NaOH, NaCl and H2SO4 media. Among the leaves extracts from the three plants used, it was observed that Yam has the best inhibition efficiency in both NaOH (alkaline), NaCl (salt) and H2SO4 (acidic) media, followed by Cassava and Maize which also showed good inhibition efficiency. The results show the very good potentialities of the leaves extracts for application in the diminution of corrosion in our various manufacturing industries.

Schiff bases are effective corrosion inhibitors for metals like mild steel, copper, and aluminum, offering high efficiency, adaptability, environmental stability, and cost-effectiveness. This study examines how substituents and molecular structures influence their corrosion-suppressing capabilities. It also analyzes the effects of metals and environmental factors such as temperature and pH on their performance. The relationship between inhibition efficiency and Schiff base concentration is explored to provide insight into their protective mechanisms. Industrial applications are discussed, particularly in coatings, mechanical engineering, and the oil and gas sectors. A key focus is on integrating nanotechnology into Schiff bases to enhance their protective properties. Innovations like nanoscale surface treatments, controlled release via nanocapsules, and nanocomposite coatings are highlighted as promising advances for next-generation corrosion prevention. The study emphasizes the need for future research into environmentally sustainable Schiff bases, novel derivatives with enhanced characteristics, and extensive industrial testing. The integration of nanotechnology is identified as a critical area for development, potentially leading to more effective and durable corrosion prevention solutions. These advancements position Schiff bases as a versatile and sustainable choice for industrial corrosion control.

The research examines the application of Artificial Intelligence (AI) in waste management systems, using Bee'ah AI City Vision in Sharjah, UAE, as a case study. Research checks the application of advanced AI models, including long short-term memory (LSTM) network and transformer-based models, in adaptation to waste collection efficiency and sustainable urban rule. Conclusions display a 30% lower environmental footprint due to a 25% decrease in operating costs, waste volume prediction, and LSTM network in passage optimization. The transformer model also enabled a 20% increase in public satisfaction by increasing the accountability of services through emotion analysis. Research reflects AI's ability to increase operational efficiency, environmental stability, and governance in the public sector, as well as the main challenges, including AI decision-making data secrecy, algorithm bias, and transparency.

Semantic-based image retrieval (SBIR) is a critical challenge at the intersection of natural language processing and computer vision. Traditional retrieval methods primarily depend on metadata annotations or low-level visual feature extraction, often failing to capture user queries' rich contextual and semantic relationships. This study introduces a novel approach that leverages knowledge graphs to enhance SBIR by structuring and representing visual concepts in a more interpretable and relational manner. Specifically, we construct a knowledge graph from the Visual Genome dataset to encode semantic relationships between objects, attributes, and scene compositions. By integrating this knowledge representation into the retrieval process, our approach improves query accuracy, enables more intuitive search mechanisms, and extends the applicability of knowledge graphs in visual information retrieval. Experimental results demonstrate the effectiveness of this method in bridging the semantic gap between textual queries and image content, paving the way for more intelligent and context-aware retrieval systems.

The integration of Supervisory Control and Data Acquisition (SCADA) systems with Industrial Internet of Things (IIoT) technologies, cloud computing, and advanced cybersecurity measures is reshaping industrial automation. This paper presents a conceptual framework for smart SCADA systems, emphasizing the role of cloud connectivity for real-time monitoring, IIoT for enhanced data acquisition, and cybersecurity to safeguard critical infrastructure. The integration of these technologies enables improved operational efficiency, predictive maintenance, and remote accessibility, fostering more scalable and flexible industrial operations. However, challenges such as data security risks, interoperability, and system complexity remain prominent. The paper discusses theoretical models to address these challenges, proposing strategies for seamless integration and robust security mechanisms. Future trends such as edge computing, AI-driven analytics, and blockchain-based security are also explored as potential avenues for advancing SCADA systems. This paper contributes to the understanding of how these technologies converge to drive the future of industrial automation while addressing the complexities of data integrity and system resilience.

Ransomware attacks have become a significant cybersecurity threat, causing severe financial and operational damage to enterprises worldwide. Traditional security measures often fail to detect and mitigate these threats before they inflict harm. This paper explores the application of machine learning (ML) techniques for the early detection of ransomware attacks in enterprise networks. By analyzing network traffic patterns, system behaviors, and anomaly detection methods, ML models can identify suspicious activities indicative of ransomware execution. The study evaluates various supervised and unsupervised learning algorithms, including decision trees, support vector machines (SVM), deep learning, and clustering techniques. Experimental results demonstrate that ML-based approaches can enhance the accuracy and efficiency of ransomware detection, minimizing response times and reducing potential losses. The findings suggest that integrating machine learning into cybersecurity frameworks can significantly improve an organization’s resilience against ransomware threats.

The reshoring of American industries has become a strategic priority to enhance domestic manufacturing resilience, reduce supply chain dependencies, and drive economic growth. Advanced lean manufacturing principles, coupled with automation, present a transformative approach to achieving cost-effective and sustainable production. This research explores the integration of smart automation, robotics, and digital lean methodologies to improve efficiency, reduce waste, and optimize operations in reshoring initiatives. By leveraging Industry 4.0 technologies such as cyber-physical systems, artificial intelligence, and real-time data analytics, manufacturers can achieve higher productivity while maintaining flexibility and quality. The study aims to identify the key enablers, challenges, and impact of advanced lean automation on reshoring efforts. Additionally, it investigates how digital lean tools and automated systems contribute to competitiveness, workforce development, and supply chain resilience in the U.S. manufacturing sector.

The research examines the application of Artificial Intelligence (AI) in waste management systems, using Bee'ah AI City Vision in Sharjah, UAE, as a case study. Research checks the application of advanced AI models, including long short-term memory (LSTM) network and transformer-based models, in adaptation to waste collection efficiency and sustainable urban rule. Conclusions display a 30% lower environmental footprint due to a 25% decrease in operating costs, waste volume prediction, and LSTM network in passage optimization. The transformer model also enabled a 20% increase in public satisfaction by increasing the accountability of services through emotion analysis. Research reflects AI's ability to increase operational efficiency, environmental stability, and governance in the public sector, as well as the main challenges, including AI decision-making data secrecy, algorithm bias, and transparency.

Smart Maintenance and Reliability Engineering (SMRE) in manufacturing leverages advanced technologies such as Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and Machine Learning (ML) to enhance asset performance, reduce downtime, and optimize maintenance strategies. By integrating predictive maintenance, condition monitoring, and real-time data analytics, SMRE improves operational efficiency and extends equipment lifespan. This paper explores the role of digital twins, cloud computing, and cyber-physical systems in revolutionizing maintenance practices. The study also discusses challenges, implementation strategies, and future trends in smart maintenance for sustainable and resilient manufacturing systems.

Earthquakes are among the hidden forces that threaten human life and the future of civilization. Earthquakes can result in landslides, ground shaking, fires, fissures, structural damage to buildings, tsunamis and damage to highways and bridges. The extent of destruction and suffering caused by an earthquake depends on: duration, magnitude, and intensity. And with the technological progress and studies in space sciences, the Earth did not receive the attention of studies and researches, especially that related to pre-earthquakes prediction. This paper presents a brief study of some modern methods for pre-earthquakes prediction through study of physical changes in the Earth's atmosphere, especially in the ionosphere.

Malware analysis is critical to cybersecurity because it enables researchers and security professionals to better understand threats, their indicators of compromise, and then provide mitigating measures. This paper presents the comparative analysis of two popular malware analysis environments, i.e, Virtual Machines (VMs) and Containerized Environments (CEs). The team evaluated these platforms based on key factors such as isolation, resource usage, startup time, scalability, operating system support and emulation capabilities. Our findings reveal that Virtual Machines offer stronger isolation and better operating system emulation, while Containerized Environments provide faster startup time, better scalability and a lower resource overhead. This study provides a valuable insight for cybersecurity professionals seeking to choose the most suitable environment for malware analysis.

This paper explores the methodologies and results of an experiment to transform Vietnamese natural language queries into SQL statements. The paper overviews existing Text2SQL models, including state-of-the-art architectures such as T5, GPT, and BERT. These models have demonstrated the ability to transform natural language into SQL with high accuracy, but still face some challenges in handling the semantics and context of the query. This study focuses on developing an effective transformation model and analysing the unique challenges of Vietnamese, a language with a different grammatical and syntactic structure than other languages. The paper also proposes a specific transformation model, combining language preprocessing techniques, a T5-based core model, and postprocessing methods to optimise the generated SQL statements. The transformation process is detailed, from input analysis to generating the final SQL statement. Experimental results and evaluation of the test model show that the proposed model can convert Vietnamese queries to SQL with high accuracy and point out future development directions, including expanding the dataset and improving the ability to handle complex cases in the future.