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The global emphasis on sustainability is driving organizations to embrace financial technology (Fintech) solutions as a means of enhancing their sustainable performance. This study seeks to unveil the intermediary role played by green finance and competitiveness, along with the moderating impact of digital transformation (DT), in the intricate relationship between Fintech adoption and sustainable performance.

Drawing on existing literature, we construct a comprehensive conceptual framework to thoroughly analyse these interconnected variables. To empirical validate of our model, a dual structural equation modelling–artificial neural network) SEM–ANN approach was employed, adding a robust layer of validation to our study’s proposed framework. A sample of 438 banking employees in Pakistan was collected using a simple random sampling technique, with 411 samples deemed suitable for subsequent analysis. Initially, data scrutiny and hypothesis testing were carried out using Smart-PLS 4.0 and SPSS-23. Subsequently, the ANN technique was utilized to assess the importance of exogenous factors in forecasting endogenous factors.

The findings from this research underscore the direct and significant influence of Fintech adoption and DT on the sustainable performance of banks. Notably, green finance and competitiveness emerge as pivotal mediators, bridging the gap between Fintech adoption and sustainable performance. Moreover, DT emerges as a critical moderator, shaping the relationships between Fintech adoption and both green finance and competitiveness. The integration of the ANN approach enhances the SEM analysis, providing deeper insights and a more comprehensive understanding of the subject matter.

This study contributes to the enhanced comprehension of Fintech, green finance, competitiveness, DT and the sustainable performance of banks. Recognizing the importance of amalgamating Fintech adoption, green finance and transformational leadership becomes essential for elevating the sustainable performance of banks. The insights garnered from this study hold valuable implications for policymakers, practitioners and scholars aiming to enhance the sustainable performance of banks within the competitive business landscape.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

Recent studies have observed rise in consumer’s ethical concerns about the online retailers while making a purchase decision. The impetus for businesses to use corporate social responsibility (CSR) is evident, but the effects of CSR motives on corresponding processes underlying cause-related marketing (CRM) patronage intention have not been thoroughly examined. This study, anchored on attribution theory, established a research model that better explains the influence of CSR motives on patronage intentions toward CRM-oriented online retailers. Additionally, this study aims to examine the moderating role of spirituality (SPT) on CSR motives and CRM patronage intention (CPI).

Primary data has been collected from 722 respondents and analyzed by using deep neural-network architecture by using the innovative PLS-SEM-ANN method to predict/rank the factors impacting CPI.

The results revealed the normalized importance of the predictors of CPI and found that value-driven motive was the strongest predictor, followed by strategic motive, SPT, age and stakeholder-driven motive. In contrast, egoistic motive, education and income were found insignificant.

The pandemic has transformed the way consumers shop and fortified the online economy, thereby resulting in a paradigm shift toward usage of e-commerce platforms. The results offer valuable insights to online retailers and practitioners for predicting patronage intentions by CSR motives and, thus, effectively engage CRM consumers by designing promotions in a way that would deeply resonate with them. This study assessed and predicted the factors influencing the CPI s, thereby guiding the online retailers to design CSR strategies and manage crucial CRM decisions.

The purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.

Based on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.

Digital twin technology enhances the quality and efficiency of assembly process planning and execution system.

It provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.