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Although elevated atmospheric CO 2 concentration (eCO 2 ) has substantial indirect effects on vegetation carbon uptake via associated climate change, their dynamics remain unclear. Here we investigate how the impacts of eCO 2 -driven climate change on growing-season gross primary production have changed globally during 1982–2014, using satellite observations and Earth system models, and evaluate their evolution until the year 2100. We show that the initial positive effect of eCO 2 -induced climate change on vegetation carbon uptake has declined recently, shifting to negative in the early 21st century. Such emerging pattern appears prominent in high latitudes and occurs in combination with a decrease of direct CO 2 physiological effect, ultimately resulting in a sharp reduction of the current growth benefits induced by climate warming and CO 2 fertilization. Such weakening of the indirect CO 2 effect can be partially attributed to the widespread land drying, and it is expected to be further exacerbated under global warming. It is unclear how indirect CO 2 effect – via associated climate change – on vegetation carbon uptake changes globally. Here, the authors show that such initial positive effect has declined recently, shifting to negative in the early 21st century.

The Loess Plateau in China has experienced a remarkable greening trend due to vegetation restoration efforts in recent decades. However, the response of precipitation to this greening remains uncertain. In this study, we identified and evaluated the main moisture source regions for precipitation over the Loess Plateau from 1982 to 2019 using a moisture tracking model, the modified WAM‐2layers model, and the conceptual framework of the precipitationshed. By integrating multiple linear regression analysis with a conceptual hydrologically weighting method, we quantified the effective influence of different environmental factors for precipitation, particularly the effect of vegetation. Our analysis revealed that local precipitation has increased on average by 0.16 mm yr−1 and evaporation by 5.17 mm yr−1 over the period 2000–2019 after the initiation of the vegetation restoration project. Regional greening including the Loess Plateau contributed to precipitation for about 0.83 mm yr−1, among which local greening contributed for about 0.07 mm yr−1. Local vegetation contribution is due to both an enhanced local evaporation as well as an increased local moisture recycling (6.9% in 1982–1999; 8.3% in 2000–2019). Thus, our study shows that local revegetation had a positive effect on local precipitation, and the primary cause of the observed increase in precipitation over the Loess Plateau is due to a combination of local greening and circulation change. Our study underscores that increasing vegetation over the Loess Plateau has exerted strong influence on local precipitation and supports the positive effects for current and future vegetation restoration plans toward more resilient water resources managements. Key Points Precipitation moisture source for Chinese Loess Plateau and its change were identified using a moisture tracking model The contribution of locally recycled moisture to Loess Plateau precipitation increased from 6.9% in 1982–1999 to 8.3% in 2000–2019 Regional greening promotes precipitation by about 0.83 mm yr−1, while local vegetation accounts for about 0.07 mm yr−1 during 2000–2019

Irrigation over the North China Plain (NCP) has been demonstrated to lower temperature by altering the surface energy budget. During past decades, the concurrence of irrigated area variation and reduced irrigation intensity prompted our investigation into whether there has been a temporal change in irrigation cooling effect over the NCP, which is largely unknown. Using historical observations in 1979–2018, we detect a shift in the cooling effect occurring around 1995, when the expansion of irrigated area was going to slow down and water‐conserving irrigation technology was boomingly introduced. After this time, the accelerated process of cooling effect (−0.0045°C year−1) switches to a decelerated one (0.0089°C year−1). Regional climate simulations also show a pronounced slowdown in irrigation‐induced cooling with the rate of 0.0081°C year−1. The irrigation‐induced cooling is expected to be weaker with the persistent reduction in agricultural water use and contribute to a more rapid warming. Plain Language Summary The North China Plain is the largest irrigated area in China. However, the irrigated agriculture situation in this fertile plain has been going through moderated expansion of irrigated area and reduced irrigation intensity. The variations in irrigation‐induced cooling effect along with this irrigation development were thus investigated based on in situ observations and model simulations. The combined analysis clearly supports a shift from enhancement to alleviation in cooling effect during past decades. The slowdown of irrigation feedback is therefore likely to enable more rapid warming in the future. Key Points The local cooling effect of irrigation during past decades is evaluated in observations and model simulations Over the North China Plain, the irrigation‐induced cooling effect has decreased since the mid‐1990s Persistent changes in irrigation water use cannot be overlooked in climate attribution studies

Irrigation has distinct impacts on extreme temperatures. Due to the carryover effect of soil moisture into other seasons, temperature impacts of irrigation are not limited to irrigated seasons. Focusing on the North China Plain, where irrigation occurs in both spring (March‐April‐May) and summer (June‐July‐August), with a higher proportion of irrigation water applied during spring, we investigate the impact of spring irrigation on summer extreme heat events. Based on partial correlation analysis of data products, we find positive correlations between spring and summer soil moisture, suggesting that spring irrigation‐induced water surplus persists into the following summer and affects regional climate by impacting surface energy partitioning. Regional climate simulations confirm cross‐seasonal climatic effects and show that spring irrigation reduces the frequency and intensity of summer extreme heat events by approximately −2.5 days and −0.29°C, respectively. Our results highlight the importance of the cross‐seasonal climatic effect of irrigation in mitigating climate extremes. Plain Language Summary Irrigation exerts a stronger impact on extreme temperatures than on mean temperatures. The North China Plain (NCP) is a typical winter wheat‐summer maize rotation planting area, where irrigation is necessary in both spring and summer, but with a higher proportion of irrigation water applied during spring. The climatic effects of spring and summer irrigation in the NCP are intertwined due to the carryover effects of soil moisture. Recently, the climatic effect of irrigation in the NCP has been extensively explored, whereas the cross‐seasonal effects of irrigation on summer extreme heat events have never been quantified. In this study, we employ the Weather Research and Forecasting model coupled with a demand‐driven irrigation algorithm to discern the effects of spring and/or summer irrigation on summer extreme heat events by means of idealized climate simulations. The results show that spring and summer irrigation significantly reduces the frequency and intensity of summer extreme heat events by approximately −6.5 days and −1.0°C, of which spring irrigation contributes about 38% and 30%, respectively. Our findings underline the importance of irrigation‐induced climate impacts in mitigating extreme heat events and emphasize that climate change adaptation planning in terms of irrigation must account for cross‐seasonal climatic effects. Key Points Effect of multi‐seasonal irrigation on summer extreme heat events is investigated Spring irrigation is beneficial for reducing summer extreme heat events Irrigation modulates the relationship between spring and summer soil moisture

Vegetation greening in China is known to cool the land surface by altering the energy budget through biophysical processes. However, its mitigation effects on extreme temperatures and the underlying mechanisms remain poorly understood. Here, we use coupled land‐atmosphere model simulations to quantitatively assess the effects of vegetation greening on summer mean and extreme land surface temperatures in Eastern China over the 2003–2018 period. We show that the modeled cooling effect on summer mean land surface temperature is more pronounced in arid Northeastern China than in humid Southeastern China, consistent with satellite‐derived temperature responses. In contrast, for extreme hot temperatures, the spatial pattern of the cooling effect reverses, largely because high temperatures accompanied by strong radiation can alleviate energy constraints on evaporative cooling in Southeastern China. These findings underscore the potential role of vegetation greening in mitigating extreme hot extremes, with important implications for local land‐based mitigation and adaptation strategies.

Carbon monoxide (CO) poisoning causes approximately 41,000 deaths annually worldwide despite being preventable. Previous studies focused primarily on mortality alone, lacked systematic socio-demographic analysis, and provided no predictive models. This study comprehensively analyzes global CO poisoning patterns using spatiotemporal methods to inform evidence-based prevention strategies. We analyzed Global Burden of Disease Study 2021 data from 204 countries (1990-2021) for age-standardized incidence, mortality, and disability-adjusted life years (DALYs). Joinpoint regression identified temporal trends with statistical precision, spatial statistics quantified geographic clustering, and ARIMA modeling projected trends through 2050. We examined associations with socio-demographic index (SDI) across regions and countries. Global age-standardized incidence rates decreased significantly by 35.1% from 12.13 (95% UI: 8.30-17.00) to 7.87 (95% UI: 5.54-10.81) per 100,000 population (annual percentage change: -1.16%, 95% UI: -1.35% to -0.96%, p < 0.001). Mortality rates declined more dramatically by 53.9% from 0.76 (95% UI: 0.66-0.91) to 0.35 (95% UI: 0.24-0.40) per 100,000 (annual change: -2.79%, 95% UI: -3.14% to -2.44%, p < 0.001). DALY rates showed the steepest reduction of 59.5% from 37.59 (95% UI: 31.75-44.76) to 15.22 (95% UI: 10.67-17.57) per 100,000 (annual change: -3.18%, 95% UI: -3.51% to -2.84%, p < 0.001). Eastern Europe demonstrated the highest burden (37.98 per 100,000 in 2021). Males experienced significantly higher mortality than females (0.50 vs 0.20 per 100,000, p < 0.001). SDI analysis revealed an inverted U-shaped relationship (Spearman's r = 0.76, p < 0.001), with peak burden at moderate development levels (SDI: 0.6-0.7). These findings directly address previous research gaps by demonstrating: (1) faster mortality decline than incidence decline indicates improved global treatment capabilities; (2) the SDI-burden relationship identifies moderate-development countries as priority intervention targets; (3) significant male predominance (2.5-fold higher mortality) supports gender-specific prevention programs; and (4) persistent Eastern European hotspots require targeted infrastructure improvements. Predictive models forecast continued decline through 2050 and enable evidence-based healthcare planning. This comprehensive analysis provides the first multi-dimensional global assessment, offering crucial evidence for differentiated prevention strategies worldwide.

This paper considers uplink and downlink transmissions in a network with radio frequency-powered Internet of Things sensing devices. Unlike prior works, for uplinks, these devices use framed slotted Aloha for channel access. Another key distinction is that it considers uplinks and downlinks scheduling over multiple time slots using only causal information. As a result, the energy level of devices is coupled across time slots, where downlink transmissions in a time slot affect their energy and data transfers in future time slots. To this end, this paper proposes the first learning approach that allows a hybrid access point to optimize its power allocation for downlinks and frame size used for uplinks. Similarly, devices learn to optimize (1) their transmission probability and data slot in each uplink frame, and (2) power split ratio, which determines their harvested energy and data rate. The results show our learning approach achieved an average sum rate that is higher than non-learning approaches that employed Aloha, time division multiple access, and round-robin to schedule downlinks or/and uplinks.

In view of the current problems of spectrum resource scarcity, return congestion, and insufficient energy utilization in the unmanned aerial vehicle (UAV)-assisted Internet of Vehicles (IoV), this paper investigates the cooperative caching and power control, and proposes a joint optimization method to improve the overall Energy Efficiency (EE) . In this method, we first propose a communication establishment threshold to control the V2V communication distance and serve as a joint optimization factor. Then we derive the closed form expressions of offloading ratio and EE of the UAV-assisted IoV, and formulate the optimization problem of maximizing EE. Due to the coupling relationship between caching strategy and transmission power, it is difficult for us to directly solve the optimization problem. Furthermore, we propose an alternating optimization algorithm for solving the optimization problem. Finally, the experimental simulation compare the propose joint optimization method with other existing optimization methods, and the simulation results prove the effectiveness and superiority of the propose joint optimization method.

Capsidiol is a sesquiterpenoid phytoalexin produced in Nicotiana and Capsicum species in response to pathogen attack. Whether capsidiol plays a defensive role and how its biosynthesis is regulated in the wild tobacco Nicotiana attenuata when the plant is attacked by Alternaria alternata (tobacco pathotype), a notorious necrotrophic fungus causing brown spot disease, are unknown. Transcriptome analysis indicated that a metabolic switch to sesquiterpene biosynthesis occurred in young leaves of N. attenuata after A. alternata inoculation: many genes leading to sesquiterpene production were strongly up-regulated, including the capsidiol biosynthetic genes 5-epi-aristolochene synthase (EAS) and 5-epi-aristolochene hydroxylase (EAH). Consistently, the level of capsidiol was increased dramatically in young leaves after fungal inoculation, from not detectable in mock control to 50.68±3.10 μg g−1 fresh leaf at 3 d postinoculation. Capsidiol-reduced or capsidiol-depleted plants, which were generated by silencing EAHs or EASs by virus-induced gene silencing, were more susceptible to the fungus. In addition, this sesquiterpene when purified from infected plants exhibited strong anti-fungal activities against A. alternata in vitro. Furthermore, an ERF2-like transcription factor was found to positively regulate capsidiol production and plant resistance through the direct transactivation of a capsidiol biosynthetic gene, EAS12. Taken together, our results demonstrate that capsidiol, a phytoalexin highly accumulated in N. attenuata plants in response to A. alternata infection, plays an important role in pathogen resistance independent of jasmonate and ethylene signaling pathways, and its biosynthesis is transcriptionally regulated by an ERF2-like transcription factor.

Mobile crowdsensing (MCS), which is a grassroots sensing paradigm that utilizes the idea of crowdsourcing, has attracted the attention of academics. More and more researchers have devoted themselves to adopting MCS in space–air–ground–sea integrated networks (SAGSINs). Given the dynamics of the environmental conditions in SAGSINs and the uncertainty of the sensing capabilities of mobile people, the quality and coverage of the sensed data change periodically. To address this issue, we propose a novel UAV-assisted cluster-based task allocation (UCTA) algorithm for MCS in SAGSINs in a two-stage process. We first introduce the edge nodes and establish a three-layer hierarchical system with UAV-assistance, called “Platform–Edge Cluster–Participants”. Moreover, an edge-aided attribute-based cluster algorithm is designed, aiming at organizing tasks into clusters, which significantly diminishes both the communication overhead and computational complexity while enhancing the efficiency of task allocation. Subsequently, a greedy selection algorithm is proposed to select the final combination that performs the sensing task in each cluster. Extensive simulations are conducted comparing the developed algorithm with the other three benchmark algorithms, and the experimental results unequivocally endorse the superiority of our proposed UCTA algorithm.