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The purpose of this paper is to produce an intelligent technique for modelling machine tool errors caused by the thermal distortion of Computer Numerical Control (CNC) machine tools. A new metaheuristic method, the cuckoo search (CS) algorithm, based on the life of a bird family is proposed to optimize the GMC(1, N) coefficients. It is then used to predict thermal error on a small vertical milling centre based on selected sensors.

A Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To enhance the accuracy of the proposed model, the generation coefficients of GMC(1, N) are optimized using a new metaheuristic method, called the CS algorithm.

The results demonstrate good agreement between the experimental and predicted thermal error. It can therefore be concluded that it is possible to optimize a Grey model using the CS algorithm, which can be used to predict the thermal error of a CNC machine tool.

An attempt has been made for the first time to apply CS algorithm for calibrating the GMC(1, N) model. The proposed CS-based Grey model has been validated and compared with particle swarm optimization (PSO) based Grey model. Simulations and comparison show that the CS algorithm outperforms PSO and can act as an alternative optmization algorithm for Grey models that can be used for thermal error compensation.

This chapter delves into the evolving identity of professionals within the field of research management and administration (RMA), examining the shifts in their roles and expectations in the changing landscape in higher education. After the introductory section, Section 2 offers a conceptual framework that emphasises identity as a dynamic process rather than a static concept. This framework sheds light on the changing roles and expectations that define the RMA profession. In Section 3, we explore the contextual backdrop of shifting expectations surrounding RMA roles while stressing the importance of recognizing the multiplicity of identities to comprehend the nuances of the RMA profession. Section 4 analyzes empirical data and explore the diverse pathways that lead individuals into the RMA profession. We uncover that a notable proportion of RMAs possess scientific training and research experience and highlight the complexities surrounding the identity of RMAs with doctoral training (DRMAs). Lastly, Section 5 discusses key observations that yield valuable insights for future research on the evolving professional identity of RMAs. We emphasise that, through self-exploration and introspection, practitioners in the field can contribute to a deeper understanding of their roles and actively shape their professional identity.