Abstract
With the advent of Information and Communication Technologies in recent decades, organizations face several challenges today. Adopting Digital Transformation (DT) offers numerous opportunities for Small and Medium Enterprises (SMEs) to improve their efficiency and operations, reaching new markets, shareholders, and customers. However, there are potential risks associated with this process. With Digital Transformation, the radius of connectivity and interconnection between devices and systems increases in Mozambique and worldwide, creating more significant space cyberattacks. As Small and Medium-sized Enterprises (SMEs) connect to the digital world and move forward with adopting innovative digital technologies, they become more vulnerable to digital security risks. Hence, managing digital security risks effectively is crucial to realising the benefits of Digital Transformation. This position paper proposes to present the research work that will culminate in the proposal to develop a framework that fits Mozambican Small and Medium Enterprises through a Design Science Research (DSR) methodology, which can help to assist Mozambican Small and Medium Enterprises in the Digital Transformation (DT) process.

Abstract
Continuous monitoring of hydrogen (H2) concentration is critical for safer use, which can be done using optical sensors. Palladium (Pd) is the most commonly used transducer material for this monitoring. This material absorbs H2 leading to an isotropic expansion. This process is reversible but is affected by the interaction with interferents, and the lifetime of Pd thin films is a recurring issue. Fiber Bragg Grating (FBG) sensors are used to follow the strain induced by H2 on Pd thin films. In this work, it is studied the stability of Pd-coated FBGs, protected with a thin Polytetrafluoroethylene (PTFE) layer, 10 years after their deposition to assess their viability to be used as H2 sensors for long periods of time. It was found that Pd coatings that were PTFE-protected after deposition had a longer lifetime than unprotected films, with the same sensitivities that they had immediately after their deposition, namely 23 and 10 pm/vol% for the sensors with 150 and 100 nm of Pd, respectively, and a saturation point around 2 kPa. Furthermore, the Pd expansion was analyzed in the presence of H2, nitrogen (N2), carbon dioxide (CO2), methane (CH4) and water vapor (H2O), finding that H2O is the main interferent. Finally, an exhaustive test for 90 h is also done to analyze the long-term stability of Pd films in dry and humid environments, with only the protected sensor maintaining the long-term response. As a result, this study emphasizes the importance of using protective polymeric layers in Pd films to achieve the five-year lifetime required for a real H2 monitoring application.

Abstract
Collaboration in extended reality (XR) environments presents complex challenges that revolve around how users perceive the presence, intentions, and actions of their collaborators. This paper delves into the intricate realm of group awareness, focusing specifically on workspace awareness and the innovative visual cues designed to enhance user comprehension. The research begins by identifying a spectrum of collaborative situations drawn from an analysis of XR prototypes in the existing literature. Then, we describe and introduce a novel classification for workspace awareness, along with an exploration of visual cues recently employed in research endeavors. Lastly, we present the key findings and shine a spotlight on promising yet unexplored topics. This work not only serves as a reference for experienced researchers seeking to inform the design of their own collaborative XR applications but also extends a welcoming hand to newcomers in this dynamic field.

Abstract

Abstract
The early and accurate detection and the grading characterization of brain cancer will generate a positive impact on the treatment plan of those patients. AI-based models can help analyze the Magnetic Resonance Imaging (MRI) to make an initial assessment of the tumor grading. The objective of this work was to develop an Al-based model to classify the grading of the tumor using the MRI. Two regions of interest were explored, with several levels of complexity for the neural network architecture, and Iwo strategies to deal with Unbalanced data. The best results were obtained for the most complex architecture (Resnet50) with a combination of weighted random sampler and data augmentation achieving a balanced accuracy of 62.26%. This work confirmed that complex problems required a more dense neural network and strategies to deal with the unbalanced data.

Abstract
With the proliferation of ODR service providers, there is a critical necessity to establish mechanisms supporting their functioning, particularly while designing ODR processes. This article aims to examine the impact of process modelling using BPMN, and of its relevance in the integration of AI into ODR processes within the EU. BPMN allows a meticulous depiction of all the ODR process steps, stakeholders, and underlying data in structured formats that are readable and interpretable by both humans and AI, which enables its integration. The advantages include predictive analysis, identification of opportunities for continuous improvement, operational efficiency, cost and time reduction, and enhanced accessibility for self-represented litigants. Additionally, the transparency afforded by explicitly incorporating AI in BPMN notation fosters a clearer comprehension of processes, facilitating management and informed decision-making. Nevertheless, it remains imperative to address ethical concerns such as algorithmic bias, fairness, and privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.