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The transition to green growth goals requires the concerted efforts of the whole society. Enterprises, as important players in the market, play a key role in promoting green and sustainable development. The rise of the concept of sustainable development has enabled more enterprises to disclose environmental, social and governance (ESG) information, and ESG behaviour is regarded as a positive strategic behaviour to implement the new development concept. This paper aims to explore the influence of ESG performance on enterprise green innovation.

This study applies a fixed effect model and the regulation effect of empirical analysis to explore the influence of ESG performance on enterprise green innovation. The object of investigation is 2014–2021 Shanghai and Shenzhen A-share listed companies.

The results of an empirical analysis outline the following conclusions: (1) ESG performance has a significant effect on enterprise green innovation, mainly by easing the pressure of the financing enterprise, fitting stakeholders’ environmental protection concept and obtaining employee organizational identity that influences enterprise green innovation. (2) Government regulation positively regulates the role of ESG performance in promoting the green innovation of enterprises. (3) Heterogeneity analysis found that the strengthening role of ESG performance on the green innovation of enterprises is stronger in green invention patents, state-owned enterprises and nonheavily polluting industries.

Despite the valuable findings, this study has a few limitations. Thus, it is necessary to extend the object of investigation by adding other Asian countries, which allows for comparison analysis and allocating best practices for promoting green innovation. Besides, innovation and ESG performance depend on the quality of institutions. In this case, the future study should incorporate the indicators that reveal the quality of institutions (corruption, transparency, digitalisation, voice, accountability, etc.).

According to the above conclusions, this paper proposes suggestions at the level of enterprises, government and investors. At the enterprise level, ESG responsibility should be strengthened, ESG information should be consciously disclosed and the quality of ESG disclosure should be improved. Government departments should play the role of supervisors, improve the construction of ESG information disclosure systems and promote the formation of ESG systems. At the social level, investors should improve the ESG information status and pay more attention to the ESG performance of enterprises.

This study fills the scientific gaps in the analysis impact of ESG performance on the green innovation of enterprises. This paper contributes to the theoretical landscape of ESG efficiency by developing approaches based on two empirical models: testing the impact of enterprise ESG performance on green innovation and testing whether government regulation plays a regulatory role in the relationship between ESG performance and green innovation. Besides, this study analysed the ESG performance and green innovation within the following categories: heavy and nonheavy polluter industries; state and nonstate-owned enterprise groups.

This study aims to solve the problem that under light-load conditions, the output voltage regulation capability is lost due to the fact that the voltage gain of the LLC resonant converter does not decrease with the increase of the switching frequency.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated under light-load conditions, the limitations of the previous method are explained and a new circuit improvement is proposed.

In this paper, an improved circuit is proposed, and the impedance Bode plot is used to verify that the circuit can effectively improve the voltage gain problem under light-load conditions. Finally, the experimental results verify the effectiveness of the proposed circuit through comparison with traditional solutions and circuits.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated, the limitations of the previous method are explained and a new circuit improvement is proposed. When compared with the previous method, the proposed circuit improvement can suppress the voltage gain increase that occurs when the switching frequency increases to a certain level.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

This paper aims to better design the resonant tank parameters for LLC resonant converter. And, it is found that under heavy load, the voltage gain is affected by junction capacitors of the primary side switching and the parasitic parameters of the secondary side diodes converted to the primary side, which will cause the voltage gain decreased when the switching frequency decreased.

This paper proposes an optimization parameters design method to solve this problem, which was based on impedance model considering the parasitic parameters of switching devices and diodes.

The effectiveness of the proposed method is verified by impedance Bode plots and experimental results.

From the perspective of impedance modeling, this paper finds the reasons for the insufficient voltage regulation capability of LLC resonant converters under heavy load and finds solutions through analysis.

Perceptions of employment histories are important insofar as they influence future job prospects. Critically, in light of the current pandemic, wherein many individuals are likely to have unanticipated employment gaps and/or temporary work experiences, this exploratory study aims to seek a better understanding of the signal associated with temporary employment histories, which is particularly germane to individuals' employment trajectories and a successful labour market recovery.

Drawing primarily on signalling theory and using a simulated hiring decision experiment, the authors examined the perceptions of temporary employment histories, as well as the period effect of COVID-19, a major exogenous event, on the attitudes of fictitious jobseekers with standard, temporary and unemployment histories.

The authors find that prior to COVID-19 unemployed and temporary-work candidates were perceived less favourably as compared to applicants employed in a permanent job. During the COVID-19 pandemic, assessments of jobseekers with temporary employment histories were less critical and the previously negative signal associated with job-hopping reversed. This study’s third wave of data, which were collected post-COVID, showed that such perceptions largely dissipated, with the exception for those with a history of temporary work with different employers.

The paper serves as a reminder to check, insofar as possible, preconceived biases of temporary employment histories to avoid potential attribution errors and miss otherwise capable candidates.

This paper makes a unique and timely contribution by focussing and examining the differential effect of economic climate, pivoted by the COVID-19 pandemic, on perceptions of temporary employment histories.

Despite the increasing importance of boundary-spanning behaviors in construction projects, the research on how leader anger expressions impact employees’ boundary-spanning behaviors remains unclear. This study aims to investigate the impact of leader anger expressions on employees’ boundary-spanning behaviors in construction projects while exploring the mediating effect of work hope and the moderating effect of power distance orientation through the lens of social information processing theory.

The empirical data were collected from a questionnaire survey of 235 employees in construction projects, and the hypotheses were tested using the PROCESS program developed by Hayes.

This study demonstrated that leader anger expressions negatively affect employees’ work hope, and work hope mediates the relationship between leader anger expressions and employees’ boundary-spanning behaviors. Besides, the stronger the employees’ power distance orientation, the stronger the negative relationship between leader anger expressions and employees’ work hope, and the above mediating effect was also stronger.

The use of cross-sectional data was unable to entirely infer a causal relationship between leader anger expressions and employees’ boundary-spanning behaviors. Additionally, the reliance on self-reported data inevitably introduced some common method bias.

Applying social information processing theory, this paper enriches the discourse on factors influencing employees’ boundary-spanning behaviors in construction projects. It also offers valuable insights for leaders in construction projects to manage their teams effectively.

When the reinforced concrete beams are reinforced by bonding steel plates to the bottom, excessive use of steel plates will make the reinforced concrete beams become super-reinforced beams, and there are security risks in the actual use of super-reinforced beams. In order to avoid the occurrence of this situation, the purpose of this paper is to study the calculation method of the maximum number of bonded steel plates to reinforce reinforced concrete beams.

First of all, when establishing the limit failure state of the reinforced member, this paper comprehensively considers the role of the tensile steel bar and steel plate and takes the load effect before reinforcement as the negative contribution of the maximum number of bonded steel plates that can be used for reinforcement. Through the definition of the equivalent tensile strength, equivalent elastic modulus and equivalent yield strain of the tensile steel bar and steel plate, a method to determine the relative limit compression zone height of the reinforced member is obtained. Second, based on the maximum ratio of (reinforcement + steel plate), the relative limit compression zone height and the equivalent tensile strength of the tensile steel bar and steel plate of the reinforced member, the calculation method of the maximum number of bonded steel plates is derived. Then, the static load test of the test beam is carried out and the corresponding numerical model is established, and the reliability of the numerical model is verified by comparison. Finally, the accuracy of the calculation method of the maximum number of bonded steel plates is proved by the numerical model.

The numerical simulation results show that when the steel plate width is 800 mm and the thickness is 1–4 mm, the reinforced concrete beam has a delayed yield platform when it reaches the limit state, and the failure mode conforms to the basic stress characteristics of the balanced-reinforced beam. When the steel plate thickness is 5–8 mm, the sudden failure occurs without obvious warning when the reinforced concrete beam reaches the limit state. The failure mode conforms to the basic mechanical characteristics of the super-reinforced beam failure, and the bending moment of the beam failure depends only on the compressive strength of the concrete. The results of the calculation and analysis show that the maximum number of bonded steel plates for reinforced concrete beams in this experiment is 3,487 mm². When the width of the steel plate is 800 mm, the maximum thickness of the steel plate can be 4.36 mm. That is, when the thickness of the steel plate, the reinforced concrete beam is still the balanced-reinforced beam. When the thickness of the steel plate, the reinforced concrete beam will become a super-reinforced beam after reinforcement. The calculation results are in good agreement with the numerical simulation results, which proves the accuracy of the calculation method.

This paper presents a method for calculating the maximum number of steel plates attached to the bottom of reinforced concrete beams. First, based on the experimental research, the failure mode of reinforced concrete beams with different number of steel plates is simulated by the numerical model, and then the result of the calculation method is compared with the result of the numerical simulation to ensure the accuracy of the calculation method of the maximum number of bonded steel plates. And the study does not require a large number of experimental samples, which has a certain economy. The research result can be used to control the number of steel plates in similar reinforcement designs.

Facing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.

Based on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.

Using the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.

With the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.

The purpose of this study is to investigate the effect of nano ZrC particles on the mechanical and electrochemical corrosion properties of FeCrAl alloys, providing a beneficial reference basis for the development of high-performance carbide reinforced FeCrAl alloys with good mechanical and corrosion properties in the future.

Nano ZrC reinforced FeCrAl alloys were prepared by mechanical alloying and spark plasma sintering. Phases composition, tensile fractography, corrosion morphology and chemical composition of nano ZrC reinforced FeCrAl alloys were analyzed by X-ray diffraction, scanning electron microscopy and energy dispersive X-ray spectroscopy, respectively. Microhardness and tensile properties of nano ZrC reinforced FeCrAl alloys were investigated by mechanical testing machine and Vickers hardness tester. Electrochemical corrosion properties of nano ZrC reinforced FeCrAl alloys were investigated by electrochemical workstation in 3.5 wt.% NaCl solution.

The results showed that addition of nano ZrC can effectively improve the mechanical and corrosion properties. However, excessive nano ZrC could decrease the mechanical properties and reduce the corrosion resistance. In all the FeCrAl alloys, FeCrAl–0.6 wt.% ZrC alloy exhibits the optimum mechanical properties with an ultimate tensile strength, elongation and hardness of 990.7 MPa, 24.1% and 335.8 HV1, respectively, and FeCrAl–0.2 wt.% ZrC alloy has a lower corrosion potential (−0.179 V) and corrosion current density (2.099 µA/cm2) and larger pitting potential (0.497 V) than other FeCrAl–ZrC alloys, showing a better corrosion resistance.

Adding proper nano ZrC particles can effectively improve the mechanical and corrosion properties, while the excessive nano ZrC is harmful to the mechanical and corrosion properties of FeCrAl alloys, which provides an instruction to develop high-performance FeCrAl cladding materials.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.