Jiping Dong

China has experienced extensive forest expansion alongside rapid urbanization and large-scale population redistribution over the past three decades, yet how these transformations have reshaped the spatial relationship between people and different forest types remains unclear. Here, we quantified changes in forest–population relationships across China from 1990 to 2020 using forest area per person (FAP), forest-proximate people (FPP), and the forest–human nexus (FHN), for all forests combined and for natural and planted forests separately. Although total forest area increased by 9.3%, population growth resulted in an 11.5% decline in FAP. The share of the population living within 5 km of any forest increased from 57.4% to 62.4%, but divergent trajectories emerged between forest types: proximity to natural forest declined from 56.2% to 51.3%, whereas proximity to planted forest increased from 30.5% to 47.5%. FHN for all forests increased by 26.2% and planted-forest FHN by 59.5%, while natural-forest FHN remained broadly stable (−1.9%). Shapley decomposition further revealed substantial spatial heterogeneity in the contributions of forest change and population redistribution, with forest change contributing most to increases in FHN for all forests and planted forests at the national scale, while the two processes acted in opposing directions for natural forests. These findings provide a more complete, population-explicit perspective on China’s evolving human–forest relationship and offer a spatial basis for sustainable forest management, conservation, restoration, and land-use planning.

Scrub typhus has emerged as a life-threatening and increasingly prevalent vector-borne disease. While the spatial and temporal distributions of scrub typhus have been studied by the research community, the main driving factors that influence the spatiotemporal patterns of the disease remain under investigation. Using Guangdong Province as a case study, we combined monthly scrub typhus case data from 2010 to 2019 and environmental and vector-related datasets with a Bayesian hierarchical mixed model to elucidate the spatiotemporal characteristics of the disease. This study revealed that the most highly endemic areas of scrub typhus are concentrated in the western and southern parts of Guangdong Province. A distinct bimodal pattern of scrub typhus was observed, with peaks typically occurring from May to July and October to November. The fitted model indicated that forest, cropland, and chigger mites were positively associated with scrub typhus transmission. Furthermore, climate and vectors were identified as key factors shaping the bimodal seasonal patterns of scrub typhus. Despite data-related limitations, including the treatment of population as a time-invariant variable and the binary simplification of vector suitability, sensitivity analyses confirmed the robustness of the model, offering valuable insights for scrub typhus prevention in Guangdong.

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Understanding the spatial dynamics and drivers of armed conflict is crucial for anticipating risk and informing targeted interventions. However, current research rarely considers the spatio-temporal clustering characteristics of armed conflicts. Here, we assess the distribution dynamics and driving factors of armed conflict from the perspective of armed conflict clusters, employing complex network dynamic community detection methods and interpretable machine learning approaches. The results show that conflict clusters vary in terms of regional distribution. Sub-Saharan Africa boasts the highest number of conflict clusters, accounting for 37.9% of the global total and covering 40.4% of the total cluster area. In contrast, South Asia and Afghanistan, despite having a smaller proportion of clusters at 12.1%, hold the second-largest cluster area, which is 18.1% of the total. The characteristics of different conflict networks are influenced by different factors. Historical exposure, socio-economic deprivation, and spatial structure are the primary determinants of conflict patterns, while climatic variables contribute less prominently as part of a broader system of environmental vulnerability. Moreover, the influence of driving factors shows spatial heterogeneity. By integrating cluster-level analysis with interpretable machine learning, this study offers a novel perspective for understanding the multidimensional characteristics of armed conflicts.

Background: The prevalence of cystic echinococcosis (CE), a widespread zoonotic disease, imposes a significant public health burden, especially in western China. However, under the background of global change, how to meet the challenge of the future risk of CE remains unclear. As global climate change, land use changes, and socio-economic factors continue to progress, the spread and intensity of CE may potentially worsen, making it crucial to assess and mitigate future risks. Methods: By employing Bayesian additive regression trees model to develop risk models for CE in animal hosts (cattle, sheep and dogs) and humans, this study mapped the current distribution of infection risk for CE and projected future risks under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The projections considered both constant and increased rates of public awareness rates regarding CE prevention in the future. Results: Current simulations indicate that the regions with a high risk of CE infection are primarily concentrated in Tibet, Qinghai, Gansu, and Xinjiang. Future projections suggest that heightened CE risks will be experienced in regions such as Yunnan, Gansu, and Sichuan will experience heightened CE risks. Notably, predictions suggest that increased public awareness is estimated to be linked to accompanied by a reduction of the population at risk by 2.72% to 3.35% in western China by 2030. Conclusion: This research offers a comprehensive understanding of the future distribution of epidemic risk for CE under climate and socio-economic changes. It highlights that enhancing public awareness regions with high-risk is a critical factor associated with reduced infection rates. Furthermore, the study offers a valuable framework for assessing the risk associated with other zoonotic diseases.

Armed conflict is a dynamic social phenomenon, yet existing research often overlooks its evolving nature. We propose a method to simulate the dynamic transformations of armed conflicts. First, we enhanced the Spatial Conflict Dynamic Indicator (SCDi) by integrating conflict intensity and clustering, which allowed for the distinction of various conflict types. Second, we established transformation rules for the SCDi, quantifying five types of transformations: outbreak, stabilization, escalation, de-escalation, and maintaining peace. Using the random forest algorithm with multiple covariates, we simulated these transformations and analyzed the driving factors. The results reveal a global trend of polarization in armed conflicts over the past 20 years, with an increase in clustered/high-intensity (CH) and dispersed/low-intensity (DL) conflicts. Stable regions of ongoing conflict have emerged, notably in areas like Syria, the border of Afghanistan, and Nepal’s border region. New conflicts are more likely to arise near these zones. Various driving forces shape conflict transformations, with neighboring conflict scenarios acting as key catalysts. The capacity of a region to maintain peace largely depends on neighboring conflict dynamics, while local factors are more influential in other types of transformations. This study quantifies the dynamic process of conflict transformations and reveals detailed changes.

Inter-state cyberattacks are increasingly becoming a major hidden threat to national security and global order. However, current prediction models are often constrained by single-source data due to insufficient consideration of complex influencing factors, resulting in limitations in understanding and predicting cyberattacks. To address this issue, we comprehensively consider multiple data sources including cyberattacks, bilateral interactions, armed conflicts, international trade, and national attributes, and propose an interpretable multimodal data fusion framework for predicting cyberattacks among countries. On one hand, we design a dynamic multi-view graph neural network model incorporating temporal interaction attention and multi-view attention, which effectively captures time-varying dynamic features and the importance of node representations from various modalities. Our proposed model exhibits greater performance in comparison to many cutting-edge models, achieving an F1 score of 0.838. On the other hand, our interpretability analysis reveals unique characteristics of national cyberattack behavior. For example, countries with different income levels show varying preferences for data sources, reflecting their different strategic focuses in cyberspace. This unveils the factors and regional differences that affect cyberattack prediction, enhancing the transparency and credibility of the proposed model.