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Background. Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long- term temporal dependencies, thus susceptible to an inherent problem of vanishing gradient. This work proposed a method based on an advanced version of RNN known as long short-term memory (LSTM) architecture, which updates recurrent weights to overcome the vanishing gradient problem. This, in turn, improves training performance. Methods. The RNN model is developed based on stack LSTM and bidirectional LSTM. The parameters like mean absolute error (MAE), standard deviation error (SDE), and root mean squared error (RMSE) are utilized as performance measures for comparison with recent state-of-the-art techniques. Results. Results showed that the proposed technique outperformed the existing techniques in terms of RMSE and MAE against all the used wind farm datasets. Whereas, a reduction in SDE is observed for larger wind farm datasets. The proposed RNN approach performed better than the existing models despite fewer parameters. In addition, the approach requires minimum processing power to achieve compatible results. [ABSTRACT FROM AUTHOR]

Sustainability has always been a concern of humankind in one form or the other. Still, it has come into sharper focus after the promulgation of the sustainable development goals in 2015 and the disruptive forces unleashed by the COVID‐19 pandemic. Supply chain management is an aspect of business operations wherein the need for sustainability has been felt more keenly. The academic and business understanding of various nuances of incorporating sustainability, particularly environmental concerns in the supply chain, is still evolving. Our study seeks to enrich the growing literature in the area by proposing to uncover a novel, logical sequence of intangible supply chain resources that can amplify the impact of green supply chain management practices (GSCMPs) on business performance. We use the dual‐theoretical lens of a resource‐based view and stakeholder theories to conceptualize the sequential mediational role of supply chain visibility, resilience, and robustness between GSCMP and performance. Analyzing data collected from 318 individuals working in the manufacturing sector in the United Kingdom, we found a positive direct association of GSCMP with performance and the serial mediational role of visibility and robustness between the two. The findings of our study are pertinent for theorists as well as managers. [ABSTRACT FROM AUTHOR]

As sustainability draws increasing attention due to the COVID‐19 pandemic, interest in the green supply chain has likewise substantially increased. The present study contributes deeper insights into the logical integration of the resource‐based characteristics that can facilitate the effective transition from traditional to green supply chains while also increasing the effectiveness of existing green supply chains. The proposed model, rooted in the resource‐based view (RBV) theory, examines the associations of supply chain connectivity (SCC), supply chain information sharing (SCIS), top management commitment (TMC) and green procurement and logistics acceptance (GPLA) with green supply chain management (GSCM). Analysis of data from employees at 381 manufacturing firms in the United Kingdom confirmed the positive associations of SCC and SCIS with TMC and GPLA, of TMC with GPLA and GSCM and of GPLA with GSCM. The results also confirmed the serial mediation effect of TMC and GPLA on the associations of both SCC and SCIS with GSCM. The study offers several practical insights and theoretical contributions, including a novel GSCM scale. [ABSTRACT FROM AUTHOR]