Geophysics plays an important role in mineral exploration, offering valuable support for reducing drilling risks, guiding target definition, and improving understanding of orebody geometry. This work presents two case studies involving the use of ground geophysical methods: one aimed at characterizing a colluvial deposit in an inactive iron mine, and another focused on estimating the thickness of an iron-rich layer in a tailing reservoir. In both cases, the materials studied have high iron content, representing opportunities to increase production without the need to develop new mining fronts.
Un spécimen mort de Lamia textor (Linnaeus, 1758) a été découvert en mai 2025 sur la commune de Saint-Marcellin-Forez dans le département de la Loire. Il s’agit de la première mention de l’espèce dans ce département. Le contexte de l’observation est donné au regard de l’écologie connue de l’espèce et de son intérêt patrimonial en région Auvergne Rhône-Alpes.
The early stages of product planning and concepting in advanced engineering domains are often hampered by high uncertainty, fragmented decision-making, and unstructured data. Traditional planning methodologies routinely lead to misalignment, inefficient risk assessments, and suboptimal product strategies. To address these challenges, we propose an AI-agentic decision intelligence (DI) framework that leverages Large Language Models (LLMs) to enhance decision-making in product planning and concept development. The proposed framework uses the transformative natural language processing capabilities and comprehensive knowledge of LLMs to capture and refine stakeholder intent, improve stakeholder engagement, and optimize workflow orchestration. Implementation of the framework is facilitated by state-of-the-art and rapidly evolving open-source tools, ensuring scalability and readiness for corporate environments. By enhancing decision confidence, adaptability, and automation, the framework provides a valuable platform for both defense and commercial product development environments.
INTRODUCTION:Friedreich ataxia (FA) is the most common hereditary ataxia in Europe, characterised by progressively worsening movement and speech impairments with a typical onset before the age of 25 years. The symptoms affect the patients' health-related quality of life (HRQoL) and psychosocial health. FA leads to an increasing need for care, associated with an economic burden. Little is known about the impact of FA on daily lives and HRQoL. To fill that gap, we will assess patient-reported, psychosocial and economic outcomes using momentary data assessment via a mobile health application (app).METHODS AND ANALYSIS:The PROFA Study is a prospective observational study. Patients with FA (n=200) will be recruited at six European study centres (Germany, France and Austria). We will interview patients at baseline in the study centre and subsequently assess the patients' health at home via mobile health app. Patients will self-report ataxia severity, HRQoL, speech and hearing disabilities, coping strategies and well-being, health services usage, adverse health events and productivity losses due to informal care on a daily to monthly basis on the app for 6 months. Our study aims to (1) validate measurements of HRQoL and psychosocial health, (2) assess the usability of the mobile health app, and (3) use descriptive and multivariate statistics to analyse patient-reported and economic outcomes and the interaction effects between these outcomes. Insights into the app's usability could be used for future studies using momentary data assessments to measure outcomes of patients with FA.ETHICS AND DISSEMINATION:Ethical approval has been obtained from the Ethics Committee of the University Medicine of Greifswald, (BB096/22a, 26 October 2022) and from all local ethics committees of the participating study sites. Findings of the study will be published in peer-reviewed journals, presented at relevant international/national congresses and disseminated to German and French Patient Advocacy Organizations.TRIAL REGISTRATION NUMBER:ClinicalTrials.gov Registry (NCT05943002); Pre-results.
