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The principal aim of this research is to acquire a deeper understanding of the opinion held by the training and development (T&D) professionals, regarding the use of artificial intelligence (AI) technology in the area of T&D. Particularly in response to the evolving needs of learners, the research aims to ascertain T&D professionals' perspective on the efficiency of AI in fostering T&D, while understanding the constraints and limitations associated with this technology.

The study is based on qualitative data. With the help of semi-structured interviews, qualitative data has been collected from 21 T&D professionals. Experts working with multinational corporations (MNCs) are selected as a study sample using a convenient sampling technique. Qualitative data were analysed using thematic analysis. Conclusions were drawn based on the results of thematic analysis.

The findings of the study have revealed a notable and rapid evolution in the requirements of learners, particularly during and post-COVID-19 period. AI-based technology has emerged as a significant contributor, offering learners distinct personalised experiences and enhanced convenience. However, the implementation of AI in training remains in its early stages and has not reached widespread adoption. The findings of the study also highlighted various challenges and limitations, while using AI-based technology for training. It has been found that AI complements rather than replaces the role of a physical trainer.

The originality of this study lies in the application of AI-based training for professional learners, from the perspective of the T&D practitioners working with MNCs in Maharashtra, India. Numerous studies that have recently been published, emphasise the areas in which AI technology can transform the T&D industry. Yet, there are currently very less studies that have attempted to understand the evolving needs of learners and support of AI-based training for the same, from the perspective of the T&D professionals working in Maharashtra, India.

In everyday life, people generally wear two layers of clothes (a knitted vest and a knitted t-shirt) during the summer. It is essential to understand which types of innerwear and outerwear maximize comfort. The primary objective of this research is to investigate the influence of layering outerwear on innerwear, as well as the air gap between two layers, on thermal comfort properties.

In this study, a total of 12 combinations were created from four vest fabrics and three T-shirt fabrics. The thermal properties (thermal conductivity, thermal resistance, thermal absorptivity, thermal diffusion and peak heat flow) were evaluated for the individual inner and outer layers. Each inner layer was layered with an outer layer to observe the effect of layering on the thermal properties. An air gap of 2 mm was introduced between the inner and outer layers to study the effect of air gap on thermal properties.

Tencel fibre exhibits higher thermal conductivity and absorptivity than cotton and polyester. Upon layering an outer layer on an inner layer, the thermal conductivity and thermal absorptivity increase to a slight extent, thermal resistance and diffusion increase drastically and the peak heat flow reduces. With an air gap between the two layers, the thermal conductivity did not improve, the difference in thermal resistance among all the combinations reduced, the thermal absorptivity of the combination textiles was lower than that of the innerwear alone, the thermal diffusion increased and the peak heat flow diminished for all the combinations.

In practice, this comprehensive thermal comfort analysis provides specific combinations of inner and outer articles of clothing that are most appropriate for enhancing comfort during the summer season.

Though there are many studies on the effect of multilayer fabrics on thermal properties, no extensive research analyses the influence of innerwear and outerwear combinations on thermal comfort properties.

This paper aims to explore the numerical study of the steady two-dimensional MHD hybrid Cu-Fe₃O₄/EG nanofluid flows over an inclined porous plate with an inclined magnetic effect. Iron oxide (Fe₃O₄) and copper (Cu) are hybrid nanoparticles, with ethylene glycol as the base fluid. The effects of several physical characteristics, such as the inclination angle, magnetic parameter, thermal radiation, viscous propagation, heat absorption and convective heat transfer, are revealed by this exploration.

Temperature and velocity descriptions, along with the skin friction coefficient and Nusselt number, are studied to see how they change depending on the parameters. Using compatible similarity transformations, the controlling equations, including those describing the momentum and energy descriptions, are turned into a set of non-linear ordinary differential equations. The streamlined mathematical model is then solved numerically by using the shooting approach and the Runge–Kutta method up to the fourth order. The numerical findings of skin friction and Nusselt number are compared and discussed with prior published data by Nur Syahirah Wahid.

The graphical representation of the velocity and temperature profiles within the frontier is exhibited and discussed. The various output values related to skin friction and the Nusselt number are shown in the table.

The new results are compared to past research and discovered to agree significantly with those authors’ published works.