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In recent years, digital transformation has become one of the most popular trends for enterprises worldwide. The global trend of digital technologies and the COVID-19 pandemic have made the growth speed of digital transformation steadier than ever. In this condition, practitioners and academic researchers believe that the Digital Maturity Model is one of the most effective weapons in helping managers and the workforce manage to transform their businesses digitally. However, the Digital Maturity Model (DMM) is a type of maturity model (MM) that is relatively new in model development and digital maturity assessment methodologies, especially when integrated into an extensive digital transformation process. With this paper, the authors aim to conduct a comprehensive review to clarify the current state of the DMM field, including its essential characteristics, popular elements belonging to its structures, the number of methods, and techniques used in developing and applying them. In addition, these papers identify significant areas of research underway. Moreover, the authors raise some challenges for the field in the capture of results by reviewing them: i) the need to standardize its component names; ii) a contextualized but low-cost DMM for SMEs to use in their business; iii) the need for positioning DMM applied processes in a master digital transformation process and in a dynamics context that help applications of DMM more efficient. The authors proposed a solution for the third challenge through a conceptual model integrating DMM into a continuous digital transformation process. Keywords: Digital Transformation; Digital Maturity Model; Continuous Transformation Process; Change Management.

In this paper, we developed an automatic system for face ageing and facial expressions modeling using 3D practical faces data. As opposed to previous works, our face ageing system individualized the ageing function based on gender and age groups. Facial expressions are modeled by a muscle system which is improved by our new interpolated formula using characteristic parameters for the structure of each face. Our experiments which were performed on a practical training data set with 3D-face in neutral expression of each person at one age have shown that the effectiveness and efficiency of proposed methods in face ageing and face expressions modeling.

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