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International audience; In this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq Al-Hadid, United Arab Emirates (UAE) was investigated. Satellite-borne synthetic aperture radar (SAR) is proposed as the main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. Results from SAR feature extraction and geospatial analyses showed detection of the areas on the site that were already under excavation and predicted new, hitherto unexplored archaeological areas. The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. The validated results can provide guidance for future on-site archaeological work. The pilot process developed in this work can therefore be applied to similar arid environments for the detection of archaeological features and guidance of on-site investigations

Temporal variability in plankton community structure and biomass is often driven by environmental fluctuations: nutrient supplies, light, stratification and temperature. But plankton time series also exhibit variability that is not strongly correlated with key phys ical variables and is distinctly nonlinear in nature. There is evidence, from both labo ratory and modeling studies, that oscillations can arise from ecological interactions alone. In the open ocean, it is challenging to establish the roles and relative importance of en vironmental versus intrinsic processes in generating the observed ecological variability. To explore this competition, we employ a marine plankton model that supports two mech anisms of intrinsic ecological variability operating at distinct frequencies: predator-prey interactions between zooplankton and phytoplankton, with timescales of weeks, and re source competition that occurs with multiple nutrients phytoplankton species, with timescales of years. The model is forced by imposing variable nutrient input rates. representing typ ical open ocean situations, with periods ranging from subseasonal to multi-annual. We find that intrinsically-driven variability generally persists in the presence of extrinsic forc ing, and that the interaction between the two can produce variability at frequencies that are not characteristic of either source. The intrinsic frequencies are found to be even more energetic when the extrinsic variability is augmented with stochastic noise. We conclude that interactions between intrinsic and extrinsic sources of variability may contribute to the wide range of observed frequencies in phytoplankton time series, and may explain why it is often difficult to relate planktonic variation to environmental variation alone International audience

Pour évaluer correctement l’agrobiodiversité, il est essentiel de comprendre comment les agriculteurs nomment et classent leurs cultures, notamment au regard de leurs méthodes de multiplication. Pourtant, les savoirs locaux sont souvent négligés dans les études génétiques, ce qui peut entraîner une sous-estimation de la diversité des cultures et entraver sa conservation et sa mobilisation pour le développement d’agrosystèmes durables.Ici, nous étudions le statut du palmier dattier barnī, la variété élite locale de l’oasis d’al-’Ulā, en Arabie saoudite. Nous avons mené une enquête ethnobotanique sur les pratiques phœnicicoles locales et généré des données sur le génome entier afin de déterminer si les palmiers barnī sont ou non exclusivement propagés de manière clonale. En outre, nous avons comparé les génomes du barnī et de deux autres palmiers d’al-’Ulā avec 112 Phoenix spp. afin de fournir un premier aperçu de l’origine de la diversité des palmiers dattiers dans cette oasis millénaire.Les résultats montrent que derrière le nom barnī se cache une seule lignée clonale, un cultivar vrai, indiquant une propagation clonale par rejets avec maintien du nom même entre des contextes de culture bien distincts à al-Ulā ou dans une oasis voisine. Néanmoins, ce cultivar est distinct du barnī très répandu à Oman. Son ascendance est comparable à celle d’autres palmiers dattiers d’Asie occidentale, mais nous avons constaté que l’ascendance d’un autre palmier de cette oasis montre une influence des palmiers dattiers d’Afrique du Nord.Ce qui se cache derrière le nom du cultivar barnī à al-’Ulā et plus loin dans la péninsule arabique a été déchiffré grâce à la combinaison de disciplines-clés que sont l’anthropologie sociale et la génétique. De futures recherches permettront de mieux comprendre la composition génétique originale de cette oasis. Understanding how farmers name and categorize their crops in relation to the way they propagate them is critical for a proper assessment of agrobiodiversity. Yet, indigenous knowledge is often overlooked in genetic studies which may result in an underestimation of crop diversity preventing its conservation and mobilization for developing sustainable agrosystem.Here, we study the status of the barnī date palm, the local elite variety of the oasis of al-‘Ulā, Saudi Arabia. We conducted an ethnobotanical survey on local phoeniculture practices and generated whole-genome data to determine whether or not barnī palms are exclusively clonally propagated. Further, we contrasted the genomes of barnī and two other palms from al-‘Ulā with 112 Phoenix spp. to provide a first insight into the origin of date palm diversity in this millennia-old oasis.Results show that behind the name barnī lies a single clone line, a true-to-type cultivar, indicating clonal propagation by offshoots with name maintenance even between distinct cultivating situations in al-Ulā and a nearby oasis. Nonetheless, it is distinct from the prominent barnī cultivated in Oman. Its ancestry is comparable to other West Asian date palms but we found that the ancestry of another palm from this oasis shows influence from North African date palm.What lies behind the cultivar name barnī in al-‘Ulā and further afield in the Arabian Peninsula has been deciphered through the key disciplinary combination of social anthropology and genetics. Future studies will provide additional insights into the original genetic make-up of this oasis. International audience

SummaryIn the context of Internet of Things, thingsourcing is poised to promote the collective behavior that should prevail among things despite their independent nature being confined into silos. By analogy with crowdsourcing where crowds of (sometimes anonymous) people are contacted for their expertise, thingsourcing abstracts crowds of things and provides the necessary mechanisms for composing things together so they collectively satisfy users' demands. However, to ensure successful discovery of things according to these demands' requirements, an ontology‐based semantic description of things is deemed necessary. This article presents an approach for enriching things' descriptions semantically in preparation for their composition with respect to specific scripts that define who will do what, when, and where. A system demonstrating semantic thingsourcing along with a case study about dairy supply‐chain is presented in this article as well.

Abstract By analogy to cells that grow, split, merge, and die, this paper applies same operations to things residing in an open Internet-of-Things (IoT) ecosystem. Despite the growing interest in IoT, things are mainly “busy” with sensing and (some) actuating, which prevents them from being responsive to changes in this ecosystem. To address this limitation, this paper proposes mutation as a novel mechanism for making things responsive and hence, capable of either satisfying “unseen” needs or seizing “unexpected” opportunities. To approve/deny mutation, a decision-making process is initiated progressing over 4 stopovers (obligation, permission, prohibition, and dispensation) along with 4 stages (awareness, pre-mutation, mutation-itself, and post-mutation) that would allow to answer questions such as why to approve/deny mutation, how to prepare mutation, and how to evaluate mutation. A testbed demonstrating the technical doability of thing mutation along with the conducted experiments are also discussed in the paper.

Abstract In this work, we present a real-time embedded implementation of an algorithm dedicated to monitoring agricultural fields. This algorithm is based on normalized indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The problem of most algorithms in this context is real-time processing, especially when we talk about applications that require time precision. The proposed implementation is based on the application of a Hardware/Software Co-design approach. For the embedded platform, we used the heterogeneous system contains CPU and GPU type XU4 and TX1. In this context, we used the parallel programming language OpenMP to have an optimal embedded implementation. The results showed that we could process 66 images/s using a desktop, 20 images/s in the XU4, and 17 images/s for TX1.

AbstractUnderstanding how thermokarst lakes on arctic river deltas will respond to rapid warming is critical for projecting how carbon storage and fluxes will change in those vulnerable environments. Yet, this understanding is currently limited partly due to the complexity of disentangling significant interannual variability from the longer‐term surface water signatures on the landscape, using the short summertime window of optical spaceborne observations. Here, we rigorously separate perennial lakes from ephemeral wetlands on 12 arctic deltas and report distinct size distributions and climate trends for the two waterbodies. Namely, we find a lognormal distribution for lakes and a power‐law distribution for wetlands, consistent with a simple proportionate growth model and inundated topography, respectively. Furthermore, while no trend with temperature is found for wetlands, a statistically significant decreasing trend of mean lake size with warmer temperatures is found, attributed to colder deltas having deeper and thicker permafrost preserving larger lakes.

AbstractThis article presents an approach to model and simulate Plug&Play social things. Confined into silos, existing (not social) things are restricted to basic operations like sensing and actuating, which deprive them from participating in the satisfaction of complex business applications. Contrarily, social things are expected to engage in collaborative scenarios and to tap into specific relations that connect them to peers when achieving these scenarios. These relations are referred to as complimentary, antagonism, and competition, and allow to develop networks of things. To capitalize on such networks, the approach to model and simulate Plug&Play social things puts forward four stages that are referred to as connecting to demystify social relations between things, influencing to examine the impact of social relations on things, playing to make things perform while considering influence, and incentivizing to reward things based on their performance. A smart system for elderly the care centers has been developed to showcase the technical doability of Plug&Play social things. The system is an integrated development environment allowing IoT engineers to define the collaboration of social things, thanks to a set of drag&drop operations.