Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.

The automobile industry is the pillar industry of the national economy. The good operation of the automobile supply chain is conducive to the sustainable development of the economy and social economy. In recent years, the popular research of automotive supply chain disruption risk management has been widely of concern by both business and academic practitioners. It is observed that most of the literature has focused only on a particular journal or field; there is a distinct lack of comprehensive bibliometric review of two decades, of research on automotive supply chain disruption risk management. This paper delivers a comprehensive bibliometric analysis that provides a better understanding not previously fully evaluated by earlier studies in the field of automotive supply chain disruption risk management. We used the 866 journal article during the period between 2000 and 2022 from the WOS database as sample data. Highlights research topics and trends, key features, developments, and potential research areas for future research. The research problems we solved are as follows: (1) Over time, how does the research in the field of automotive supply chain disruption risk management progress? (2) Which research areas and trends are getting the most attention in the field of automotive supply chain disruption risk management? (i) to recognize the scholarly production; (ii) the most productive authors; (iii) the most productive organization; (iv) the most cited articles; and (v) the most productive countries. (3) What is the research direction of automotive supply chain disruption risk management in the future? Also discusses the shortcomings of literature and bibliometric analysis. These findings provide a potential road map for researchers who intend to engage in research in this field.

Chicken plumage color is an inheritable phenotype that was naturally and artificially selected for during domestication. The Baicheng You chicken is an indigenous Chinese chicken breed presenting three main feather colors, lavender, black, and yellow plumages. To explore the genetic mechanisms underlying the pigmentation in Baicheng You chickens, we re-sequenced the whole genome of Baicheng You chicken with the three plumage colors. By analyzing the divergent regions of the genome among the chickens with different feather colors, we identified some candidate genomic regions associated with the feather colors in Baicheng You chickens. We found that EGR1, MLPH, RAB17, SOX5, and GRM5 genes were the potential genes for black, lavender, and yellow feathers. MLPH, GRM5, and SOX5 genes have been found to be related to plumage colors in birds. Our results showed that EGR1 is a most plausible candidate gene for black plumage, RAB17, MLPH, and SOX5 for lavender plumage, and GRM5 for yellow plumage in Baicheng You chicken.

Identifying the key factors influencing agricultural carbon emissions and accurately predicting future trends are essential for achieving carbon peak and carbon neutrality goals. This study aims to assess carbon emissions in agriculture from 1997 to 2022, construct an accurate model to identify the key influencing factors, and predict carbon emissions in agriculture from 2023 to 2030 with an intelligent prediction system to discuss risk management. Additionally, the Dagum method was employed to explore regional differences in agricultural carbon emissions across China. The results reveal that China’s agricultural carbon emissions exhibited a fluctuating trend from 1997 to 2022, peaking in 2015, followed by a period of decline and a moderate rebound in recent years. Elastic Net Regression identified eleven key variables, including Agricultural Machinery Level (MA), Numbers of Agricultural Tools (AT), and Agricultural Industrial Structure Upgrading (AICE), as major determinants of agricultural carbon emissions. Furthermore, the RF-PSO method demonstrated the highest predictive accuracy, forecasting a minor peak in agricultural carbon emissions in 2027, followed by stabilization. Regionally, imbalances in emissions were observed, with the intensity of transvariation accounting for 37.078% of the disparity. Therefore, the Chinese government is advised to implement region-specific strategies for controlling agricultural carbon emissions, cultivate new high-quality agricultural productivity, and promote advanced technologies.

Reducing agricultural carbon emissions is key to promoting the sustainable development of agriculture. Carbon sources play a significant role in the carbon emissions of China’s planting industry. Researching the principles of evolutionary trends of carbon sources regarding carbon emissions in China’s planting industry helps formulate scientific policies to control such emissions in the industry. This paper adopted an emission factor approach from the IPCC to estimate the CO₂ emissions of all kinds of carbon sources in China’s planting industry from 1997 to 2017. On the basis of the data, the principles of dynamic evolution in China’s planting industry and six carbon sources were analyzed by the kernel density estimation approach. Notably, the study discovered that carbon emissions peaked in 2015. In terms of the contributions of various carbon sources to the carbon emissions of the planting industry, sorted by chemical fertilizers, agricultural diesel oil, agricultural films, pesticides, agricultural irrigation, and seeding, their contribution rates were 60.82%, 13.95%, 12.88%, 9.83%, 1.88%, and 0.64%. At the same time, the kernel density results show that there was an increasing trend in carbon emissions across the whole of China’s planting industry and six kinds of carbon sources nationwide, with apparent “multipolarization”. From the perspective of various regions, the carbon emissions of chemical fertilizers, diesel oil, films, and pesticides in China’s planting industry had an evolutionary trend of multipolarization in central regions, while there was an evolutionary trend of monopolarization in eastern and western regions. The carbon emissions of seeding and irrigation had a similarly evolutionary trend in eastern, central, and western regions. Basically, they all had a double increase pattern in carbon emissions and regional differences. Therefore, China’s government needs a target to set up long-term mechanisms to ensure a stable and orderly reduction in carbon emissions in the planting industry, leading its development from the traditional planting industry to a climate-smart planting industry.

As the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). To verify the prediction effect of the RF-BPNN combined model, experiments were performed by using the energy efficiency data set in the UCI database, and the model was evaluated with five indicators: mean absolute error, root mean square deviation, mean absolute percentage error, correlation coefficient, and coincidence index. Compared with the random forest, BP neural network model, and other existing models, respectively, it is proven by the experimental results that the RF-BPNN model possesses higher prediction accuracy and better stability.