###### Abstract
Thus, the authors carried out an SLR, through which it was possible to identify five main issues about degradation--residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance--as major performance measures to investigate the performance of the FUS. In this study, it was understood that the degradation rate would affect the life and production rate of different configurations of FUS. Moreover, it was possible to realize that the considered upgradation model and predictive maintenance, along with advanced analytics procedures of the manufacturing systems, are valuable and enable the systems to run with higher production rates while increasing the life of systems. Moreover, it was also possible to explore three research objectives related to system configuration flexibility to improve the proposed FUS and identify further research opportunities in this field. [PERSON] et al. focus on co-authorship networks analysis to discover collaboration patterns among Italian researchers. Through their study about the analysis of behaviors of a large community of researchers and their correlations between the underlying environments, the authors determined a set of grouping rules by law or specific institutional policies that enabled conclusions about their performance and, importantly, affecting metrics to evaluate the quality of their research carried out. To this end, the coauthors created a procedure to craft a large dataset of Italian academic researchers by considering a set of performance indices and co-authorship information. Through their study, the authors could automate the association of profiles and the mapping of publications to reduce the use of computational resources. Moreover, the authors presented several examples of how the information extracted from the datasets can help better understand the dynamics influencing scientific performances. [PERSON], [PERSON], and [PERSON] refer in their work to leveraging explainable AI to support cryptocurrency. The authors clarify that this research area has been attracting the attention of many researchers and continues to be a very important research focus for private and professional traders and investors. They further mention that forecasting financial markets can be properly approached by using algorithmic trading systems based on AI models, which are becoming more and more developed. Moreover, the authors state that such approaches usually suffer from a lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. Additionally, they mention that this is particularly critical for cryptocurrency investors because studying the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts' decisions. Thus, in their paper, the authors propose a new visual analytics tool to support domain experts in explaining AI-based cryptocurrency trading systems. To further describe the rationale behind AI models, their proposed approach exploits an established method, the SHapley Additive exPlanations, which, according to their results, allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. [PERSON] approached design technology and AI-based decision-making models for digital twin engineering. This study proposes comprehensive technology (methodological approach) for digital twin design to accelerate its engineering. The author clarifies that this kind of technology consists of design steps, methods, and models and provides systems synthesis of digital twins for a complex system (object or process) operating under uncertainty that can reconfigure in response to internal faults or environment changes and perform preventive maintenance. The author mentions that the proposed technology structure was developed based on a simulation model using situational \"what-if\" analysis and based on fuzzy logic methods.