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At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm.

A road network is the key foundation of any nation’s critical infrastructure. Pavements represent one of the longest-living structures, having a post-construction life of 20–40 years. Currently, most attempts at maintaining and repairing these structures are performed in a reactive and traditional fashion. Recent advances in technology and research have proposed the implementation of costly measures and time-intensive techniques. This research presents a novel automated approach to develop a cognitive twin of a pavement structure by implementing advanced modelling and machine learning techniques from unmanned aerial vehicle (e.g., drone) acquired data. The research established how the twin is initially developed and subsequently capable of detecting current damage on the pavement structure. The proposed method is also compared to the traditional approach of evaluating pavement condition as well as the more advanced method of employing a specialized diagnosis vehicle. This study demonstrated an efficiency enhancement of maintaining pavement infrastructure.

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Electric vehicles (EVs) are an alternative architecture in the automotive industry that provide reduced emissions. This research has developed a switch reluctance motor (SRM) in-wheel drivetrain for an EV. SRM drivetrains are cheaper and do not use rare earth elements unlike a permanent magnet motor (PMM). Conversely, the in-wheel SRM has a drawback of an increased mass on the suspension when compared with an equivalent power output PMM drivetrain. This situation results in an increased mass at the wheels; hence, a suspension analysis is required. This paper discusses the suspension dynamics evaluated using a quarter-car simulation of an in-wheel SRM EV and compares it to the internal combustion engine (ICE) vehicle. The simulation used step loads derived design scenarios, namely (1) sprung, (2) unsprung and (3) driver's seat. Further Bode plot analysis techniques were used to determine the ride comfort range for the developed EV.