Abstract
Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, and applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited applications such as motor rehabilitation or communication. This paper aims to offer a comprehensive review of recent electroencephalogram (EEG)-based BCI applications in the medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, and emotion recognition. Moreover, the current challenges and future trends of BCIs were also discussed, including personal privacy and ethical concerns, network security vulnerabilities, safety issues, and biocompatibility.

Abstract

Abstract
Information Systems (IS) projects are critical for organizational development, both in the private and public sectors. The relevance and complexity inherent in this type of project require management to be fully aware of the factors that influence success. This study contributes to the literature on public-sector IS project management by providing a comprehensive set of Success Factors (SFs) for different levels of the public administration. The research method comprised a literature review, six case studies of central government, local government, and other types of administration, and a questionnaire-based survey of public sector IS experts. Forty-four SFs were identified, described, and organized in nine categories: organization and environment; strategy; project; scope; project manager and project team; stakeholders; vendors; clients and users; and monitoring and control. Our results add a new perspective to the theoretical body of knowledge on the SFs for IS projects in the public sector. © 2025 Copyright held by the owner/author(s).

Abstract

Abstract
Digital Transformation Models (DTM) and Digital Maturity Models (DMM) are two artefacts that guide the planning and implementation of Digital Transformation (DT) initiatives. When used in a combined approach, a DTM-DMM pairing could support DT managers and practitioners, as DTs are holistic and complex initiatives with high failure rates. However, no study has yet systematically addressed the compatibility amongst artefacts. This paper, therefore, aims to analyse the compatibility between academic DTMs and DMMs. Based on architectural compatibility and conceptual similarity, we provide a structured and replicable mixed methods approach to assessing artefact compatibility. To achieve this, we start with a systematic literature review to identify existing academic DTMs and DMMs, analyse the models and group them according to their scope. After, we employ quantitative similarity analysis techniques (Term Frequency-Inverse Document Frequency and Bidirectional Encoder Representations from Transformers combined with Cosine Similarity) and perform a qualitative compatibility analysis to establish ground truth. Based on this analysis, we apply the Receiver Operating Characteristic Curve technique to define threshold values for compatibility assessment. The threshold values were used to suggest compatible DTM-DMM pairings, resulting in nine DTM-DMM binomials for Small and Medium-sized Enterprises. The findings support managers and practitioners in selecting DTM-DMM pairs to guide DT initiatives while offering academics a mixed-methods approach based on the similarity analysis field to evaluate artefact compatibility systematically. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

Abstract
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions. The ''Greening AI with Software Engineering'' workshop,1 funded by the Centre Europ´een de Calcul Atomique et Mol´eculaire (CECAM) and the Lorentz Center, provided an interdisciplinary forum for 29 participants, from practitioners to academics, to share knowledge, ideas, practices, and current results dedicated to advancing green software and AI research. The workshop was held February 3-7, 2025, in Lausanne, Switzerland. Through keynotes, flash talks, and collaborative discussions, participants identified and prioritized key challenges for the field. These included energy assessment and standardization, benchmarking practices, sustainability-aware architectures, runtime adaptation, empirical methodologies, and education. This report presents a research agenda emerging from the workshop, outlining open research directions and practical recommendations to guide the development of environmentally sustainable AI-enabled systems rooted in software engineering principles.