Yang Liu

Aligning academic research with policymaking is vital for addressing China's energy challenges. This study introduces an AI-driven framework combining BERTopic, semantic similarity analysis, and deep reinforcement learning (DRL) to evaluate alignment between 106,661 English-language academic papers and 618 national-level policy documents. Topic modeling reveals strong convergence in themes such as “Coal mining and geological formations,” which account for 15.73 % of academic publications, while “Safety regulations and worker protection” dominate policy texts at 13.35 %. In contrast, emerging topics like “Digital economy and carbon transformation” remain underrepresented, with a popularity score of only 0.13. Semantic similarity analysis across 22 policy and 27 academic topics yields an average cosine similarity of 0.23, with only 12.5 % of topic pairs exceeding 0.4, underscoring thematic misalignment. Structurally, policy networks are 15.9 times denser and exhibit 30 × higher clustering coefficients than scientific networks, indicating more centralized but less diversified discourse. DRL-based prioritization identifies “Power systems and renewable integration” as the top-performing theme (Q-value = 1.6225), highlighting opportunities for targeted energy transition policies. These quantitative results offer empirical evidence to guide theme-based policy adaptation and foster actionable science-policy integration.
•Integrates AI methods to assess research-policy alignment in energy governance.•Identifies thematic gaps in China's energy policy using NLP and deep learning.•Uses deep reinforcement learning for dynamic policy prioritization.•Demonstrates framework scalability across environmental and policy domains.•Provides actionable insights for evidence-based energy policy decisions.

This study explores food safety law research using the Latent Dirichlet Allocation (LDA) model, analyzing 2540 papers from the Web of Science (1996–2024). Nine key themes were identified: Public Health and Food Safety, Health Management and Supply Chain, Implementation Strategies and Consumer Rights, Behavior and Policy Regulation, Welfare and Impact Assessment, Purchasing Patterns and Product Performance, Food Industry Regulations and Control Measures, Sectoral Support and Risk Assessment, and Agricultural Systems and Environmental Concerns. The latter theme received the most attention, highlighting a focus on sustainable agriculture and environmental stewardship. The study notes a decline in research on Health Management and Supply Chain and Implementation Strategies and Consumer Rights, possibly due to their maturation or the rising importance of environmental issues. Secondary analysis of Agricultural Systems and Environmental Concerns revealed an interdisciplinary nexus of chemistry, environment, and health. Geographical analysis showed diverse national priorities: Australia and Japan emphasized regulatory and consumer themes, while France and India prioritized agricultural sustainability. High entropy values in China and the U.S. indicated broad research themes, while lower entropy in Azerbaijan and Bulgaria reflected concentrated focus. The findings offer actionable recommendations tailored to stakeholders. Policymakers should prioritize adaptive regulations that address emerging health threats and integrate real‐time data systems. Industry professionals are encouraged to adopt advanced technologies, such as blockchain and AI, to enhance food safety and traceability. Researchers are advised to explore intersections between food safety, public health, and environmental sustainability to inform evidence‐based policy reforms. These steps aim to address global challenges, promote public health, and ensure resilient food systems.

1. Biosecurity activities primarily include pre-border and border quarantine, post-border surveillance and post-border eradication. Budget allocated to quarantine and surveillance activities ultimately influence the expenditure and success rate of eradication campaigns. Optimal portfolio allocation examined in previous research is susceptible to potential severe uncertainties existing in ecology and in the behaviour of invasive species itself. These uncertainties, together with a limited budget, make it difficult for decision makers to allocate the total management budget to each biosecurity activity in a robust manner.

2. Info-gap decision theory is applied to model the severe uncertainty in invasive species management, and robust optimize the total management cost.
This research shows that using a combination of pre-border and border quarantine (to reduce the incursion probability) and post-border surveillance (to enable early detection and rapid response), enables decision makers to be more robust to potential uncertainty. Further, it is reported that investment in quarantine that is more cost-effective should outweigh that in surveillance, in line with precautionary principle.

3. Increasing the estimated population threshold for surveillance detection also gains more robustness.

4. Synthesis and applications: Portfolio allocation options developed in this research provide decision makers with a way to manage the invasive species spatially, cost-effectively and confidently by allocating the total management budget in a robust manner. The methods outlined in this research can not only be applied to invasive species, but also the conservation of endangered species that are constrained by severe uncertainty in ecological modelling and limited resources.

Comprehensive vegetation cover in grasslands is a crucial indicator of grassland health and ecological balance, holding significant importance for scientifically sound grassland management and ecological environment monitoring. Satellite remote sensing inversion methods can provide full coverage for the scientific assessment of comprehensive vegetation cover in a given area. However, the sampling for inversion modeling often relies on conventional field survey methods, which are not only labor-intensive but also subject to high subjectivity, making it difficult to achieve satisfactory modeling results. This paper explores the use of multispectral low-altitude unmanned aerial vehicle (UAV) aerial photographs for sample point positioning, and employs three methods—RGB image clustering extraction, NRG image clustering extraction, and NDVI threshold extraction—to quantitatively estimate grassland comprehensive vegetation cover. The experimental results for Yuanmou County in Yunnan indicate that for areas with very high comprehensive vegetation cover, all three methods cannot achieve high assessment accuracy due to the interference of yellowed vegetation. In regions with lower comprehensive vegetation cover, NRG image clustering extraction and NDVI threshold extraction can achieve higher accuracy, with NRG images being more conducive to visual interpretation, and the NDVI threshold extraction method being simpler and more efficient.

This study leverages the BERTopic algorithm to analyze the evolution of research within precision agriculture, identifying 37 distinct topics categorized into eight subfields: Data Analysis, IoT, UAVs, Soil and Water Management, Crop and Pest Management, Livestock, Sustainable Agriculture, and Technology Innovation. By employing BERTopic, based on a transformer architecture, this research enhances topic refinement and diversity, distinguishing it from traditional reviews. The findings highlight a significant shift towards IoT innovations, such as security and privacy, reflecting the integration of smart technologies with traditional agricultural practices. Notably, this study introduces a comprehensive popularity index that integrates trend intensity with topic proportion, providing nuanced insights into topic dynamics across countries and journals. The analysis shows that regions with robust research and development, such as the USA and Germany, are advancing in technologies like Machine Learning and IoT, while the diversity in research topics, assessed through information entropy, indicates a varied global research scope. These insights assist scholars and research institutions in selecting research directions and provide newcomers with an understanding of the field's dynamics.

Invasive species eradication campaigns often fail due to stochastic arrival events, unpredictable detectability and incorrect resource allocation. Severe uncertainty in model parameter estimates may skew the eradication policy results. Using info-gap decision theory, this research aims to provide managers with a method to quantify their confidence in realizing successful eradication of particular invasive species within their specified eradication budgets (i.e. allowed eradication cost) in face of information-gaps. The potential introduction of the Asian house gecko Hemidactylus frenatus to Barrow Island, Australia is used as a case study to illustrate the model. Results of this research demonstrate that, more robustness to uncertainty in the model parameters can be earnt by (1) increasing the allowed eradication cost (2) investment in pre-border quarantine and border inspection (i.e. prevention) or (3) investment in post-border detection surveillance. The combination of a post-border spatial dispersal model and info-gap decision theory demonstrates a novel and spatially efficient method for managers to evaluate the robustness of eradication policies for incursion of invasive species with unexpected behaviour. These methods can be used to provide insight into the success of management goals, in particular the eradication of invasive species on islands or in broader mainland areas. These insights will assist in avoiding eradication failure and wasteful budget allocation and labour investment.

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3·5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.