Promoting sustainable development in both urban and rural areas has become a globally shared concern. Urban–rural interactions can contribute to wealth creation, poverty alleviation, employment growth, and improved social equity. It has been widely recognized as a key strategy for addressing sustainability challenges, although it may also bring some negative impacts, such as labor outmigration and imbalanced capital allocation. However, the impacts of urban–rural interactions on rural development and how these impacts contribute to sustainable rural transformation require further in-depth investigation.

In light of these pressing global ecological challenges, there is a compelling need to develop ecological theories and applied technologies for understanding, managing, and conserving macroecological systems at regional to global scales. Such efforts are essential to safeguarding natural resource environments, maintaining ecosystem stability, and ensuring the sustainability of human societies.

 In recent decades, the infusion of statistics and dynamic equations into geography has shifted the discipline from a descriptive endeavor to a modern science that builds predictive models through hypothesis testing. This progress, however, prompted doubts about the representativeness of sampling, the validity of discarded outliers, and the uniqueness of assumed distributions, parameters, and equations.

The dynamics of soil organic carbon (SOC) play a critical role in the global carbon cycle. In context of global warming, numerous experimental studies have reported temperature-sensitive responses of SOC. However, the limited temporal frequency and spatial density of repeated sampling and whole-profile SOC observations have hindered the understanding of large-scale, long-term spatiotemporal patterns of SOC and their responses to environment changes under global warming, thereby constraining the ability to accurately predict the global carbon cycle.

This study analyzes 20 years of data from the Food and Drug Administration’s Adverse Event Reporting System (FAERS) to reveal trends in drug-induced fatty liver disease (DIFLD), identifying high-risk drug classes and vulnerable populations, and highlighting the need for region-specific pharmacovigilance and tailored interventions.