Stopping of energetic sulfur and bromine ions in dense hydrogen plasma

The concepts of communicative space, media sphere and public sphere are sometimes used like synonyms one of the other. However, according to us, they are three different concepts: public sphere and media sphere are two distinct spaces symbolic systems which, both, are anchored in communicative spac

INVESTIGATION OF THE TRANSMISSION AND STOPPING OF LIGHT IONS PASSING THROUGH A PLASMA TARGET

Transmission and energy losses of 2 MeV/u Carbon and Sulphur beams passing through a plasma target, have been extensively investigated. A hydrogen plasma ignited by an electrical discharge was coupled to the Orsay Tandem beam accelerator. Fluctuations in beam transmission have been observed and attributed to a magnetic focusing effect generated during the plasma evolution. Energy loss measurements were performed on the basis of time of flight techniques and indicate an enhanced stopping power of the plasma relative to its cold matter equivalent

A deep‐learning framework for enhancing habitat identification based on species composition

Aims The accurate classification of habitats is essential for effective biodiversity conservation. The goal of this study was to harness the potential of deep learning to advance habitat identification in Europe. We aimed to develop and evaluate models capable of assigning vegetation-plot records to the habitats of the European Nature Information System (EUNIS), a widely used reference framework for European habitat types. Location The framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus). Methods We leveraged deep-learning techniques, such as transformers (i.e., models with attention components able to learn contextual relations between categorical and numerical features) that we trained using spatial k-fold cross-validation (CV) on vegetation plots sourced from the European Vegetation Archive (EVA), to show that they have great potential for classifying vegetation-plot records. We tested different network architectures, feature encodings, hyperparameter tuning and noise addition strategies to identify the optimal model. We used an independent test set from the National Plant Monitoring Scheme (NPMS) to evaluate its performance and compare its results against the traditional expert systems. Results Exploration of the use of deep learning applied to species composition and plot-location criteria for habitat classification led to the development of a framework containing a wide range of models. Our selected algorithm, applied to European habitat types, significantly improved habitat classification accuracy, achieving a more than twofold improvement compared to the previous state-of-the-art (SOTA) method on an external data set, clearly outperforming expert systems. The framework is shared and maintained through a GitHub repository. Conclusions Our results demonstrate the potential benefits of the adoption of deep learning for improving the accuracy of vegetation classification. They highlight the importance of incorporating advanced technologies into habitat monitoring. These algorithms have shown to be better suited for habitat type prediction than expert systems. They push the accuracy score on a database containing hundreds of thousands of standardized presence/absence European surveys to 88.74%, as assessed by expert judgment. Finally, our results showcase that species dominance is a strong marker of ecosystems and that the exact cover abundance of the flora is not required to train neural networks with predictive performances. The framework we developed can be used by researchers and practitioners to accurately classify habitats

Stopping of multicharged ions in dense and fully ionized hydrogen

This communication aims to clarify what is information seen as an activity in the workplace, through an object : the document. More specifically it aims to address what is at stake with editorial processes within organizations and what do these processes reveal or organizational and managerial changes