Abstract
The International Symposium on Applied Reconfigurable Computing (ARC) is an annual forum for the discussion and dissemination of research, notably applying the Reconfigurable Computing (RC) concept to real-world problems. The first edition of ARC took place in 2005, and in 2024, ARC celebrated its 20th edition. During those 20 years, the field of reconfigurable computing saw a tremendous growth in its underlying technology. ARC contributed very significantly to the presentation and dissemination of new ideas, innovative applications, and fruitful discussions, all of which have resulted in the shaping of novel lines of research. Here, we present selected papers from the first 20 years of ARC, that we believe represent the corpus of work and reflect the ARC spirit by covering a broad spectrum of RC applications, benchmarks, tools, and architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper examines the role of digital twins (DTs) in promoting sustainability within seaport operations and logistics. DTs have emerged as promising tools for enhancing seaport performance. Despite the recognized potential of DTs in seaports, there is a paucity of research on their practical implementation and impact on seaport sustainability. Through a systematic literature review, this study seeks to elucidate how DTs contribute to the sustainability of seaports and to identify future research and practical applications. We reviewed and categorized 68 conceptual and practical digital applications into ten core areas that effectively support economic, social, and environmental objectives in seaports. Furthermore, this paper proposes five preliminary potential applications for DTs where practical implementations are currently lacking. The primary findings indicate that DTs can enhance seaport sustainability by facilitating real-time monitoring and decision-making, improving safety and security, optimizing resource utilization, enhancing collaboration and communication, and supporting the development of the seaport ecosystem. Additionally, this study addresses the challenges associated with DT implementation, including high costs, conflicting stakeholder priorities, data quality and availability, and model validation. The paper concludes with a discussion of the implications for seaport managers and policymakers. © The Author(s) 2024.

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PurposeAfrican ports play a vital role in the continent’s economy and international trade. While African ports are essential for the competitiveness of African countries, their low level of digitalization presents significant challenges. This study aims to explore how digitalization initiatives implemented by African countries since 2018 are contributing to addressing the sustainable development goals (SDGs).Design/methodology/approachA qualitative analysis approach is adopted by exploring 19 case studies identified by the World Ports Sustainability Program. It explores how African ports have contributed to addressing the 17 SDGs, the positioning of these initiatives according to sociotechnical systems theory and the role these initiatives can play in reducing asymmetries in performance between African countries.FindingsThe results indicate that the most strongly addressed SDGs are 8, 9 and 17. The technical dimension stands out as the main objective of these projects to the detriment of the social and organizational components. Finally, the findings reveal that these initiatives have not significantly reduced performance gaps between African countries.Originality/valueThis study explores the under-researched nexus between digitalization and sustainable development. It uniquely contextualizes digital initiatives within the SDGs. The value lies in its potential to guide policymakers, port operators and stakeholders in leveraging digital transformation. Moreover, the relevance of this study is amplified as Africa seeks to integrate more fully into the global trade system while addressing pressing challenges related to resource management, sustainable development and socio-economic disparities.

Abstract
Due to the high variability in Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs), hidden stratification, and batch effects, generalizing beyond the training distribution is one of the main challenges in Deep Learning (DL) for Computational Pathology (CPath). But although DL depends on large volumes of diverse and annotated data, it is common to have a significant number of annotated samples from one or multiple source distributions, and another partially annotated or unlabeled dataset representing a target distribution for which we want to generalize, the so-called Domain Adaptation (DA). In this work, we focus on the task of generalizing from a single source distribution to a target domain. As it is still not clear which domain adaptation strategy is best suited for CPath, we evaluate three different DA strategies, namely FixMatch, CycleGAN, and a self-supervised feature extractor, and show that DA is still a challenge in CPath.