Legacy systems pervade the IT landscape, presenting a formidable challenge for organizations striving to stay abreast of the swiftly evolving technological terrain. This research paper delves into the intricacies of modernizing legacy systems, with a keen focus on achieving successful application overhauls. Through a comprehensive exploration, the paper underscores the criticality of legacy system modernization, scrutinizes diverse approaches and methodologies, and elucidates key insights through compelling case studies. By grappling with the challenges and seizing the opportunities entwined with modernizing legacy systems, organizations can adeptly craft strategies that pave the way for a seamless transition towards updated, efficient, and scalable applications.

This research paper unravels the power of Bidirectional Encoder Representations from Transformers (BERT), a cutting-edge language representation model, in the realm of comment sentiment analysis. By focusing on two main aspects the working principles of BERT and the methodology of comment sentiment analysis - the paper aims to captivate readers with an engaging exploration of this exciting field. In this paper, we delve into the remarkable workings of BERT, showcasing its exceptional ability to grasp context, semantics, and sentiment in natural language text. Through a comprehensive examination of its underlying architecture, intricate pre-training process, and ingenious fine-tuning techniques, readers will gain a profound understanding of how BERT transforms raw language into rich, contextualized representations. Building upon this foundation, the paper unveils the methodology employed in our comment sentiment analysis project. Here, we take readers on an enthralling journey through the data collection and pre-processing phases, carefully selecting appropriate evaluation metrics, and ingeniously designing a sentiment classification framework. Along the way, we address the challenges of word sense disambiguation and tackle the nuances of neutral sentiments. By venturing into the uncharted territory of comment sentiment analysis using BERT, this research paper becomes more than just an invaluable resource for practitioners and researchers. It serves as a seductive invitation to unlock the hidden treasures within user opinions and sentiments, enabling us to leverage this invaluable wealth of information across various domains. With insightful findings and actionable recommendations, this paper fuels the pursuit of advanced sentiment analysis techniques, propelling the field towards enhanced comprehension and optimal utilization of user sentiments in the ever-evolving digital landscape.

The separation between structural engineering and building energy research has limited the ability of urban studies to capture interaction-driven performance at collective scales. This study develops and applies an integrated structural–energy co-optimization framework at the urban block scale to evaluate the role of adaptive structural systems in reducing energy demand under humid subtropical conditions. Using a representative urban block composed of heterogeneous low- to mid-rise buildings in: content Reference[oaicite:0] {index=0}, the research couples reduced-order structural modeling with time-resolved energy simulation and rule-based adaptive control. Quantitative results demonstrate that coordinated structural and envelope adaptivity can simultaneously reduce block-level cooling demand and inter-story drift, revealing non-linear trade-offs captured through Pareto optimization. The findings confirm that block-scale coordination outperforms isolated building-level adaptation and that targeted intervention in dominant buildings yields system-wide benefits. The study establishes structural adaptivity as an active energy-modulating mechanism and provides a transferable methodological foundation for performance-driven sustainable urban design.