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This study aims to propose a new composite metal fin structure to enhance heat transfer efficiency during the phase change energy storage (PCES) process in a hot water oil displacement system.

PCES numerical unit is developed by varying the radii of annular fins and the number of corrugated fins. The impact of the finned structure on melting characteristics, energy storage performance and rate of heat storage is analyzed.

This study indicate the presence of non-uniform melting behavior in PCES unit during the heat charging process, which can be mitigated by increasing the number of corrugated fins and the radius of annular fins.

The impact of the finned structure on melting characteristics, energy storage performance and rate of heat storage is analyzed. This study indicates the presence of non-uniform melting behavior in PCES unit during the heat charging process, which can be mitigated by increasing the number of corrugated fins and the radius of annular fins.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

This study aims to examine the ramifications of corporate environmental, social and governance (ESG) investing in zombie firms and considers how external funding support may moderate this relationship given the sustainable nature of ESG performance, which often incurs costs.

Panel regression analyses used data from China’s A-share listed companies from 2011 to 2019, resulting in a data set comprising 6,054 observations.

Despite firms’ additional financial burdens, corporate ESG investing emerges as a catalyst in resurrecting zombie firms by attracting investor attention. Further analysis underscores the significance of funding support from entities such as the government and banks in alleviating ESG cost pressures and enhancing the efficacy of corporate ESG investing. Notably, the positive impact of corporate ESG investing is most pronounced in non-heavily polluting and non-state-owned firms. The results of classification tests reveal that social (S) and governance (G) investing yield greater efficacy in revitalizing zombie firms compared to environmental (E) investing.

This research enriches the discourse on corporate ESG investing and offers insights for governing zombie firms and shaping government policies.

By extending the domain of ESG research to encompass zombie firms, this paper sheds light on the multifaceted role of corporate ESG investing. Furthermore, this study comprehensively evaluates the influence of external funding support on the positive outcomes of ESG investing, thereby contributing to the resolution of the longstanding debate on the relationship between ESG performance and corporate financial performance, particularly with regard to ESG costs and benefits.

This paper aims to explore the impact of green finance on the heterogeneity of enterprise green technology innovation and the underlying mechanism between them.

Using the data of China's A-share listed enterprises from 2008 to 2020 and the fixed effect model, the authors empirically explore the relationship and mechanism between green finance and green technology innovation by constructing the green finance index while considering both the quality and quantity of innovation.

The study suggests that green finance is positively related to the quality and quantity of enterprise green technology innovation, while green finance is more effective in stimulating the quality of green technology innovation than quantity. In addition, alleviating financial mismatch and improving the quality of environmental information disclosure are core mechanisms during the process of green finance facilitating green technology innovation. Furthermore, green finance exerts a more positive effect on the quality and quantity of green technology innovation with large-size enterprises, heavily polluting industries and enterprises in the eastern region.

This paper enriches the literature on green finance and green technology innovation and provides practical significance for green finance implementation.