FID4SA-Repository
Not a member yet
21181 research outputs found
Sort by
Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling
No full textInternational audienceSatellite microwave observations from 1.4 to 36 GHz already showed sensitivity to several geophysical parameters of sea ice such as Sea Ice Concentration (SIC), Sea Ice Thickness (SIT) or snow depth. The main goal of this article is to provide a realistic and comprehensive characterization of the sea ice and its snow cover that explains the microwave observations during a whole year using a radiative transfer model. For this purpose, we construct a unique dataset of passive microwave observations, to mimic the future Copernicus Imaging Microwave Radiometer (CIMR), along with the active microwave scatterometer data (ASCAT). CIMR database is used to classify sea ice microwave signatures in their spectral dimension with a machine learning technique while ASCAT data are used to help interpret the results of the classification. Classification results are then interpreted with a state-of-art sea ice and Snow Microwave Radiative Transfer model (SMRT) for all highlighted signatures and all seasons. Results make it possible to identify the specific behaviors from the observation co-variabilities for SIC, SIT, and snow structure. Our analysis underlined the role of the depth hoar over multi-year ice, for the interpretation of scattering signals in winter. Scattering signals that appear in late summer are explained by the presence of superimposed ice. This characterization will benefit from future advances in SMRT development, as well as the improved observations of future satellite missions
Combined LC-MS/MS and Molecular Networking Approach Reveals Antioxidant and Antimicrobial Compounds from Erismadelphus Exsul Bark.
No full textInternational audienceErismadelphus exsul Mildbr bark is widely used in Gabonese folk medicine. However, little is known about the active compounds associated with its biological activities. In the present study, phytochemical profiling of the ethanolic extract of Erismadelphus exsul was performed using a de-replication strategy by coupling HPLC-ESI-Q/TOF with a molecular network approach. Eight families of natural compounds were putatively identified, including cyclopeptide alkaloids, esterified amino acids, isoflavonoid- and flavonoid-type polyphenols, glycerophospholipids, steroids and their derivatives, and quinoline alkaloids. All these compounds were identified for the first time in this plant. The use of molecular networking obtained a detailed phytochemical overview of this species. Furthermore, antioxidant (2,2-diphenyl-1-picryl-hydrazylhydrate (DPPH) and ferric reducing capacity (FRAP)) and in vitro antimicrobial activities were assessed. The crude extract, as well as fractions obtained from Erismadelphus exsul, showed a better reactivity to FRAP than DPPH. The fractions were two to four times more antioxidant than ascorbic acid while reacting to FRAP, and there was two to nine times less antioxidant than this reference while reacting to DPPH. In addition, several fractions and the crude extract exhibited a significant anti-oomycete activity towards the Solanaceae phytopathogen Phytophthora infestans in vitro, and, at a lower extent, the antifungal activity against the wheat pathogen Zymoseptoria tritici had growth inhibition rates ranging from 0 to 100%, depending on the tested concentration. This study provides new insights into the phytochemical characterization and the bioactivities of ethanolic extract from Erismadelphus exsul bark
Quand la ville réagit à la menace terroriste: Quels effets sur les ambiances ordinaires ?
No full textInternational audienc
Les inégalités au sein du système éducatif français
No full textLa question des inégalités au sein du système éducatif se pose à de nombreuses échelles. Les chercheurs et les acteurs de la communauté éducative se sont interrogés sur les moyens de permettre à tous les élèves d’accéder aux savoirs sans accroître les inégalités. Cette recherche permet de faire un état des lieux sur l’histoire du système éducatif ainsi que de ces propositions pour pallier les inégalités entre les élèves. De plus, elle permet de questionner la différenciation ainsi que les pratiques enseignantes associées, comme solution pour résorber ces inégalités
Les intérêts des activités de négociation graphique dans l'acquisition des normes en orthographe grammaticale
No full textL’ objectif de ce mémoire est de montrer dans quelle mesure les activités de négociation graphique favorisent l’acquisition des normes en orthographe grammaticale chez les élèves de CE2. Pour cela sont pratiquées en classe des dictées innovantes telles que la dictée du jour ou la dictée négociée. Ces dictées innovantes permettent un travail de négociation graphique et sont reconnues par les didacticiennes en orthographe et en grammaire. Les intérêts de ces activités de négociation graphique sont dégagés par un travail d’analyse des échanges produits en classe lors de la pratique de ces dictées innovantes
Energy-efficient Fog Computing-enabled Data Transmission Protocol in Tactile Internet-based Applications
No full textInternational audienceSensor nodes are one of the basic elements in the Tactile Internetbased fog computing architecture. They provide a huge amount of data to the network due to the widespread real-world applications that use these types of wireless devices. This huge number of data, transmitted by the sensor devices to the fog gateway and then to the cloud, leads to high communication costs, increased power consumption, and high latency at the fog gateway. These challenges would be considered as a hurdle in the Tactile Internetbased fog system. To tackle these challenges, this paper proposes an Energy-efficient Fo g Co mputing-enabled Da ta Transmission (EFoCoD) protocol in Tactile Internet-based Applications. The protocol works on sensor devices level in the Tactile Internet-based fog computing architecture. EFoCoD protocol executes a Lightweight Data Redundancy Elimination (LiDaRE) Algorithm at the sensor level to reduce the collected data before transferring them to the smart fog gateway. To study the performance of the EFoCoD, it was compared to its counterpart protocols in the literature just like ATP and PFF. Simulation results show that EFoCoD outperforms these protocols in terms of energy consumption, transmitted data, and data accuracy
Les formes de groupement en danse à l'école primaire : quel impact sur la motivation et l'engagement des élèves ? Sur le climat de classe en général ?
No full textCette étude a pour but d’observer des élèves d’une classe à double niveau et double cycle CE2-CM1 lors d’une séquence de danse. Les élèves seront répartis en dyades dissymétriques avec, dans la mesure du possible, un CE2 avec un CM1. L’ observation de ces élèves doit nous amener à déterminer si le travail sous cette forme de groupement couplée à du tutorat au sein des dyades permet de motiver et in fine d’engager davantage les élèves –et en particulier les garçons– dans l’APSA danse. Au cours de cette étude, nous distribuerons un questionnaire aux élèves qui précèdera la séquence de danse puis un nouveau questionnaire à la fin de celle-ci. Enfin, nous avons également choisi de mettre en place des entretiens semi-directifs afin de pouvoir augmenter les informations nous permettant de répondre à notre problématique et ainsi d’affirmer ou infirmer nos hypothèses
Consensual based classification as emergent decisions in a complex system
No full textInternational audienceIn massive multi-agent systems that are used to model some complex systems, emergence is a key feature that allows to model high-level states of such systems. According to this perspective, the work we introduce in this paper entails the handling of emergence in massive multi-classifiers that we consider as complex systems. We aim to build a collaborative system for supervised data classification that we expect to provide better performance, compared to conventional classifiers. Modeled as a multi-agent system, the massive multi-classifier is composed of a high number of agents that are interconnected according to a given neighborhood. Each agent plays the role of a weak classifier. At the micro-level, the elementary interaction between agents consists of combining their respective classification results. Every agent, according to the majority vote rule, combines its result with those of its neighbors by taking into account their respective performances. This process is iterated continuously in a cyclic manner within the neighborhood of each agent. Therefore, a complex dynamic will be created within the system. After a certain time, this complex dynamic stabilizes, allowing the exhibition of an emergent structure that will be observed at the macro-level and is considered as a consensual class prediction for the data we want to classify. Obtained experimental results and the comparison with conventional classifiers show the potential of the approach to enhance classification and to be an alternative for classifier combination and aggregation
