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This paper aims to clarify the relationship between foreign direct investment (FDI) and carbon intensity. This study uses the dynamic panel data model to study and provide fresh evidence for the issue.

This study first uses the dynamic panel data model to consider the endogeneity problem, and applies a system-generalized method of moments estimator to study the effect of FDI on carbon intensity using the panel data of 188 countries during 1990-2013.

The result shows that FDI has a significant negative impact on carbon intensity of the host country. After considering the other factors, including share of fossil fuels, industrial intensity, urbanization level and trade openness, the impact of FDI on carbon intensity is still significantly positive. In addition, FDI also has a significant negative impact on carbon intensity of high-income countries and middle- and low-income countries.

This paper offers two contributions to the literature on the effect of FDI on carbon intensity. From a methodological perspective, this paper is the first to apply a dynamic panel data model to study the effect of FDI on carbon intensity using worldwide panel data. Second, this paper is the first to analyze the effect of FDI on carbon intensity in different countries with different income levels separately.

The calculation of the shear capacity of inclined section for prestressed reinforced concrete beams is an important topic in the design of concrete members. The purpose of this paper, based on the truss-arch model, is to analyze the shear mechanism in prestressed reinforced concrete beams and establish the calculation formula for shear capacity.

Considering the effect of the prestressed reinforcement axial force on the angle of the diagonal struts and regression coefficient of softening cocalculation of shear capacity is established. According to the shape of the cracks of prestressed reinforced concrete beams under shear compression failure, the tie-arch model for the calculation of shear capacity is established. Shear-failure-test beam results are collected to verify the established formula for shear bearing capacity.

Through theoretical analysis and experimental beam verification, it is confirmed in this study that the truss-arch model can be used to analyze the shear mechanism of prestressed reinforced concrete members accurately. The calculation formula for the angle of the diagonal struts chosen by considering the effect of prestress is accurate. The relationship between the softening coefficient of concrete and strength of concrete that is established is correct. Considering the effect of the destruction of beam shear plasticity of the concrete on the surface crack shape, the tie-arch model, which is established where the arch axis is parabolic, is applicable.

The formula for shear capacity of prestressed reinforced concrete beams based on this theoretical model can guarantee the effectiveness of the calculation results when the structural properties vary significantly. Engineers can calculate the parameters of prestressed reinforced concrete beams by using the shear capacity calculation formula proposed in this paper.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

Most existing studies are confined to model beam tests, which cannot reflect the actual strengthening effects provided by prestressed carbon-fiber-reinforced polymer (CFRP) plates to existing bridges. Hence, the actual capacity for strengthening existing bridges with prestressed CFRP plates is becoming an important concern for researchers. The paper aims to discuss these issues.

Static load tests of in-service prestressed concrete hollow slabs before and after strengthening are conducted. Based on the results of the tests, the failure characteristics, failure mechanism and bending performance of the slabs are compared and analyzed. Nonlinear finite element method is also used to calculate the flexural strength of the strengthened beams prestressed with CFRP plates.

Test results show that prestressed CFRP plate strengthening technology changes the failure mode of hollow slabs, delays the development of deflection and cracks, raises cracking and ultimate load-carrying capacity and remarkably improves mechanical behavior of the slab. In addition, the nonlinear finite element analyses are in good agreement with the test results.

Strengthening with prestressed CFRP plates has greater advantages compared to traditional CFRP plate strengthening technology and improves active material utilization. The presented finite element method can be applied in the flexural response calculations of strengthened beams prestressed with CFRP plates. The research results provide technical basis for maintenance and reinforcement design of existing bridges.

The purpose of this study is to investigate the effects of stud height, stud diameter, ultimate stress of stud and concrete strength on the static behaviour of studs in push-off tests based on the ductile fracture theory.

Push-off tests of headed stud shear connectors with different heights and diameters used in concrete of various strengths were designed and implemented. A finite element model was established based on a ductile fracture criterion of ML15 cold-heading steel with stress triaxiality and Lode angle parameter. Based on the results of the parametric study of the numerical model, equations were proposed to evaluate the effect of stud height h_s, stud area A_s, concrete strength f_c and stud ultimate strength f_su used in concrete of various strengths on the static behaviour of studs.

The typical failure phenomenon observed among the test specimens was the fracture of the shank of studs. The microscopic images of the stud fracture surfaces and the verified finite element model indicate that the studs were fractured as a result of the combined action of tension and shear.

A new method for calculating ultimate load P_u and ultimate slip S_u is proposed in this paper. In the method, P_u is linearly related to f_su^0.2143, A_s^0.7790, h_s^0.0974, f_c^0.2065. S_u is linearly related to f_su^1.078, A_s^0.4681, h_s^(−0.3135), f_c^(−0.3480).

This study aims to focus on the resistance to elevated temperatures of up to 700ºC of high-performance concrete (HPC) compared to ordinary Portland concrete (OPC) with regards to mass loss and residual compressive and flexural strength.

Two mixtures were developed to test. The first mixture, OPC, was used as the control, and the second mixture was HPC. After 28 days under water (per Chinese standard), the samples were tested for compressive strength and residual strength.

The test results showed that at elevated temperatures of up to 500ºC, each mixture experienced mass loss. Below this temperature, the strength and the mass loss did not differ greatly.

When adding a 10 per cent silica fume, 25 per cent fly, 25 per cent slag to HPC, the compressive strength increased by 17 per cent and enhanced the residual compressive strength. A sharp decrease was observed in the residual flexural strength of HPC when compared to OPC after exposure to temperatures of 700ºC.

Forest as a vital natural resource in China plays an irreplaceable important role in safeguarding ecological security and human survival and development. Due to the vast territory, huge population and widespread forest landscape of China, forest management is a complex system involving massive data and various management activities. To effectively implement sustainable forest management, the big data technology has been utilized to analyze China’s forestry resources. Thus, the purpose of this paper is to clarify the role of big data technology in China’s forest management.

In this paper, the authors revisited the roles of big data in forest ecosystem monitoring, forestry management system development, and forest policy implementation.

It demonstrates that big data technology has a great potential in forest ecosystem protection and management, as well as the government’s determination for forest ecosystem protection. However, to deepen the application of big data in forest management, several challenges still need to be tackled.

Thus, enhancing modern science and technology to improve big data, cloud computing, and information technologies and their combinations will contribute to tackle the challenges and achieve wisdom of forest management.