Easy Global Market (France)

It is possible to deduce from recent scientific findings general properties of a family of plants, the Lamiaceae, that have excellent value for medical practice.The deductions of Pliny the Elder can be shown to be still valid today and the plants understood as the antiviral agents they are.

One of the goals of the iFishIENCi project is to bring to market the iFishIENCi Biology Online Steering System (iBOSS) that significantly improves production control and management for all fish aquaculture systems. Within iBOSS, smart functions can be implemented to take advantage of data from multiple sources to give better insight for the farmer, optimize production, better production planning, etc. The Report on final Smart feeding AI aims to implement one such smart function: iBOSS Smart Feeding. By using an AI approach, it is expected appropriate correlations between fish behaviour, feeding regime and water quality can be found, and used for controlling feeder and achieve optimal feeding operation. While a lot of data is available, it has not yet been explored how to concatenate this into one model for optimizing feeding efficiency. Thus, an approach to integrate multiple sources of feeding efficiency quantification in to one, is proposed.

This paper describes a model-based prognosis method for the health management of a planetary rover. Using a hybrid model of the rover, including a continuous part and a discrete part, a prognoser is generated that relies on the hybrid particle Petri nets (HPPN) data structure. The prognosis process uses the current diagnosis of the system to predict its future states and to determine its end of life (EOL) or its remaining useful life (RUL). The HPPN-based prognoser is initialized with a stochastic scaling algorithm that selects the diagnosis hypotheses with the highest beliefs. The SSA provides a compromise between performance and available computational resources through the setting of scaling parameters. The prognoser then uses the future commands to determine the hypotheses over the rover future trajectory and the RUL/EOL. The set of future hypotheses associated with their belief degrees forms the current rover prognosis. The prognosis method is tested on different scenarios, with different scaling parameters, considering whether the future commands are known or not. Experimental results show that the approach is robust to real system data and computational performance constraints.

Abstract Zn1‐xMgxO microcrystals are produced in the 0.15 ≤ x ≤ 1 composition range by calcination of ZnO + Mg(OH)2 after hydro micro mechanical activation according to the patent WO2018065735A1. The structural properties of the samples have revealed the cohabitation of wurtzite and rock‐salt phases for x values ranging from 0.15 up to 0.6 with a clear increase of the proportion of cubic phase with x. A single cubic phase is observed in the range 0.66 ≤ x < 1. From the purity of these samples produced at very low cost, it is expected that they will be used as precursors for growth of advanced light emitters integrated into an already existing process as they exhibit exceptionally efficient (and robust with T) light emission in the ultraviolet region.

The Internet of Things (IoT) concept has attracted a lot of attention from the research and innovation community for a number of years already. One of the key drivers for this hype towards the IoT is its applicability to a plethora of different application domains. However, infrastructures enabling experimental assessment of IoT solutions are scarce. Being able to test and assess the behavior and the performance of any piece of technology (i.e., protocol, algorithm, application, service, etc.) under real-world circumstances is of utmost importance to increase the acceptance and reduce the time to market of these innovative developments. This paper describes the federation of eleven IoT deployments from heterogeneous application domains (e.g., smart cities, maritime, smart building, crowd-sensing, smart grid, etc.) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day. The paper summarizes the resources that are made available through a cloud-based platform. The main contributions from this paper are twofold. In the one hand, the insightful summary of the federated data resources are relevant to the experimenters that might be seeking for an experimental infrastructure to assess their innovations. On the other hand, the identification of the challenges met during the testbed integration process, as well as the mitigation strategies that have been implemented to face them, are of interest for testbed providers that can be considering to join the federation.

Context Information Management (CIM) platforms have tended to rely on mostly hierarchical information models with weak semantics and predefined APIs, falling short of the requirements for interoperation with other platforms and flexibility of data utilizationand federation. In order to improve cross-domain federation capabilities for existing CIM platforms, we propose an evolution framework by combining the “property graph” information model with RDF-based semantic modeling. We describe this evolution for what concerns: context information modeling, context information representation, query patterns and architecture. For each of these aspects, we introduce the problem space, the evolution strategy and provide examples based on existing popular CIM platforms. This evolution framework is designed to meet the requirement of backward compatibility to existing CIM platforms, while bringing graph-based semantic evolution based on the property graph model for CIM interoperability and cross-domain federation. This framework is currently evolved by the ETSI Industry Specification Group on “cross-cutting Context Information Management” (ETSI ISG CIM) for standardization.

IoT deployments generate data of the real world in an automated fashion without direct user involvement. With increasing scale of these IoT deployments the extraction of the right knowledge about the real world from a vast amount of IoT data and efficient decisions is a challenging endeavor. While solutions to deal with large amounts of IoT data are slowly emerging, potential users of IoT solutions, or policy makers find it difficult to assess the actual usefulness of investing in IoT deployments or selecting adequate deployment strategies for a particular business domain. Despite the recent hype generated by consultancy companies and IoT vendors about the IoT, there is still a lack of experience in assessing the utility and benefits of IoT deployments as many IoT deployments are still in their early stages. In order to capture such experience quicker and derive best practices for IoT deployments, systematic tools, or methodologies are required in order to allow the assessment of the goodness or usefulness of IoT deployments and a comparison between emerging IoT deployments to be performed. This chapter addresses the existing gap and proposes a novel benchmarking framework for IoT deployments. The proposed framework is complementary to the emerging tools for the analysis of big data as it allows various stakeholders to develop a deeper understanding of the surrounding IoT business ecosystem in a respective problem domain and the value proposition that the deployment of an IoT infrastructure may bring. It also allows a better decision making for policy makers for regulatory frameworks.

Smart Cities have emerged as an efficient infrastructure to contribute to so-called global sensing or situation-awareness applications. One example of large scale deployment of sensors in the city is the SmartSantander test-bed. Most of the deployment so far propose traditional scalar physical measures such as temperature or luminosity for a number of environment-related applications. The EAR-IT project moves a step further and proposes large-scale real-life experimentations of intelligent acoustics for supporting high societal value applications. One scenario that will be demonstrated is an on-demand acoustic data streaming feature for surveillance systems and management of emergencies. In this paper, we will present experimentations on streaming encoded acoustic data on low-resources devices. We will highlight the main sources of delays assuming no flow control nor congestion control to determine the best case performance level and will demonstrate that streaming acoustic data can be realized in a multi-hop manner on the SmartSantander infrastructure.