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This protocol defines the procedure to conduct a systematic literature review on adaptive middleware support for the Internet of Things (IoT) and Cyber-physical Systems (CPS). The mentioned concepts deal with smart interactive objects which provide a set of services, but they look into the problem from various perspectives. We especially look into middleware design decisions for reactive/proactive adaptations. Following a systematic literature review (SLR) in the selection procedure, we selected 62 papers among 4,274 candidate studies. To this end, we applied the classification and extraction framework to select and analyze the most influential domain-related information. In addition to the academic database, we took advantage of the use-cases provided by our industrial partners within the CPS4EU 2 project. This document clarifies the primary studies' selection process. The analysis of the studies, discussion, and solution proposals will be presented separately in a journal article.

In 2018, the EU Court of Justice ruled that gene edited organisms “are GMOs and are, in principle, subject to the obligations laid down by the GMO directive [EU Directive 2001/18/EC]”. While the EU Court of Justice has established an equivalence between gene edited organisms and GMOs, how have national institutions and committees from EU member states positioned themselves regarding the use of gene editing in agriculture? In order to answer this question, this article examines and compares 11 official reports and position statements from 7 European countries: Germany, France, the Netherlands, Italy, Spain, Denmark, and Sweden. The various kinds of issues that are addressed and argumentsthat are made in the reports are coded into large categories (innovation, risk, ethics, legislation, etc.) and are analysed. The paper discusses the main similarities and differences in terms of how the governance of gene editing is problematized. For instance, while some reports consider gene editing in terms of technology, risk and regulation, others situate gene editing within larger debates about agriculture, intellectual property, ethics, public participation, and the responsibility of scientists. The paper aims to provide a useful resource to broaden debates on the future regulation of gene editing within and beyond Europe. It also calls for an analysis of the objectification of gene editing: how are gene editedorganisms rendered tangible, discussable and public via policy processes? How are they tied to national territories, identities, histories or products and how does this (re)nationalizing of gene edited organisms matter within and beyond EU member states?

The recent and fast development of automatic transcription software is accompanied by a growing heterogeneity of formats to save the output of such a task. TEI P5 can be helpful to simplify workflows and bring in more coherence in digitization pipelines. We present a twofold modelization in TEI which brings together essential information resulting from the transcription phase with the editorial layers. The usefulness of this modelization is illustrated with several examples showing how such an approach can be leveraged at different stages of a digitization pipeline.

In Senegal, as in many developing countries, the agricultural sector plays a key role in the economy. As well as supplying food, agriculture is the most important source of employment, especially for women. Through the Plan for an Emerging Senegal (PES) the Senegalese government is implementing an ambitious financing plan to improve the productivity of the agricultural sector and to improve women's employment opportunities. Our study assesses the impact of two PES measures (investing in the agricultural sector and an increase in production subsidies for the agricultural sectors) on economic growth, women's employment, poverty and inequality using a dynamic computable general equilibrium model linked to a micro-simulation model. While on the one hand, we find that investing in the agricultural sectors has positive impacts on growth, improves women's work opportunities, decreases poverty and inequality both in the short and long term, however increasing production subsidies in the agricultural sectors has positive effects only in the short term and does not reduce inequality or poverty in the long term.

The difficulty to measure or predict species community composition at fine spatio-temporal resolution and over large spatial scales severely hampers our ability to understand species assemblages and take appropriate conservation measures. Despite the progress in species distribution modeling (SDM) over the past decades, SDM have just begun to integrate high resolution remote sensing data and their predictions are still entailed by many biases due to heterogeneity of the available biodiversity observations, most often opportunistic presence only data. We designed a European scale dataset covering around ten thousand plant species to calibrate and evaluate SDM predictions of species composition in space and time at high spatial resolution (~ten meters), and their spatial transferability. For model training, we extracted and harmonized five million heterogeneous presence-only records from selected GBIF datasets and 6 thousand exhaustive presence-absence surveys both sampled during 2017-2021. We associated species observations to diverse environmental rasters classically used in SDMs, as well as to 10 m resolution RGB and Near-Infra-Red satellite images and 20 years-time series of climatic variables and satellite point values. The evaluation dataset is based on 22 thousand standardized presence-absence surveys separated from the training set with a spatial block hold out procedure. The GeoLifeCLEF 2023 dataset is open access and the first benchmark for researchers aiming to improve the prediction of plant species composition at a very fine spatial grain and at continental scale. It is a space to explore new ways of combining massive and diverse species observations and environmental information at various scales. Innovative AI-based approaches, in particular, should be among the most interesting methods to experiment with on the GeoLifeCLEF 2023 dataset. Comment: The full dataset is freely available at the link below (perennial repository) for academic use or other non-commercial use: https://lab.plantnet.org/seafile/d/936fe4298a5a4f4c8dbd/

This contribution documents the contrasting fate of three key actors of the retail industry since the mid-nineties: Carrefour, Wal-Mart and Amazon. Stylized facts about their respective financial trajectories and a description of their engagement with digitalization allow to identify their distinct dynamics. Through a combination of accounting, business and economic analyses, this paper clarifies the underlying logics of profit making in the context of retail digitalization and provides new insights concerning the role of fixed costs leveraging in the digital age.

In defense-related remote sensing applications, such as vehicle detection on satellite imagery, supervised learning requires a huge number of labeled examples to reach operational performances. Such data are challenging to obtain as it requires military experts, and some observables are intrinsically rare. This limited labeling capability, as well as the large number of unlabeled images available due to the growing number of sensors, make object detection on remote sensing imagery highly relevant for self-supervised learning. We study in-domain self-supervised representation learning for object detection on very high resolution optical satellite imagery, that is yet poorly explored. For the first time to our knowledge, we study the problem of label efficiency on this task. We use the large land use classification dataset Functional Map of the World to pretrain representations with an extension of the Momentum Contrast framework. We then investigate this model's transferability on a real-world task of fine-grained vehicle detection and classification on Preligens proprietary data, which is designed to be representative of an operational use case of strategic site surveillance. We show that our in-domain self-supervised learning model is competitive with ImageNet pretraining, and outperforms it in the low-label regime.

International audience; This chapter explores the different forms of environmental violence practised against humans and nature (including the cannabis plant) in a context of intensive cannabis cultivation. In particular, it examines the effects of the industrial farming of cannabis since the 1960s on water, land, forests, animals and farmers. It also investigates the exploitation of the labour force, which has comprised vulnerable populations, especially women and landless agricultural workers since Rifian agriculture was first integrated into colonial capitalism in Morocco. While this study focuses on the Moroccan territory, the situation presented is by no means unique at the global level. The phenomenon can be seen developing both within a framework of prohibition and when legal and illegal actors adopt a capitalist system of exploitation, as is the case in California

In Mali’s current context where the crops sector is particularly exposed and vulnerable to agricultural drought, this study assesses the economy-wide impacts of such events and the potential effectiveness of some adaptation strategies. Using a dynamic computable general equilibrium model, we conduct counterfactual simulations of various scenarios accounting for different levels of intensity and frequency of droughts over a 15-year period. We first show how mild, moderate, and intense droughts currently experienced by the country affect its economic performances and considerably degrade its households’ welfare. We also show how these negative impacts could be aggravated in the future by the likely increased number of intense droughts threatened by global climate change. However, we finally show that there appears to be some room for Mali to manoeuvre in terms of drought-risk management policies, such as fostering the use of drought-tolerant crop varieties, improving drought early warning systems or extending irrigation capacities.