Can the development of digital construction reduce enterprise carbon emission intensity? New evidence from Chinese construction enterprises

IntroductionWith the rapid development of digital technology and its deep integration with the engineering and construction field, digital construction has become an effective way for low-carbon transformation in the construction industry. However, there is a gap of empirical research between digital construction and carbon emissions. MethodsThis paper empirically investigates the impact of digital construction level on carbon emission intensity and the mechanism of action by using the two-way fixed effects model and mechanism testing based on the panel data of 52 Shanghai and Shenzhen A-share listed companies in China’s construction industry from 2015 to 2021. ResultsThe findings indicate that the improvement of digital construction level can significantly decrease the carbon emission intensity of construction enterprises, and the conclusions still hold after robustness tests and discussions on endogeneity issues such as replacing core explanatory variables, replacing models, using instrumental variables method, system GMM model and difference in differences model. According to a mechanism analysis, digital construction can curb carbon emission intensity by enhancing the R&D innovation capacity and total factor productivity of enterprises. Furthermore, the heterogeneity analysis shows that the improvement of digital construction level in state-owned enterprises as well as civil engineering construction enterprises can better contribute to reducing carbon emission intensity. DiscussionThis paper will provide a reference for the synergistic optimization of digital construction development and carbon emissions reduction in construction enterprises. The research conclusions are going to promote the digital transformation of the construction industry to accelerate the achievement of the carbon peaking and carbon neutrality goals

An Immune Detector-Based Method for the Diagnosis of Compound Faults in a Petrochemical Plant

Aiming at the serious overlap of traditional dimensionless indices in the diagnosis of compound faults in petrochemical plants, we use genetic programming to construct optimal indices for that purpose. In order to solve the problem of losing some useful fault feature information due to classification processing, during the generation of the dimensionless index immune detector, such as reduction and clustering, we propose an integrated diagnosis method using each dimensionless index immune detector. Simulation results show that this method has high diagnostic accuracy

Recent Advances in RecBole: Extensions with more Practical Considerations

RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update requests. This motivates us to make some significant improvements on our library, so as to meet the user requirements and contribute to the research community. In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole. In general, we focus on the flexibility and efficiency of RecBole in the past few months. More specifically, we have four development targets: (1) more flexible data processing, (2) more efficient model training, (3) more reproducible configurations, and (4) more comprehensive user documentation. Readers can download the above updates at: https://github.com/RUCAIBox/RecBole.Comment: 5 pages, 3 figures, 3 table

Marine Gas Hydrate: Geological Characterization, Resource Potential, Exploration, and Development

Natural gas hydrate is critical for its tremendous potential to impact the energy supply field, accelerate global warming if methane reaches the atmosphere, and affect the safety of deep-sea oil and gas production [...

Sentiments classification in stock network public opinion space based on long-short memory convolution neural network

Deep learning is used to deal with natural language processing problems. Some are based on phrases and some are based on words. This article is inspired by the pixel level in the CV world and therefore retrains the neural network from a character perspective. Neural networks do not need to know about word lookup table or word2vec in advance, and the knowledge of these words is often high-dimensional and it is difficult to apply to convolutional neural networks. In addition, our long-short term memory convolutional neural networks no longer need to know the syntax and semantics in advance. The purpose of this paper is to analyse the investor's psychological characteristics and investment decision-making behaviour characteristics, to study the investor sentiment in the network public opinion space

Sentiments classification in stock network public opinion space based on long-short memory convolution neural network

Deep learning is used to deal with natural language processing problems. Some are based on phrases and some are based on words. This article is inspired by the pixel level in the CV world and therefore retrains the neural network from a character perspective. Neural networks do not need to know about word lookup table or word2vec in advance, and the knowledge of these words is often high-dimensional and it is difficult to apply to convolutional neural networks. In addition, our long-short term memory convolutional neural networks no longer need to know the syntax and semantics in advance. The purpose of this paper is to analyse the investor's psychological characteristics and investment decision-making behaviour characteristics, to study the investor sentiment in the network public opinion space

A Novel Key-Frame Extraction Approach for Both Video Summary and Video Index

Existing key-frame extraction methods are basically video summary oriented; yet the index task of key-frames is ignored. This paper presents a novel key-frame extraction approach which can be available for both video summary and video index. First a dynamic distance separability algorithm is advanced to divide a shot into subshots based on semantic structure, and then appropriate key-frames are extracted in each subshot by SVD decomposition. Finally, three evaluation indicators are proposed to evaluate the performance of the new approach. Experimental results show that the proposed approach achieves good semantic structure for semantics-based video index and meanwhile produces video summary consistent with human perception

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We conduct research from the perspective of user groups and analyze the differences in the users' attention and posting order in different time periods to vectorize stocks and build relationships from the generatedx vectors. This provides a new perspective for the complex network cconstruction and community division of network public opinion space. The experiment result show that we can get the community division consistent with reality using our model

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Aiming at the shortcomings of the traditional frequency domain analysis method, such as failure to find early faults, the misjudgement and omission of fault types, and failure to diagnose complex faults, a new approach is developed, which is different from the existing technical route in the field of fault diagnosis, by closely following real-time online, intelligent and accurate requirements in the field of monitoring and fault diagnosis of large rotating machinery. Combining immune mechanism, dimensionless index, support vector machine and other artificial intelligence technologies, linked with the particularity of fault diagnosis problems, a fault diagnosis classification algorithm based on memory sequence is proposed, and an intelligent fault diagnosis system based on a dimensionless immune detector and support vector machine was developed. Finally, the system was applied to a compressor unit in a petrochemical enterprise and good results were achieved

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