Hasil untuk "History of Africa"

Professor Dr. Friedrich Ruttner

J. Tunbridge, G. Ashworth

J. Dewey, W. C. Pitman, W. Ryan et al.

Eric Hobsbawm, Terence Ranger

R. Morley

Alexander Thomson

List of Tables Acknowledgements 1. Introduction: State, civil society and external interests 2. History: Africa's pre-colonial and colonial inheritance 3. Ideology: Nationalism, socialism, populism and state capitalism 4. Ethnicity: Ethnic groups, 'tribes' and political identity 5. Social Class: The search for class politics in Africa 6. Legitimacy: Neo-patrimonialism, personal rule and the centralisation of the African state 7. Coercion: Military intervention in African politics 8. Sovereignty: External influence on African politics 9. Sovereignty Again: Neo-colonialism, structural adjustment and Africa's political economy 10. Authority: The crises of accumulation, governance and state collapse 11. Democracy: Re-legitimising the African state? 12. Conclusions: State and civil society in post-colonial Africa

Wenyin Loh, M. Tang

There is a lack of high-quality evidence based on the gold standard of oral food challenges to determine food allergy prevalence. Nevertheless, studies using surrogate measures of food allergy, such as health service utilization and clinical history, together with allergen-specific immunoglobulin E (sIgE), provide compelling data that the prevalence of food allergy is increasing in both Western and developing countries. In Western countries, challenge-diagnosed food allergy has been reported to be as high as 10%, with the greatest prevalence noted among younger children. There is also growing evidence of increasing prevalence in developing countries, with rates of challenge-diagnosed food allergy in China and Africa reported to be similar to that in Western countries. An interesting observation is that children of East Asian or African descent born in a Western environment are at higher risk of food allergy compared to Caucasian children; this intriguing finding emphasizes the importance of genome-environment interactions and forecasts future increases in food allergy in Asia and Africa as economic growth continues in these regions. While cow’s milk and egg allergy are two of the most common food allergies in most countries, diverse patterns of food allergy can be observed in individual geographic regions determined by each country’s feeding patterns. More robust studies investigating food allergy prevalence, particularly in Asia and the developing world, are necessary to understand the extent of the food allergy problem and identify preventive strategies to cope with the potential increase in these regions.

Soheil Zorofchian Moghadamtousi, Mehran Fadaeinasab, Sonia Nikzad et al.

Annona muricata is a member of the Annonaceae family and is a fruit tree with a long history of traditional use. A. muricata, also known as soursop, graviola and guanabana, is an evergreen plant that is mostly distributed in tropical and subtropical regions of the world. The fruits of A. muricata are extensively used to prepare syrups, candies, beverages, ice creams and shakes. A wide array of ethnomedicinal activities is contributed to different parts of A. muricata, and indigenous communities in Africa and South America extensively use this plant in their folk medicine. Numerous investigations have substantiated these activities, including anticancer, anticonvulsant, anti-arthritic, antiparasitic, antimalarial, hepatoprotective and antidiabetic activities. Phytochemical studies reveal that annonaceous acetogenins are the major constituents of A. muricata. More than 100 annonaceous acetogenins have been isolated from leaves, barks, seeds, roots and fruits of A. muricata. In view of the immense studies on A. muricata, this review strives to unite available information regarding its phytochemistry, traditional uses and biological activities.

R. Cowling, D. Richardson, S. M. Pierce

P. Curtin

Historians have begun to show a new interest in the slave trade. Recent developments in historical demographv, economic history, and the history of Africa have solved some of the old problems and posed new ones. The mere passage of time makes it possible to go beyond the largely humanitarian concerns of the nineteenth-century writers, concerns that arose out of the great debate over slavery as a question of policy. We can now accept the trade as an evil and move on to the problem of why and how it took place for so many centuries and on such a scale. The recent trend toward a world-historical perspective and away from parochial national history also calls for a new approach to the broad patterns of Atlantic history. Social and economic development on the tropical shores of the Atlantic was a single p. -cess, regardless of the theoretically self-contained empires of mercantilist Europe. From the late sixteenth century to the early nineteenth, the central institution was the plantation, located in tropical America, worked by slave labor from tropical Africa, but directed by Europeans and producing tropical staples for European consumption. The broader patterns of society and economy were much the same in all the plantation colonies, regardless of metropolitan control. These patterns were not only different from those of Europe; they were also different from those of European settlements in temperate North America, the Indian Ocean trading

A. Leaché, M. Fujita

Toufiq Musah, Chantelle Amoako-Atta, John Amankwaah Otu et al.

Brain tumors are among the deadliest cancers worldwide, with particularly devastating impact in Sub-Saharan Africa (SSA) where limited access to medical imaging infrastructure and expertise often delays diagnosis and treatment planning. Accurate brain tumor segmentation is crucial for treatment planning, surgical guidance, and monitoring disease progression, yet manual segmentation is time-consuming and subject to inter-observer variability. Recent advances in deep learning, based on Convolutional Neural Networks (CNNs) and Transformers have demonstrated significant potential in automating this critical task. This study evaluates three state-of-the-art architectures, SwinUNETR-v2, nnUNet, and MedNeXt for automated brain tumor segmentation in multi-parametric Magnetic Resonance Imaging (MRI) scans. We trained our models on the BraTS-Africa 2024 and BraTS2021 datasets, and performed validation on the BraTS-Africa 2024 validation set. We observed that training on a mixed dataset (BraTS-Africa 2024 and BraTS2021) did not yield improved performance on the SSA validation set in all tumor regions compared to training solely on SSA data with well-validated methods. Ensembling predictions from different models also lead to notable performance increases. Our best-performing model, a finetuned MedNeXt, achieved an average lesion-wise Dice score of 0.84, with individual scores of 0.81 (enhancing tumor), 0.81 (tumor core), and 0.91 (whole tumor). While further improvements are expected with extended training and larger datasets, these results demonstrate the feasibility of deploying deep learning for reliable tumor segmentation in resource-limited settings. We further highlight the need to improve local data acquisition protocols to support the development of clinically relevant, region-specific AI tools.

Pierfrancesco Siena, Pasquale Claudio Africa, Michele Girfoglio et al.

Cardiovascular diseases are a leading cause of death in the world, driving the development of patient-specific and benchmark models for blood flow analysis. This chapter provides a theoretical overview of the main categories of Reduced Order Models (ROMs), focusing on both projection-based and data-driven approaches within a classical setup. We then present a hybrid ROM tailored for simulating blood flow in a patient-specific aortic geometry. The proposed methodology integrates projection-based techniques with neural network-enhanced data-driven components, incorporating a lifting function strategy to enforce physiologically realistic outflow pressure conditions. This hybrid methodology enables a substantial reduction in computational cost while mantaining high fidelity in reconstructing both velocity and pressure fields. We compare the full- and reduced-order solutions in details and critically assess the advantages and limitations of ROMs in patient-specific cardiovascular modeling.

Pierfrancesco Siena, Pasquale Claudio Africa, Michele Girfoglio et al.

This paper deals with the development of a Reduced-Order Model (ROM) to investigate haemodynamics in cardiovascular applications. It employs the use of Proper Orthogonal Decomposition (POD) for the computation of the basis functions and the Galerkin projection for the computation of the reduced coefficients. The main novelty of this work lies in the extension of the lifting function method, which typically is adopted for treating nonhomogeneous inlet velocity boundary conditions, to the handling of nonhomogeneous outlet boundary conditions for the pressure, representing a very delicate point in the numerical simulations of the cardiovascular system. Moreover, we incorporate a properly trained neural network in the ROM framework to approximate the mapping from the time parameter to the outflow pressure, which in the most general case is not available in closed form. We define our approach as "hybrid", because it merges physics-based elements with data-driven ones. At full order level, a Finite Volume method is employed for the discretization of the unsteady Navier-Stokes equations while a two-element Windkessel model is adopted to enforce a valuable estimation of the outflow pressure. Numerical results, firstly related to a 2D idealized blood vessel and then to a 3D patient-specific aortic arch, demonstrate that our ROM is able to accurately approximate the FOM with a significant reduction in the computational cost.

Ahmed Jaheen, Abdelrahman Elsayed, Damir Kim et al.

Brain cancer affects millions worldwide, and in nearly every clinical setting, doctors rely on magnetic resonance imaging (MRI) to diagnose and monitor gliomas. However, the current standard for tumor quantification through manual segmentation of multi-parametric MRI is time-consuming, requires expert radiologists, and is often infeasible in under-resourced healthcare systems. This problem is especially pronounced in low-income regions, where MRI scanners are of lower quality and radiology expertise is scarce, leading to incorrect segmentation and quantification. In addition, the number of acquired MRI scans in Africa is typically small. To address these challenges, the BraTS-Lighthouse 2025 Challenge focuses on robust tumor segmentation in sub-Saharan Africa (SSA), where resource constraints and image quality degradation introduce significant shifts. In this study, we present EMedNeXt -- an enhanced brain tumor segmentation framework based on MedNeXt V2 with deep supervision and optimized post-processing pipelines tailored for SSA. EMedNeXt introduces three key contributions: a larger region of interest, an improved nnU-Net v2-based architectural skeleton, and a robust model ensembling system. Evaluated on the hidden validation set, our solution achieved an average LesionWise DSC of 0.897 with an average LesionWise NSD of 0.541 and 0.84 at a tolerance of 0.5 mm and 1.0 mm, respectively.

Carina M. Schlebusch, P. Skoglund, P. Sjödin et al.

Giuseppe Alessio D'Inverno, Saeid Moradizadeh, Sajad Salavatidezfouli et al.

The complexity of the cardiovascular system needs to be accurately reproduced in order to promptly acknowledge health conditions; to this aim, advanced multifidelity and multiphysics numerical models are crucial. On one side, Full Order Models (FOMs) deliver accurate hemodynamic assessments, but their high computational demands hinder their real-time clinical application. In contrast, Reduced Order Models (ROMs) provide more efficient yet accurate solutions, essential for personalized healthcare and timely clinical decision-making. In this work, we explore the application of computational fluid dynamics (CFD) in cardiovascular medicine by integrating FOMs with ROMs for predicting the risk of aortic aneurysm growth and rupture. Wall Shear Stress (WSS) and the Oscillatory Shear Index (OSI), sampled at different growth stages of the thoracic aortic aneurysm, are predicted by means of Graph Neural Networks (GNNs). GNNs exploit the natural graph structure of the mesh obtained by the Finite Volume (FV) discretization, taking into account the spatial local information, regardless of the dimension of the input graph. Our experimental validation framework yields promising results, confirming our method as a valid alternative that overcomes the curse of dimensionality.

Safoura Tanbakouei, Rui-Lin Cheng, Binlong Ye et al.

The CM chondrites are characterized as primary accretionary rocks which originate from primitive water-rich asteroids formed during the early Solar System. Here, we study the mineralogy and organic characteristics of right CM and one ungrouped chondrite to better understand their alteration history; Queen Alexandra Range 93005 (QUE 93005), Murchison, LaPaz Icefield 02333 (LAP 02333), Miller Range (MIL 13005), Mackay Glacier 05231 (MCY 05231), Northwest Africa 8534 (NWA 8534), Northwest Africa 3340 (NWA 3340), Yamato 86695 (Y-86695), and the ungrouped carbonaceous chondrite Belgica 7904 (B-7904). Raman spectroscopy has been employed to detect the presence of organic carbon in the samples, specifically through the G band at approximately 1580 cm-1 and D band at around 1350 cm-1. The properties of organic matter in meteorites serve as valuable indicators for characterizing the structure and crystallinity of carbonaceous materials and estimating their thermal metamorphism degree. The R1 parameter, defined as the peak height ratio of the D and G bands, provides a quantifiable measure of this structural organization. Raman spectra are used to show the general mineralogy, thermal history and heating stage of CM and ungrouped chondrites. X-ray diffraction patterns further indicate the mineralogical compositions of the samples. Visible to near-infrared (VNIR) and attenuated total reflection (ATR) reflectance spectra illustrate the trends related to their mineralogy and furthermore infer aqueous alteration, thermal history of CM carbonaceous chondrites, formation and evolution of their parent bodies.

Jamal El-Ouahi

This study explores how scientometric data and indicators are used to transform science systems in a selection of countries in the Middle East and North Africa. I propose that scientometric-based rules inform such transformation. First, the research shows how research managers adopt scientometrics as 'global standards'. I also show how several scientometric data and indicators are adopted following a 'glocalization' process. Finally, I demonstrate how research managers use this data to inform decision-making and policymaking processes. This study contributes to a broader understanding of the usage of scientometric indicators in the context of assessing research institutions and researchers based on their publishing activities. Related to these assessments, I also discuss how such data transforms and adapts local science systems to meet so-called 'global standards'.

Munachimso Blessing Oguine, Chidera Godsfavor Oguine, Kanyifeechukwu Jane Oguine

Recent advancements in technology have necessitated a paradigm shift in the people use technology necessitating a new research field called Human-Machine collaboration. ChatGPT, an Artificial intelligence (AI) assistive technology, has gained mainstream adoption and implementation in academia and industry; however, a lot is left unknown about how this new technology holds for Human-Machine Collaboration in Africa. Our survey paper highlights to answer some of these questions. To understand the effectiveness of ChatGPT on human-machine collaboration we utilized reflexive thematic analysis to analyze (N= 51) articles between 2019 and 2023 obtained from our literature search. Our findings indicate the prevalence of ChatGPT for human-computer interaction within academic sectors such as education, and research; trends also revealed the relatively high effectiveness of ChatGPT in improving human-machine collaboration.