Microblog, as one of the most popular social networking services, plays an increasingly significant role in communication and information propagation. Among the numerous studies on social network, one critical problem is identifying the influencers (or opinion leaders) and quantifying the influence strength of each individual effectively. This paper focuses on the problem of measuring users' influence in microblog network. We define respectively, the social influence in terms of the ability of interactivity driven and the breadth of information dissemination in global network. With the analysis of the characteristics of interactive behaviors and the way of information spread, the principal factors indicating influence are explored, which include the quantity and quality of followers, the quality of tweets, the ratio of retweeting and the similarity of users' interests. Although so many metrics have been taken into account to measure influence in proposed User Relative Influence Measure Model and User Network Global Influence Model, our Influence Rank Algorithm to implement the models is only O(e) on time complexity. Finally, the experimental evaluations and comparisons with related algorithms on million-user-level dataset demonstrate the efficiency and effectiveness of Influence Rank Algorithm.

Paving thickness and evenness are two key factors that affect the paving operation quality of earth-rock dams. However, in the recent study, both of the key factors characterising the paving quality were measured using finite point random sampling, which resulted in subjectivity in the detection and a lag in the feedback control. At the same time, the on-site control of the paving operation quality based on experience results in a poor and unreliable paving quality. To address the above issues, in this study, a novel assessment and feedback control framework for the paving operation quality based on the observe–orient–decide–act (OODA) loop is presented. First, in the observation module, a cellular automaton is used to convert the location of the bulldozer obtained by monitoring devices into the paving thickness of the levelling layer. Second, in the orient module, the learning automaton is used to update the state of the corresponding and surrounding cells. Third, in the decision module, an overall path planning method is developed to realise feedback control of the paving thickness and evenness. Finally, in the act module, the paving thickness and evenness of the entire work unit are calculated and compared to their control thresholds to determine whether to proceed with the next OODA loop. The experiments show that the proposed method can maintain the paving thickness less than the designed standard value and effectively prevent the occurrence of ultra-thick or ultra-thin phenomena. Furthermore, the paving evenness is improved by 21.5% as compared to that obtained with the conventional paving quality control method. The framework of the paving quality assessment and feedback control proposed in this paper has extensive popularisation and application value for the same paving construction scene.

Accurate assessment of the compaction quality of sandy gravel materials remains challenging because representative features are difficult to extract from roller acceleration signals. Existing intelligent compaction (IC) indicators are largely derived from handcrafted spectral features and therefore cannot fully capture the multi-periodicity, local cyclic patterns, and long-range temporal dependencies embedded in the signals. To address this limitation, this study proposes a multi-period 2D temporal convolution and hierarchical dependency learning framework for automatic compaction quality assessment. The framework first identifies dominant periods and folds one-dimensional acceleration sequences into a two-dimensional temporal representation, enabling joint characterization of intra-period and inter-period variations. It then employs 2D temporal convolution to learn local cyclic features and a binary tree-structured hierarchical dependency module to extract multiscale short- and long-range temporal dependencies. Finally, the learned features are mapped to dry density through a multilayer perceptron, and a new compaction quality index, MPNT, is established. Field validation on a large concrete-faced rockfill dam in northwest China with 105 in-situ test points demonstrates that MPNT achieves the highest correlation with rolling passes (R2 =0.8544) and dry density (R2 =0.8882), and improves prediction accuracy by 14.34% compared with ECP, while outperforming CMV, CCV, THD, and W. MPNT also exhibits superior robustness under complex working conditions.

Since China joined the Tokyo Agreement, China has been committed to reducing carbon emissions and launched a pilot carbon trading market in 2013 and has established preliminary markets in 8 cities. This article will outline the development status of the eight pilot markets, the upcoming carbon trading system and possible problems. Compared with the first stage of the EU carbon emission market (denoted as 2005-2007) to the present, a feasible solution for the Chinese market is obtained. Through a summary analysis of the international market, especially the European market, we found that there is still a huge gap between China and the mature international market. China does not have a unified carbon pricing mechanism, and in terms of carbon allowance allocation principles, carbon emission coverage sources are still not enough to meet market demand compared with the above-mentioned markets. Therefore, the Chinese market should seek to change, and can learn from other markets to speed up its construction progress. It is necessary to clarify the laws and regulations on the construction of the carbon market to ensure the safe operation of the national market, as well as to unify the trading system and improve the carbon trading market. This study contributes to the healthy development of China's carbon market and the achievement of China's green development goals.