National Science Library

Identification of potential disruptive technologies is crucial for formulating science, technology, and innovation policies. Existing studies not only exhibit limitations in semantic analysis and multi-dimensional relationship modeling but also fail to incorporate external influences such as policy, market, and social factors. To address these limitations, a large language model (LLM) is employed to extract sentence-level technical directions from heterogeneous scientific texts for topic modeling with BERTopic. Subsequently, multiple co-occurring themes within individual documents are synthesized with themes extracted from their respective citing literature to construct hyperedges, thereby encapsulating the underlying mechanisms of knowledge recombination and evolutionary trajectories. Furthermore, tracking the evolution of dynamic hypergraphs across time slices facilitates the extraction of multidimensional signals, thereby enabling a more forward-looking and systematic detection of early-stage disruptive technology seeds. Finally, we leverage an LLM to architect a multi-agent system incorporating perspectives from technology, enterprises, policy, industry associations, and venture capital to simulate the inherent conflicts and potential coordination of evaluation criteria among different stakeholders, thereby filtering candidate technologies with the highest market development potential. Applying our framework to an empirical study on hydrogen energy, we identified three candidate technologies. These results were subsequently cross-validated through converging evidence from market trends (Google Trends) and expert literature, confirming the framework's scientific validity and practical utility. Our approach provides an interpretable and scalable paradigm for pinpointing disruptive technologies transcending the limits of traditional bibliometrics.

Polyphenols are a diverse group of plant-derived bioactives that have been investigated as multi-target candidates for the potential prevention and management of neurodegenerative diseases (NDDs). We conducted an integrated bibliometric and mechanistic scoping review covering 12 polyphenol classes and seven major NDDs using records from PubMed, Embase, the Cochrane Library, and Web of Science (1940-2024). Research landscapes and emerging themes were mapped using keyword co-occurrence, clustering analyses, and BERTopic modeling. Mechanistic evidence was synthesized across core pathways, including oxidative stress, neuroinflammation, proteostasis (amyloid/tau and α-synuclein), mitochondrial dysfunction, and cholinergic modulation, to link preclinical findings with clinical outcomes. Publication output increased markedly after 2000, with China and the United States contributing the most records. Four persistent hotspots were identified: (1) antioxidant and neuroprotective effects (e.g., resveratrol, curcumin); (2) anti-inflammatory activity and intracellular signaling; (3) cognition, aging, and sex-specific responses in clinical research; and (4) animal models of memory impairment. Clinically investigated interventions include epigallocatechin gallate, Ginkgo biloba extracts, olive/cocoa polyphenols, and flavonoid-rich mixtures; however, limited bioavailability and heterogeneous trial designs constrain the strength of effect estimates. Advances in delivery systems, computational screening, and precision nutrition may improve translation. Overall, polyphenols show multi-target neuroprotective potential, but larger and more standardized clinical trials are needed to support evidence-based nutritional strategies for NDD prevention and management.

Abstract Establishing an environmentally sustainable food system is crucial for mitigating climate change. To address the research gap, this study employs multiple methods/models to assess the inequality in per capita food-related GHGs (PFGHGs), and identify the drivers of the disparities. Results indicate that PFGHGs in China have increased dramatically, with more pronounced changes in rural areas. The urban-rural gap has undergone a process of “slow decrease - rapid increase - rapid decrease”. As for the drivers, the consumption levels have reduced regional inequalities, whereas the effects of urbanization and income levels have expanded inequalities. Educational level, consumption level, Engel’s coefficient, and income level exhibit different impacts on inequality, with improvements in consumption level, educational level, and a decrease in the Engel’s coefficient narrowing the gap, while an increase in income level can widen it. The research provides a scientific basis for emission reduction strategies at the micro-level.