The possibilities of the Ambient Assisted Living (AAL)/Enhanced Living Environments (ELE) concept in the environment of a smart home were investigated to improve accessibility and improve the quality of life of a person with disabilities. This paper focuses on the concept of predictive information for a person with disabilities in a smart home environment concept where artificial intelligence (AI) and machine learning (ML) systems use data on the user’s preferences, habits, and possible incident situations. A conceptual mathematical model is proposed, the purpose of which is to provide predictive user information from defined data sets. This paper defines the taxonomy of communication technologies, devices, and sensors in the environment of the user’s smart home and shows the interaction of all elements in the environment of the smart home. Through the integration of assistive technologies, it is possible to adapt the home to users with diverse types of disabilities and needs. The smart home environment with diverse types of sensors whose data are part of sets defined by a mathematical model is also evaluated. The significance of establishing data sets as a foundation for future research, the development of ML models, and the utilization of AI is highlighted in this paper.

Research on smartphone lifespan is of high interest nowadays due to the growing number of smartphone users and the environmental impact associated with device turnover. Although the concept of smartphone lifespan varies in the literature, research defines the in-use lifespan as the period during which the user finds the device useful, i.e., until it becomes obsolete. Smartphone obsolescence is primarily influenced by both technological and psychological factors, making them the key determinants of the device’s lifespan. For this reason, it is essential to dedicate more research efforts to understanding smartphone lifespan and to developing clear guidelines and policies that can shift users’ perceptions, thereby improving lifespan estimation and encouraging prolonged use. This review synthesizes EU regulations and scientific literature, gathering comprehensive knowledge on key segments and events that affect the in-use lifespan of smartphones. Based on the data, several statistics were generated to provide a better understanding of the term “lifespan,” its multiple estimations, obsolescence issues, and factors affecting the estimated length of the in-use lifespan. Additionally, the research material was used to design a smartphone lifecycle model within the business process management software ARIS and to identify the End of Life (EoL) phase accordingly.

Climate change is an urgent issue, and the current mindset of private ownership, particularly of private vehicles, needs to shift. Shared mobility is rapidly emerging as a key part of the solution to contemporary transportation challenges, driven by technological advancements and the growing demand for more sustainable travel options. This paper provides a comprehensive analysis of shared mobility operational models in Europe, focusing on carsharing and its current research on fleet optimization, bikesharing, and scooter sharing. The study draws on three scientific literature databases, with searches centered on keywords relevant to Shared Mobility. This study contributes to the literature by defining each Shared Mobility modality and examining the different operational models.