Abstract
This research aims to leverage digital drawing as a non-verbal language to transcend the communication barriers faced by individuals with partial to complete locked-in syndrome (LIS). It will explore the possibility of using the human body as an interface, through assistive technology, in accordance with its limitation in functionality, to facilitate social reconnection and emotional expression through drawing. This approach is grounded in the understanding that creative expression and communication are fundamental human needs and can significantly impact the well-being and quality of life of individuals with severe motor impairments. This paper will focus on the development of the drawing activities. These activities will run under a mixed reality set that can be tailored by caregivers or therapists to the end-user's needs and preferences, ensuring functionality and user satisfaction through an accessible, enriching, and emotionally rewarding experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation.

Abstract
The use of Convolutional Neural Network (CNN) models for image classification tasks has gained significant popularity. However, the lack of interpretability in CNN models poses challenges for debugging and validation. To address this issue, various explanation methods have been developed to provide insights into CNN models. This paper focuses on the validity of these explanation methods for ordinal regression tasks, where the classes have a predefined order relationship. Different modifications are proposed for two explanation methods to exploit the ordinal relationships between classes: Grad-CAM based on Ordinal Binary Decomposition (GradOBDCAM) and Ordinal Information Bottleneck Analysis (OIBA). The performance of these modified methods is compared to existing popular alternatives. Experimental results demonstrate that GradOBD-CAM outperforms other methods in terms of interpretability for three out of four datasets, while OIBA achieves superior performance compared to IBA.

Abstract
Here we define a new unification algorithm for terms interpreted in semantic domains denoted by a subclass of regular types here called deterministic regular types. This reflects our intention not to handle the semantic universe as a homogeneous collection of values, but instead, to partition it in a way that is similar to data types in programming languages. We first define the new unification algorithm which is based on constraint generation and constraint solving, and then prove its main properties: termination, soundness, and completeness with respect to the semantics. Finally, we discuss how to apply this algorithm to a dynamically typed version of Prolog.

Abstract

Abstract
The transition to circular business models poses significant challenges, particularly for Small and Medium Enterprises (SMEs). These challenges arise from different perspectives. Strategic alignment and technological barriers are just two of them. This paper aims to explore how Industry 4.0 technologies and Enterprise Architectures can facilitate the implementation of circular business models. By analyzing their role in overcoming key obstacles, the study explores the potential of these technologies in driving sustainable business transformation. The findings indicate that while integrating circularity into business practices remains complex, Enterprise Architectures, through the adoption of Industry 4.0 technologies, can mitigate some barriers. Ultimately, the synergy between technological innovation and circular business models can accelerate the shift towards sustainability.