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International audience; Hyperspectral imagery classification is a challenging task due to the large number of spectral bands, and low number of labeled samples. To overcome these issues, we propose a novel approach for hyperspectral image classification based on feature selection and semi-supervised hypergraph convolutional network working with small number of labeled samples. Firstly, we propose a new unsupervised feature selection method based on an information theoretic criterion. Relevant spectral features are automatically selected while preserving the physical properties of hyperspectral data. Secondly, we construct a spectro-spatial hypergraph in order to represent the complex relationships between pixels. Finally, we propose a semi-supervised hypergraph convolutional network which integrates local vertex features and hypergraph topology in the convolutional layers. The aim of this step is to preserve the spectro-spatial features and to cope with the high correlation between hypernodes during classification. The main advantage of the proposed approach is to allow the automatic selection of relevant spectral bands while preserving the spatial and spectral features. In addition, by accounting for the relationship between pixels leads to improved classification results even when the number of labeled samples is low. Experiments are conducted on two real hyperspectral images show that the proposed approach reaches competitive good performances, and achieves better classification performances compared to state-of-the-art methods.

The Angular Resolution Limit (ARL) is a fundamental statistical metric to quantify our ability to resolve two closely-spaced narrowband far-field complex sources. This statistical quantity, is defined as the minimal angular deviation between the two sources to be separated for a prefixed detection-based performance. In this work, we assume that the sources of interest are corrupted by a compound-Gaussian noise. In the standard literature, denoting with δ the true distance between the two sources, the derivation of the ARL is based on the statistical distribution of the Generalized Likelihood Ratio Test (GLRT) for a binary test where there is only one source under the null hypothesis (i.e., δ = 0 ) and two sources under the alternative hypothesis δ ≠ 0. In literature, the true angular distance (TAD) is generally considered as an unknown deterministic parameter, then a maximum likelihood-based estimation of δ is exploited in the GLRT. In this paper, breaking away from existing contributions, we suppose that the TAD is a random variable, Gaussian distributed, meaning that δ ∼ N ( δ 0 , σ δ 2 ) . The TAD uncertainty can have many causes as for instance moving sources or/and platform, antenna calibration error, etc. In this work, a generic and flexible (but common) statistical model of the uncertain knowledge of the TAD is preferred instead of a too much specified error model. The degree of randomness (or uncertainty) is quantified by the ratio ξ = δ 0 2 / σ δ 2 . The standard framework of the GLRT is no longer feasible for our problem formulation. To cope with the compound Gaussian noise modeling and the random model of the TAD, a powerful upper bound from information/geometry theory is exploited in this work. More precisely, a new expected Chernoff Upper Bound (CUB) on the minimal error probability is introduced. Based on the analysis of this upper bound, we show that the expected-CUB is highly dependent on the degree of uncertainty, ξ. As a by-product, the optimal s-value in the Chernoff divergence for which the expected-CUB is the tightest upper bound is analytically studied and the role of the mean value δ0 in the ARL context is analyzed.

International audience; This paper proposes a novel approach based on adaptive dimensionality reduction (ADR) and a semi-supervised 3-D convolutional neural network (3-D CNN) for the spectro-spatial classification of hyperspectral images (HSIs). It tackles the problem of curse of dimensionality and the limited number of training samples by selecting the most relevant spectral bands. The selected bands should be informative, discriminative and distinctive. They are fed into a semi-supervised 3-D CNN feature extractor, then a linear regression classifier to produce the classification map. In fact, the proposed semi-supervised 3-D CNN model seeks to extract the deep spectral and spatial features based on convolutional encoder-decoder to enhance the HSI classification. It uses several 3-D convolution and max-pooling layers to extract these features from the selected relevant bands. The main advantage of the proposed approach is to reduce the high dimensionality of HSI, preserve the relevant spectro-spatial information and enhance the classification using few labeled training samples. Experimental studies are carried out on three real HSI data sets: Indian Pines, Pavia University, and Salinas. The obtained results show that the proposed approach performs better than other deep learning-based methods including CNN-based methods, and significantly improves the classification accuracy of HSIs.

Internet of Mobile Things (IoMT) is a new paradigm of the Internet of Things (IoT) where devices are inherently mobile. While mobility enables innovative applications and allows new services, it remains a challenging issue as it causes disconnection of nodes and intermittent connectivity, which negatively impacts the network operation and performance. In addition, energy saving presents a real challenge for networks with limited resources. In this context, a new energy efficient and mobility aware routing protocol called EMA-RPL is proposed based on the well-known Routing Protocol for Low power and Lossy Networks (the RPL standard). Unlike the RPL which is basically designed for static devices, EMA-RPL enables better sustaining of connectivity of mobile nodes and conserving energy. The proposed protocol integrates an enhanced mobility detection method through a continuous control of the distance between the mobile node and its attachment, a novel point of attachment prediction based on the new location of the mobile node, and an efficient replacement strategy preserving the mobile node energy. EMA-RPL overcomes and mitigates problems caused by the mobility of nodes. Simulations using Cooja/Contiki show that EMA-RPL outperforms both the RPL and its mobility aware variant (MRPL) in terms of handover delay, data loss rate, signaling cost and energy consumption.

International audience; The Internet of Things (IoT) ecosystem includes a plethora of devices equipped with heterogeneous communication interfaces. They exhibit different mobility patterns and hardware constraints as memory, battery and processing power. On the other hand, IoT applications are overlayed on top of these constrained-devices imposing stringent requirements in term of data availability, data coherence, and response latency. To cope with these challenges, Named Data Networking (NDN) architecture is positioned in the middle layer to act as the networking layer. By providing easy data access thanks to the unique and location-independent content names, in-network caching and name-based routing, NDN expects to hide from IoT applications the complexity and diversity of the underlying Things by adapting the network operation to their features. In this paper, we focus on data availability requirements threatened by high IoT network dynamics related to sensors mobility. We address the producer mobility issue in NDN/IoT networks using the routing-based approach. We propose a novel and efficient forwarding algorithm named AFIRM in order to support producer mobility, and compare its performances to those of other relevant solutions based on the routing approach.

International audience; An objective of satellite remote sensing is to predict or characterize the land cover change (LCC) over time. Sometimes we are capable of describing the changes of land cover with a probability distribution. However, we need sufficient knowledge about the natural variability of these changes, which is not always possible. In general, uncertainties can be subdivided into aleatory and epistemic. The main problem is that classical probability theory does not make a clear distinction between aleatory and epistemic uncertainties in the way they are represented, i.e., both of them are described with a probability distribution. The aim of this paper is to propagate the aleatory and epistemic uncertainty associated with both input parameters (features extracted from satellite image object) and model structure of LCC prediction process using belief function theory. This will help reducing in a significant way the uncertainty about future changes of land cover. In this study, the changes prediction of land cover in Cairo region, Egypt for next 16 years (2030) is anticipated using multi-temporal Landsat TM5 satellite images in 1987 and 2014. The LCC prediction model results indicated that 15% of the agriculture and 6.5% of the desert will be urbanized in 2030. We conclude that our method based on belief function theory has a potential to reduce uncertainty and improve the prediction accuracy and is applicable in LCC analysis.

International audience; Spectro-spatial dimensionality reduction in HyperSpectral Images (HSI) classification is a challenging task due to the problem of curse dimensionality, i.e. the high number of spectral bands and the heterogeneity of data. In this context, many dimensionality reduction methods have been developed to overcome the high correlation between bands and the redundancy of information in order to improve the classification accuracy. Most of these methods represent the original HSI as a set of vectors. Therefore, they only exploit spectral properties, neglecting the spatial information, i.e. the spatial rearrangement is lost. To jointly take advantage of spatial and spectral information, HSI has been recently represented as a tensor. In order to preserve the spatial and spectral information, we develop a hybrid method using both the Tensor Locality Preserving Projections method (TLPP) projecting the original data into a lower subspace and the Constrained Band Selection method (CBS) to select the relevant bands. These two methods will be jointly used to get high-level quality classification. Moreover, since the two obtained classifications are uncertain and imprecise, we propose to fuse them using the Dempster-Shafer's Theory (DST) to obtain an accurate classification preserving the spectro-spatial information. The proposed approach has been applied on real HSI showing its efficiency compared with conventional dimensionality reduction methods.

Background The aim of this study was to review, systematically, evidence concerning the link between the ACTN3 R577X polymorphism and the rates and severity of non-contact injuries and exercise-induced muscle damage in athletes and individuals enrolled in exercise training programs. Methods A computerized literature search was performed in the electronic databases PubMed, Web of Science, and SPORTDiscus, from inception until November 2020. All included studies compared the epidemiological characteristics of non-contact injury between the different genotypes of the ACTN3 R577X polymorphism. Results Our search identified 492 records. After the screening of titles, abstracts, and full texts, 13 studies examining the association between the ACTN3 genotypes and the rate and severity of non-contact injury were included in the analysis. These studies were performed in 6 different countries (Spain, Japan, Brazil, China, Republic of Korea, and Italy) and involved a total participant pool of 1093 participants. Of the studies, 2 studies involved only women, 5 studies involved only men, and 6 studies involved both men and women. All the studies included were classified as high-quality studies (≥6 points in the Physiotherapy Evidence Database (PEDro) scale score). Overall, evidence suggests there is an association between the ACTN3 R577X genotype and non-contact injury in 12 investigations. Six studies observed a significant association between ACTN3 R577X polymorphism and exercise induced muscle damage: two with non-contact ankle injury, three with non-contact muscle injury, and one with overall non-contact injury. Conclusion The present findings support the premise that possessing the ACTN3 XX genotype may predispose athletes to a higher probability of some non-contact injuries, such as muscle injury, ankle sprains, and higher levels of exercise-induced muscle damage.

International audience; The Internet of Things (IoT) is a way to address and communicate with objects and their virtual representations. The exponential development of Smartphone, tablet, sensors and their applications has created very complex systems. From here, the concept of M2M (Machine-to-Machine) has emerged to provide communication and service architecture without human intervention. The European Telecommunications Standards Institute (ETSI) has proposed a horizontal architecture that can fit to several domains: transport, energy, e-health, etc. A crucial aspect is the management of devices in a system where equipment can be used during decades. Several protocols exist, in this article we explain the OMA DM management protocol, and we describe its connection to the ETSI-M2M platform. An extension of device management is proposed with an autonomic capability.

International audience; Les communautés bactériennes associées aux racines dePanicum turgide, exposés à des conditions arides, ont été investis-associée à une combinaison d'approches culturelles et métataxonomiques. Technologie traditionnelle basée sur la culture-ont été utilisées et 32 ​​isolats de racines irradiées ont été identifiés comme appartenant àActinobactéries, Bacteroidetes, Firmicutes et Proteobacteria embranchements. Quatre souches actinobactériennes ont étéRésistant aux rayonnements ionisants (IR) :Microbactériesp. PT8 (4,8 kGy (kGy)),Microcoquesp.PT11 (4,4 kGy),Kocuria rhizophilaPT10 (2,9 kGy) etPromicromonospora paniciPT9T(2,6 kGy), basésur le Ddixdose nécessaire pour une réduction de 90 % des unités formant colonies (UFC). Concernant l'enquêtedes communautés microbiennesin situ, analyses métataxonomiques de la diversité des microorganismes résistants aux IRassociée aux racines irradiées a révélé une dominance marquée des Actinobactéries (46,6%) etProtéobactéries (31,5%) par rapport aux Bacteroidetes (4,6%) et Firmicutes (3,2%). Irradiation gamma nonn'ont fait que modifier la structure des communautés bactériennes, mais ont également affecté leurs propriétés fonctionnelles.Des analyses comparatives des profils métaboliques ont indiqué l'induction de plusieurs voies liées à l'adaptation.au stress oxydatif dans les racines irradiées, comme la réparation de l'ADN, la synthèse de métabolites secondaires, la réactionenzymes atténuant les espèces oxygénées (ROS),etc. P. turgidumest emblématique des plantes adaptées au désert.Jusqu'à présent, aucun autre travail n'a porté sur le profil microbien des racines irradiées de cexérophyte.

International audience

International audience; Purpose:We studied the effect of two interval training programs of varying intensities (100% vs. 110% of maximal aerobic velocity [MAV]) on hematocrit (Ht), hemoglobin (Hb), and plasma volume variations (PVV) in young men.Methods: Thirty-nine male volunteered were assigned to two control groups (CG100, n = 9 and CG110, n = 10), and two training groups (one with 100% MAV [EG100, n = 10] and one with 110% MAV [EG110, n = 10]). All participants performed a maximal graded exercise test and an intermittent exercise (IE) protocol. Blood was collected at rest, at the end of the IE and after 15 min of recovery, before and after 8-weeks-training. Interval training (IT) sessions consist of 30s IE run at 100% or 110% MAV with 30s recovery at 50% MAV.Results: After training, time to exhaustion (TTE) was improved in EG110 (+613.7) and EG100 (+397.5). This improvement was greater in EG110 than EG100 (p

International audience; Local search algorithms constitute a growing area of interest to approximate the Pareto sets of multi-objective combinatorial problem instances. In this study, we focus on the multi-objective knapsack problem and its optimization thanks to a multi-population based cooperative framework. The proposed approach, Wϵ-CMOLS, uses a multi-objective local search algorithm based on quality indicator (IBMOLS), initially presented by Basseur and Burke in 2007, and integrates it into a cooperative model. The idea is to optimize the overall quality of a Pareto set approximation by evolving several sub-populations in parallel, each population executing a different configuration of IBMOLS. The algorithm uses a weighted version of the epsilon quality indicator by means of different weight vectors. The populations cooperate through sharing a non-dominated archive, which stores the best compromises found during the optimization process, and which is used to re-initialize regularly each sub-population. Wϵ-CMOLS is compared with state-of-the-art algorithms such as IBEA, NSGA-II and SPEA2. Experiments highlight that the use of a cooperative model as well as a weighted indicator to guide the search toward different directions, can lead to interesting results for the multi-objective knapsack problem.

International audience; Barbary red deer (Cervus elaphus barbarus) is a protected rare subspecies of red deer. The study of its Protostrongylidae fauna based only on sporadic necropsy of naturally dead animals is difficult. Therefore diagnosis of lungworms rely mainly on the identification of the first stage larvae (L1). The L1 of the different species are not readily diagnosed on morphological basis since much variation is recorded within and among dorsal-spined larvae belonging to various species. The aim of this study was to identify the dorsal-spined lungworm larvae of the Barbary red deer. A discriminant function was established, using the measurements of L1 lungworms recorded from red deer in the literature, then applied to identify 220 dorsal-spined larvae extracted from 25 Tunisian Barbary red deer fresh fecal samples. Also the ITS2 region of rDNA of four pools of larvae (n = 25-60) were amplified, sequenced and analyzed. Using discriminant analysis of morphological traits, Elaphostrongylus cervi and Varestongylus sagittatus were identified. Molecular identification confirmed the presence of E. cervi which is the most prevalent species. This study represents the first identification of V. sagittatus and E. cervi in North Africa.

HighlightsWe propose to merge a set of business process fragments to construct new processes.We extend path matrices to systematically compile fragments' components.We ensure that the behavior of initial fragments is preserved.We ensure that the designer is alerted of undesirable behaviors.We prove the effectiveness of our approach through experimentations. In the field of business process management, adopting efficient building strategies can improve the quality of companies' business processes. The reuse of existing business processes or even fragments of them is a practical approach to build complete business processes or coarser-grained process fragments. In the present paper, we deal with the merge of a set of business process fragments for the construction of new complete processes. Our merge mechanism relies on a particular path matrix, that we call gateway path matrix. We use gateway path matrices to represent business process fragments to systematically compose shared components with individual ones. Moreover, our approach ensures that the resulting business process fragments subsume the behavior of initial ones and allows for adding new execution scenarios while controlling undesirable ones. In fact, we detect newly generated behaviors, and alert process designers of undesirable ones through behavioral constraints. We provide extensive experimental results derived from an implementation of our approach applied on a well-known industrial library of business process fragments.

International audience; Chemical compounds of orange peel extracts were identified and their antioxidant activities were determined. The inhibiting effect on acidic steel corrosion brought by the extract and selected antioxidant compounds (neohesperidin, naringin, ascorbic acid) was evaluated separately by electrochemical methods. Whatever the extract concentration, a significant inhibition is observed, whereas selected antioxidant compounds show only a slight effect. Both electrochemical impedance spectroscopy results and scanning electron microscopy observations after immersion reveal that the inhibiting efficiency of orange peel extract is not only due to the antioxidant activity of its compounds but also to the precipitation of a surface film.

International audience; Hyalomma scupense (Acari, Ixodidae) is a common tick species found in several areas in North Africa, Asia and South Europe and an efficient natural vector of bovine tropical theileriosis (Theileria annulata), a livestock disease with an important economic impact. For one year, 1938 ticks were collected on cattle in several Corsican slaughterhouses; 168 of them were morphologically identified as H. scupense. This result was confirmed by genetic identification using sequences of mitochondrial cytochrome c oxidase subunit I (COI) and ribosomal internal transcribed spacer 2 (ITS2) genes. The presence of 2 different stages (adults and nymphs), collected in various areas of the island, indicates that a population of H. scupense is established in Corsica. However, bovine tropical theileriosis has not been diagnosed on the island so far.

International audience; The aim of this work was to investigate the impact of almond gum as coating agent to decrease the oil absorption and to increase the moisture content of fried potato chips meaning a Box–Behnken design. The studied parameters were: almond gum concentration (0–20 g/L), frying time (30–120 s) and frying temperature (160–190 °C). Results showed that the increase of almond gum concentration up to 20 g/L decreased the oil uptake of potato chips by 34% and increased the moisture by 29.5%. Optimal coating and frying conditions found with RSM were thus achieved with 20 g/L aqueous almond gum, 75 s frying time at 160 °C frying temperature. Additionally, the study of sensorial analysis (color, appearance, crispiness, taste, odor, appetence) showed that the chips coated with almond gum have an overall acceptability better than the uncoated potato chips, and the ones coated with arabic gum. The textural analysis of potato chips showed that the hardness (rigidity) decreased significantly by coating either with almond gum or arabic gum. The results obtained through this work showed that coating with almond gum improves the sensorial and nutritional qualities of potato chips.