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Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

Heating, ventilation and air-conditioning (HVAC) systems account for approximately half of all energy usage in the operational phase of a building's lifecycle. The disproportionate amount of energy usage in HVAC systems against other utilities within buildings has proved a huge cause for alarm, as this practice contributes significantly to global warming and climate change. This paper reviews the status and current trends of energy consumption associated with HVAC systems with the aim of interrogating energy efficiency practices for improving HVAC systems' consumption in buildings in the context of developing countries.

The study relied predominantly on secondary data by analysing the relevant body of literature and proposing conceptual insights regarding best practices for improving the energy efficiency of HVAC systems in buildings. The systematic review of the literature (SLR) was aided by the PRISMA guiding principle. Content analysis technique was adopted to examine germane scholarly articles and finally grouped them into themes.

Based on the SLR, measures for enhancing the energy efficiency of HVAC systems in buildings were classified based on economic considerations ranging from low-cost measures such as the cost of tuning the system, installing zonal control systems, adopting building integrated greenery systems and passive solar designs to major approaches such as HVAC smart technologies for energy management which have multi-year pay-back periods. Further, it was established that practices to improve energy efficiency in buildings range from integrated greening system into buildings to HVAC system which are human-centred and controlled to meet human modalities.

There is a need to incorporate these energy efficiency practices into building regulations or codes so that built environment professionals would have a framework within which to design their buildings to be energy efficient. This energy efficient solution may serve as a prerequisite for newly constructed buildings.

To this end, the authors develop an integrated optimization conceptual framework mimicking energy efficiency options that may complement HVAC systems operations in buildings.

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

Leveraging the technology acceptance model (TAM) and the stimulus–organism–response (S–O–R) theory, this paper aims to investigate how the utilitarian and hedonic factors in virtual reality (VR) technologies affect consumers' intention to travel in the endemic phase of COVID-19. At the same time, the study incorporated emotional engagement and two forms of trust as possible organisms for this model.

Through snowball sampling, data collected from 263 respondents were analysed using the partial least square structural equation modelling (PLS-SEM).

The findings revealed that among the different forms of hedonic and utilitarian factors, all but perceived entertainment has a significant positive relationship to emotional engagement. Additionally, emotional engagement positively influences trust in the product and seller. However, the results show that only trust in the seller has a significant relationship with travelling intention. Predictive analysis shows that the model displays a strong predictive power.

This study differentiates from the existing literature by investigating the effect of VR technologies on the two different forms of trust and emotional engagement on travelling intention. This study extends earlier studies by supplementing the explanatory perspective with a predictive focus, which is particularly important in making sound recommendations on managerial decision-making.