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Cross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.

The concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.

The application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.

This is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

The purpose of this paper is to target on designing a short-term load prediction framework that can accurately predict the cooling load of office buildings.

A feature selection scheme and stacking ensemble model to fulfill cooling load prediction task was proposed. Firstly, the abnormal data were identified by the data density estimation algorithm. Secondly, the crucial input features were clarified from three aspects (i.e. historical load information, time information and meteorological information). Thirdly, the stacking ensemble model combined long short-term memory network and light gradient boosting machine was utilized to predict the cooling load. Finally, the proposed framework performances by predicting cooling load of office buildings were verified with indicators.

The identified input features can improve the prediction performance. The prediction accuracy of the proposed model is preferable to the existing ones. The stacking ensemble model is robust to weather forecasting errors.

The stacking ensemble model was used to fulfill cooling load prediction task which can overcome the shortcomings of deep learning models. The input features of the model, which are less focused on in most studies, are taken as an important step in this paper.

The purpose of the research is to determine the current contradictions and perspectives of convergence of social development and economic growth for the purpose of formation of the scientific and methodological basis of targeted and efficient state regulation of these processes, which would allow for their harmonization and systemic acceleration.

The authors use correlation analysis for calculating the correlation of the rate of economic growth (according to the forecast of the IMF) and the indicators of qualify of life, calculated by Numbeo, and the index of economy digitization, calculated by the IMD. The research is performed based on the 2020 data. On the basis of the established dependencies, the authors use the method of hierarchy analytics of T.L. Saaty for determining the contribution of social development into economic growth.

The authors substantiate the existence of close interconnection between social development and economic growth and determine substantial differences in this interconnection in developed countries (correlation – 52%), where only purchasing power of population and society's digitization contribute into acceleration of economic growth, and in developing countries (correlation – 48%), where quality of life, environment protection, living standards and society's development level contribute to acceleration of economic growth.

It is proved that in the course of the increase of the level of social development, it contradicts economic growth – due to which the possibilities of state regulation of the interconnection of these processes are limited. The authors develop a conceptual model of convergence of these processes through the prism of phases of the economic cycle. The compiled model reflects the authors' recommendations at each phase of the economic cycle, due to which state regulation of socioeconomic development will become targeted and efficient.

In China, private equity placement (PEP) has become the most important equity refinancing method because most listed firms issue new stocks in this method. However, previous literature has not paid much attention to the impact of political connections on PEP. In this paper, the authors aim to focus on the effect of ultimate ownership types and political connections on approval, approval time, approval results and proceeds of PEP. Besides that the authors also explore the influence of different types and levels of political connections on PEP.

This study investigates the impact of ultimate ownership and political connections of private firms on the approval of PEPs. The authors obtain a final sample of 1,651 private placement events of Chinese-listed firms. To test the hypothesis that the authors developed in this paper, the authors use empirical models from the existing literature about political connections and corporate finance. They establish multiple linear regressions to test Hypothesis 1 and 3 and introduce a logit model to test Hypothesis 2.

First, this study documents that state-owned firms have significant advantages over private firms in approval procedure. Second, political connections seem to help private firms obtain approval of placements from China Securities Regulatory Commission. Third, political connections through government officers are not useful for firms to obtain refinance resources, whereas the connections of being members of Chinese People’s Political Consultative Conference and People’s Congress are the two valuable types of political connections to help private firms obtain approval.

This paper has three main contributions to the previous literature. The first contribution is to provide an evidence for the relation between political connections and PEP approval procedures. The second contribution is to provide a comparison between government officer’s connection and social title’s connection. The third contribution of this paper is to reveal the influence of non-disclosed political connection on PEP approval. All the three contributions are important for understanding the relation between political connections and firm refinancial policy.

The purpose of this paper is to investigate the relationship between potential affecting factors and the local communities’ willingness to pay (WTP) for housing earthquake insurance (HEI) in the context of ethnic minority communities.

A literature review was done to identify possible factors affecting WTP for HEI. Fieldwork was conducted in 2017 in Dali Minority Autonomous Prefecture, where the first Chinese HEI was launched in 2015. Interviews were done in two earthquake-prone counties, as the main ethnic minority communities in the area. A total of 536 questionnaires were collected and used as empirical data for testing the impacts mechanism.

Respondents’ risk perception, risk exposure, self-prevention behaviors, government aid, insurance experience and sociodemographic characteristics were hypothesized as theoretical indicators correlated to WTP for HEI. Empirical analysis results predict that WTP for HEI is significantly influenced by risk perception, insurance experience, government aid, and age and out-migrating labors. It is evident that higher risk perception and more insurance experience lead to stronger desire for HEI coverage. However, dependency on government aid negatively affects WTP for HEI. Moreover, WTP for HEI is negative in relation to age and out-migrating labors. Surprisingly, ethnic-culture factors were not statistically significant to WTP for HEI.

This paper is an attempt to identify and verify factors affecting WTP for HEI, bridging the gap of inadequate research on WTP for HEI in ethnic minority communities.

The purpose of this paper is to construct the algebraic traveling wave solutions of the (3 + 1)-dimensional modified KdV-Zakharov-Kuznetsve (KdV-Z-K) equation, which can be usually used to express shallow water wave phenomena.

The authors apply the planar dynamical systems and invariant algebraic cure approach to find the algebraic traveling wave solutions and rational solutions of the (3 + 1)-dimensional modified KdV-Z-K equation. Also, the planar dynamical systems and invariant algebraic cure approach is applied to considered equation for finding algebraic traveling wave solutions.

As a result, the authors can find that the integral constant is zero and non-zero, the algebraic traveling wave solutions have different evolutionary processes. These results help to better reveal the evolutionary mechanism of shallow water wave phenomena and find internal connections.

The paper presents that the implemented methods as a powerful mathematical tool deal with (3 + 1)-dimensional modified KdV-Z-K equation by using the planar dynamical systems and invariant algebraic cure.

By considering important characteristics of algebraic traveling wave solutions, one can understand the evolutionary mechanism of shallow water wave phenomena and find internal connections.

To the best of the authors’ knowledge, the algebraic traveling wave solutions have not been reported in other places. Finally, the algebraic traveling wave solutions nonlinear dynamics behavior was shown.

The absence of government intervention and market supervision cannot effectively promote green process innovation in manufacturing industries. As a new government regulation approach, environmental taxes provide a platform to internalize the externality of environmental pollution. This paper empirically investigates the impact of environmental taxes on green process innovation and the moderating effects of industry pollution heterogeneity and green credit.

This research collects manufacturing industry data ranging from 2008 to 2020, resulting in a total of 351 observations. Time-individual, two-way fixed effect models are constructed to examine the hypotheses.

The results indicate environmental taxes have an inverted-U effect on green process innovation in manufacturing industries. Implementation intensity of the current environmental taxes on China's manufacturing industries does not reach an inflection point. Further analysis suggests that environmental taxes exert influence on the inverted-U relationship with low-pollution industries displaying a steeper curvilinear pattern than high-pollution industries. Moreover, the analysis shows that green credit plays a moderating role in the inverted-U relationship, as low green credit provides more limited stimulus than high green credit in terms of the effect of environmental taxes on green process innovation.

This study offers empirical evidence to accommodate negative externalities of corporate production and provides new perspectives in nudging corporate green-process innovation.

This paper verifies the effect of environmental taxes on green process innovation amid industry pollution heterogeneity by introducing an industrial-level analysis unit. This study improves the means by which environmental taxes are measured. Existing literature has narrowly used pollution discharge fees as a proxy for environmental taxes. The authors have summed up the taxes on vehicle and vessels, urban land use, urban maintenance and construction, vehicle purchases, waste gas, wastewater and solid waste to measure the effect of environmental taxes in this study.

This paper aims to propose a framework for optimizing the pose in the assembly process of the non-ideal parts considering the manufacturing deviations and contact deformations. Furthermore, the accuracy of the method would be verified by comparing it with the other conventional methods for calculating the optimal assembly pose.

First, the surface morphology of the parts with manufacturing deviations would be modeled to obtain the skin model shapes that can characterize the specific geometric features of the part. The model can provide the basis for the subsequent contact deformation analysis. Second, the simulated non-nominal components are discretized into point cloud data, and the spatial position of the feature points is corrected. Furthermore, the evaluation index to measure the assembly quality has been established, which integrates the contact deformations and the spatial relationship of the non-nominal parts’ key feature points. Third, the improved particle swarm optimization (PSO) algorithm combined with the finite element method is applied to the process of solving the optimal pose of the assembly, and further deformation calculations are conducted based on interference detection. Finally, the feasibility of the optimal pose prediction method is verified by a case.

The proposed method has been well suited to solve the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the effectiveness of the method with an example of the shaft-hole assembly.

The method proposed in this paper has been well suited to the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the method with an example of the shaft-hole assembly.

The different surface morphology influenced by manufacturing deviations will lead to the various contact behaviors of the mating surfaces. The assembly problem for the components with complex geometry is usually accompanied by deformation due to the loading during the contact process, which may further affect the accuracy of the assembly. Traditional approaches often use worst-case methods such as tolerance offsets to analyze and optimize the assembly pose. In this paper, it is able to characterize the specific parts in detail by introducing the skin model shapes represented with the point cloud data. The dynamic changes in the parts' contact during the fitting process are also considered. Using the PSO method that takes into account the contact deformations improve the accuracy by 60.7% over the original method that uses geometric alignment alone. Moreover, it can optimize the range control of the contact to the maximum extent to prevent excessive deformations.