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Renewable energy, such as wind power and photovoltaic power, has uncertain and intermittent characteristics and zero marginal cost characteristics. The traditional power market mechanism is difficult to adapt to the new power system with a high proportion of renewable energy, and the original market system needs to be reformed. This paper discusses the application of a VCG auction mechanism in the electricity market, proposes a two-stage VCG market-clearing model based on the VCG mechanism, including the day-ahead market and the real-time market, and discusses the nature of the VCG mechanism. In order to address the discrepancy between the actual output of stochastic generator sets in the real-time market and their pre-scheduled output in the day-ahead market due to prediction deviations, a method for calculating punitive costs is proposed. A reallocation method based on market entities' contributing factors to budget imbalance is proposed to address the issue of budget imbalance under the VCG mechanism, in order to achieve revenue and expenditure balance. Through an example, the incentive compatibility characteristics of the VCG mechanism are verified, the problems of the locational marginal pricing (LMP) mechanism in the stochastic electricity market with a high proportion of renewable energy are analyzed, the electricity prices of the LMP mechanism and the VCG mechanism under different renewable energy proportions are compared, and the redistribution of the budget imbalance of the VCG mechanism is analyzed. [ABSTRACT FROM AUTHOR]

This paper examines the construction and maintenance of Chinese Han Buddhist temples in modern China against the backdrop of societal transformation. Initially, it analyzes the profound impact of social changes since the mid-19th century on Buddhist monasteries, including political turmoil, economic development, and urbanization. Furthermore, the paper explores how temples were reconstructed and revitalized within this historical context, highlighting the monastic community's unwavering commitment to protecting the Dharma and ensuring its enduring presence. Additionally, this paper also explores the role of charismatic monks in enhancing the sanctity of temples and the influence of Buddhist institutional frameworks on the dynamics of state and society. The study employs a multifaceted analysis to understand the complex interplay between temple construction, economic development, and the cultural heritage of Buddhism in China. [ABSTRACT FROM AUTHOR]

Drawing on Foucault's theoretical framework of "space and power", this paper examines the discursive construction of "knowledge" in the context of Chinese Buddhist education. It traces the historical transformation of Chinese Buddhist education from the traditional "Sangha Forest"(the monastic community; 叢林 Conglin) style education to the Buddhist Academy, and analyzes how modern Buddhism reshaped its social image and function from a faith-based to a knowledge-based culture. Furthermore, this paper explores the reasons why modern Buddhism requires "knowledge" as a bridge between its worldly and transcendental dimensions, and the roles of elite laymen and monasteries as "Buddhist Institutes" in the new discursive practice. [ABSTRACT FROM AUTHOR]

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government's incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption. [ABSTRACT FROM AUTHOR]

Endosymbionts living in plants and insects are pervasive. Ficus (Moraceae) has very special inflorescences (which we also call figs) enclosed like an urn, and such inflorescence is usually parasitized by fig wasps. Ficus breeds fig wasp larvae in its figs and adult fig wasps pollinate for Ficus, Ficus and its obligated pollinator formed fig-fig wasp mutualism. Previous studies have found that this confined environment in figs may have provided protection for fig wasps and that this has left some imprints on the genome of fig wasps during the coevolution history of figs and fig wasps. Research on the diversity of both bacteria and fungi in figs are fewer. Our study explored the diversity of endosymbionts in Ficus hirta figs. We utilized high-throughput sequencing and biological database to identify the specific microorganism in figs, then conducted microorganism communities' diversity analysis and function annotation analysis. As a result, we identified the dominant endosymbionts in figs, mainly some insect internal parasitic bacteria and fungi, plant pathogen, endophytes, and saprotroph. Then we also found bacteria in Ficus hirta figs were more diversified than fungi, and bacteria communities in female figs and functional male figs were different. These findings may give us more insight into the coevolution and interaction among endosymbiont, fig, and fig wasp. [ABSTRACT FROM AUTHOR]

Most of the previous studies exploring the changing patterns of wetland in the Yellow River Delta (YRD) were conducted based on sparse time-series images, which ignored its severe environmental gradient and rapid evolution process of the wetland. The changes in the dominant factors in the evolution of the wetland in the YRD are not clear. This study used the dense time-series Sentinel-2 images to establish a wetland database of the YRD, and then analyzed the spatial distribution characteristics of, and temporal changes in, the wetland during 2015–2022. Finally, the dominant factors of the spatio-temporal evolutions of the wetland were explored and revealed. The results showed the following. (1) During 2015–2022, the wetland in the YRD was dominated by artificial wetland, accounting for 54.02% of the total wetland area in the study area. In 2015–2022, the total wetland area increased by 309.90 km2, including an increase of 222.63 km2 in natural wetlands and 87.27 km2 in artificial wetlands. In the conversion between wetland types, 218.73 km2 of artificial wetlands were converted into natural wetlands, and 75.18 km2 of natural wetlands were converted into artificial wetlands. The patch density of rivers, swamps, and salt pans increased, showing a trend of fragmentation. However, the overall degree of landscape fragmentation in wetlands weakened. The trend of changes in the number of patches and landscape shape index was the same, while the trend of changes in Shannon's diversity index and Contagion index was completely opposite. (2) Natural factors, such as precipitation (0.51, 2015; 0.65, 2016), DEM (0.57, 2017; 0.47, 2018; 0.49, 2020; 0.46, 2021), vegetation coverage (0.59, 2019), and temperature (0.48, 2022), were the dominant influencing factors of wetland changes in the YRD. The dominant single factor causing the changes in artificial wetlands was vegetation coverage, while socio-economic factors had lower explanatory power, with the average q value of 0.18. (3) During 2015–2022, the interactions between the natural and artificial factors of the wetland changes were mostly nonlinear and showed double-factor enhancement. The interactions between temperature and sunshine hours had the largest explanatory power for natural wetland change, while interactions between precipitation and vegetation coverage, and between temperature and vegetation coverage, had large contribution rates for artificial wetland change. The interactions among natural factors had the greatest impacts on wetland change, followed by interactions between natural factors and socio-economic factors, while interactions among socio-economic factors had more slight impacts on wetland change. The results can provide a scientific basis for regional wetland protection and management. [ABSTRACT FROM AUTHOR]

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder–decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network. [ABSTRACT FROM AUTHOR]

3D object detection in LiDAR point clouds has been extensively used in autonomous driving, intelligent robotics, and augmented reality. Although the one-stage 3D detector has satisfactory training and inference speed, there are still some performance problems due to insufficient utilization of bird's eye view (BEV) information. In this paper, a new backbone network is proposed to complete the cross-layer fusion of multi-scale BEV feature maps, which makes full use of various information for detection. Specifically, our proposed backbone network can be divided into a coarse branch and a fine branch. In the coarse branch, we use the pyramidal feature hierarchy (PFH) to generate multi-scale BEV feature maps, which retain the advantages of different levels and serves as the input of the fine branch. In the fine branch, our proposed pyramid splitting and aggregation (PSA) module deeply integrates different levels of multi-scale feature maps, thereby improving the expressive ability of the final features. Extensive experiments on the challenging KITTI-3D benchmark show that our method has better performance in both 3D and BEV object detection compared with some previous state-of-the-art methods. Experimental results with average precision (AP) prove the effectiveness of our network. [ABSTRACT FROM AUTHOR]

Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years. Most of the existing graph convolution methods take all the joints of the human skeleton as the overall modeling graph, ignoring the differences in the movement patterns of various parts of the human, and cannot well connect the relationship between the different parts of the human skeleton. To capture the unique features of different parts of human skeleton data and the correlation of different parts, we propose two new graph convolution methods: the whole graph convolution network (WGCN) and the part graph convolution network (PGCN). WGCN learns the whole scale skeleton spatiotemporal features according to the movement patterns and physical structure of the human skeleton. PGCN divides the human skeleton graph into several subgraphs to learn the part scale spatiotemporal features. Moreover, we propose an adaptive fusion module that combines the two features for multiple complementary adaptive fusion to obtain more effective skeleton features. By coupling these proposals, we build a whole and part adaptive fusion graph convolution neural network (WPGCN) that outperforms previous state-of-the-art methods on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400. [ABSTRACT FROM AUTHOR]

Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder–decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird's eye view) object detection compared with some previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]