Abstract
Artificial Intelligence (AI) is increasingly recognized as a transformative force with the potential to reshape sustainability practices across sectors, including Higher Education Institutions (HEIs). However, limited empirical research has explored how the academic community within HEIs perceives the role of AI in advancing sustainability. This study explores the potential of AI to support sustainability transformation within HEIs through a structured participatory workshop approach. Focusing on the perspectives of the academic community—students, professors, and researchers—the study engaged participants in collaborative activities aligned with the UNEP Sustainable University Framework’s key dimensions: Teaching and Research, Environment and Climate, People and Society, and Administration and Governance. Data were collected using Mentimeter surveys and creative group outputs, including thematic newsletters envisioning AI applications in 2030. Results show strong participant optimism regarding AI's capacity to enhance efficiency, governance, and educational innovation, while also highlighting ethical and social considerations, such as inequality and the responsible use of AI. Comparisons of participants’ initial and final perceptions reveal a shift from operational concerns, centered on “time,” toward broader strategic thinking that emphasizes “efficiency,” “critical thinking,” and sustainability. The study reflects on the importance of inclusive HEIs’ community engagement, interdisciplinary approaches, and governance frameworks in realizing AI-driven sustainability initiatives in HEIs, while identifying avenues for future research to deepen and expand these insights. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Developing information retrieval (IR) systems that enable access across multiple languages is crucial in multilingual contexts. In Timor-Leste, where Tetun, Portuguese, English, and Indonesian are official and working languages, no cross-lingual information retrieval (CLIR) solutions currently exist to support information access across these languages. This study addresses that gap by investigating CLIR approaches tailored to the linguistic landscape of Timor-Leste. Leveraging an existing monolingual Tetun document collection and ad-hoc text retrieval baselines, we explore the feasibility of CLIR for Tetun. Queries were manually translated into Portuguese, English, and Indonesian to create a multilingual query set. These were then automatically translated back into Tetun using Google Translate and several large language models, and used to retrieve documents in Tetun. Results show that Google Translate is the most reliable tool for Tetun CLIR overall, and the Hiemstra LM consistently outperforms BM25 and DFR BM25 in cross-lingual retrieval performance. However, overall effectiveness remains up to 26.95% points lower than that of the monolingual baseline, underscoring the limitations of current translation tools and the challenges of developing an effective CLIR for Tetun. Despite these challenges, this work establishes the first CLIR baseline for Tetun ad-hoc text retrieval, providing a foundation for future research in this under-resourced setting.

Abstract
Cancer is a leading cause of mortality worldwide, with breast and lung cancer being the most prevalent globally. Early and accurate diagnosis is crucial for successful treatment, and medical imaging techniques play a pivotal role in achieving this. This paper proposes a novel pipeline that leverages generative artificial intelligence to enhance medical images by combining synthetic image generation and super-resolution techniques. The framework is validated in two medical use cases (breast and lung cancers), demonstrating its potential to improve the quality and quantity of medical imaging data, ultimately contributing to more precise and effective cancer diagnosis and treatment. Overall, although some limitations do exist, this paper achieved satisfactory results for an image size which is conductive to specialist analysis, and further expands upon this field's capabilities.

Abstract
An all-passive, multipoint, and multiparameter optical monitoring system was developed and deployed in an industrial environment for the simultaneous measurement of methane concentration and other physical parameters. Methane is detected via rapid wavelength modulation spectroscopy (WMS) at 1648.2 nm and 4 MHz frequency. An attenuation invariant quantity defined by the peaks at 0, 4, and 8 MHz of the fast Fourier transform (FFT) of temporal signal is employed, characterized, and validated. Other parameters can concomitantly be measured by fiber Bragg grating (FBG) sensors operating in the 1520-1590 nm range. In the deployed system, the tested parameter was the temperature, which is an important quantity for gas monitoring. The system features a modular architecture that enables scalability up to 16 384 sensing points with an estimated less than 20-min acquisition cycle. In its current deployment, it monitors methane and temperature at eight locations using a single optical network. The system is intended to be used onshore and offshore platforms where the usual monitoring protocol consists of manual measurements usually performed three to four times a year and involves personal displacement and risky situations. Field tests at an onshore natural gas treatment unit (NGTU) demonstrated reliable performance and effective event detection, including undocumented nocturnal emissions, maneuvers at main shut-off valve, and partial plant shutdowns and restarts.

Abstract

Abstract
This paper presents a narrative review of power electronic converter topologies and architectures for energy conversion in green hydrogen production systems. A technical analysis is made regarding multiple performance metrics including total harmonic distortion (THD), current ripple, power factor, efficiency, and system complexity. The review examines AC/DC conversion topologies, covering both passive rectifiers and active front-end configurations with diverse transformer arrangements and harmonic mitigation techniques. DC/DC converter architectures are also analysed, distinguishing between non-isolated and isolated topologies, with emphasis on their operational principles, performance characteristics and practical suitability for hydrogen electrolyzer integration. Comparative analysis tables quantify key performance metrics between topologies across different power ranges and operational constraints. Additionally, the paper presents emerging technologies and modular converter structures, including current-source rectifiers, multiport converters, and modular multilevel architectures, alongside the transition toward solid-state transformers. Overall, this work provides a holistic review of the complete energy conversion chain from grid interface to electrolyzer, offering valuable insights for decision-support in topology selection in large-scale hydrogen production systems. In contrast with existing reviews, which often focus on individual conversion stages, this review seeks to fill a gap in the literature by summarizing the most relevant and technically mature topologies across the full chain with an evolutionary perspective. © (2026), (Universidade do Porto - Faculdade de Engenharia). All rights reserved.