This work investigates the KdV-Calogero Bogoyavlenkskii-Schiff equation in modified (2+1) dimensions. The Lie symmetries are created using Lie group analysis, and the similarity solutions are then found using the symmetries. Using the multiple exp-function approach, numerous wave solutions are obtained. Furthermore, we use the multiplier approach to express the conserved currents, which is essential for comprehending the nature of non-linear equations, particularly in engineering and physics.

Environmental conditions have become more unstable; therefore, innovative and eco-friendly methods of food production are urgently required. Most existing hydroponic systems lack the capacity for real-time responses and decision-making based on integrated data, similar to contemporary farms. This document outlines the creation of an advanced hydroponic farming system that utilizes Internet of Things (IoT) sensors and a digital twin (DT) simulator to address these challenges. A completely monitored and continuously assessed hydroponic farming simulator operating on a Raspberry Pi, employing various sensors, data management and processing, and automated environmental regulation. The development of this intelligent hydroponic farming system employs a dual-model machine learning pipeline: one that identifies plant diseases through image analysis, and another that assesses plant nutrient levels based on sensor data. The data from the two models are combined using a cloud-based DT, enabling remote access to the DT and offering closed-loop control for irrigation, nutrient dosing, and management of all environmental factors related to crop growth in a hydroponic setting. This research showcases the capability to develop scalable, data-focused precision agriculture solutions that can adapt to the demands of today’s agricultural environment by combining all elements of IoT sensing, machine learning, and DT simulations into one functional hyperphysical system.

The increasing digitalisation of critical energy infrastructure has elevated cybersecurity from a traditional information technology concern to a broader software engineering and risk governance challenge. While previous studies have examined cyber threats, regulatory requirements, and operational resilience independently, limited attention has been given to integrating secure software engineering practices with cyber risk governance in the energy sector. This study addresses that gap through a PRISMA 2020-based systematic literature review. A total of 1,092 records were identified from Scopus, Web of Science, Google Scholar, and authoritative institutional sources, of which 70 studies met the final inclusion criteria. The review synthesises evidence on IT/OT convergence, ransomware, supply-chain vulnerabilities, artificial intelligence, and emerging cybersecurity regulations affecting critical energy systems. The findings indicate that effective cyber resilience requires security to be embedded throughout the software development lifecycle rather than treated solely as a compliance activity. Based on the reviewed literature, a four-pillar framework encompassing regulatory compliance, operational technology security, third-party risk management, and AI governance is proposed and aligned with SSDLC and DevSecOps principles. The framework promotes secure-by-design architectures, continuous security validation, and lifecycle-oriented risk management for critical infrastructure applications. This study contributes an integrated software engineering perspective that connects cyber risk governance with secure software development practices. The proposed framework offers practical guidance for software engineers, cybersecurity practitioners, and policymakers seeking to strengthen resilience, improve compliance readiness, and support the secure evolution of critical energy infrastructure.