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In response to the rapid development of highly integrated multifunctional electronic devices, developing advanced multifunctional composite phase change materials (PCMs) that integrate thermal management, solar-thermal conversion and microwave absorption has become increasingly essential. Herein, we propose a bionical strategy to design neural network-like (carbon nanofiber) CNF@Co/C aerogels by growing ZIF-67 in situ on bacterial cellulose (BC) and subsequent calcination strategies. After the encapsulation of thermal storage unit (paraffin wax, PW), the obtained multifunctional composite PCMs (PW-CNF@Co/C aerogel) are composed of “soma” (Co/C polyhedra), “axon” (porous CNF) and thermal storage unit (PW). Importantly, the composite PCMs show a high solar-thermal conversion efficiency of 95.27% benefiting from the synergism of “soma” with strong local surface plasmon resonance (LSPR) effect and “axon” with enhanced photon transmission path. More attractively, the composite PCMs also display good microwave absorption capacity with a minimum reflection loss (RL) of -26.8 dB at 10.91 GHz owing to the synergy of magnetic and dielectric components along with abundant polarization and multiple reflections. Our developed functionally integrated composite PCMs provide a prospective application of highly integrated and miniaturized electronic devices in complex and changeable outdoor environments.

Abstract Neither pristine phase change materials (PCMs) nor metal‐organic frameworks (MOFs) can be driven by optical/electrical/magnetic triggers for multiple energy conversion and thermal storage, which cannot satisfy the requirements of multi‐scenario applications. Herein, a three‐dimensional interconnected forest‐type array carbon network anchored by Co nanoparticles serving as optical/electrical/magnetic multimode triggers was developed through in situ growth of two‐dimensional MOF nanosheet arrays on pre‐carbonized melamine foam and subsequent high‐temperature carbonization. After the encapsulation of polyethylene glycol, the resulting composite PCMs simultaneously integrate fascinating photothermal, electrothermal, magnetothermal conversion and storage for personal thermotherapy. Benefiting from the synergistic enhancement of forest‐type array carbon heterostructure and Co nanoparticles, composite PCMs exhibit high thermal/electrical conduction and strong full‐spectrum absorption capacities. Resultantly, low‐energy photoelectric triggers are sufficient to drive high‐efficiency photothermal/electrothermal conversion and storage of composite PCMs (93.1%, 100 mW/cm2; 92.9%, 2.5 V). Additionally, composite PCMs also exhibit excellent encapsulation stability without liquid phase leakage, long‐term thermal reliability and multiple energy conversion and storage stability after multiple cycles. The proposed photoelectromagnetic multimode triggers are aimed to inspire innovation and accelerate major breakthroughs in advanced responsive composite PCMs toward multiple energy utilization and personal thermotherapy.

Knowing how functional diversity varies across environmental gradients is crucial in investigating biodiversity formation and community assembly processes. The majority of studies concerning functional diversity are based on one fixed plot size, resulting in weakened sensitivity of the spatial patterns to sampling resolution. This weakness may obscure the true mechanisms behind community assemblage. In this study, we utilized hyperspectral imagery collected by Orbita hyperspectral satellites (OHS) to explore the spatial distribution patterns of functional traits and diversity in alpine meadows at four sampling radii (25 m, 50 m, 100 m, 200 m). We examined how functional traits and their diversity responded to variations in climatic and soil conditions at different sampling resolutions. Our findings revealed a significant influence of solar radiation and soil depth on the patterns of functional traits in alpine meadows. Scale dependency was evident in the functional richness (FRic), functional evenness (FEve), and functional dispersion (FDis) of different traits, indicated by a notable difference from the null model. In the presence of stronger soil nutrient and water limitation, chlorophyll a content (CAC), specific leaf area (SLA), leaf thickness (LT), and plant height (Height) exhibited increased clustering and convergence at larger sampling resolutions, illustrating environmental filtering in habitat patch assembly in the alpine meadow. For the first time, this study explores scale dependency in functional diversity patterns in alpine meadows using satellite-based hyperspectral imagery. It unveils the environmental filtering effect of soil nutrient limitation on shaping vegetation patch structures at a landscape level from a remote sensing perspective. The study underscores the significance of accounting for sampling resolution in future related research.

Abstract Recent concurrent processes of vegetation greening and reduced resilience (the capacity to recover from disturbances) worldwide have brought many uncertainties into sustainable ecosystems in the future. However, little is known about the conditions and extent to which greening affects resilience changes. Here we assess both vegetation dynamics and resilience in China’s Loess Plateau from 2000 to 2020 using satellite-based vegetation data and an early warning indicator. Our results reveal an overall greening trend in vegetated areas, while resilience shifted from gains to losses at a breakpoint in 2010. Vegetation greening generally contributed to resilience gains, whereas increased temperature and precipitation variability contributed to the resilience loss observed in 2011–2020. These findings provide empirical evidence that vegetation greening does not necessarily correspond to an increase in resilience. We therefore recommend integrating resilience indicators into ecological restoration and conservation efforts to gain a more comprehensive understanding of vegetation states and support effective ecosystem stewardship.

To meet the personalized distribution needs of customers, comprehensively consider customer value, the urgency of customer needs, and the impact of priority distribution to the customer on the enterprise, and based on regional restrictions, put forward vehicle-drone joint distribution path optimization problem considering customer priority. First, the goal is to minimize the sum of total distribution cost and customer priority cost integrating soft time windows and constructing a path optimization model of the vehicle-drone joint distribution. Second, a two-stage hybrid algorithm is proposed for the problem model. In the first stage, the deep neural network and the grid search improved support vector machine algorithm (DNN-GSM-SVM) are used to screen and classify customer priority features, and in the second stage, adaptive large-scale neighborhood search improved genetic algorithms (ALNS-GA) are used to solve the problem of vehicle-drone joint distribution path planning problem. Finally, combined with the numerical example, the optimization scheme of the vehicle-drone joint distribution path considering priority is analyzed. Compared with the three algorithms and error analysis, the effectiveness of the model and the two-stage algorithm was verified. Compared with the results of the scheme that does not consider priority, the results show that priority can significantly improve customer satisfaction. The efficiency of the vehicle-drone joint distribution was verified by comparing the three scenarios.

Forest aboveground biomass (AGB) estimation is crucial for carbon cycle studies and climate change mitigation actions. However, because of limitations in timely and reliable forestry surveys and high-resolution remote sensing data, producing a fine resolution and spatial continuous forest AGB map of China is challenging. Here, we combined 4789 ground-truth AGB measurements and multisource remote sensing data such as a recently released forest canopy-height product, optical spectral indexes, topographic data, climatological data, and soil properties to train a random forest regression model for forest AGB estimation of China at 30-m resolution. The accuracy of the estimated AGB can yield R2 = 0.67 and RMSE = 70.71 Mg/ha. The nationwide estimates show that the average forest AGB and total forest carbon storage were 97.57 ± 23.85 Mg/ha and 11.06 Pg C for the year 2019, respectively. The value of AGB uncertainty ranges from 0.68 Mg/ha to 37.80 Mg/ha, and the average AGB uncertainty was 4.32 ± 1.75 Mg/ha. The forest AGB estimates of China in this study correspond reasonably well with the AGB estimates derived from the forestry and grassland statistical yearbook at the provincial level (R2 = 0.61, RMSE = 30.15 Mg/ha). In addition, we found that previous AGB products generally underestimate the forest AGB compared with our estimated AGB at the pixel-level and ground-truth AGB measurements. The high-resolution forest AGB map provides an important alternative data source for forest carbon cycle studies and can be used as a baseline map for forest management and conservation practices.

Abstract This article aims to study the influence of random crowd loading on the perceived vibration response of pedestrians. Firstly, a vertical vibration response analysis method considering pedestrian perception was established based on the random crowd walking model. Secondly, change rules of maximum vibration response of pedestrians, occurrence time and position interval under different random walk models were compared and analyzed. Finally, the vibration response reduction factor was defined by studying the correlation between the maximum vibration response of pedestrians and the peak acceleration of the structure, and the approximate calculation method of the maximum vibration response of pedestrians was proposed. The results show that the maximum acceleration perceived by pedestrians obeys the normal distribution under the four crowd walking models, the response distribution of ordered arrangement model (OAM) is larger than that of the other three models; The location and occurrence time of the maximum response depend on the distribution of pedestrian locations on the footbridge, and there is no significant change with the increase of population density. In addition, the distribution of OAM and stochastic arrival model (SAM) are consistent, which is concentrated in the middle of the total time-history. In contrast, the distribution of stochastic distribution model (SDM) and dynamic equilibrium model (DEM) are relatively uniform. The maximum error between the calculated acceleration maximum value and the actual acceleration value felt by the pedestrian is less than 5%. These results can provide reference for quantitative evaluation of pedestrian-induced vibration comfort.

Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth, and severe anthropogenic-induced grassland ecosystem function declines have been reported recently. Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies. Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate (e.g., light, water, and wind), and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions. However, the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated. Here, we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China. Aboveground net primary productivity (ANPP) and resilience, two indicators of grassland ecosystem functions, were measured from field data and satellite remote sensing data. Terrestrial laser scanning data were collected to measure canopy structure (represented by mean height and canopy cover). Our results showed that plant species diversity was positively correlated to canopy structural traits, and negatively correlated to human activity intensity. Canopy structure was a significant indicator for ANPP and resilience, but their correlations were inconsistent under different human activity intensity levels. Compared to plant species diversity, canopy structural traits were better indicators for grassland ecosystem functions, especially for ANPP. Through structure equation modeling analyses, we found that plant species diversity did not have a direct influence on ANPP under human disturbances. Instead, it had a strong indirect effect on ANPP by altering canopy structural traits. As to resilience, plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover. This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances, which should be included in future grassland monitoring and management.

Mineral admixtures are increasingly being employed in concrete production to replace cement, thus reducing carbon dioxide emissions. However, leaching of silicon can exert a significant influence on the cement hydration process. In this study, density functional theory was used to study the influence of [H2SiO4]2- on the adsorption and dissociation of water on the C3S surface in the initial hydration process. The results demonstrate that [H2SiO4]2- can promote the dissociation of water molecules around it. This is mainly due to the fact that the two non-hydroxylated O atoms of [H2SiO4]2- can adsorb the dissociated H from water molecules, thus preserving more active ionic O sites on the C3S surface, thereby promoting the dissociation of water molecules. The charge transfer and bonding mechanisms of the adsorption of [H2SiO4]2- with its hydration shell were also calculated. This work provides an atomic-level understanding of the influence of the aluminum phase from the mineral admixture on the initial hydration of cement and also offers certain assistance for the widespread application of mineral admixtures.

The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic river system in the middle reaches of the Yangtze River and the partial reticulated river system in the northern part of the Zhejiang Province as its primary investigation areas. Considering the structure and attributes of AIS data, we introduce a novel algorithm known as the Combination of DBSCAN and DTW (CDDTW) to identify regional navigation characteristics of ships. Subsequently, we develop a real-time ship trajectory prediction model (RSTPM) to facilitate real-time ship trajectory predictions. Experimental tests on two distinct types of river sections are conducted to assess the model’s reliability. The results indicate that the RSTPM exhibits superior prediction accuracy when compared to conventional trajectory prediction models, achieving an approximate 20 m prediction accuracy for ship trajectories on inland waterways. This showcases the advancements made by this model.

Abstract This article aims to study the quantitative calculation of pedestrian-induced footbridge vibration comfort. Firstly, the analytical expression for the vibration response was derived. In addition, the simplified formula of the vibration response under resonance condition was put forward. The proposed analytical solution was compared with the numerical solution and the experimental result. Secondly, the analytical method was used to calculate the acceleration response under different crowd densities. The peak acceleration distribution and cumulative probability were analyzed. Finally, the cumulative probability that exceeded the acceleration limit was proposed as the comfort evaluation index, and the improved annoyance rate model was used to verify the proposed evaluation method. The results demonstrate that the analytical method can efficiently calculate the dynamic response of footbridges. Errors between the analytical results and the experimental results are less than 6.2%. Vibration comfort is negatively correlated with crowd density and walking speed. Furthermore, the errors between the proposed discomfort probability values and the calculated annoyance rate results are within 6%.

Leaf anatomical traits are responsive to environmental factors and thus highly plastic in their development and biochemical function. However, how they vary across large geographical scales and the main driving factors remain unclear, especially for forest plants. Here, by combining field sampling with plant semi-thin sectioning methods, eight anatomical traits (including leaf epidermis and mesophyll traits, leaf midrib and vascular traits) that can fully reflect leaf structure were measured in 60 plant species inhabiting eight forests in eastern China, and their geographical patterns and possible determinants were explored. Our results showed that leaf anatomical traits varied significantly among different plant functional types (PFTs), and phylogeny had a negligible effect on them. At the species level, leaf anatomical traits exhibited significant geographical patterns, and the thickness of leaf tissues tended to increase from boreal to tropical forests. Compared to leaf epidermis and mesophyll traits, leaf midrib and vascular traits showed similar and more significant geographical patterns. Moreover, the spatial variations in leaf anatomical traits were mainly determined by the shifts in PFTs, while climate and soil nutrient availability had relatively marginal effects. Our findings emphasized that leaf anatomical trait coordination could reflect the resource trade-off and utilization strategies of plants, which is expected to improve our understanding of the responses and adaptation mechanisms of plants to environmental changes.

Abstract Background Larch (Larix Mill.) forests are widely distributed in the upper parts of mountainous areas in China, playing vital roles in constructing mountain landscapes and maintaining mountain environments. Despite their importance, our knowledges on the large-scale patterns of structure characteristics and the relationships between different structure variables are unclear. In this paper, we investigated 155 plots from 11 natural larch forest types across the country to explore the biogeographic patterns of the structure characteristics and the allometric relationships between different structure variables for Chinese larch forests. Results The structure characteristics were significantly different among larch forest types. For different larch forest types, the power function fits the relationships between tree height and diameter at breast height (DBH), average DBH and stem density, and taper and stem density well, but with different exponents among larch forest types. The power exponents of the allometric relationships between tree height and DBH for different larch forest types varied from 0.61 to 0.93 (mean = 0.86) by standard major axis regression (SMA), and from 0.51 to 0.78 (mean = 0.56) by ordinary least square regression (OLS). The 50%, 75% and 95% quantile regression (QR) and OLS indicated that the average DBH and taper of the L. gmelinii forests, L. gmelinii var. principis-rupprechtii forests, and L. sibirica forests were significantly correlated with stem density. Conclusions The relationship between tree height and DBH showed a power function relationship for all larch forest types in China, but with different exponents. Overall, stem density was negatively correlated with average DBH and taper. The Sect. Larix forests exhibited stand density effect. Our findings provide an important basis for recognizing the biogeographic patterns of structure factors and for the management of larch forests in China.

Alien species invasions are considered to be one of the multiple key drivers that trigger ecological regime shifts in ecosystem structure and function. Predicting population collapse and understanding the self-reinforced feedback mechanisms that erode the resilience of native species are two major challenges in invasion ecology. However, to date, empirical evidence of species invasion-induced regime shifts remains scarce, and the minimum required data to generate early warning signals (EWSs) before critical transitions remains unclear, despite its importance for ecosystem management. By combining 80-year (i.e., 1936–2016) fishery data from Lake Qiandao and a theoretical competition model, we provide evidence that relative to nutrient enrichment and climate change, stocking alien fish species of silver carp (Hypophthalmichthys molitrix) and bighead carp (Aristichthys nobtilis) is the most important driver leading to the collapse of local fish populations. Detectable EWSs indicated by the variance, autocorrelation and composite resilience indicators require at least ten years of data before an abrupt decline, demonstrating that a long-term ecological monitoring program is necessary for providing insight into resilience dynamics. A structural equation model (SEM) suggests that the combined effects of eutrophication, high water levels and carp stocking-induced water clarity decline may play an important role in the reduced fitness of local fish populations, which subsequently increase the carrying capacity of alien fish populations. Our results demonstrate that stocking alien carps result in competitive exclusion of local fish populations, especially in the current context of water eutrophication and global climate changes. Furthermore, quantifying the minimum required time series length with detectable EWS across various ecosystems should be further studied given that this information is critical for flexible management policies to take actions to avert the harmful transitions.

It is significant to achieve the scientific forecast and quantitative analysis of construction output. In most existing construction economic forecasting methods, both time series models and BP neural network fail to consider the change in relevant influencing factors. This paper introduced the support vector machine (SVM) to solve the above problems based on the grid search method (GSM) optimization model. First, based on constructing an index system of influencing factors of the gross industrial output, a grey relational method is adopted to verify the correlation between the eight factors and output. Furthermore, a SVM forecast model of the gross output is constructed with the relative datasets and influencing factors of the construction industry in Hubei from 2001 to 2016 as a training sample, while the parameters are optimized using the GSM. Then, the model is used to forecast and analyze the gross output from 2017 to 2020 while checking errors. Finally, according to systematic comparison analyses among three forecast models, including the GSM-SVM model, BP neural network, and grey GM (1,1), the results showed that the GSM-SVM forecast model processed the higher solution accuracy and generalization ability. The effectiveness and reliability of our proposed model in the field of construction output forecasting are verified. It can provide a more effective modeling and forecasting method for the gross output value of the construction industry.

Given the important role of protected areas (PAs) in biological conservation and the huge investment required to establish and manage them, it is essential to accurately assess the effectiveness of PAs. Previous studies typically used the difference between the PA and a non-protected area to measure the effectiveness and presented many space constraints to enhance the comparability between treatment and control groups. In contrast, however, researchers have given less consideration to time constraints, and there is still no consensus on the choice of period for assessing the effectiveness. Here, we explored this issue and assessed the effectiveness of 2,975 PAs worldwide using the difference in differences (DID) model. We found that 56.2% of PAs were effective in maintaining plant productivity, and PAs in forests were more effective than those in non-forests. However, the effectiveness of PAs in improving ecological stability was limited. The stability rose only in the regions where PAs could effectively maintain plant productivity (e.g., needleleaf forest, woodland or Central Europe). Further, an event-study analysis showed that global PAs had been playing a positive and persistent role in maintaining plant productivity. In addition to providing these new assessment results, we compared the assessment results derived from different methods and confirmed that the impact of not using the DID model was greater than that of not using the propensity score matching (PSM) method. We therefore recommend the use of the DID model in future effectiveness assessments.

Increasing total factor productivity is the key to promoting high-quality economic development. This paper starts with land supply as an institutional source factor and discusses the impact of the regional differentiation of land supply on total factor productivity in China. Based on the panel data of 273 cities in China from 2003 to 2017, this paper measures total factor productivity (TFP) using the DEA-Malmquist index, decomposes it into technical progress (TE), pure technical efficiency (PE) and scale efficiency (SE), and analyzes the effect of the regional differentiation of land supply on TFP using the spatial Durbin model and mediating model. This study finds the following: (1) At the national level, the tilting of land supply to the central and western regions leads to a contradiction between land supply and demand, which hinders TFP by suppressing TE, PE and SE. (2) At the regional level, the reduction in land supply in the eastern region expands the technological substitution rate, induces the substitution effect of enterprise innovation, and thus promotes TE, but inhibits PE and SE under the resource constraint and congestion effect, which hinders the improvement of TFP. The estimates for the central and western regions are generally consistent with the national results; again, as land supply increases, this hinders TFP by suppressing TE, PE and SE. (3) In terms of the mechanism of influence, the decrease in land supply in the eastern region promotes TE and TFP by promoting urban innovation and inhibiting SE through productive services agglomeration; the increase in land supply in the central and western regions hinders TE and TFP by inhibiting urban innovation, and hinders SE through manufacturing agglomeration. The conclusion of this paper has great significance for deepening the structural reform of land supply and promoting high-quality economic development.

Due to the leaps of progress in the 5G telecommunication industry, commodity pricing and consumer choice are frequently subject to change and competition in the search for optimal supply and demand. We here utilize a two-stage extensive game with complete information to mathematically describe user-supplier interactions on a social network. Firstly, an example of how to apply our model in a practical 5G wireless system is shown. Then we build a prototype that offers multiple services to users and provides different outputs for suppliers, where in addition, the user and supplier quantities are independently distributed. Secondly, we then consider a scenario in which we wish to maximize social welfare and determine if there is a perfect answer. We seek the subgame perfect Nash equilibrium and show that it exists, and also show that when both sides reach it, social welfare likewise reaches its maximum. Finally, we provide numerical results that corroborate the efficacy of our approach on a practical example in the 5G background.

Abstract The decomposition of litter from forest tree species is a fundamental process in the carbon (C) cycle of terrestrial ecosystems and is closely related to ongoing climate change. However, the spatial distribution of the forest litter decomposition rate (k) and its potential response to changing air temperature (temperature sensitivity) remain poorly understood. Here, we estimated the spatial pattern of forest plant k values in China by performing a random forest model based on 433 standardized k values from 124 published studies. Nine potential predictors, including climate‐related factors, vegetation characteristics, and soil physical and chemical factors, were considered in the model. The results of spatial extrapolation indicated that the average k value for China's forests was 0.53, and the mean annual temperature (MAT) was the most important factor. We also mapped the spatial pattern of the temperature sensitivity of the k value (Q10) by using a moving window method. We found that the Q10 values had considerable variation (from 0.05 to 11.68, 95% confidence interval, CI) across forest types and regions. The Q10 values were lower in the warmer regions (primarily in the sub‐tropical evergreen forests) and higher in the semi‐humid regions (primarily in the temperate deciduous forests and boreal Larix forests) than those in the other regions of China. However, the Q10 values of the broadleaved forests were higher than those of coniferous forests. These results suggest that the temperature sensitivity of the litter decomposition rate will decline under the ongoing global warming. Changing patterns of precipitation will also affect not only the forest litter decomposition rate but also its temperature sensitivity.

Lakes play a key role in the global water cycle, providing essential water resources and ecosystem services for humans and wildlife. Quantifying long-term changes in lake volume at a global scale is therefore important to the sustainability of humanity and natural ecosystems. Yet, such an estimate is still unavailable because, unlike lake area, lake volume is three-dimensional, challenging to be estimated consistently across space and time. Here, taking advantage of recent advances in remote sensing technology, especially NASA’s ICESat-2 satellite laser altimeter launched in 2018, we generated monthly volume series from 2003 to 2020 for 9065 lakes worldwide with an area ≥ 10 km2. We found that the total volume of the 9065 lakes increased by 597 km3 (90% confidence interval 239–2618 km3). Validation against in situ measurements showed a correlation coefficient of 0.98, an RMSE (i.e., root mean square error) of 0.57 km3 and a normalized RMSE of 2.6%. In addition, 6753 (74.5%) of the lakes showed an increasing trend in lake volume and were spatially clustered into nine hot spots, most of which are located in sparsely populated high latitudes and the Tibetan Plateau; 2323 (25.5%) of the lakes showed a decreasing trend in lake volume and were clustered into six hot spots—most located in the world’s arid/semi-arid regions where lakes are scarce, but population density is high. Our results uncovered, from a three-dimensional volumetric perspective, spatially uneven lake changes that aggravate the conflict between human demands and lake resources. The situation is likely to intensify given projected higher temperatures in glacier-covered regions and drier climates in arid/semi-arid areas. The 15 hot spots could serve as a blueprint for prioritizing future lake research and conservation efforts.

Network segregation—a critical problem in real-life networks—can reveal the emergence of conflicts or signal an impending collapse of the whole system. However, the strong heterogeneity of such networks and the various definitions for key nodes continue to pose challenges that limit our ability to foresee segregation and to determine the main drivers behind it. In this paper, we show that a multi-agent leader–follower consensus system can be utilized to define a new index, named leadership, to identify key leaders in real-life networks. And then, this paper explores the emergence of network segregation that is driven by these leaders based on the removal or the rewiring of the relations between different nodes in agreement with their contribution distance. We finally show that the observed leaders-driven segregation dynamics reveals the dynamics of heterogeneous attributes that critically influence network structure and its segregation. Thus, this paper provides a theoretical method to study complex social interactions and their roles in network segregation, which ultimately leads to a closed-form explanation for the emergence of imbalanced network structure from an evolutionary perspective.

China has been experiencing significant climate and land use changes over the past decades. The way in which these changes, particularly a warming hiatus and national ecological restoration projects that occurred almost concurrently in the late 1990s, have influenced vegetation net primary productivity (NPP), is not well documented. Here, we estimated annual and seasonal changes in China’s NPP between 1982 and 2015 using the Carnegie-Ames-Stanford Approach model and examined their shifting years (SHYs) caused by the switch in climatic factors and the restoration projects. Our analyses revealed that the growth of annual, spring and summer NPP stalled in 1997 or 1998, while the trend of autumn NPP increased in 1992 at the national scale. We also showed that the changes in the NPP trends were more sensitive to the warming hiatus in spring and autumn, as well as in the temperate monsoonal region and the Tibetan Plateau, while the larger trend of autumn NPP in eastern China after the SHY was strongly coupled with increased monsoonal precipitation. Although the starting years of the restoration projects were partially consistent with the SHYs of the NPP trends, the projects were likely playing minor roles in enhancing NPP increase. Our findings can be applied for ecological risk assessment and future management of ecological restoration projects in the context of global change.

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.. [ABSTRACT FROM AUTHOR]

Overlapping graph clustering is essential to understand the nature and behavior of real complex systems including human interactions, technical systems and transportation network. However, in addition of topological structure, many real-world networked systems contain spare factors, i.e., attributes of networks. Despite the considerable efforts that have been made in graph clustering, they only concentrate on the topological structure, which lack a profound understanding of cluster configuration on attributed graphs. To address this great challenge, in this article, we propose a new overlapping graph clustering algorithm by integrating the topological and attributive information into a cluster potential flame (CPG). Firstly, a generalized definition of the utility function is provided, which measures the payoff of each node based on different node-to-cluster distance functions. It is worth mentioning that the model we proposed is able to associate with the classic ordinal potential game well. Then, we define the measures of both tightness and the homogeneity in each cluster, and introduce a novel two-way selection mechanism. The goal is to extend the flexibility of the cluster potential game, so that one can achieve a win-win situation between nodes and clusters. Finally, a distributed and heterogeneous multiagent system (DHMAS) is carefully designed based on a fast self-learning algorithm (SLA) for attributed overlapping graph clustering. Two series of experiments are implemented in multi-types datasets and the results verify the effectiveness and the scalability after the comparison with the most advanced approaches of literature. [ABSTRACT FROM AUTHOR]