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The main purpose of the research paper is to study the effect of refinement of primary silicon on sliding wear behavior of commercially available LM-28 alloy and correlate these with the micro-structural and mechanical properties. It is well known that the refinement of primary silicon increases the toughness, ultimate tensile stress (UTS) and wear resistance of the Al-Si alloys at ambient temperature. But exact addition level is not clearly mentioned in the paper; hence, the present paper throws light on the addition level for refinement of primary silicon present in LM-28 alloy.

In the present paper, commercially available LM-28 alloy was prepared in the laboratory before and after the addition of Cu-P master alloy. Further wear studies will be carried out at room temperature. Wear properties are correlated with microstructure and mechanical properties.

Improvements in mechanical properties were observed after refinement of the primary silicon present in LM-28 alloy.

Further studies are required regarding in-depth investigation of refinement of primary silicon and its effects on the life of the components prepared by this alloy.

The data obtained from the research paper help the manufacturers of automotive, aerospace and marine components, such as pistons, cylinder heads and blocks, etc.

The present research work will be an essential information required for the students of undergraduate, postgraduate and research scholars to carry out research at an R&D centre.

Experiments are conducted at an R&D centre and analyses were carried out for structural changes, mechanical properties and wear properties. Further, the results are compared with other researcher’s data.

Aluminium is the most preferred material in engineering structural components because of its excellent properties. Furthermore, the properties of aluminium may be enhanced through metal matrix composites and an in-depth investigation on the evolved properties is needed in view of metallurgical, mechanical and tribological aspects. The purpose of this study is to explore the effect of TiC addition on the tribological behavior of aluminium composites.

Aluminium metal matrix composites at different weight percentage of titanium carbide were produced through powder metallurgy. Produced composites were subjected to sliding wear test under dry condition through Taguchi’s L9 orthogonal design.

Optimal process condition to achieve the minimum wear rate was identified though the main effect plot. Sliding velocity was identified as the most dominating factor in the wear resistance.

The production of components with improved properties is promoted efficiently and economically by synthesizing the composite via powder metallurgy.

Though the investigations on the wear behavior of aluminium composites are analyzed, reinforcement types and the mode of fabrication have their significance in the metallurgical and mechanical properties. Thus, the produced component needs an in-detail study on the property evolution.

X‐ray lithography is an important technique in micro fabrication used to obtain structures and devices with a high aspect ratio. The X‐ray exposure takes place in a system composed of a mask and a photoresist deposited on a substrate (with a gap between mask and resist). Predictions of the temperature distribution in three dimensions in the different layers (mask, gap, photoresist and substrate) and of the potential temperature rise are essential for determining the effect of high flux X‐ray exposure on distortions in the photoresist due to thermal expansion. In this study, we develop a three‐dimensional numerical method for obtaining the temperature profile in an X‐ray irradiation process by using a hybrid finite element‐finite difference scheme for solving three‐dimensional parabolic equations on thin layers. A domain decomposition algorithm is then obtained based on a parallel Gaussian elimination for solving block tridiagonal linear systems. The method is illustrated by a numerical method.

The purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.

A model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.

This modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.

The research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.

The study implies that transport operations would actually need to be re-organized so as to deal with lowering CO₂ footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).

The growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.

Despite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

This study aims to formulate a behavioural credit scoring models for Indian small and medium enterprises (SME) entrepreneurs using certain behavioural and psychological constructs. Two separate models are built which can predict the credit default and wilful default of the borrowers, respectively. This research was undertaken to understand whether certain psychological and behavioural factors can significantly predict the borrowers’ credit and wilful default.

A questionnaire survey was undertaken by SME entrepreneurs of two Indian states, i.e. Uttar Pradesh and Maharashtra. The questionnaire had two dependent variables: wilful default and credit default and nine independent variables. The questionnaire reliability and validity were ensured through confirmatory factor analysis (CFA) and further a model was built using logistic regression.

The results of this study have shown that certain behavioural and psychological traits of the borrowers can significantly predict borrowers’ default. These variables can be used to predict the overall creditworthiness of SME borrowers.

The findings of this research indicate that using behavioural and psychological constructs, lending institutions can easily evaluate the credit worthiness of those borrowers, who do not have any financial and credit history. This will enhance the capability of financial institutions to evaluate opaque SME borrowers.

There are very few numbers of studies which have considered predicting the credit default using certain psychological variables, but with respect to Asian market, and especially India, there does not exist a single significant study which has tried to fulfil such research gap. Also, this is the first study that has explored whether certain psychological factors can predict the wilful default of the borrowers. This is one of the most significant contributions of this research.

Human-artificial intelligence (AI) collaboration, as a new form of cooperative interaction, has been applied in brainstorming activities. This study aims to explore the impact of performance-reward expectancy (PRE) and creative motivation (CM), along with the search for ideas in associative memory (SIAM) theory, on participants' AI collaboration intent (AICI).

The research employs an online survey targeting users with brainstorming experience. Structural equation modeling (SEM) is applied to analyze the data and validate the proposed hypotheses.

PRE shows a positive correlation with both intrinsic motivation (IM) and extrinsic motivation (EM). Furthermore, EM significantly and positively influences AICI, while IM has a negative significant effect. Additionally, the study confirms the mediating role of social inhibition (SI) between EM and AICI.

This study examines the intent to collaborate with AI in brainstorming, filling a gap in existing research. It integrates SIAM theory to analyze how performance rewards and creative motivation influence this intent. Findings reveal that performance-based rewards effectively motivate creative engagement, but high intrinsic motivation may lead to lower intent to collaborate due to autonomy concerns and trust issues. The study emphasizes the need for an open environment and offers practical insights for fostering AI collaboration while addressing challenges like social inhibition and resistance among participants.

This study provides practical insights for creative teams and individuals, emphasizing the importance of integrating AI in brainstorming to unlock its full potential. While performance rewards are effective, social inhibition may still lead participants to have negative attitudes toward AI collaboration. Creating an open and inclusive environment is essential. Additionally, the “individual + AI” model may provoke resistance among highly intrinsically motivated participants, necessitating training and improved AI transparency to build trust. Although focused on the Chinese market, the findings are applicable globally, highlighting the need to explore effective AI integration methods for innovation.

Our study found that PRE can positively influence intrinsic and extrinsic motivation in creative activities. This finding provides new evidence for our understanding of the role of performance-reward mechanisms in stimulating creativity. At the same time, we also explored how factors such as social inhibition and production blocking can affect individuals’ willingness to work with AI by influencing creativity motivation. This provides new insights to better understand how AI in teams affects individual psychology and team dynamics. These findings not only enrich our understanding of innovation and teamwork but also provide valuable references and directions for future research.

This study systematically examines the influence of PRE on CM within the context of AI-assisted brainstorming for the first time. It further investigates how SIAM theory regulates this process and ultimately shapes participants' willingness to engage in AI collaboration. The findings offer theoretical and practical guidance on designing incentive mechanisms to enhance engagement in AI-supported brainstorming and provide new perspectives on the application of AI in team innovation activities.