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Transportation-related pollution is expected to decrease when using battery electric cars. This will not only address energy and environmental issues but also promote reform and transformation in the zero-emission automotive industry. To craft policy interventions and promotional initiatives, manufacturers need to comprehend the techno-psychological perspectives of automotive users on the adoption of electric cars. Therefore, this study aims to test a “perception-attitude-intention” linking framework built upon the “Unified Theory of Technology Acceptance and Use” (UTAUT) and analyze the behavioral intentions of existing automobile users to embrace battery electric cars.

The conceptual model tests the underlying direct paths, the mediation of attitudes and the moderating gender effects in predicting users’ attitudes and behavioral intentions to adopt battery electric cars using a techno-psychological approach from UTAUT. “Structural equation modeling” is used to analyze the model using the 361 valid online responses received from conventional car owners.

The results show that behavioral intentions are directly predicted by UTAUT measures with attitudes and indirectly through its mediation and gender moderation. The results support the “Perceptions-Attitudes-Intentions” linkage model that explains the phenomenon of electric car adoption. However, the mediating and moderating paths between facilitating conditions and intentions do not support the model. In addition, the research corroborates that men have a stronger effect than women on behavioral intentions to prefer battery electric cars.

This work may assist manufacturers and regulators in developing marketing policies to encourage consumers’ adoption of battery electric cars and potentially improve their favorable perception of these vehicles.

This study contributes to the comprehension of how UTAUT constructs shape consumers’ attitudes and behavioral intentions regarding the adoption of battery cars equipped with emission-free technology. This study validates the grounded framework “perception-attitude-intention” linkage model, which also describes gender-wise differences toward electric car adoption in the backdrop of Indian sustainable transportation.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

In vitro millifluidic cultures with perfusion are essential tools to analyse and understand the interactions between cells, their matrix and multi-cell populations. The purpose of this paper is to focus on the design and development of a 3D-printed template that can be used for fabrication of a clear view poly (dimethyl siloxane) (PDMS) device. The major objective is to obtain a transparent device prototype that allows perfusion culture of two cell types for multiple days that can be imaged using laser scanning confocal microscopy.

The authors used a two-step approach for achieving the final geometric structure at a faster timeline and lower cost. The first part focuses on comparing the fidelity of the printing templates using fused deposition modelling (FDM) and stereolithography (SLA) printers for a range of dimensions. They then show that the complex geometry chip with connection chambers can be printed using low resolution low cost FormLab SLA printer. The final optimized design was then printed using high-resolution Projet 6000 SLA printer to obtain smoother structures.

In this work, the authors have shown that the FormLab SLA printer yields significantly lower error for printing complex design geometries as compared to FDM printer. Result shows that FormLab printer can be used to achieve a minimum dimension of 0.5 mm. They then use the printer to optimize the device dimension for the culture chip which requires several iterations of printing and experimenting. They showed the two-step protocol of printing the optimized template in a high-resolution SLA printer and further fabricating a clear view millifluidic PDMS device that is compatible confocal microscopy imaging. They used this culture chip for perfusion culture of two cell type, and the controlled fluidic exchange between the two chambers led to the formation of neuroglia junction.

One of the major bottlenecks for obtaining complex geometry in mili/microfluidic device by 3D printing is the need of multiple iterations on printing. This makes the tuning of dimension significantly expensive. Another challenge is to obtain a smooth surface of PDMS that leads to a leak proof clear view device compatible for laser based confocal imaging. The combination of two printers plays a crucial role for the rapid prototyping of the imaging device with flow control. The proposed approach lowers the cost for prototyping of in vitro culture chip with complex geometries to improve on biological research demanding multi-chamber fluidic device.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.