The widespread adoption of electric vehicles (EVs) necessitates advanced energy management strategies to alleviate range anxiety and improve overall energy efficiency. This study presents a novel framework for optimizing energy consumption in EVs by integrating driver behavior patterns, road conditions, and environmental factors. Utilizing a comprehensive dataset of 3395 high-resolution charging sessions from 85 EV drivers across 25 workplace locations, we developed a multi-modal prediction model that captures the complex interactions between driving behavior and environmental conditions. The proposed methodology employs a combination of driving scenario recognition and reinforcement learning techniques to optimize energy usage. Specifically, we utilize contrastive learning to extract meaningful representations of driving states by leveraging the symmetric relationships between positive pairs and the asymmetric nature of negative pairs and implement graph attention networks to model the intricate relationships between road environments and driving behaviors. Our experimental results demonstrate that the proposed framework achieves a significant reduction in energy consumption compared to baseline methods, with an average improvement of 17.3% in energy efficiency under various driving conditions. Furthermore, we introduce an adaptive real-time optimization strategy that dynamically adjusts vehicle parameters based on instantaneous driving patterns and environmental contexts. This research contributes to the advancement of intelligent energy management systems for EVs and provides insights into the development of more efficient and environmentally sustainable transportation solutions.

The increasing complexity of Very Large Scale Integration (VLSI) circuits and Radio Frequency (RF) systems demands sophisticated electromagnetic field analysis capabilities that traditional Electronic Design Automation (EDA) tools struggle to provide efficiently. This research presents a novel Physics-Informed Neural Network (PINN) framework specifically tailored for high-fidelity electromagnetic field approximation in VLSI and RF Electronic Design Automation applications. The proposed methodology integrates Maxwell's equations with advanced neural network architectures to enable accurate field prediction across diverse frequency ranges from DC to millimeter-wave operations while maintaining computational efficiency suitable for interactive design workflows. Through comprehensive validation across representative VLSI interconnect structures, RF passive components, and millimeter-wave integrated circuits, our PINN framework demonstrates superior accuracy compared to conventional moment-based methods while achieving computational speedup factors of 45-150× for typical design scenarios. The framework successfully captures complex electromagnetic phenomena including substrate coupling, crosstalk mechanisms, and frequency-dependent losses with mean absolute errors below 3.8% across frequency ranges spanning DC to 300 GHz. The adaptive mesh-free formulation eliminates geometric discretization constraints that limit traditional EDA tools, enabling seamless analysis of irregular conductor geometries and multi-layer dielectric stackups common in advanced semiconductor processes. Real-time field visualization capabilities facilitate intuitive understanding of electromagnetic coupling mechanisms, supporting design optimization workflows that were previously computationally prohibitive. The framework incorporates specialized handling of conductor loss mechanisms, dielectric dispersion effects, and substrate characteristics specific to semiconductor manufacturing processes, ensuring practical relevance for modern VLSI and RF design challenges. Validation against commercial EDA software demonstrates comparable accuracy for standard benchmarks while providing substantial performance advantages for parametric analysis and optimization applications critical to contemporary circuit design methodologies.

The proliferation of large language models (LLMs) has intensified the need to utilize large quantities of computational resources, which causes the cost of operation and infrastructure issues to grow. The traditional one-cloud environments have limited scalability and cost-effectiveness due to strict provisions policy and skewed workload allocation. The new possibilities of dynamic scaling and workload balancing between heterogeneous cloud providers have emerged due to recent improvements in multi cloud orchestration, but such solutions still have significant problems concerning the cost optimization, elasticity, and communication overhead. This paper attempts to resolve these shortcomings by offering a scalability framework of the commercially viable LLMs that incorporates adaptive model compression, predictive auto-scaling, and federated orchestration mechanisms on the multi cloud settings. The solution will utilize the concept of elasticity of Amazon SageMaker and distributed topology management solutions to dynamically deploy compute resources without compromising on performance (Liberty et al., 2020; Wei et al., 2025). Moreover, the model also includes auto-scaling techniques that reduce the cost variations in line with the unpredictable demand as noted by Alharthi et al. (2024). The cost-per-inference of 38% reduction and latency acquisitive scheduling (Zhu et al., 2024) are experimentally validated by showing a reduction by up to 38 percent and an increase in cross-cloud load balance by 25 percent. The results indicate that it is possible to implement cost-effective, scalable implementation of LLMs in multi-cloud systems without compromising throughput or reliability, which provides a viable way of implementing AI at large scale in various cloud systems.