The Akbarpur block is positioned in the Kanpur district. A detailed analysis of the soils in this area was performed to evaluate specific macronutrients. The macronutrients assessed included Organic Carbon, Total Nitrogen, Phosphorus, Potassium, Calcium, and Magnesium. This investigation, conducted during the 2023-24 timeframe (from June to March), focused on an extensive chemical examination of soil samples gathered from diverse locations within the district during different seasons. Soil samples were analyzed at three-month intervals (June, September, December, and March) to capture seasonal changes. The pH measurements indicated a highly alkaline soil composition, with values ranging from 8.1 to 12.6. The results generally suggested that the Organic Carbon levels were elevated in nearly all seasons. Conversely, the Akbarpur soil displayed a deficiency in Total Nitrogen, while Phosphorus and Potassium were present in adequate amounts, and an excess of Calcium and Magnesium was observed consistently across all seasons..

Generative artificial intelligence (AI) is revolutionizing education by providing tools that can personalize learning, deliver adaptive feedback, and create innovative educational content. As educational institutions integrate these technologies, understanding student adoption becomes vital. Factors such as awareness, perceived usefulness, ease of integration, technical support, user experience, and institutional backing influence how students embrace and utilize generative AI tools. The impact on learning outcomes is significant, with these tools enhancing personalized learning, boosting engagement, and improving academic performance. However, challenges related to usability, technical issues, and ethical considerations must be addressed. Effective support and feedback mechanisms are crucial for optimizing the benefits of generative AI in education..

Plant modelling in industrial operations addresses efficiency and complexity through computational methods. This study explores integrating fact tables and multidimensional models to analyse key metrics like production outputs and energy consumption. Fact tables centralize data, while dimensions such as time and location provide contextual analysis. Computational modelling, enhanced by tools like Simulink, simulates plant dynamics, enabling scenario evaluation and optimization. Techniques like star and snowflake schemas organize data for efficient querying, supporting operational insights. The study also highlights real-time validation and Industry 4.0 technologies for automation and predictive maintenance. Future research emphasizes AI integration, multi-stage production models, and broader industrial applications, enhancing adaptability and efficiency..

This study explores a robust authentication mechanism combining Elliptic Curve Cryptography (ECC) and Advanced Machine Learning (ADV-ML), specifically Random Forest, to address modern cybersecurity challenges. ECC is leveraged for its computational efficiency, offering strong security with shorter key lengths, making it ideal for resource-constrained environments. It ensures secure encryption, key exchange, and digital signatures, providing a foundation for safeguarding sensitive data. The integration of ADV-ML enhances system adaptability by analysing user behaviours, environmental contexts, and detecting anomalies. Random Forest’s predictive capabilities enable dynamic security adjustments, improving authentication accuracy and mitigating evolving threats. Additional layers of steganography and watermarking secure hidden data and verify authenticity, respectively. The multi-layered approach, validated through rigorous simulations, demonstrates a high probabilistic success rate of 97.73%, ensuring robust encryption, anomaly detection, and data integrity. This framework is a scalable solution for sectors requiring high-security standards, including healthcare, finance, and government systems, supporting seamless, user-friendly authentication while safeguarding privacy..

The rising incidence of Type 2 diabetes is a major public health problem that needs new approaches for earlier identification and intervention. Beyond acknowledging the obvious harm, traditional diagnostic methods detect the disease after such a significant metabolic shift has taken place; as a result, there is a crucial need for innovative predictive tools..