Multimodal large language models have shown powerful abilities to understand and reason across text and images, but their massive size and computational cost limit real-world deployment. This research systematically examines how multimodal models can be made more efficient without severely sacrificing performance. By analyzing lightweight architectures, visual token compression strategies, efficient training methods, and compact language backbones, the study maps out the technical pathways that reduce memory demand and inference cost. The work highlights how efficiency-focused design enables multimodal intelligence to move beyond cloud-based systems toward broader, more accessible applications, including mobile devices and edge computing environments.

Machine learning (ML) is rapidly emerging as a powerful tool to improve the safety, reliability, and long-term performance of marine structures exposed to harsh ocean environments. This study presents a comprehensive review of ML and deep learning algorithms applied to marine engineering, highlighting how they enhance structural design, construction efficiency, and real-time maintenance. The work introduces a novel modeling framework that integrates mechanical principles with data-driven algorithms, improving interpretability and prediction accuracy. It also outlines key challenges such as data scarcity, environmental uncertainty, and model transparency, offering guidance for future research. The review provides valuable insights for structural engineers seeking to adopt ML technologies for next-generation ocean infrastructure.

On November 8, 2025, the Faculty of Education at East China Normal University hosted a grand ceremony to present the second Global Award for Innovation in Education Research Methods. Five distinguished scholars—Ruth Hayhoe, Stephen Raudenbush, Manabu Sato, Shanmai Wang, and Rupert Wegerif—from Canada, the United States, Japan, China, and the United Kingdom were honored with this year’s award.

Hong Kong, China, December 30, 2025 --- Insilico Medicine (3696.HK), a clinical-stage drug discovery and development company driven by generative artificial intelligence (AI), is successfully listed on the Hong Kong Stock Exchange today, becoming the first AI-driven biotech company to go public in Main Board under Chapter 8.05 listing rules of the HKEX. This initial public offering (IPO) raised a total of HKD 2.277 billion, achieving the largest biotech IPO in Hong Kong this year, as in the size of fundraising.