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The purpose of the paper is to study the dry sliding wear behavior of as-cast and heat-treated zinc-aluminum (ZA-27) alloy, reinforced with silicon carbide and graphite particles.

The alloy and composite samples were prepared with stir casting technique. Heat treatment was carried out for samples at a temperature of 370°C followed by quenching in water at room temperature. Subsequently, the heat-treated samples were aged at 180°C and quenched in water at room temperature. The wear tests were carried using pin-on-disc apparatus at room temperature at different applied loads, sliding speed and sliding distance.

The wear volume loss of as-cast samples was more compared with heat treated samples. Composites exhibited improved wear resistance than base alloy.

Hybrid metal matrix composites with heat treatment has exhibited superior wear behavior in dry sliding conditions.

The purpose of this paper is to produce Al6061 metal matrix composites reinforced with silicon carbide (SiC) and graphite particulates and study their wear behavior and also to develop artificial neural network model to predict the mass loss of hybrid composites.

The hybrid composites were produced by using stir casting process. The experiments were conducted based on the central composite rotatable design matrix using pin‐on‐disc wear testing machine. The set of data collected from the experimental values were used to train a back propagation (BP) learning algorithm with one hidden layer network. In artificial neural network (ANN) training module, four input vectors were used in the construction of proposed network namely, weight percentage of SiC particles, weight percentage of graphite particles, applied load and sliding distance. Mass loss was the output to be obtained from the proposed network. After training process, the test data collected from the experimental values were used to check the accuracy of proposed ANN model.

The results show that the well trained one hidden layer network have smaller training errors and much better generalization performance and can be successfully used for the prediction of mass loss of hybrid aluminium metal matrix composites.

In this paper the ANN method was adopted to predict the mass loss of hybrid composites. It was found that artificial neural network can be successfully used for prediction of mass loss of composites.

The purpose of this paper is to provide an alternative framework that will assist in understanding the adoption of digital food shopping. The coronavirus disease 2019 (COVID-19) pandemic has exacerbated the demand for digital shopping, but the adoption of digital shopping for food has not accelerated as fast as in other product categories. This study considered the role of socio-cultural factors to understand the reason for slow adoption of digital technology to access food. A cultural framework that can be used to investigate socio-cultural factors in this context was lacking, however, this paper provides a discussion of social and cultural factors and developed measurement scales to assist in understanding cultural change acceptance in consumers' adoption of digital technology to purchase food.

Using Hayes' process analysis, this paper investigated how cultural acceptance – mediated by consumer affection and appeal and measuring the moderated effects of digital trust (DT) – determined the eventual impact on consumer intention to adopt digital food retailing. This paper also considered moderated mediation with parallel mediations (consumer affection and appeal, digital convenience (DC) and consumer digital readiness) interacting with DT and consumer learning.

The authors found that cultural acceptance of digital technology (CADT) is an antecedent to the adoption of digital shopping for food, but this is also mediated by consumers' appeal and affection for digital technology and consumers' digital readiness.

This study also indicates that DT influences consumer appeal and affection (CAA), especially amongst female consumers.

The paper represents an empirical investigation of a new conceptual framework that considers socio-cultural factors to understand consumers' use of digital technology in food shopping which has been an existing knowledge gap in current literature.

It has generally been anticipated that the growth of Internet technology and e-commerce would result in virtual grocery shopping (VGS) becoming a normal way of life for consumers worldwide. However, the adoption of VGS, except in China and other Asian countries, has been quite slow and there is little understanding for this reason. Using Canada as a research context, the purpose of this study was to investigate the attitudes of consumers towards VGS with a focus on their technological readiness and the impact of the optimisation of consumer learning.

A quantitative research methodology was undertaken using cluster analysis with descriptive statistics to segment the different groups of consumers from a sample of 1,034 adult respondents. Structural equation modelling (SEM) was then used to test a theoretical model for consumers’ intention to adopt VGS.

The study found that the attitudes of consumers towards virtual shopping, convenience motivation, perceived ease of use (PEOU), perceived risk and consumer learning are all factors that impact consumers' intention to adopt virtual food shopping. The research also identified four segments of consumers in the Canadian market based on their attitudes and intention to adopt VGS. These results allow grocers to target the consumer groups favourable to VGS and provide insights on the factors that can be manipulated via marketing strategies to reach these consumers.

Retailers are provided with insights on consumers behaviour that will allow them to target specific segments with shopping modalities.

This research investigated VGS, focussing on consumer learning as a socio-cultural influence as well as the consumer's technological readiness as an intention to adopt to this modality of shopping for food. These constructs have not been investigated by previous studies on food grocery shopping.

This study aims to replace petroleum-based lubricating oils with sustainable biomaterials, addressing issues associated with existing alternatives, such as poor performance, high cost and limited availability.

The transformation of agricultural waste cardanol, a nonedible vegetable oil that is abundantly available, into green cardanyl acetate (CA) biolubricating ester oil. The potential of CA as a base stock for lubricants is validated by assessing its lubrication performance.

CA exhibited a higher viscosity index, flash point and thermal stability than commercially available mineral-based (CTL3, coal-to-liquid) and synthetic (PAO2, poly-alpha-olefin) lubricant base stocks. Moreover, CA exhibits excellent anticorrosivity properties as well as PAO2 and CTL3. The tribological properties of CA were evaluated, and the results show that CA exhibits a smaller average wear scar diameter (WSD) of 0.54 mm than that of PAO2 (0.85 mm) and CTL3 (0.90 mm). In extreme pressure tests, acylated CA demonstrated the highest last nonseizure load capacity at 510 N, outperforming commercial CTL3 (491 N) and PAO2 (412 N). All results demonstrate that CA displays an excellent series of base stock properties.

The novelty of this work lies in the utilization of renewable agricultural waste, cashew nut shell liquid, to produce a high-value biolubricant as an alternative to commercial fossil-based lubricants. The renewable nature, low cost, and large-scale availability of raw materials pave a new path for the production and application of biolubricants, showcasing the immense potential of converting agricultural waste into high-value products.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0064/

Purpose – The purpose of this chapter is to review the methodological and empirical underpinnings of transport network screening, or management, as it relates to improving road safety. As jurisdictions around the world are charged with transport network management in order to reduce externalities associated with road crashes, identifying potential blackspots or hotspots is an important if not critical function and responsibility of transport agencies.

Methodology – Key references from within the literature are summarised and discussed, along with a discussion of the evolution of thinking around hotspot identification and management. The theoretical developments that correspond with the evolution in thinking are provided, sprinkled with examples along the way.

Findings – Hotspot identification methodologies have evolved considerably over the past 30 or so years, correcting for methodological deficiencies along the way. Despite vast and significant advancements, identifying hotspots remains a reactive approach to managing road safety – relying on crashes to accrue in order to mitigate their occurrence. The most fruitful directions for future research will be in the establishment of reliable relationships between surrogate measures of road safety – such as ‘near misses’ – and actual crashes – so that safety can be proactively managed without the need for crashes to accrue.

Research implications – Research in hotspot identification will continue; however, it is likely to shift over time to both closer to ‘real-time’ crash risk detection and considering safety improvements using surrogate measures of road safety – described in Chapter 17.

Practical implications – There are two types of errors made in hotspot detection – identifying a ‘risky’ site as ‘safe’ and identifying a ‘safe’ site as ‘risky’. In the former case no investments will be made to improve safety, while in the latter case ineffective or inefficient safety improvements could be made. To minimise these errors, transport network safety managers should be applying the current state of the practice methods for hotspot detection. Moreover, transport network safety managers should be eager to transition to proactive methods of network safety management to avoid the need for crashes to occur. While in its infancy, the use of surrogate measures of safety holds significant promise for the future.

The purpose of this paper is to model the boundary layer flow and heat transfer of dusty fluid with suspended nanoparticles over a stretching surface. The effect of multiple slip and nonlinear thermal radiation is taken into the account. Adequate similarity transformations are used to obtain a set of nonlinear ordinary differential equations to govern formulated problem. The resultant non-dimensionalized boundary value problem is solved numerically using the RKF-45 method. The profiles for velocity and temperature, which are controlled by thermophysical parameters, are presented graphically. Based on these plots, the conclusion is given and the obtained numerical results are tabulated. Observed interesting fact is that the SiO₂-water nanoparticles show a thicker thermal boundary layer than TiO₂-water nanoparticles.

The governing partial differential equations are approximated to a system of nonlinear ordinary differential equations by using suitable similarity transformations. An effective fourth–fifth-order Runge–Kutta–Fehlberg integration scheme numerically solves these equations along with a shooting technique. The effects of various pertinent parameters on the flow and heat transfer are examined.

Present results have an excellent agreement with previous published results in the limiting cases. The values of skin friction and wall temperature for different governing parameters are also tabulated. It is demonstrated that the SiO₂-water nanoparticles show a thicker thermal boundary layer than TiO₂-water nanoparticles. It is interesting to note that the dusty nanofluids are found to have higher thermal conductivity.

This paper is a new work related to comparative study of TiO₂ and SiO₂ nanoparticles in heat transfer of dusty fluid flow.

The main theme of this paper is the effect of viscous dissipation Darcy–Forchheimer flow and heat transfer augmentation of a viscoelastic fluid over an incessant moving needle.

The governing partial differential equations of the current problem are diminished into a set of ordinary differential equations using requisite similarity transformations. Energy equation is extended by using Cattaneo–Christov heat flux model with variable thermal conductivity. By applying boundary layer approximation system of equations is framed.

Convective condition is also introduced in this analysis. Obtained set of similarity equations are then solved with the help of efficient numerical method four–fifth-order RKF-45.

The outcomes of various pertinent parameters on the velocity, temperature distributions are analysed by using portraits.

The purpose of this paper is to investigate the flow and heat transfer of power-law fluids over a non-linearly stretching sheet with non-Newtonian power-law stretching features.

The governing non-linear partial differential equations are reduced to a series of ordinary differential equations by suitable similarity transformations and the numerical solutions are obtained by the shooting method.

As the temperature power-law index or the power-law number of the fluids increases, the dimensionless stream function, dimensionless velocity and dimensionless temperature decrease, while the velocity boundary layer and temperature boundary layer become thinner for other fixed physical parameters. The thermal diffusivity varying as a function of the temperature gradient can be used to present the characteristics of flow and heat transfer of non-Newtonian power-law fluids.

Unlike classical works, the effect of power-law viscosity on the temperature field is considered by assuming that the temperature field is similar to the velocity field with modified Fourier’s law heat conduction for power-law fluid media.