Abstract

Abstract
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
Optical fibers have been extensively applied in optical sensing platforms for their large bandwidth, stability, light weight and accessibility. This work presents a theoretical analysis of an optical fiber surface plasmon resonance system for refractometric sensing applications. The device consists of a multilayer hyperbolic metamaterial (HMM) composed of concentric Au/TiO2 alternate layers in optical fiber matrix. HMMs exhibit hyperbolic dispersion (HD) and the interaction of different plasmonic modes at each interface of the HMM is reported to enhance light-matter coupling, leading to an increased refractometric sensitivity. The HD and its effects on sensor performance are numerically investigated by effective medium theory (EMT) and backed by the exact transfer matrix method (TMM). The maximum sensor performance was attained for a configuration with 2 bilayers with 30 nm thickness for a metal fill fraction (rho) of 0.7, achieving a figure of merit (FOM) of 18.45. A direct comparison with a plasmonic Au optical fiber sensor returned an optimized FOM of 5.74, therefore achieving over a three-fold increase in sensor performance, assessing the potential of HMM as highly refractometric sensitive platforms.

Abstract

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.

Abstract
Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match both identities, allowing serious security breaches. In this work, we apply a frequency-based explanation method from the area of explainable face recognition to shine a light on how FR models behave when processing a bona fide or attack pair from a frequency perspective. In extensive experiments, we used two different state-of-the-art FR models and six different morphing attacks to investigate possible differences in behavior. Our results show that FR models rely differently on different frequency bands when making decisions for bona fide pairs and morphing attacks. In the following step, we show that this behavioral difference can be used to detect morphing attacks in an unsupervised setup solely based on the observed frequency-importance differences in a generalizable manner.