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The construction of smart cities holds the potential to drive digital innovation in the construction industry through various means, such as enhancing supply and demand. This study echoes the urgent need for the construction industry to overcome development challenges. Hence, it is necessary to study the extent and ways in which smart city policies promote digital innovation in the construction industry.

This study treats China’s smart city policies as quasi-natural experiments. Using a dataset of Chinese prefecture-level cities from 2007 to 2021 and a difference-in-differences model, the study scrutinizes the impact of smart city policies on digital innovation within the construction industry.

The study reveals a substantial positive influence of smart city policies on digital innovation in the construction industry. In addition, the study explains these results by analysing supply-side and demand-side mechanisms. Moreover, the effect of smart city pilot policies on promoting digital innovation within the construction industry displays noteworthy heterogeneity across cities at different regional and political levels.

By exploring the impact and mechanisms of smart city policies on digital innovation in the construction industry, this research contributes to a more comprehensive and profound comprehension of the role of policies in facilitating the digital transformation of the construction sector. It is a valuable reference for policymakers and industry practitioners aiming to advance digital development.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

The abnormal behaviors of staff at petroleum stations pose significant safety hazards. Addressing the challenges of high parameter counts, lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models, this paper proposes GTB-ResNet, a network designed to detect abnormal behaviors in petroleum station staff.

Firstly, to mitigate the issues of excessive parameters and computational complexity in 3D ResNet, a lightweight residual convolution module called the Ghost residual module (GhostNet) is introduced in the feature extraction network. Ghost convolution replaces standard convolution, reducing model parameters while preserving multi-scale feature extraction capabilities. Secondly, to enhance the model's focus on salient features amidst wide surveillance ranges and small target objects, the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction. Lastly, to address the challenge of short time-series features leading to misjudgments in similar actions, a bidirectional gated recurrent network is added to the feature extraction backbone network. This ensures the extraction of key long time-series features, thereby improving feature extraction accuracy.

The experimental setup encompasses four behavior types: illegal phone answering, smoking, falling (abnormal) and touching the face (normal), comprising a total of 892 videos. Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7% with a model parameter count of 4.46 M and a computational complexity of 3.898 G. This represents a 4.4% improvement over 3D ResNet, with reductions of 90.4% in parameters and 61.5% in computational complexity.

Specifically designed for edge devices in oil stations, the 3D ResNet network is tailored for real-time action prediction. To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices, a lightweight residual module based on ghost convolution is developed. Additionally, to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations, a triple attention mechanism is introduced during feature extraction to enhance focus on salient features. Moreover, to overcome the potential for misjudgments arising from the similarity of actions, a Bi-GRU model is introduced to enhance the extraction of key long-term features.

Night lighting reflects the prosperous development of economic and the increasingly rich and colorful cultural life. Currently, various technical standards, protocols and management specifications have been developed to build a safe, comfortable and economical lighting environment. However, prevailing evaluation systems focus on objective indexes of illumination and have ignored environmental characteristics and subjective feelings and lacked consideration of regional culture, economic benefit, management and maintenance. In this context, a lighting evaluation system combining subjective and objective is proposed for the first time in this study to explore approaches to guide the development of a healthy and comfortable urban night-time environment.

Existing research and relevant lighting standards are analyzed and an evaluation model with a logical hierarchy is constructed by combining with the evaluation theory that is set based on people and the environment. The index weights were scientifically determined on the basis of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The rationality and validity of the proposed evaluation system is verified in accordance with field projects and case studies.

Taking into account traditional and cultural factors, the evaluation model established has an acceptable accuracy. Evaluation based on subjective-objective combination can provide a scientific basis for the management and optimization of night lighting.

The proposed evaluation system can serve as a guiding reference for other areas of cultural identity and esthetic perspective.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

This study aimed to identify a specific taxonomy of entry modes for international construction contractors and to develop a decision-making mechanism based on case-based reasoning (CBR) to facilitate the selection of the most suitable entry modes.

According to the experience orientation of the construction industry, a CBR entry mode decision model was established, and based on successful historical cases, a two-step refinement process was carried out to identify similar situations. Then the validity of the model is proved by case analysis.

This study identified an entry mode taxonomy for international construction contractors (ICCs) and explored their decision-making mechanisms. First, a two-dimension model of entry mode for ICCs was constructed from ownership and value chain dimensions; seven common ICC entry modes were identified and ranked according to market commitment. Secondly, this study reveals the impact mechanism of the ICC entry mode from two aspects: the external environment and enterprise characteristics. Accordingly, an entry mode decision model is established.

Firstly, sorting out the categories of entry mode in the construction field, which provide an entry mode list for ICCs to select. Secondly, revealing the impact mechanism of ICC entry mode, which proposes a systematic decision-making system for the selection of ICC entry mode. Thirdly, constructing a CBR entry mode decision-making model from an empirical perspective, which offers tool support and reduces transaction costs in the decision-making process.

The study on entry modes for ICCs is still in the preliminary exploratory stage. The authors investigate the entry mode categories and decision-making mechanisms for ICCs based on Uppsala internationalization process theory. It widens the applied scope of Uppsala and promotes cross-disciplinary integration. In addition, the authors creatively propose a two-stage retrieval mechanism in the CBR model, which considers the order of decision variables. It refines the influence path of the decision variables on ICCs' entry mode.

The purpose of this paper is to explore the high-quality development (HQD) strategy of Chinese mineral resource enterprises, which is important for Chinese mineral resource enterprises to improve the efficiency and benefit of resource utilization, reduce the intensity of resource and energy consumption and gradually form resource-saving and environment-friendly enterprises.

This study establishes an evaluation index system with four dimensions: economy, environment, society and management innovation. The entropy value method assigns weights to them and then uses the system dynamics (SD) model for case simulation.

The results of the SD simulation conclude that the fulfillment of social responsibility and the implementation of management innovation can accelerate the realization of HQD of mineral resource enterprises; profitability plays a crucial role in economic indicators; the improvement of energy-saving volume has the most significant impact on environmental benefits; the social contribution is the key element to measure social indicators; and the sales rate of core products has the most significant impact on the benefits of management innovation.

Based on the few studies on the evaluation of the development strategy of mineral resource enterprises, this study establishes an evaluation index system that considers the interactions between indicators, combines the entropy value method with SD and uses the SD model to comprehensively and systematically analyze the impact and degree of each factor on the HQD of mineral resource enterprises.

The paper aims to clarify the influence of the equivalent particles number (EPN) change on the flow velocity characteristic.

The paper opted for an exploratory study using PIV technology to obtain the transient flow toxicity vector of oil in the square pipeline.

The paper provides empirical insights about the influence of EPN on the flow average velocity which is most prominent in the middle of the pipeline, and smaller EPN values have a greater impact.

These influence laws of EPN can be used to obtain the dynamic characteristics of oil, which provides theoretical support for oil pollution control and effective treatment measures and lays a preliminary foundation for the online monitoring of particles in oil.

Green entrepreneurship (GE) is a novel concept in business and enhances environmentally friendly production and operation activities for “sustainable development” (SD). The aim of this study is to determine the drivers that contribute to the growth and success of “micro, small, and medium enterprises” (MSMEs) in the manufacturing sector in India. The study also examines the mutual and cause-and-effect relationships among these identified drivers.

The study used integrated research methodology and identified nine key drivers of GE (GEDs) through extensive literature reviews, theoretical perspectives (i.e. “resource-based view” (RBV), “natural resource-based view” (NRBV) and “critical success factor theory” (CSFT)), and expert opinions. Further, “total interpretive structural modeling” (TISM) and “matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) analysis are used here to develop a hierarchical model and cluster the drivers, and fuzzy “decision-making trial and evaluation laboratory” (fuzzy-DEMATEL) is used to develop causal relationships among the drivers. Further, a sensitivity analysis is conducted to ensure the robustness of the results.

Results indicated that green manufacturing and operation capability development, green business process management and attitudes toward developing sustainable business models significantly impacted GE and SD. The findings of this study help managers, policymakers, and practitioners gain an in-depth understanding of the drivers of GE.

The study considers a limited number of drivers and is specific to Indian manufacturing MSMEs only. Further, a limited number of experts from different enterprises are considered for data analysis. This study is also based on interrelationships and their relative importance based on multicriteria decision-making techniques. This study aids government decision-making, policy formulation and strategic decision-making for manufacturing businesses in achieving SD goals. In addition, this research also encourages green entrepreneurs to start eco-driven companies and facilitate the use of environmentally friendly goods to offset environmental challenges and accomplish sustainable development goals.

This study proposes an integrated methodology that will benefit managers, practitioners and others in developing strategies and innovations to improve and develop green practices. This study further helps with responsive, sustainable business development in various manufacturing MSMEs.

The ancient town of Lijiang is a representative place of ethnic minorities in China’s southwest border area jointly built by many ethnic groups. Its rich and diversified history, culture and architecture as well as its artistic and spiritual values need to be better retained and explored.

The protection and inheritance of Lijiang’s cultural heritage will be improved through the construction of digital memory resources. To guide Lijiang’s digital memory construction, this study explores strategies of digital memory construction by analyzing four case studies of well-known memory projects from China and America.

From the case studies analysis, factors of digital memory construction were identified and compared. Factors led to the discussion of strategies for constructing the digital memory of Lijiang within its design, construction and service phases.

The ancient town of Lijiang is a famous historical and cultural city in China, and it is also a representative place of ethnic minorities in the border area jointly built by many ethnic groups. The rich culture should be preserved and digitalized to offer better use for the whole nation.