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The green innovation behavior of construction enterprises is the key to reducing the construction industry's carbon emissions and realizing the green transformation of the construction industry. The purpose of this study is to reveal the evolutionary mechanism of green innovation behavior in construction enterprises.

This study is based on resource-based theory, Porter's hypothesis and signaling theory. First, a measurement model of the green innovation behavior of construction enterprises was constructed from three aspects: environmental regulation, enterprise resources and public opinion through hierarchical analysis. Then, the state values of the measurement model of green innovation behavior of construction enterprises were calculated through the time series data from 2011–2018. Finally, the Markov chain model was used to predict the evolutionary trend of green innovation behavior of construction enterprises, and the accuracy of the prediction effect of the Markov chain model was verified using the time series data of 2019.

The Markov chain model of green innovation behavior of construction enterprises constructed in this study has high accuracy. This model finds that the transition of the growth state of green innovation behavior in China's construction industry is fluid and predicts the evolution trend of the innovation behavior of construction enterprises. In the future, the green innovation behavior of construction enterprises has a probability of 70.17% to be in a continuous growth state and 40.27% to be in a rapid growth state.

Based on the Markov chain model of green innovation behavior of construction enterprises, this study finds that the transition of the growth state of green innovation behavior of construction enterprises in China has the characteristics of liquidity. In addition, it reveals the development process of the green innovation behavior of construction enterprises from 2011–2018 and predicts the evolution trend of the green innovation behavior of construction enterprises.

The surging market demand for green construction materials has brought opportunities for construction materials enterprises' greenwashing behavior (GWB). This study aims to establish the causal relationship among the influencing factors of GWB and reveal the key influencing factors from the perspective of Chinese construction materials enterprises under multi-agent interactions.

This study is based on stakeholder theory, resource-based theory and the green development behavior and performance of industrial enterprises (GDBP-IE). First, with the literature analysis, an index framework of the influencing factors of enterprises' GWB was constructed from five dimensions (including 15 factors): environmental regulation, public scrutiny, market environment, corporate resources and corporate green development (GD) performance. Second, the interactive relationship among influencing factors was obtained by a questionnaire survey. Finally, the data are processed and analyzed with the grey-DEMATEL (Decision-making Trial and Evaluation Laboratory) method.

Among the factors, corporate information transparency has the greatest impact on the other factors, and consumer green preferences are most influenced by others. The most critical and important factor is the corporate social performance factor. In China, corporate social performance, corporate information transparency, corporate size and media supervision are the key factors influencing the GWB of construction materials enterprises.

This study provides a new perspective on the literature related to GWB by considering multi-agent interactions and extends the evidence from the construction materials industry for research on the drivers or influencing factors of enterprises' bad environmental behavior. Furthermore, it adds insights from China for further research on the governance strategies of GWB in other countries.

This study aims to identify and quantitatively evaluate the factors that influence the construction and demolition waste (CDW) policy instrument choice, and provide a systematic framework for local governments to select the appropriate policy instruments with a consideration of their specific situations.

Literature review and expert interviews were conducted to identify the key factors. These factors were quantitatively evaluated via a questionnaire survey. Exploratory factor analysis (EFA) was employed to explore the underlying structure of these variables and organize the factors into a systematic framework.

Resource, clarity of policy problem and goal, legitimacy, acceptance of targeted group and environmental concern of public are the five top variables, which should be emphasized in the selection of CDW policy. The respondents with experience in policymaking and those without experience had slightly different views on the importance of these variables. A total of six latent factors were identified and organized into a framework.

The questionnaire survey was conducted with government staff via a snowball sampling method, which may affect the representativeness of conclusions to a certain extent.

This study provides a useful reference for local government to choose appropriate policies to address the CDW problem. Matching attributes of policy instruments with the policy goal and the problem is the starting point in choice of the CDW policy. The matched instruments will be further narrowed by social context, i.e. environmental concern and socioeconomic situation. The final selection must undergo bargaining between policymakers and targeted groups.

Policy instruments choice is a complicated process, which is shaped by the interaction of many influential factors. However, the impact of these factors on CDW policy choice is largely overlooked. This study will contribute to the knowledge body pertaining to policy selection in the CDW field.

Digital humanities database is one of the essential tools in digital humanities research area. Therefore, examining the usage of digital humanities database in academic papers is conducive to assessing the value of digital humanities database for scientific research activities and improving the construction of digital humanities infrastructure.

This paper constructs an evaluation system of digital humanities database from the perspective of academic influence and social influence, with mention frequency, usage motivation, platform access data, usage region and usage discipline as indicators and takes China Biographical Database Project as the empirical object to explore the usage of digital humanities database in China.

The data analysis result demonstrates that digital humanities databases are widely used and recognized in China. However, the problem of low actual usage remains.

This paper constructs the digital humanistic database's evaluation system and discusses applying the digital humanistic database in China, which provides a new perspective and method for the influence evaluation study of the digital humanistic database.

Financial performance has been paid attention at an unprecedented level, which can be confirmed as a fact that the quantitative expansion of financial performance evaluation work. The purpose of this study is to propose a more appropriate model for financial performance evaluation under the unbalanced development.

This paper introduces the differentiation criteria to eliminate the deviation caused by the same principle for multiple performance evaluation objects whose development are unbalanced; Then the generalized grey number is adopted to describe the value of performance evaluation index; and the information entropy weight is used to obtain the index weight to reduce the artificial judgment error; Finally, the generalized grey information entropy weight TOPSIS evaluation model is constructed.

Empirical research shows that in the new evaluation model, the differentiated possibility function effectively eliminates the deviation caused by the same principle, the application of information entropy weight reduces the human judgment error, and the value of generalized grey number further enhances the closeness of the results. Moreover, it is also found that in different scenarios, an adaptive performance evaluation model should be selected to match scientifically reasonable results.

The proposed model offers a solution for financial performance evaluation considering unbalanced development among cities. It can be realized by determining the differentiation possibility function matrix, and then the information entropy weight TOPSIS evaluation model can be constructed. This model reflects the actual situation, improves the performance evaluation accuracy, and can be used under similar conditions.

Omnidirectional mobile robots with a special type of wheel structure can realize flexible motion with all three degrees of freedom in a plane. But the driving method brings large disturbance, which affects motion accuracy and stability. This study aims to improve the motion control accuracy of the omnidirectional mobile platform with MY3 wheels (MY3-OMR), a new fuzzy active disturbance rejection control (FADRC) method with adaptivity is proposed.

Based on the basic mechanical structure and drive characteristics of MY3-OMR, the dynamics model of the system is established. The linear active disturbance rejection control (LADRC) system is designed to reduce the interference of nonlinear factors in this dynamics model. A fuzzy controller is introduced to realize the online adjustment of the parameters of the LADRC, which further improves the anti-disturbance performance of the system.

The control method proposed in this paper is compared and analyzed with other methods by simulation and experiment. Results show that the proposed method has better tracking and robustness, which effectively improves the control accuracy of trajectory tracking of MY3-OMR.

A FADRC method with adaptivity is proposed by combining fuzzy control and LADRC. The motion accuracy and anti-interference ability of the MY3-OMR are improved by this control method, which lays a foundation for the subsequent application of MY3-OMR.

The purpose of this study which resulted in this work is to propose an optimization method of sensors distribution for structural impact localization.

This paper presents a multi-objective optimization study of a novel sensors distribution technique, where two optimization objective functions are considered: sensors number and sensors location optimization performance index. In addition, the finite element analysis, the time-frequency transform and the principal component analysis are combined to quantize the above objective functions. The non-dominated sorting genetic algorithm II (NSGA-II) is used to acquire Pareto solutions.

The effectiveness of this method is validated through a prototype laboratory called the piezoelectric intelligent structure where promising results are obtained.

An optimization method of this novel sensors distribution technique is built and produced a set of efficiency solutions for the real-world problem of impact localization where two conflicting objectives are involved.

The purpose of this paper is to introduce a participatory library service based on artificial intelligence (AI).

AI technologies and various technologies for facilitating the use of the currently existing libraries and the third-party resources are combined in the new mobile and social networking environments to provide an innovative real-time virtual reference service. Special aesthetic design and library marketing measures are adopted to expand the gains of the service. Questionnaire survey, in-depth interview, and statistical analysis are conducted to evaluate the effects of the service.

A smart talking robot called Xiaotu (female) is developed. This robot is regarded as a promising new online reference service modus operandi. Four factors contribute to the success of the robot, namely, AI, self-learning, vivid logo and language, and modular architecture.

Xiaotu presents a participatory library service, in which users participate in the resources collection and become content co-creators. Her presence at anytime and anywhere on any kind of terminal maximizes her potential for the delivery of virtual reference services. Xiaotu has the potential to be a general reference robot or a costumed institute robot.

AI is adopted in libraries to form an innovative online reference service. The participatory library service is practiced through a high-featured interactive communication. The aesthetic design of Xiaotu and the related promotions are new in libraries as well.

Few of the established risk identification methods refer to low-severity yet high-frequency safety risks data that may lead to several safety risks being ignored, thus reducing the potential of learning from a considerable number of cases. The purpose of this study is to explore a new valid method based on preaccident safety supervision data to identify these minor construction safety risks during routine construction operations.

A total of 329 official construction safety supervision reports containing 5,159 safety problem records from Shanghai between 2016 and 2018 served as raw material for in-depth analysis. Given the characteristics of the data collected, text mining integrated with natural language processing was applied to review the supervision reports and group safety risks automatically.

This study clarifies the way in which the supervision data should be employed to analyze high-frequency–low-severity safety risks. From these data, seven unsafe-act-related and nine unsafe-condition-related risks are identified. Regarding unsafe-act-related risks, inappropriate human behaviors could usually occur in personnel management, contract management, expense management, material management and acceptance work. For unsafe-condition-related risks, hoisting, scaffolding and reinforcement works are the main generators of onsite safety hazards during construction operations.

The study includes implications for project managers and supervisors to facilitate more effective proactive risk management by paying more attention to collecting and employing the supervision data established in each routine inspection.

Whereas previous research focused on analyzing severe accidents, this study seeks to identify the high-frequency–low-severity construction safety risks using the preaccident supervision data. The findings could provide a new thought and research direction for construction safety risk management.