A new study published in Engineering reveals that NSUN2, a key protein involved in RNA modification, significantly contributes to cardiac hypertrophy and heart failure by activating the LARP1–GATA4 axis. This research not only deepens our understanding of the molecular mechanisms underlying heart failure but also identifies NSUN2 as a potential therapeutic target for preventing and treating cardiac diseases.

Huawei’s new “Agentic-AI Core” lets 6G networks write their own control procedures: natural-language intents become executable missions, composed on the fly from verified, third-party building blocks and orchestrated by AI agents with no human coding required.

Discover how generative AI is transforming video communication in a groundbreaking new paradigm. Learn about the innovative techniques and future prospects of generative video communication, which promises more efficient, adaptable, and immersive video services. Explore the challenges and potential solutions in this emerging field.

A new study published in Engineering reveals that dihydrotanshinone I (DHT), a compound from Salvia miltiorrhiza, can induce autophagic cell death in ovarian cancer by targeting sortilin 1 (SORT1). This discovery offers a potential new therapeutic strategy for treating ovarian cancer through disrupting the autophagy–lysosome pathway.

A new study published in Engineering explores how coal power flexibility can support global decarbonization efforts. Researchers find that retrofitting coal plants for flexibility enhances renewable energy integration, reduces carbon emissions, and offers economic benefits. The study highlights the importance of policy support and technological innovation in transforming coal power to meet future energy needs sustainably.

Researchers from Southeast University have developed a novel amplifying and filtering reconfigurable intelligent surface (AF-RIS) that enhances wireless communication for 6G networks. The AF-RIS achieves significant signal amplification, frequency selectivity, and beam-steering capabilities while reducing hardware costs and power consumption. This innovation could revolutionize wireless relay systems in next-generation networks.

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to underwater cables—and is critical for safe renewable energy development. Traditional numerical simulations and experiments require enormous computational resources, yet often fail to capture multiscale turbulence and long-term system behavior. This review highlights how machine learning (ML) is emerging as a powerful solution for analyzing, predicting, and even controlling FSI systems. Key progress spans feature detection, reduced-order modeling, physics-informed neural networks, and reinforcement-based flow control. By leveraging data-driven models to extract hidden patterns and reconstruct flow fields, ML shows promise in improving efficiency, predictive accuracy, and automated control across ocean engineering applications, positioning itself as a transformative tool for next-generation design.

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.

Researchers from Southeast University have developed a compact, dual-band, dual-polarized phased array for B5G/6G millimeter-wave communication. The array integrates four independent beamforming systems on a single printed circuit board, supporting concurrent operations at 28 GHz and 38 GHz. It features scalable architecture, broad bandwidth, and high spectral efficiency, making it ideal for future high-speed wireless applications.

Abstract

Purpose – We introduce a novel method for comparing the prices and the delta and vega risks for European options by considering Liu’s stock model of uncertainty and the stochastic Black and Scholes model. We aim to reveal the differences between the prices and risks under both approaches.

Design/methodology/approach – We develop an uncertainty approach to estimate the Greek letters delta and vega risks and establish two comparison criteria based on order relations and matrix norm metrics. The method is tested through a numerical experiment, incorporating a wide range of experimental and market parameters for strike price and maturity options and expert views for asset volatilities and preference levels.

Findings – We find four key facts: prices and risks of European call options differ significantly between the two approaches; uncertain young out-of-the-money call options are costly and riskier than the stochastic counterparts; the expert preference level is more important than the volatility for uncertain call options’ premiums and risks; and these, in turn, are sensitive for young options and across all strike prices.

Practical implications – We design a static delta hedging strategy for call options under uncertainty and find that although it is more expensive, it may offer better hedging than the stochastic counterpart. Thus, market hedgers may benefit more from the uncertainty framework rather than the stochastic one.

Originality/value – Our findings are summarized in a set of facts that could be considered to develop innovative foundations to support future research in artificial intelligence for financial risk management using the uncertainty theory.