Molecular features of new Peach Latent Mosaic Viroid variants suggest that recombination may have contributed to the evolution of this infectious RNA.

peer reviewe

Occurrence of target-site resistance to neonicotinoids in the aphid Myzus persicae in Tunisia, and its status on different host plants

This is the peer reviewed version of the following article: Kamel Charaabi, Sonia Boukhris-bouhachem, Mohamed Makni, and Ian Denholm, ‘Occurrence of target‐site resistance to neonicotinoids in the aphid Myzus persicae in Tunisia, and its status on different host plants’, Pest Management Science, Vol. 74(6): 1297-1301, June 2018, which has been published in final form athttps://doi.org/10.1002/ps.4833 Under embargo until 19 December 2018. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.BACKGROUND: The R81T mutation conferring target-site resistance to neonicotinoid insecticides in Myzus persicae was first detected in France and has since spread across much of southern Europe. In response to recent claims of control failure with neonicotinoids in Tunisia, we have used a molecular assay to investigate the presence and distribution of this target-site mutation in samples collected from six locations and six crops attacked by M. persicae. RESULTS: The resistance allele containing R81T was present at substantial frequencies (32–55%) in aphids collected between 2014 and 2016 from northern Tunisia but was much rarer further south. It occurred in aphids collected from the aphid's primary host (peach) and four secondary crop hosts (potato, pepper, tomato and melon). Its absence in aphids from tobacco highlights complexities in the systematics of M. persicae that require further investigation. CONCLUSION: This first report of R81T from North Africa reflects a continuing expansion of its range around the Mediterranean Basin, although it remains unrecorded elsewhere in the world. Loss of efficacy of neonicotinoids presents a serious threat to the sustainability of aphid control.Peer reviewe

Hybrid Energy Storage Systems Based on Redox-Flow Batteries: Recent Developments, Challenges, and Future Perspectives

Recently, the appeal of Hybrid Energy Storage Systems (HESSs) has been growing in multiple application fields, such as charging stations, grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance, e.g., efficiency and lifespan. Most recent studies on HESS mainly focus on power management and coupling between the different ESSs without a particular interest in a specific type of ESS. Over the last decades, Redox-Flow Batteries (RFBs) have received significant attention due to their attractive features, especially for stationary storage applications, and hybridization can improve certain characteristics with respect to short-term duration and peak power availability. Presented in this paper is a comprehensive overview of the main concepts of HESSs based on RFBs. Starting with a brief description and a specification of the Key Performance Indicators (KPIs) of common electrochemical storage technologies suitable for hybridization with RFBs, HESS are classified based on battery-oriented and application-oriented KPIs. Furthermore, an optimal coupling architecture of HESS comprising the combination of an RFB and a Supercapacitor (SC) is proposed and evaluated via numerical simulation. Finally, an in-depth study of Energy Management Systems (EMS) is conducted. The general structure of an EMS as well as possible application scenarios are provided to identify commonly used control and optimization parameters. Therefore, the differentiation in system-oriented and application-oriented parameters is applied to literature data. Afterwards, state-of-the-art EMS optimization techniques are discussed. As an optimal EMS is characterized by the prediction of the system’s future behavior and the use of the suitable control technique, a detailed analysis of the previous implemented EMS prediction algorithms and control techniques is carried out. The study summarizes the key aspects and challenges of the electrical hybridization of RFBs and thus gives future perspectives on newly needed optimization and control algorithms for management systems

Molecular Evidence of Tomato Yellow Leaf Curl Virus-Sicily Spreading on Tomato, Pepper and Bean in Tunisia

Unusual symptoms including yellowing, stunting, curling, crumpling and plant size reduction were observed in tomato fields and green houses in Tunisia. These symptoms, generally associated with the Tomato yellow leaf curl virus (TYLCV) complex, have become increasingly common in recent years. In order to ascertain the molecular characteristics of Tunisian isolates by PCR, both the coat protein gene and the intergenic region of eleven isolates were amplified using specific primers, and sequenced. The PCR procedure also allowed the amplification of viral DNA fragments using a bean total DNA as a template. Phylogenetic analysis suggested that these Tunisian isolates clustered with a Sicilian isolate of TYLCSV-Sic. This is the first report of the involvement of this viral species in Phaseolus vulgaris disease

Un service de navigation omniprésent sur les smartphones

La navigation pédestre est un domaine de recherche en pleine croissance qui vise à développer des services assurant le positionnement et la navigation en continu des personnes à l'extérieur comme à l'intérieur de bâtiments. Dans cette thèse, nous proposons un prototype de service pour la navigation pédestre ubiquitaire qui tient compte des préférences de l'utilisateur et de la technologie de positionnement optimale disponible. Notre objectif principal est d'estimer, d'une façon continue, la position d'un piéton muni d'un smartphone. En premier lieu, nous proposons un nouvel algorithme, nommé UCOSA, qui permet de sélectionner la technologie de positionnement à adopter à tout moment le long du processus de navigation. L'algorithme UCOSA commence par inférer la nécessité de déclencher un processus de "handover" (changement de technologie) entre les technologies de positionnement détectées (i.e. quand les zones de couvertures se chevauchent) en utilisant la technique de la logique floue. Ensuite, il sélectionne la technologie optimale à l'aide d'une fonction qui calcule un score pour chaque technologie disponible et qui se compose de deux parties. La première partie représente les poids, calculés en utilisant la méthode d'analyse hiérarchique (AHP). Tandis que, la deuxième partie fournit les valeurs normalisées des paramètres considérés. L'algorithme UCOSA intègre aussi la technique de positionnement à l'estime appelé PDR afin d'améliorer le calcul de la position du smartphone. En second lieu, nous portons l'intérêt à la technique de positionnement par empreintes RSS dont le principe consiste à calculer la position du smartphone en comparant les valeurs RSSs enregistrées, en temps réel, avec les valeurs RSSs stockées dans une base de données (radiomap). La majorité des radiomaps sont représentées sous forme de grilles composées de points de référence (PR). Nous proposons une nouvelle conception de radiomap qui ajoute d'autres PRs au centre de gravité de chaque carré de la grille. En troisième lieu, nous abordons le problème de la construction du graphe modélisant un bâtiment multi-étages. Nous proposons un algorithme qui crée tout d'abord un graphe plan pour chaque étage, séparément, et qui relie ensuite les différents étages par des liens verticaux. En dernier lieu, nous étudions un nouvel algorithme nommé SIONA qui calcule et qui affiche d'une manière continue le chemin entre deux points situés à l'intérieur ou à l'extérieur d'un bâtiment. Plusieurs expériences réelles ont été réalisées pour évaluer les performances des algorithmes proposés avec des résultats prometteurs en termes de continuité et de précision (de l'ordre de 1.8 m) du service de navigation.Pedestrian navigation is a growing research field, which aims at developing services and applications that ensure the continuous positioning and navigation of people inside and outside covered areas (e.g. buildings). In this thesis, we propose a ubiquitous pedestrian navigation service based on user preferences and the most suitable efficient available positioning technology (e.g. WiFi, GNSS). Our main objective is to estimate continuously the position of a pedestrian carrying a smartphone equipped with a variety of technologies and sensors. First, we propose a novel positioning technology selection algorithm, called UCOSA for the complete ubiquitous navigation service in indoor and outdoor environments. UCOSA algorithm starts by inferring the need of a handover between the available positioning technologies on the overlapped coverage areas using fuzzy logic technique. If a handover process is required, a score is calculated for each captured Radio Frequency (RF) positioning technology. The score function consists of two parts: the first part represents the user preferences weights computed based on the Analytic Hierarchy Process (AHP). Whereas, the second part provides the user requirements (normalized values). UCOSA algorithm also integrates the Pedestrian Dead Reckoning (PDR) positioning technique through the navigation process to enhance the estimation of the smartphone's position. Second, we focus on the RSS fingerprinting positioning technique as it is the most widely used technique, which principle is to return the smartphone's position by comparing the real time recorded RSS values with the radiomap (i.e. a database of previous stored RSS values). Most of radiomap are organized in a grid, formed or Reference Point (RP): we propose a new design of radiomap which complements the grid with other RPs located at the center of gravity of each grid square. Third, we address the challenge of constructing a graph for a multi-floor building. We propose an algorithm that starts by creating the horizontal graph of each floor, separately, and then, adds vertical links between the different floors. Finally, we implement a novel algorithm, called SIONA that calculates and displays in a continuous manner the pathway between two distinct points being located indoor or outdoor. We conduct several real experiments inside the campus of the University of Passau in Germany to evaluate the performance of the proposed algorithms. They yield promising results in terms of continuity and accuracy (around 1.8 m indoor) of navigation service

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%