Industry 4.0 is characterized by smart manufacturing, implementation of Cyber Physical Systems (CPS) for production, i.e., embedded actuators and sensors, networks of microcomputers, and linking the machines to the value chain. It further considers the digital enhancement and reengineering of products. It is also characterized by highly differentiated customized products, and well-coordinated combination of products and services, and also the value added services with the actual product or service, and efficient supply chain. All these challenges require continuous innovation and learning, which is dependent on people and enterprise's capabilities. Appropriate management approaches can play a vital role in the development of dynamic capabilities, and effective learning and innovation climate. This paper aims at offering a viewpoint on best suitable management practices which can promote the climate of innovation and learning in the organization, and hence facilitate the business to match the pace of industry 4.0. This paper is one of the initial attempts to draw the attention towards the important role of management practices in industry 4.0, as most of the recent studies are discussing the technological aspect. This paper also suggests empirical and quantitative investigation on these management approaches in the context of industry 4.0.

This paper studies modelling and dynamic analysis of underactuated capsule systems exhibiting friction-induced hysteresis. The motion mechanism is novel in utilizing internal centripetal torques generated by a vibration micro-motor mounted on the platform. Up to now, most investigations in frictional interactions towards capsule systems were confined into static or quasi-dynamic circumstance, where it is difficult to facilitate online use and control. It is the first time the dynamic frictional characteristics (non-reversible drooping and hysteresis) are studied towards these systems. An analytical study is primarily conducted to reveal the non-reversible characteristic for the static friction, the pre-sliding regime as well as the pure sliding regime, and the frictional limit boundaries are identified. Subsequently, the studies are mainly focused on dynamic analysis, including friction-driven vibrational responses and qualitative changes induced by control parameter (mass ratio) in capsule dynamics. It is found that the models predict periodic responses for the parameters considered and the average capsule velocity can be controlled through proper tuning of the control parameter around identified control points. The results demonstrate good captions of experimentally observed frictional characteristics, quenching of friction-induced vibrations and satisfaction of energy requirements.

Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates. We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFW can achieve equal or higher accuracy than the best classifier within the group.