International Research Journal of Scientific Reports and Reviews

The significance of reskilling, upskilling, and having good managers during the digital transition is discussed in this study, which analyses the influence of Industry 4.0 technologies on the manufacturing companies’ skill requirements. The authors assert that the introduction of data-based systems, robots, and AI into the manufacturing process has transformed the industry in such a way that there is now a high demand for workers who not only have technical, cognitive, and soft skills but also possess a mix of these qualities. Digital communication, data interpretation, and cross-functional cooperation are now indispensable skills for the industry. The authors point out that most of the firms still face difficulties like a lack of willingness to adapt, inadequately trained staff, and the absence of skilled managers. This is where the reskilling and upskilling initiatives come into play, as they not only help the organisations to cope with the changing demands but also increase the digital capabilities of their workforce. The research encompassed worker readiness assessments, technological adoption, training programs, and managerial issues as the key factors to be examined. The quantitative analysis included a total of thirty-one respondents from manufacturing units located in the NCR region. The long-term competitiveness of organisations through the implementation of Industry 4.0 practices requires a commitment to continuous learning, efficient training, and strong management support. The findings indicated that, while the relationships between the variables were significant, the strength was not extremely high.

Abstract: Large language models (LLMs) such as GPT and BERT have revolutionized natural language processing but impose enormous computational and energy costs due to their massive parameter sizes and quadratic attention complexity. This study introduces LoRA-S, a unified framework that combines Low-Rank Adaptation (LoRA) with sparse attention mechanisms to achieve energy-efficient and scaljournalable training of transformer-based LLMs. By freezing pretrained weights and injecting low-rank trainable matrices into attention and feed-forward layers, LoRA reduces the number of trainable parameters by over 90%, significantly lowering gradient computation and memory overhead. Simultaneously, sparse attention restricts token interactions to structured subsets, cutting attention-related FLOPs from 100 G to 7 G in WikiText-2 experiments. Comparative analysis across Full Fine-Tuning, LoRA, and LoRA-S demonstrates that LoRA-S achieves the lowest energy consumption of 22,380 J (6.22 Wh) while maintaining competitive task performance, with perplexity of 115.26 on WikiText-2 and sentiment classification accuracy of 73.90% on IMDB. Pareto frontier analysis confirms LoRA-S as an optimal trade-off between computational efficiency and predictive capability, enabling resource-constrained and eco-friendly model deployment. These results establish LoRA-S as a practical step toward Green AI, providing a novel, integrated approach to minimize FLOPs and parameter updates without substantially compromising LLM performance.