Electric Vehicles are becoming world’s primary modes of transportation for minimizing operating costs, reducing reliance on fossil fuels, lowering carbon emissions, and enhancing public health and urban air quality. Electric Vehicles are facing the major problems of limited battery range, battery life, charging stations and long charging times. This research work proposes an installation of Solar PV array on EV’s rooftop integrating with Battery Energy Storage System to power the drive of an Electric Vehicle to reduce the charging and discharging cycles of the battery and enhancing the battery life. The solar PV panels are fixed onto the body of vehicle so as to be exposed to the Sun light. The electric power generated by PV cells is directly fed to the DC drive during daytime while the vehicle is moving and batteries are charged during daytime while the vehicle is parked.

The Finite Element Method (FEM) has been the foundation of computational structural analysis for a very long time; yet, because to its high computing demand, it has limits when used to applications that are data-intensive, real-time, and large-scale. In response, this research presents a hybrid framework that combines traditional finite element method (FEM) with artificial intelligence (AI), more especially supervised deep learning, in order to improve the effectiveness and scalability of mathematical models of structural systems. The AI-Enhanced FEM framework that has been proposed has been trained on verified FEM datasets, and it has demonstrated the ability to accurately approximate displacement and stress fields across a wide range of structural scenarios. These scenarios include beam deflection, plate bending, and stress concentration around geometrical discontinuities. The model is validated by presenting six comprehensive numerical examples, with the predictions made by AI reaching an accuracy that is within 1–3% of the findings obtained by traditional finite element methods (FEM) and giving up to 500 times quicker calculation. Cross-validation using analytical benchmarks, physics-based feature embedding, and domain-informed neural network design are the three methods that are used to ensure that the methodological rigor is maintained. The talk focusses on the practical benefits as well as the theoretical implications that are associated with hybridizing numerical and data-driven models. This approach is positioned as a revolutionary step towards real-time structural analysis, digital twins, and intelligent infrastructure systems. This study highlights the connection between numerical rigor and machine learning, therefore opening the way for engineering simulations that are interpretable, adaptable, and computationally economical.

SDVs get advanced as a result of applying artificial intelligence (AI), yet they become increasingly difficult to test. The conventional techniques such as the scripted examinations or random scenarios are not sufficient to test all of the likelihood of driving that may contain peculiar and hazardous elements. The present research paper claims to introduce a novel conceptualization using agentic AI and generative AI (with no contact to real-life phenomena) to experiment in SDV attributes more effectively. The agentic system of AI will be autonomous that is, they plan, learn and innovate overtime. The Generative AI assists in supporting millions of test cases, unusual road dynamics, driver behavior and cyber-attacks. It experimented with its framework using modified systems of driver-assistance (ADAS) and the experiment was very successful. In comparison, agentic AI contains three times more opportunities to go off the rails relative to scripted tests and exceeding a regulation failure relative to what random tests. It also enabled the vehicles to be competent to pass safety standards like FMVSS and the ISO 26262 standard as risks may be noticed promptly. The model minimized the use of the network without the report of mines to the testing speed and coverage which was enabled with a combination of edge resources and cloud-based resources. This was what was called continuous learning as the system continued to improve as more vehicles provided the data. The result has already been proven that AI-based testing is scalable, viable and applicable to use in complex conditions. According to the study, agentic AI could greatly contribute to smarter and safer SDV testing that would give engineers the opportunity to identify and eliminate issues extremely quickly. Such an approach will go toward the adoption of faster and have more confidence in self-driving cars.

The integration of Artificial Intelligence (AI) into financial planning processes has revolutionized traditional budgeting and forecasting methodologies. This paper delves into the application of AI-driven predictive models within Oracle's Enterprise Planning and Budgeting Cloud Service (EPBCS) to automate financial projections, thereby fostering dynamic and adaptive budgeting frameworks. Through a comprehensive analysis, we contrast traditional budgeting approaches with AI-enhanced methods, explore the core functionalities and limitations of EPBCS, and examine the implementation of AI-powered predictive models. We also address the challenges associated with AI adoption in financial planning and propose best practices for seamless integration. Our findings suggest that while AI integration offers substantial improvements in forecasting accuracy and operational efficiency, careful consideration of data governance, model interpretability, and compliance is imperative for successful implementation.

The main aim of this manuscript is to present the innovative applications of the Euclidean distance algorithm (EDA) for the development of the dual-axes solar tracking system. By positioning the solar panel perpendicular to the sun, this system intends to optimize solar energy harvesting efficiency. Applied using a quantitative approach, the EDA helped to find the optimum solar panel angle continuously depending on the sun's location. The system was developed using LabVIEW software and data acquisition (DAQ) system. The design and installation of the dual-axes solar tracking system included evaluation of performance metrics under different weather conditions. Data was collected over a set time to assess the effectiveness of system. Results established that the dual-axes solar tracking system significantly increased energy output. The algorithm was effectively used to improve the system’s performance by accurately finding the optimal solar panel position throughout the day. By aiming the likelihood of higher solar energy efficacy through effective algorithmic techniques, the results provide the prospect for farther research and development in solar energy systems. By directing the invention of more effective renewable energy solutions, the knowledge gained may ultimately help to execute sustainability goals.

The rapid evolution of mobile-enabled real-time payment systems, coupled with the proliferation of Internet of Things (IoT) devices, necessitates robust security and efficiency measures. Traditional cloud-centric payment architectures face challenges such as high latency, data exposure, and security vulnerabilities. This paper proposes a secure edge-based IoT integration framework to enhance the security, scalability, and real-time processing of mobile payments. By leveraging edge computing, blockchain technology, and AI-driven anomaly detection, the framework reduces latency, optimizes transaction processing, and strengthens security through decentralized authentication and encryption mechanisms. The proposed approach mitigates cyber threats, ensures data integrity, and enhances system resilience while maintaining seamless user experience. Performance evaluations demonstrate reduced transaction delays, improved security, and increased scalability, making it a viable solution for next-generation mobile payment ecosystems.

The study examines the value of AI-based predictive project risk management to enhance the yield of large-scale financial software programmes, where complexity, regulatory pressure and intensity of data play to the detriment of success. Through an explanatory secondary mixed-methods process, the study combines the evidence provided by recent scholarly literature, industry reports, graphical performance data and an actual case study of Microsoft and Deloitte. The most important findings are summarised as follows: the AI-based predictive analytics are much more efficient in comparison with traditional risk management techniques. This allows identifying the risk earlier, detecting it more often, and monitoring the risk in real-time: financial, operational, and scheduling risks. Empirically, it has been shown that there are high gains in scheduling effectiveness (a maximum of 85%), resource use (a maximum of 80%), as well as fraud and financial risks detection accuracy (more than 25% improved). The findings of case studies also make it clear that AI applications minimise the delay of projects, cost overruns, and project failures by proactively mitigating and supporting decisions based on data. Nevertheless, the analysis also reveals such essential challenges as limitations in data quality, challenges in explaining the algorithms, the need to integrate with the legacy systems, and skills deficits. In general, the study finds that AI-driven predictive risk management is a scalable, adaptable and better variant of the traditional methods and has a great prospect of improving the success of big-scale financial software projects.

Magnetic levitation (maglev) systems exhibit strong nonlinearity and intrinsic instability, making them vulnerable to parameter uncertainties and unforeseen external disturbances, especially in demanding environmental conditions. This paper presents a disturbance observer-based control (DOBC) integrated with a higher-order sliding mode (HOSM) controller to enhance the stability and performance of the maglev system. Traditional sliding mode control (SMC) methods often suffer from high-frequency oscillations, known as chattering, and require precise knowledge of system parameters, which may not always be available. To overcome these challenges, the proposed DOBCbased HOSM controller effectively estimates and compensates for disturbances while ensuring robust position tracking. The controller’s performance is evaluated through simulations conducted on a predefined reference trajectory, both in the presence and absence of external disturbances. Comparative analysis with conventional integer-order proportional-integral-derivative (IOPID) and standard HOSM controllers demonstrates the superior disturbance rejection capability and improved tracking accuracy of the proposed approach.

Forensic accounting has emerged as a pivotal mechanism in uncovering complex corporate fraud in an increasingly volatile and technology-driven business environment. This study examines the multifaceted role of forensic accountants in detecting, investigating, and preventing fraudulent financial activities within corporate entities. It highlights how forensic accounting integrates accounting expertise, investigative skills, digital analytics, and legal knowledge to reveal financial irregularities that traditional auditing methods may overlook. The abstract underscores the growing relevance of forensic tools such as data mining, ratio analysis, predictive modelling, and digital forensics in identifying red flags across financial statements and transactional data. Furthermore, it explores the significance of professional scepticism, ethical judgement, and regulatory compliance in enhancing fraud detection effectiveness. The study also reflects on notable corporate scandals that have shaped the evolution of forensic accounting practices, emphasising the need for stronger internal controls, transparency, and governance frameworks. Overall, forensic accounting is positioned as an indispensable discipline for strengthening corporate accountability and mitigating financial misconduct. It concludes that organisations must invest in specialised forensic expertise to safeguard assets, protect stakeholder interests, and foster a culture of ethical financial reporting.

Misconfigurations of cloud services stay a critical security factor that is regularly followed by downtimes, breaches, and financial losses. In the case of detection, it becomes apparent that the traditional methods like manual auditing and preprogrammed rule-based systems are not easy to scale, and are not very adaptive in their nature, while on the other hand, the more advanced methods like machine learning models have drawbacks of their own including those of data requirements and of generalizing the model. In this study, we discuss the use of large language models, Google’s Gemini in particular, for the zero-shot cloud misconfigurations detection. Based on the natural language understanding of LLMs, the study shows how such systems can be used to discover subtleties in cloud dynamics and new security threats. The proposed framework called SARGE takes a set of cloud configuration files, analyses them for misconfigurations, and generates recommendations that do not need to be particular to specific tasks. The implementation includes cloud platforms, using Terraform for testing purposes, Docker for scalability purposes. Experiments demonstrate that proposed LLMs achieve higher accuracy than traditional approaches in identifying new misconfigurations while operating at scale and being easy to interpret. To the best of the author’s knowledge, this research fills gaps in the existing literature and provides a novel solution for cloud security that alleviates challenges of previous solutions. It also creates the basis for further studies concerning LLMs incorporation into cloud-native security solutions for enhancing efficient threat detection and mitigation.