Wang Mianzhi, a recipient of the inaugural title of "Master of Chinese Medicine" and a tenured professor at Beijing University of Chinese Medicine, was the principal founder and pioneer of the discipline of Formulas of Chinese Medicine in China. Born into a distinguished family of traditional Chinese medicine practitioners in Nantong, Jiangsu Province, he was the 19th−generation heir of the Wang family's medical tradition. He served as editor−in−chief of the first nationally unified textbook of Formulas of Chinese Medicine, established the organizational principle of "formulas governed by therapeutic methods," and made the discipline a core course in Chinese medicine education. Academically, he advocated that "formulas derive from therapeutic methods, and methods are established according to pattern differentiation," emphasizing the study of formula theory and the flexible modification of classical formulas. He was highly skilled in treating refractory and serious diseases such as immune−related infertility and aplastic anemia. He also introduced traditional Chinese medicine into China's manned spaceflight program by developing the "Taikong Yangxin Wan" (Space Heart−Nourishing Pill), which effectively improved astronauts' cardiovascular function and metabolic status, contributing to the success of the Shenzhou−5 and Shenzhou−6 missions. Throughout his life, Wang Mianzhi devoted himself to theoretical research, clinical practice, textbook development, and the training of talent in the field of Formulas of Chinese Medicine, making indelible contributions to the establishment and advancement of this discipline and to the modernization and internationalization of traditional Chinese medicine.

As river geomorphology research increasingly relies on large−scale literature integration and analysis, the challenge of extracting structured knowledge from unstructured texts has become central to advancing digital and semantic approaches in geomorphological science. This study proposes a GPT−based method for automatic knowledge extraction in the field of fluvial geomorphology. A structured Few−shot prompt framework was designed to guide the model in identifying geomorphic entities and their relations, enabling the generation of standardized knowledge triples in the form of <entity₁, relation, entity₂>. The workflow consists of corpus preparation, prompt engineering, model invocation, and structured output. Applied to 30 high−quality English−language journal articles, the approach extracted a total of 7605 knowledge triples, encompassing 22815 entities and relations. Evaluation results demonstrate that the method achieves high accuracy (Precision=0.834), good recall (Recall=0.789), and stable overall performance (F1=0.862). Nonetheless, limitations remain in the handling of abstract logic, long−range dependencies, and relation directionality. Future improvements may involve domain−specific model fine−tuning, integration of geographic ontologies, and multimodal data fusion. This research provides a practical solution for intelligent knowledge construction in fluvial geomorphology and expands the methodological toolkit at the intersection of geographic information science and artificial intelligence.

With the acceleration of urbanization, light pollution caused by the widespread use of artificial light at night (ALAN) has become a global environmental issue. Early research on the ecological impacts of light pollution primarily focused on a limited number of vertebrate taxa but has since expanded to encompass diverse taxonomic and ecological groups. Here, we systematically review the impacts of light pollution across different ecological groups, including potential threats to human health, disruptions of animal circadian rhythms and behavior, alterations in plant phenology and physiological processes, and changes in microbial community composition and function. The ecological effects of light pollution exhibit strong species specificity and context dependence, with response intensity being influenced by light intensity, spectral composition, and exposure conditions. Despite considerable advances, our understanding of the mechanisms underlying ecological responses to light pollution and its long−term consequences at higher levels of ecological organization remains limited. Future research should further elucidate the mechanisms driving ecological responses to light pollution, improve our capacity to predict ecological risks, and advance ecologically friendly lighting technologies and management strategies to address the growing challenges posed by light pollution.

The problem of urban light pollution in China is becoming increasingly prominent, and accelerating the improvement of the system for preventing and controlling light pollution has become an urgent need. This article systematically reviews the regulations, policies, and practical experience of light pollution prevention and control in typical international countries and regions, and deeply analyzes the current situation, management system, and institutional standards of light pollution prevention and control in China. The study proposes to strengthen joint supervision among multiple departments, clarify the responsibilities of departments such as ecological environment, housing and construction, and transportation, and implement the responsibilities of source prevention and control and local government zoning management; Accelerate the improvement of the institutional system centered on overall planning, source prevention and control, classified management, and zoning control (requiring strict protection of special protection areas), and strengthen the promotion of green lighting and public participation supervision; synchronize and improve key standards such as light environment quality, emission limits, monitoring and evaluation, and zoning control, build a scientific, rigorous, collaborative and efficient light pollution prevention and control system, and continuously enhance governance capabilities and levels.

Light exposure is a critical environmental factor influencing human circadian rhythms, emotional regulation, and metabolic health. In recent years, it has become a central variable in the field of healthful lighting research. As relevant studies continue to accumulate, methodological paradigms are evolving from static to dynamic approaches, and from environment−based to individual−centered models. This paper reviews the development of light exposure research across four dimensions: theoretical mechanisms, indicator systems, measurement techniques, and health−related evidence. By summarizing five mainstream research paradigms, the paper identifies an ongoing shift from short−term experiments based on predefined lighting conditions to dynamic modeling of ocular light exposure in real−life settings, namely the concept of personal light exposure history. Further analysis reveals three major challenges facing the field: insufficient tools for measuring long−term health effects, a lack of standardized data frameworks for individualized exposure, and the high cost of accurate exposure history acquisition. In response to these issues, the paper proposes a development pathway focusing on high−fidelity measurement, data structure design, and predictive modeling. On this basis, a framework for light pollution assessment and governance is further developed, with personal light exposure history as its core. This framework proposes using individual health risk as the basis for quantifying pollution intensity, enabling a shift from measurability to evaluability and controllability in lighting governance. The study provides a theoretical foundation and methodological support for advancing both the mechanistic understanding and practical implementation of healthful lighting.

The convergence of communication and intelligence is initiating a revolutionary and disruptive transformation of the communication system. This paper first analyzes the current research of communication and artificial intelligence technologies, highlighting the necessity of their deep integration. It then presents an original paradigm called ComAI, and discusses on its guiding theory, namely semantic information theory, which reveals that synonymy is the essential characteristic of semantic information. Starting from the concept of synonymous mapping, a semantic information measurement system is constructed, and the semantic source coding theorem, semantic channel coding theorem, and semantic rate-distortion coding theorem, semantic channel coding, and semantic rate−distortion coding are proved. Furthermore, a series of key technologies of the ComAI system are discussed. Finally, it outlines the future application prospects of ComAI. To support the construction of China's intelligent communication infrastructure system, it is imperative to achieve breakthroughs in core technologies and enhance China's overall competitiveness in the field of information and communication.