This study investigates the role of IT system and social affordances in shaping consumers’ post-purchase intentions within the metaverse, employing the Theory of Affordance. Utilizing a sequential mixed-methods research design, Study 1 identifies critical affordances and their experiential outcomes, while Study 2 empirically tests their impact using structural equation modeling on survey data from 410 metaverse users. Results reveal that IT system and social affordances significantly influence repurchase and recommendation intentions, with interactive experience mediating these relationships. This research advances understanding of metaverse commerce, providing theoretical insights and practical guidelines for enhancing consumer engagement and loyalty in virtual environments.
The world’s energy demand is rising rapidly and conventional energy sources are at risk of exploitation. Uncontrolled emissions and a significant price increase in commercial fuel promoted the search for new diesel engine substitutes. This study focuses on biodiesel production from non-edible waste cottonseed oil (NWCSO), which could serve as a renewable and sustainable alternative for petroleum products. It involves using a novel crude exoenzyme (CEE) produced from Priestia endophytica SSP to transesterify NWCSO through the sonication process. The ultrasound-assisted enzyme-mediated transesterification process (UETP) was optimized using one variable analysis at a time and response surface methodology analysis to obtain the maximum biodiesel yield (99 Non-edible waste cottonseed oil biodiesel production using novel crude exoenzyme of Priestia endophytica SSP. Ultrasound-assisted enzyme-mediated transesterification process for biodiesel production, showing a maximum yield of 99
Wire Arc Additive Manufacturing (WAAM) is emerging as a cost-effective and material-efficient deposition technique; however, optimizing process parameters for dissimilar metal deposition remains a significant challenge. This study examines the impact of Gas Metal Arc Welding (GMAW) parameters, specifically current and deposition speed, on bead geometry and hardness during the deposition of Inconel 617 (IN 617) on a stainless steel 304L (SS 304L) substrate. An L9 orthogonal design was employed to conduct bead-on-plate depositions, followed by detailed macrostructural and microstructural analyses. Machine learning models (linear regression and random forest regression) were developed to predict bead width, bead height, and hardness, and Bayesian optimization was applied to identify the optimal parameter combination. Bead width increased with higher current, while bead height decreased with increasing deposition speed. Microstructural characterization revealed the presence of cellular, equiaxed, and columnar dendritic morphologies, along with unmixed zones at the interface. FESEM-EDS confirmed the segregation of alloying elements and the formation of Cr and Mo-rich carbides, as well as delta ferrite in the interfacial region. Bayesian optimization identified 200 A current and 180 mm/min deposition speed as optimal parameters, producing maximum bead width (13.7 mm), bead height (7.27 mm), and hardness (179.2 HV). Overall, the integration of machine learning prediction with process optimization provides an effective strategy for improving deposition quality and mechanical performance in dissimilar WAAM of nickel-based superalloys.
The shipping industry plays a critical role in global trade, but it also faces major sustainability challenges that affect important global goals like SDG 14 (Life Below Water) and SDG 13 (Climate Action). This study creates a framework to promote sustainable change using Industry 4.0 technologies, such as IoT and Blockchain. The approach includes a literature review and expert consultation to identify and categorize 36 technological enablers across four areas: environmental, social/organizational, managerial, and supply chain. To rank these enablers, we used the Robust Best Worst Method (RBWM), and we validated the results with machine learning classifiers to ensure the framework's reliability and accuracy. Our key findings show that renewable energy integration, eco-friendly design, blockchain applications, and planning algorithms are the most important factors for sustainability. This new framework provides practical guidance for policymakers and industry stakeholders to improve sustainable practices. It aims to support informed investment and operational choices, especially in developing economies where Industry 4.0 adoption is not fully explored.
Traditional super-resolution methods often struggle to capture fine details and extract features, especially at higher frequency which leads to poor reconstruction of images. Further some SR methods neglect the significance of complexity while designing deeper networks. Deeper networks are challenging to train and have greater computational load which limits the performance of SR method making it less compatible for other devices. To address this problem, we propose a novel Multi-Scale Attention Residual Convolutional Neural Network(MSARCNN). The model combines eight multi-scale attention residual convolution and a Dilated Convolution Block(DCB). Each MSARCB comprises of a squeeze and excitation block which recalibrates feature maps by emphasizing informative channels and a Pixel Attention Block(PAB) which utilizes attention-based weighting to enhance local feature representation. The MSARCB employs multi-scale hierarchical feature extraction with the help of parallel convolution layers with varying channels and DCB with dilation rates of 1,3,5 and 7 which helps in capturing both spatial features and fine details by enlarging the effective receptive field without increasing the number of learnable parameters. Experiments on four benchmark dataset demonstrate that the proposed model significantly outperforms other state-of-the-art lightweight SR methods, providing a exceptional balance of reconstruction performance, model complexity and parameter count.
