Systems biology has traditionally relied on network abstractions and mechanistic models to study complex biological systems. However, advances in model expressiveness and data availability have not translated into proportional improvements in mechanistic understanding. We suggest that a key limitation may arise from a structural mismatch: prevailing pairwise interaction models fail to capture the inherently higher-order organization of biological systems. Across molecular, cellular, and ecological scales, system behavior is governed by cooperative, conditional, and context-dependent multi-body interactions that cannot be faithfully represented by pairwise projections alone. To address this challenge, we advocate a paradigm shift toward explicit higher-order structural representations combined with data-driven, learnable dynamical models. Within this framework, artificial intelligence enables the inference of governing dynamical rules and the discovery of mechanisms operating on higher-order structures, while large language models can accelerate hypothesis generation and the integration of prior knowledge. Together, these advances point toward a unified, generative approach to systems biology that moves beyond descriptive networks toward an interpretable, mechanism-driven understanding of the processes underlying biological function.

Continuous sign language recognition (CSLR) aims to model the temporal evolution of visual gestures to recognize continuous semantic units, which is of great significance for applications in deaf communication assistance and intelligent human–computer interaction. While existing methods emphasize local segment modeling and long-range dependency capture, they often overlook the critical role of global semantic context in overall video comprehension—an oversight that contradicts the inherently context-dependent nature of sign language. Moreover, sign language videos frequently contain a large number of visually similar but semantically meaningless motions. These misleading segments are easily misperceived as valid glosses, thereby degrading recognition accuracy. To address these challenges, we propose GANet (Gloss-Aware Network), a novel CSLR framework with cross-modal input adaptability. Inspired by the hierarchical structure of "book–chapter–content", GANet explicitly models global context to guide local understanding while effectively suppressing irrelevant motion noise. Specifically, we introduce a Global Context Modeling Module to capture semantic patterns across frames and an auxiliary task to enhance the model's ability to learn high-level structural semantics. In addition, we propose a Gloss-Aware Module that leverages global semantics to model the spatiotemporal occurrence of glosses, thereby improving the recognition of meaningful gestures. Extensive experiments on multiple benchmark datasets demonstrate that GANet outperforms existing methods, validating its effectiveness, robustness, and broad adaptability to both RGB (red, green, and blue) and event-based data.

The utilization of liquefied natural gas (LNG) cold energy represents an important approach to improving the efficiency of the LNG value chain. Existing research on LNG cold energy utilization has mainly focused on steady-state simulations and key parameter optimization, while studies on dynamic simulation remain relatively limited. To address this gap, this study develops a dynamic model of a boil-off gas (BOG) re-liquefaction system coupled with an Organic Rankine Cycle (ORC) power generation unit driven by LNG cold energy, with particular emphasis on system dynamic stability. The effects of disturbances in certain parameters, such as temperature and mass flow rate, on system stability and dynamic response are investigated. The results indicate that when the LNG mass flow rate increases by 5%, the BOG re-liquefaction rate rises from 2,006 kg/h to approximately 2,100 kg/h (about 5%), while the ORC power output even increases from 60 kW to around 65 kW (about 8%), demonstrating the importance of sufficient cold energy for the ORC system. In contrast, a ±1 °C variation in BOG temperature has a limited impact on ORC power output (generally less than 2%), but it significantly affects the BOG re-liquefaction rate, which can increase by up to 10%. This study provides valuable insights into the dynamic operational characteristics of the BOG-ORC system and highlights the potential of utilizing LNG cold energy for both BOG re-liquefaction and power generation.