Abstract
Introduction: Surveys are a key method for collecting data, used in contexts ranging from national censuses to customer satisfaction assessments. Despite their clear utility, recent literature highlights their inaccessibility to people with disabilities. Objective: This paper outlines the development of a survey completion tool accessible to people with disabilities. Methodology: Key steps included identifying requirements for designing an accessible survey completion tool, implementing them, testing the tool with people with disabilities, and refning it based on identifed issues and suggested changes. Results: Overall, the tool was found to be accessible to people with diverse disabilities.

Abstract
Digitalisation is mandatory for today’s companies. Living in the Era of Industry 4.0, the phenomenon of digital transformation cannot be ignored. Intending to support manufacturing companies in their digitalisation processes, the present paper reflects the work that has been carried on, to support the digital transition for manufacturing companies in the region of Tâmega e Sousa. This region is considered to be an industrial region located in the North of Portugal, but lagging in terms of digital technology adoption. In a theoretical framework, it is expected to identify the most relevant factors to promote a successful digital strategy. Supported by a Science Design methodology, a platform was developed to support the measurement of the maturity or digital companies’ readiness levels. To collect the necessary data were performed questionnaires. First, in a face-to-face approach and later through the platform developed. The (preliminary) results are based on a sample of 53 companies (pilot test). From this data, it was possible to identify some trends: 1) some behaviours indicate that the region is still in the digitisation phase; 2) the digitisation focus is in the processes dimension; 3) even performing a digital transition, companies do not invest in in-house IT solutions. Copyright © 2025 Inderscience Enterprises Ltd.

Abstract

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.