Egyptian Initiative for Personal Rights

Archaeological research conducted as part of the PCR and TerriFer project in the Bourgogne-Franche-Comté region has identified Puisaye as a major center of primary iron production, active from the Iron Age through to the end of the Middle Ages. A total of 110 slag heaps were investigated and sampled through surface surveys, excavations and waste collection. The bloomery slags were classified based on their morphology, chemical composition (by ICP-MS-AES), and mineralogical (by optical microscopy, SEM-EDS and XRD) characteristics. These data were cross-referenced with 120 radiocarbon dates, allowing us to associate specific slag types with distinct periods of activity and to track technological changes over time. For the early periods, changes are mostly characterized by an evolution of the architecture of the furnaces. The late Iron Age and Antiquity are periods of experimentation with new practices, and a time of major innovation in the way that techniques were conducted. For the Middle Ages, a new uniformity in the smelting production is documented. This makes Puisaye a key region for studying the long-term evolution of iron smelting techniques in Western Europe.

Abstract As Brain-Machine Interfaces (BMIs) advance toward clinical integration, ensuring cognitive stability under uncertainty has become a foundational requirement. This work departs from conventional decoder-centric paradigms by introducing a cognitively introspective architecture that reasons through its own instability.This study introduces a modular neuro-symbolic inference engine designed to anticipate internal divergences in user intent—referred to as cognitive faults—before they manifest as behavioral errors.The system comprises six interpretable modules, each dedicated to resolving distinct dimensions of epistemic conflict, from hypothesis generation and contradiction detection to arbitration and controlled reset. These modules operate under a dynamic meta-inference layer capable of introspecting symbolic traces and triggering adaptive reconfiguration.Evaluations conducted across three high-fidelity simulated BMI contexts—motor imagery decoding, attentional drift, and inhibitory control failure—demonstrated robust early fault detection, achieving sub-30 ms intervention latency and 92.4% accuracy under rare-event conditions.Beyond detection, the framework supports symbolic traceability and schema-based introspection, ensuring interpretability and system accountability. Its architecture is implementation-ready for neuromorphic substrates and aligns with emerging cognitive safety standards. Rather than post hoc explainability, this model embeds real-time epistemic transparency as a structural principle.This approach offers a viable pathway for embedding self-corrective inference mechanisms in BMI systems, contributing to the development of cognitively aligned and resilient human–machine interaction.

This paper provides guidance for building and maintaining infrastructure for participatory AI efforts by sharing reflections on building World Wide Dishes (WWD), a bottom-up, community-led image and text dataset of culinary dishes and associated cultural customs. We present WWD as an example of participatory dataset creation, where community members both guide the design of the research process and contribute to the crowdsourced dataset. This approach incorporates localised expertise and knowledge to address the limitations of web-scraped Internet datasets acknowledged in the Participatory AI discourse. We show that our approach can result in curated, high-quality data that supports decentralised contributions from communities that do not typically contribute to datasets due to a variety of systemic factors. Our project demonstrates the importance of participatory mediators in supporting community engagement by identifying the kinds of labour they performed to make WWD possible. We surface three dimensions of labour performed by participatory mediators that are crucial for participatory dataset construction: building trust with community members, making participation accessible, and contextualising community values to support meaningful data collection. Drawing on our findings, we put forth five lessons for building infrastructure to support future participatory AI efforts.

This paper investigates the uses of the concept of the habitable zone (HZ) in recent exoplanetary research. A corpus of articles (n=24) published between 2022 and 2024 was constructed, and each text was analyzed with regard to its mobilization of the

The article offers a new approach to constructing modality in a literary text as a semiosis of the individual author’s view from the perspective of intersemiotic translation. This development has spurred a growing interest in exploring the interdisciplinary phenomena of translation, focusing on dichotomies such as language and consciousness, language and thought, and language and culture while incorporating contemporary concepts of natural intelligence and semiotic systems. The anthropocentric approach to studying linguistic phenomena highlights the complexity of modality, a subject of ongoing debate regarding its ontological essence and interdisciplinary nature. Based on linguo-cognitive and anthropocentric paradigms, the subjective factor is essential in utterance construction, requiring a third link – the creator, informant, and speaker. The construction of modality in literary translation is a complex intersemiotic process involving the reproduction of verbal and non-verbal means, adaptation to cultural contexts, and the creation of new modal meanings. This creative process combines verbal and non-verbal elements, adapts them culturally, and generates new meanings, highlighting the translator’s crucial role in intersemiotic activity.

Islamic scholars have made several efforts to translate the Qur’ān into various languages of the world, with English being the most prominent target language. This article uses textual analysis to study translated verses of the Qur’ān and shows that the predominant result of subjectivity analysis was objectivity by a contribution of 63% with a preponderant non-ironic tone. There is a close balance between the topic categories of entity and concept in the sentiment analysis and agreement in different textual elements polarity of 72% unipolarity. Interestingly, the verses with positive polarity outweigh those with negative polarity, with a significant number of verses reflecting non-polar texts. A topic sentiment analysis of the verses showed the prevalence of non-polarity over the positive, negative, neutral, strong positive, then strong negative polarity. Sūrat al-Baqarah holds the greatest polarity count. This investigation paves the way for a series of in-depth analyses for the examination of religious texts. Also, it sheds light on the necessity of the control of the language translation of verses to deliver the same or close impression to that of the original religious text. Refinement of the text analysis algorithm may be required in the quality control of the translation. Nevertheless, the current study provides direct evidence of the future ability of this type of program to derive conclusions from religious texts in an acceptable manner.

The study focuses on applying advanced machine learning approaches, namely deep learning models and ensemble learning, to develop an automated system for Cassava Brown Streak Disease (CBSD) detection using robotic systems. We leverage an extensively used dataset to evaluate our proposed methodologies. The dataset comprises thousands of labeled images illustrating various stages of CBSD infection. Our experiment is furnished with high-resolution cameras and sophisticated image-processing algorithms. We finetune AmoebaNet and ResNeXt-101 32x16d models using the dataset to distinguish between healthy and diseased cassava plants. Ensemble learning is then applied to consolidate the prediction outputs from both models, consequently enhancing overall diagnostic accuracy. The implemented system showcases exceptional performance, delivering high precision and recall rates in recognizing CBSD cases. Through automation, our solution significantly diminishes the reliance on human expertise, streamlines the diagnostic procedure, and extends the reach of CBSD detection applications. This pioneering research marks a significant stride forward in fortifying food security and promoting sustainable agriculture in regions affected by CBSD. Further details about the dataset usage will be discussed in the Methodology section.

Defining inequality as the social, economic and political challenges of our time, this book will examine SDG10 to look ahead at how policy action might engage multiple stakeholders, involve diverse sectors and address gaps between policy and implementation to tackle key inequalities within and among countries.

Depuis mai 2009 la FAO met en œuvre un projet régional financé par la Belgique intitulé « Gestion des connaissances et Genre : Capitalisation des bonnes pratiques en appui à la production agricole et à la sécurité alimentaire » (http://www.capitalisation-bp.net). Pour construire une démarche de capitalisation adaptée aux organisations d'appui au monde rural nigérien et burkinabè, quoi de plus normal que de se référer à ceux et celles qui ont déjà de l'expérience et des résultats dans ce domaine ! Voici donc une bibliographie commentée sur la capitalisation d'expériences appliquée au développement international en général, et au développement rural et agricole en particulier. La liste se focalise sur l'approche francophone, elle est donc loin d'être exhaustive. Elle contient une sélection de références pour comprendre la démarche et comment elle est mise en pratique. De l'article au livre en passant par la vidéo, les documents commentés sont tous disponibles intégralement et gratuitement en téléchargement sur internet.