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Plant organ biomass allocation and morphological characteristics are important functional traits. The responses of plant root, stem, and leaf traits to heterogeneous habitats in floodplain wetlands are highly important for understanding the ecological adaptation strategies of riparian plants. However, the patterns of these responses remain unclear. In a floodplain wetland in the middle reaches of the Heihe River, we studied the responses of the root, stem, and leaf morphological traits and biomass allocation of Leymus secalinus to varying habitat conditions. We measured these traits in three sample plots, delineated based on distance from the riverbank: plot I (near the riparian zone, 50–150 m from the riverbank), plot II (middle riparian zone, 200–300 m from the riverbank), and plot III (far riparian zone, 350–450 m from the riverbank). The results showed that in plot I, L. secalinus tended to have slender roots and stems and small leaves, with a biomass allocation strategy that maximized the root–shoot ratio (RSR). In plot II, L. secalinus had thick stems and moderate leaf and root patterns, and the RSR values were between those of plot I and plot III. In plot III, L. secalinus had thin and short stems and large leaves; furthermore, among the root morphological structures, plot III had the shortest Rhizome length (RL) and longest Rhizome diameter (RD), and the RSR was the lowest. Moreover, there was a significant correlation between organ biomass and leaf thickness, stem length, RD, and RL in the three habitats (p < 0.05). By balancing the biomass allocation among organs, wetland plants in floodplains balance changes in root, stem, and leaf morphological characteristics to improve their environmental adaptation. [ABSTRACT FROM AUTHOR]

Multi-agent reinforcement learning (MARL) aims to study the behavior of multiple agents in a shared environment. Existing communication-based MARL methods seldom consider the case of communication interference. However, such situations are not rare in real-world inter-agent communication. The majority of previous MARL methods struggle to design effective communication techniques for better cooperation between agents without considering communication reliability or channel capacity constraints. In addition, these models are typically not ready to be extended to large-scale multi-agent systems. To address these issues, in this paper, we propose a method named the Attentional CommUnicaTion FramEwork (ACUTE), which enables efficient communication between agents in a dynamic environment and improves the effectiveness of decision-making by using the most useful information from other agents. Specifically, we introduce an attention mechanism for the feature extraction of information during communication which determines the importance of messages received by agents. We evaluate the performance of our approach under different channel capacity constraints. Experimental results show that our model can efficiently exploit messages transmitted in unreliable channels for higher returns when compared to existing methods and can be applied to large-scale multi-agent systems. [ABSTRACT FROM AUTHOR]

The spatiotemporal evolution of population urbanization and its relationship with economic variables are important aspects of socioeconomic research and essential for revealing the mechanism of urban construction and evolution. To study the spatial heterogeneity and influencing mechanisms of population urbanization in the upper reaches of the Yellow River, this study investigated the spatial distribution and dynamic evolution of population urbanization using nuclear density analysis, exploratory spatial data analysis and the geographical detector model. The results showed that the rate of population urbanization in the upper reaches of the Yellow River increased annually since 2000 and that the nuclear density curve changed from a single–peaked mode in 2000 to a double–peaked mode in 2018. The spatial distribution of the population urbanization level was uneven, that is, high in the north and low in the south, with substantial spatial agglomeration and spatial autocorrelation. The main distribution of hot spots was in the Yinchuan area in the north, while cold spots were distributed mainly in the south. Analysis revealed that changes in population urbanization level were mainly attributable to the influence of economic and employment opportunities, such as regional GDP, GDP per capita, proportion of tertiary industry in GDP, and total retail sales of consumer goods. [ABSTRACT FROM AUTHOR]