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Plant nutrient stoichiometry indicates the balance of plant internal nutrients and its nutrient-use strategies in response to environmental changes. However, the responses of nutrient stoichiometry in different wetland plant organs under agricultural intervention are poorly understood. Here, we compared the nitrogen (N), phosphorus (P), and the ratio of N:P in the plant organs (leaves, stems, roots, and root hair) of a typical helophyte plant (Glyceria spiculosa) in reference, drained, nutrient-rich, and cultivated wetlands (CW) located downstream of the Tumen River in Northeast China. Compared with that in reference wetlands (RW), the results indicate that the average N content in plant leaves, stems, roots, and root hair in nutrient-rich wetlands (NW) was significantly higher by 76, 61, 56, and 39%, respectively (p < 0.05), whereas the N content of roots and root hair in drained wetlands (DW) was significantly higher by 17 and 32%, respectively (p < 0.05). It was found that plant root P increased only in nutrient-rich and DW (p < 0.05). Interestingly, the agricultural interventions significantly affected soil N and P availability, resulting in positive effects on plant leaves, stems, roots, and root hair. Nutrient stoichiometry analysis showed the highest increase in plant leaf N:P ratio in NW, followed by that in drained and CW, but its ratio in root and root hair showed no significant changes under different agricultural interventions, which suggests that G. spiculosa allocates nutrients differently in different organs under agricultural interventions. These results imply that plant nutrient stoichiometry should incorporate various plant organs for an in-depth understanding of plant strategies against environmental changes.

Simple Summary: The Lower Tumen River basin habitat at the Sino-North Korean border is crucial for reestablishing Amur leopards in the Korean Peninsula, where they once thrived. However, except for the Jingxin–Dapanling (JD) and Mijiang (MJ) corridors, most areas have become impassable due to human activities and urbanization. In this study, we evaluated the effectiveness of the MJ corridor by analyzing the species abundance, forest structure, landscape features, and disturbance factors. Our findings indicate that leopard activity is predominantly concentrated in the northern part of the corridor, with little to no presence in the middle and southern regions near the North Korean border. Human disturbances, forest structure, and infrastructural obstacles seem to impede the movement of leopards. To ensure the resurgence of the leopard population in the Korean Peninsula, it is imperative to mitigate or eliminate the impacts of these hindrances. This entails reducing human disturbances, enhancing forest structure, and removing infrastructural barriers. Such efforts are vital to facilitate the revival of the Amur leopards in their former range in the Korean Peninsula. The interconnected forest regions along the lower Tumen River, at the Sino-North Korean border, provide critical habitats and corridors for the critically endangered Amur Leopard (Panthera pardus orientalis). In this region, there are two promising corridors for leopard movement between China and North Korea: the Jingxin–Dapanling (JD) and Mijiang (MJ) corridors. Past studies have confirmed the functionality of the JD corridor, but leopards' utilization of the MJ corridor has not yet been established or confirmed. In this study, we assessed the functionality of the MJ corridor. The study area was monitored using camera traps between May 2019 and July 2021. We also analyzed 33 environmental and vegetation factors affecting leopard survival and analyzed leopard movement. In the Mijiang area, the Amur leopard was mainly active in the region adjacent to the Northeast China Tiger and Leopard National Park and did not venture into area near the North Korean border. The complex forest structure allowed leopards to move into the Mijiang area. However, the high intensity of human disturbance and manufactured physical barriers restricted further southward movement. Therefore, human-induced disturbances such as grazing, mining, farming, logging, and infrastructure development must be halted and reversed to make the Mijiang region a functional corridor for the Amur leopard to reach the North Korean forest. This necessitates inter-governmental and international cooperation and is essential for the long-term survival of the Amur leopard. [ABSTRACT FROM AUTHOR]

This study is an attempt at combining James Phelan’s rhetorical approach to narrative, particularly his theory of narrative progression, and Nie Zhenzhao’s theory of the Sphinx factor. It takes as its subject of study the appellations of the protagonist in 《陆犯焉识》(Prisoner Lu Yanshi, 2011) by Yan Geling, a major overseas Chinese female writer. It delineates how the narrative progression of the novel is governed by the dynamics of Lu Yanshi’s varied appellations which are bound with varied ethical identities and ethical duties. It argues that the varied appellations serve as the instabilities of the narrative. At the story level, the protagonist transforms from a proud saint to a humble human being, as a result of the discovery of his animal factor which comes to the surface under extreme circumstances. At the level of discourse, circuitous narration and double focalizations create tensions between the protagonist’s understanding of and readers’ responses to the ethical implications of the appellations, thus helping the readers to see the ethical message conveyed by the novel – a complete human consists of both the human factor and the animal factor; he is defective when solely controlled by either of them.

With the large-scale deployment of sensors, both the smart grid and the power communication network should jointly deal with different kinds of big data. The fusion of both networks should bring unpredictable accidents, even leading a catastrophic destruction in our lives. However, data fusion (DF) and coordination treatment for two networks will greatly improve system performance, reduce system complexity, and improve the precision and control ability of both networks. Situation awareness (SA) is the key function for DF and accident avoidance for both networks with different network structures, data types, system mechanisms, and so on. This paper use tensor computing to provide a general data model for heterogeneous and multidimensional big data generated from smart grid and power communication network. A novel data fusion scheme is designed with multidimensional tensors. Deep reinforcement learning (DRL) algorithms are utilized to construct an optimal SA strategy based on tensor big data. A multi-agent actor-critic (MAAC) algorithm is used to achieve an optimal SA policy and improve system performance. The proposed DF and SA schemes based on tensor computing and DRL provide useful guidance for smart grid and power communication networks from theory and practice. [ABSTRACT FROM AUTHOR]