Climate change has altered precipitation regimes and shifted the end of the vegetation growing season (end of the season [EOS]), with consequences for terrestrial carbon and water cycles. However, the impacts of precipitation regimes, that is, total precipitation ( P _total ) and precipitation frequency ( P _freq ), on EOS remain unclear. Using satellite‐derived phenology data from 1982 to 2020, we examined the effects of precipitation regimes on EOS across the mid‐ to high‐latitudes of the Northern Hemisphere. We found that both increased P _total and decreased P _freq delayed EOS, with the effect of reduced P _freq likely mediated through increased maximum temperature and elevated soil moisture resulting from higher precipitation intensity. We further developed a process‐based autumn phenology model that incorporated the effect of P _freq , which significantly improved model performance. These findings underscore the influence of precipitation regimes on EOS and highlight the need to account for both precipitation frequency and intensity when projecting vegetation carbon uptake under future climate scenarios.

Litter decomposition regulates the quantity and quality of plant-derived carbon (C) and nitrogen (N) inputs to soil and is closely associated with microbial community structure. However, how elevated ozone (O₃) and nitrogen (N) addition interactively affect residual litter inputs and their associations with soil microbial communities remains poorly understood, especially in agroforestry systems. Here, we conducted a 12-month in situ litter decomposition experiment using two poplar clones (107 and 546) under ambient or elevated O₃ with or without N addition (60 kg N ha⁻¹ yr⁻¹) at an O₃-FACE platform in northern China. Litter mass and chemical traits were measured during decomposition, and endpoint soil microbial community structure was characterized using phospholipid fatty acid (PLFA) profiling. Treatment effects and litter–microbe associations were evaluated using linear mixed-effects models, correlation analysis, and redundancy analysis (RDA). Endpoint litter mass remaining was significantly affected by O₃, clone identity, and their interactions with N addition, while endpoint litter chemical traits showed trait-specific responses. PLFA-derived microbial community indices also showed treatment- and clone-dependent responses, particularly in bacterial groups, AM fungi, and the fungal-to-bacterial ratio. Endpoint litter mass remaining showed the strongest statistical association with PLFA-derived microbial community structure, whereas individual nutrient concentrations showed weaker independent effects. These findings suggest that O₃- and N-induced changes in residual litter quantity and quality are associated with shifts in PLFA-derived microbial community structure. Because PLFA characterizes microbial community structure rather than process rates, these findings should be interpreted as evidence of structural microbial reorganization associated with altered residual litter inputs, rather than direct evidence of changes in C or N cycling rates.

Purpose: Mental disorders represent a major public health challenge among women of childbearing age (WCBA, 15– 49 years), yet comprehensive evidence on long-term trends, socioeconomic disparities, and future trajectories remains limited. Using data from the Global Burden of Disease (GBD) 2021, this study aimed to quantify the burden of mental disorders among WCBA from 1990 to 2021 and to project future trends. Methods: Incidence, prevalence, and disability-adjusted life years (DALYs) of mental disorders among WCBA were obtained from GBD 2021. Temporal trends from 1990 to 2021 were assessed using joinpoint regression analysis. Bayesian age–period–cohort (BAPC) modeling was applied to forecast the burden of ten major mental disorders for the next 15 years. Analyses were stratified by age group, geographic region, country, and socio-demographic index (SDI). Results: In 2021, mental disorders imposed a substantial global burden among WCBA, with depressive and anxiety disorders accounting for the majority of cases. Marked geographic and socioeconomic disparities were observed, with consistently higher burdens in high-SDI regions. Age-specific patterns differed by disorder, with anxiety disorders peaking in younger women and depressive disorders peaking in midlife. Trend analyses revealed a decline in burden indicators between 2005 and 2010, followed by a pronounced rebound after 2019. Projections indicate that the incidence and prevalence of depressive and anxiety disorders will continue to rise, whereas the burden of bipolar disorder and schizophrenia is expected to stabilize or decline. Conclusion: The burden of mental disorders among WCBA shows substantial inequalities across regions and SDI levels and is projected to increase further for common disorders. These findings highlight the need for forward-looking, equity-oriented mental health policies, with targeted prevention, early intervention, and resource allocation strategies tailored to high-risk populations and regions.

In recent years, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed to tackle constrained multi-objective optimization problems (CMOPs). However, most of them still struggle to achieve a good balance among convergence, diversity, and feasibility. To address this issue, we develop an adaptive constraint-boundary learning-based two-stage dual-population evolutionary algorithm for CMOPs, referred to as CL-TDEA. The evolutionary process of CL-TDEA is divided into two stages. In the first stage, two populations cooperate weakly through environmental selection to enhance the exploration ability of CL-TDEA under constraints. In particular, the auxiliary population employs an adaptive constraint-boundary learning mechanism to learn the constraint boundary, which in turn enables the main population to more effectively explore the constrained search space and cross infeasible regions. In the second stage, the cooperation between the two populations drives the search toward the complete constrained Pareto front (CPF) through mating selection. Here, the auxiliary population provides additional guidance to the main population, helping it escape locally feasible but suboptimal regions by means of the proposed cascaded multi-criteria hierarchical ranking strategy. Extensive experiments on 54 test problems from four benchmark suites and three real-world applications demonstrate that the proposed CL-TDEA exhibits superior performance and stronger competitiveness compared with several state-of-the-art methods.