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The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

Xinjiang is located in arid northwest China, which is a key area for promoting the high-quality development of the regional ecological environment. In recent years, against a background of increasing human activities and rapid natural changes, Xinjiang has faced enormous ecological challenges. This paper utilizes land-use data from 2000 to 2020 to verify the region’s current state of the ecosystem. Additionally, it uses the value equivalent factor per unit area, ecosystem service value (ESV) loss and gain matrix, and double-factor spatial autocorrelation analysis to study the spatial and temporal variabilities of ESV in Xinjiang and its attribution to spatial correlation. The results show that (1) the ESV in Xinjiang exhibits an overall increasing trend during 2000–2020, with a total increase of about CNY 18.202 billion. Regulation-service ESV takes the main position in the single-service function, accounting for about 67.18% of the total ESV. In northern Xinjiang, the ESV demonstrates a decreasing trend, dropping by about CNY 16.885 billion, while in southern Xinjiang, the ESV shows an increasing trend, rising by CNY 35.086 billion. (2) For the study period, the main loss of ESV in Xinjiang is the conversion of ecological land with a high ESV into cropland or barren land with a low ESV. The conversion of bare land to grassland led to the largest increase in ESV (about CNY 209.308 billion), whereas the conversion of grassland to barren land led to the largest loss (about CNY 183.046 billion). (3) There are positive correlations among ESV, net primary productivity (NPP), and human activity intensity (HAI). However, all of the relationships weaken year by year. The spatial agglomeration of ESV ∩ NPP is significantly greater than that of ESV ∩ HAI, so NPP is the dominant factor in the spatial correlation of ESV in Xinjiang. The findings of this study provide a scientific basis for promoting high-quality regional ecological development in China’s arid northwest.

Graph contrastive learning (GCL) has gained considerable attention as a self-supervised learning technique that has been successfully employed in various applications, such as node classification, node clustering, and link prediction. Despite its achievements, GCL has limited exploration of the community structure of graphs. This paper presents a novel online framework called Community Contrastive Learning (Community-CL) for simultaneously learning node representations and detecting communities in a network. The proposed method employs contrastive learning to minimize the difference in the latent representations of nodes and communities in different graph views. To achieve this, learnable graph augmentation views using a graph auto-encoder (GAE) are proposed, followed by a shared encoder that learns the feature matrix of the original graph and augmentation views. This joint contrastive framework enables more accurate representation learning of the network and results in more expressive embeddings than traditional community detection algorithms that solely optimize for community structure. Experimental results demonstrate that Community-CL achieves superior performance compared to state-of-the-art baselines in community detection. Specifically, the NMI of Community-CL is reported to be 0.714 (0.551) on the Amazon-Photo (Amazon-Computers) dataset, which represents a performance improvement of up to 16% compared with the best baseline.

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.

Zooplankton play a pivotal role in connecting primary producers and high trophic levels, and changes in their temporal and spatial distribution may affect the entire marine ecosystem. The spatial and seasonal taxonomic composition patterns of mesozooplankton in Bohai Bay were investigated in relation to a number of water parameters. Bohai Bay is a eutrophic semi-enclosed bay with dynamic physico-chemical conditions influenced by terrestrial inputs and seawater intrusion. The results showed that under the condition of terrigenous input, the diversity of mesozooplankton species near the eutrophic Haihe River Estuary and Jiyun River Estuary was lower than that in the central Bohai Bay, with gelatinous Oikopleura dioica as the dominant species. The mesozooplankton diversity was highest in the bay mouth affected by seawater intrusion, and the dominant oceanic species, mainly copepods Corycaeus affinis, Calanus sinicus, and Oithona similis, entered the inner bay from the bay mouth. Meanwhile, the abundance of mesozooplankton in summer was significantly higher than that in autumn. Compared with historical data, the dominant species in Bohai Bay has evolved from arrow worm Sagitta crassa to copepod Paracalanus parvus, probably due to global warming, indicating the effects of human activities on the succession of mesozooplankton community.