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.

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.

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.

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

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.

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.

Increasing international trade and tourist activities raise the likelihood that invasive species will be introduced and result in damage to the economy, environment and society. There are primarily three biosecurity activities for managing invasive species; namely pre-border and border quarantine (quarantine, hereafter) to prevent incursion, post-border surveillance (surveillance, hereafter) to search for newly introduced species and, post-border eradication (eradication, hereafter) to remove the last individual of the invasive species. Preventing the introduction of invasive species is an effective and economical management approach. Intensive surveillance is necessary to prevent newly introduced populations from establishing and spreading, particularly in high-risk areas. Once the invasive species becomes established, eradication can be difficult and resource intensive. Appropriate budget allocation for future incursions, is important to ensure early detection and a rapid response in order to successfully eradicate the organism. When eradication is not feasible, containment and asset-based protection could be implemented. These management options are not considered in this research. When confronted with biological invasions, decision-makers need to react quickly, however, they are often hindered by sparse and/or contradictory information. This is because ecological systems, and the species they contain, are variable and highly complex, resulting in severe uncertainty in ecological model parameters. The existence of severe uncertainty can lead to flawed and inefficient decisions. Within the limited time prior to an incursion response, it is usually impractical for decision makers to collect sufficient information and put forward more informed responses. Info-gap decision theory can be applied to model and manage such severe uncertainty. Info-gap decision theory (IGDT) is developed for decision-making under severe uncertainty. Either the decision maker is risk seeking or risk averse, the uncertainties are thus favourable or detrimental respectively. While opportuneness searches for possibilities of extra gains for the optimistic decision makers, robustness can guarantee return for conservative decision makers, with minimum requirements always satisfied. The confidence in realizing desired goals under severe uncertainty is quantified and evaluated using robustness. Info-gap decision theory has been widely applied in ecosystem management. This research extends the application of IGDT by determining the estimated population threshold for surveillance detection and guiding robust budget allocation to biosecurity activities. This research models the cost-effective management of a potential incursion of the Asian house gecko (Hemidactylus frenatus) (AHG) Duméril & Bibron, 1836 onto Barrow Island (BWI), Australia using info-gap decision theory. The AHG is an excellent hitchhiker and one of the most widespread reptiles world-wide. In its introduced range, this species has caused the extinction of several gecko species and is believed to carry novel parasites. Barrow Island is one of the most significant conservation reserves in Western Australia. The operation of industries on the island may increase the possibility of invasive species incursions. A strict quarantine management system (QMS) has been implemented on BWI to protect the valuable biodiversity of the island. AHG is one of the most frequently detected vertebrate invasive species at the border and was detected and eradicated on BWI in March 2015. BWI is currently free of the AHG (Chevron Corporation, 2021). Due to its widespread presence on the mainland of Australia and many overseas sites, its known invasiveness and its potential impact on the biodiversity of BWI, AHG has been identified as a priority invasive species for biosecurity management. This research examines the robustness of expenditure that the decision makers would like to spend on surveillance and eradication. This research also enables decision makers to determine a robust portfolio of funds across the three biosecurity activities (quarantine, surveillance and eradication) against errors in the model parameter estimates. It is demonstrated that quarantine is more efficient than surveillance and eradication for managing the AHG. However, combining quarantine and surveillance works in a more robust manner against the underlying uncertainty in ecological modelling. Nevertheless, investment in quarantine should still outweigh that in surveillance. Increasing budget allocated to either quarantine or surveillance results in a larger annual budget, but decreases the total budget limit (i.e. the maximum total budget that decision makers may allocate to all three biosecurity activities) and increases robustness. More investment in eradication increases the robustness by increasing the probability of eradication success. The model used to describe the spread of AHG after its incursion (spatial (i.e. spread among multiple locations) or local (i.e. spread at one location) dispersal model) and the estimated population threshold (i.e. estimated tolerable population size in which the surveillance programme is designed to detect at least one individual of AHG if it is present, ) both change the robustness achieved by the same budget limit. The spatial spread model has been demonstrated to work more efficiently than the local spread model. Transportation exploited by the AHG among locations could facilitate its spread and influence the invasion probability. Results vary when uncertainty is modelled in more fundamental parameters of model of detection probability. The situation in which is shown as robust-dominant and thus preferred in two of the data chapters and opportune-dominant in the first chapter of surveillance program design. Environmental damage and reputational, economic, ecological and societal impacts caused during the eradication campaigns and by the invasive species are not considered in this research. It is possible that the estimated population threshold could decrease when these are considered. The model in this research can be extended to include additional activities (e.g. delimiting the infested area, monitoring to confirm eradiation success) to inform a more coordinated and comprehensive biosecurity system. This research model can also be extended to include temporal factors reflected by annual discount rate or by dynamic models related to varying population sizes (e.g. detection rate). Robust management of other invasive species (e.g. agricultural pests, marine invasive species and weeds), and conservation of endangered species in a data-poor environment can be achieved by adapting the three info-gap analysis models to the situations. A full decision analysis, weighing the costs and benefits of each biosecurity activity, is required to identify the risks and consequences of each biosecurity action on a case by case basis. This research provides a tool to assist in the completion of such decision analysis. IGDT as an alternative decision-making method has been applied in this research to present a quantitative bioeconomic modelling framework, thus providing a feasible and theoretical basis to assist in making informed decisions in a cost-effective and robust way. This research provides a reference for other islands and even mainlands for the purpose of managing other invasive species, but also provides scientific support for improving the rational biosecurity framework. Keywords: invasive species, biosecurity, quarantine, surveillance, eradication, portfolio allocation, decision making, info-gap decision theory, robustness, opportuneness, spatial spread model, Asian house gecko, Barrow Island.

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.