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In order to achieve sustainable development that balances economic growth, environmental protection, and social well-being and ensures a sustainable future, strict environmental regulations and sustainable nuclear energy production may play a vital role. Empirical works are insufficient when measuring the effects of strict environmental policies and nuclear energy production on sustainable development. This research aims to close this gap by examining how environmental policy stringency and nuclear energy production contribute to sustainable development in the top 17 nuclear energy-generating countries between 1995 and 2021. The research uses the linear and nonlinear CS-ARDL and PMG-ARDL models to achieve this goal. The linear model suggests that environmental policy stringency and nuclear energy production contribute to long-term sustainable development. In the nonlinear model, a positive change in environmental policy stringency and nuclear energy production causes long-run sustainable development to grow, while a negative change in environmental policy stringency and nuclear energy production hinders long-run sustainable development. Furthermore, environmental technologies, human capital, financial development, trade liberalization, and research and development expenditures are crucial for fostering long-run sustainable development. In contrast, the natural resource rents hurt sustainable development. These findings suggest that policymakers should consider combining strict environmental regulations and nuclear energy in devising policies for sustainable development.

Abstract Graph convolutional network (GCN) is an effective tool for feature clustering. However, in the text classification task, the traditional TextGCN (GCN for Text Classification) ignores the context word order of the text. In addition, TextGCN constructs the text graph only according to the context relationship, so it is difficult for the word nodes to learn an effective semantic representation. Based on this, this paper proposes a text classification method that combines Transformer and GCN. To improve the semantic accuracy of word node features, we add a part of speech (POS) to the word-document graph and build edges between words based on POS. In the layer-to-layer of GCN, the Transformer is used to extract the contextual and sequential information of the text. We conducted the experiment on five representative datasets. The results show that our method can effectively improve the accuracy of text classification and is better than the comparison method.

Abstract The objective of crowd counting is to learn a counter that can estimate the number of people in a single image. So far, most of the proposed work evaluates the crowd density by fitting the constructed density map corresponding to the sample. The performance of those algorithms depends on a large amount of carefully prepared data. However, a significant problem with crowd data sets is the difficulty of labeling. To address such a situation, utilizing object counting data in few‐shot scenes is considered and an efficient algorithm to extract the meta‐information is proposed, thus improving the accuracy and convergence rate of the crowd counting tasks. Specifically, the counting network is trained with only object counting tasks constructed on different domains during the meta‐training phase. Then, the meta‐counter is testing on crowd counting tasks in the meta‐testing stage. Experimentally, it is demonstrated that the above way improves the converge rate and accuracy of crowd counting tasks on three crowd counting datasets when meta‐training on ten‐type object counting tasks.

With the widespread adoption of Internet of Things (IoT) technology, the increasing number of IoT devices has led to a rise in serious network security issues. Botnets, a major threat in network security, have garnered significant attention over the past decade. However, detecting these rapidly evolving botnets remains a challenge, with current detection accuracy being relatively low. Therefore, this study focuses on designing efficient botnet detection models to enhance detection performance. This paper improves the initial population generation strategy of the Dung Beetle Optimizer (DBO) by using the centroid opposition-based learning strategy instead of the original random generation strategy. The improved DBO is applied to optimize Catboost parameters and is employed in the field of IoT botnet detection. Performance comparison experiments are conducted using real-world IoT traffic datasets. The experimental results demonstrate that the proposed method outperforms other models in terms of accuracy and F1 score, indicating the effectiveness of the proposed approach in this field.

As one of the most successful recommendation techniques, collaborative filtering provides a useful recommendation by associating an active user with a crowd of users who share the same interests. Although some achievements have been achieved both in theory and practice, the efficiency of recommender systems has been negatively affected by the problems of cold start and data sparsity recently. To solve the above problems, the trust relationship among users is employed into recommender systems to build a learning model to further promote the prediction quality and users' satisfaction. However, most of the existing social networks-based recommendation algorithms fail to take into account the fact that users with different levels of trust and backgrounds, that is, user's social status and homophily have different degrees of influence on their friends. In this paper, a novel social matrix factorization-based recommendation method is proposed to improve the recommendation quality by fusing user's social status and homophily. User's social status and homophily play important roles in improving the performance of recommender systems. We first build a user's trust relationship network based on user social relationships and the rating information. Then, the degree of trust is calculated through the trust propagation method and the PageRank algorithm. Finally, the trust relationship is integrated into the matrix factorization model to accurately predict unknown ratings. The proposed method is evaluated using real-life datasets including the Epinions and Douban datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing social networks-based recommendation algorithms.