Indexing Uncertain Categorical Data over Distributed Environment

International audienceToday, a large amount of uncertain data is produced by several applications where the management systems of traditional databases incuding indexing methods are not suitable to handle such type of data. In this paper, we propose an inverted based index method for effciently searching uncertain categorical data over distributed environments. We adress two kinds of query over the distributed uncertain databases, one a distributed probabilis-tic thresholds query, where all results sastisfying the query with probablities that meet a probablistic threshold requirement are returned, and another a distributed top k-queries, where all results optimizing the transfer of the tuples and the time treatment are returned

Development of a New Slit-Slotted Shaped Microstrip Antenna Array for Rectenna Application

Abstract-These paper presents a new 3X3 array design using a microstrip patch array antenna to operate at 2.45GHz. The aim of this antenna array construction is to obtain high directivity. The element of the array is microstrip square patch antenna using V-shaped symmetric-slit along with rectangular slot in diagonal direction at the centre of square patch radiator to achieve the circularly polarized radiation and each element is fed by inset feed. The size and feed position of the single microstrip square patch is determined through the theoretical design and CST microwave studio software simulation. Based on which an array of six elements with equal sizes and equal spacing is designed on a planar substrate. The simulation results in this paper can be used as design reference for the practical design of the rectenna

An overview of the welfare of animals used for scientific and educational purposes in Algeria

This study describes the welfare and animals used for scientific and educational purposes in the field of laboratory animal sciences in Algeria. The aim of this study is to provide an overview of the status of the care and use of animals and to improve implementing plans and animal welfare measures. A literature review was performed using online databases and reference lists of the US National Library of Medicine to assess the prevalence of animal use for research in Algeria between 2013 and 2017. Also a retrospective study was conducted using the Pasteur Institute of Algeria report for 2015 to assess the prevalence of animal use in both teaching and research. The first workshop on animal experimentation was organized in 2013 in collaboration with international animal laboratory organizations (ICLAS and OIE) and involving the participation of universities, research centers, veterinary schools and the Pasteur Institute of Algeria. In addition, after accreditation of the Algerian Association of Experimental Animal Sciences, a number of training workshops and courses relating to laboratory animal sciences were organized. In Algeria the use of laboratory animals in research and education is a subject of debate regarding the need to establish regulations and to propose an appropriate ethical framework for the use of animals. Finally, some actions have been already taken in Algeria to promote the ethical use of animals but many more sustainable actions are needed and require cooperation, harmonization of policies and establishment of regional and international networks for experience exchange

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Gestion des données incertaines dans un environnement distribué

Ces dernières années, les données deviennent incertaines en raison du fleurissement des technologies de pointe qui participent continuellement et de plus en plus dans la production d’une grande quantité de données incertaines. Surtout, que certains nombres d’applications ou l’incertitude est omniprésentes sont distribuées dans la nature, e.g. Des réseaux de capteur distribués, l’extraction de l’information, l’intégration de données, le réseau social, etc. Par conséquent, malgré que ‘incertitudes a été étudier dans la littérature des bases de données centralisé, il reste toujours des défis à relever dans le contexte des bases de données distribuées. Dans ce travail, nous nous concentrons sur le type de données qui est composé d’un ensemble d’attributs descriptifs, qui ne sont ni numériques, ni en soi ordonnés en aucune façon, à savoir des données catégoriques. Nous proposons deux approches pour la gestion de données catégorielles incertaines dans un environnement distribué. Ces approches sont construites sur une technique d’indexation hiérarchique et des algorithmes distribués pour efficacement traiter certain types de requêtes sur des données incertaines dans un environnement distribué Dans la première approche, nous proposons une technique d’indexation distribuée basée sur la structure d’index inversée pour efficacement rechercher des données catégoriques incertaines dans un environnement distribué. En utilisant cette technique d’indexation, nous adressons deux types de requêtes sur les bases de données incertaines distribuées (1) une requête de seuils probabiliste distribuée, où les réponses obtenues satisfont l’exigence de seuil de probabilités (2) une requêtes probabiliste de meilleurs k-réponse, en assurant l’optimisation de transfert du tuples des sites interrogés au site de coordinateur en un temps réduit . Des expériences empiriques sont conduites pour vérifier l’efficacité et l’efficacité de la méthode proposée en termes de coûts de communication et le temps de réponse. La deuxième approche se concentre sur les requêtes Top-k , on propose un algorithme distribué à savoir TDUD. Son but est de trouves les meilleurs k réponses sur des données catégorielles incertaines distribuées en un seul tour seul de communication. Pour aboutir à ce but, nous enrichissons l’index incertain global proposé dans la première approche avec d’autres informations qui résument les indexes locaux afin de minimiser le coût de communication, De plus, en utilisant les moyennes de dispersion de probabilité de chaque site, on peut prévoir le nombre de sites qu’on doit interroger afin d’avoir les meilleurs k réponse, ainsi élaguer les sites qui ne fournis pas de réponse, ce qui engendre un meilleur temps d’exécution et moins de transfert de tuples. Des expériences vastes sont conduites pour vérifier l’efficacité de la méthode proposée en termes de coûts de communication et le temps de réponse. Nous montrons empiriquement que l’algorithme lié est presque optimal, dans lequel, il peut typiquement récupérer les meilleurs k-réponses en communiquant un nombre restreint de tuples dans un seul tour seul.In recent years, data has become uncertain due to the flourishing advanced technologies that participate continuously and increasingly in producing large amounts of incomplete data. Often, many modern applications where uncertainty occurs are distributed in nature, e.g., distributed sensor networks, information extraction, data integration, social network etc. Consequently, even though the data uncertainty has been studied in the past for centralized behavior, it is still a challenging issue to manage uncertainty over the data in situ. In this work, we focus on the type of data records that are composed of a set of descriptive attributes, which are neither numeric nor inherently ordered in any way namely categorical data. We propose two approaches to managing uncertain categorical data over distributed environments. These approaches are built upon a hierarchical indexing technique and a distributed algorithm to efficiently process queries on uncertain data in distributed environment In the first approach, we propose a distributed indexing technique based on inverted index structure for efficiently searching uncertain categorical data over distributed environments. By leveraging this indexing technique, we address two kinds of queries on the distributed uncertain databases (1) a distributed probabilistic thresholds query, where its answers are satisfy the probabilistic threshold requirement (2) a distributed top k-queries, optimizing, the transfer of the tuples from the distributed sources to the coordinator site and the time treatment. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method in terms of communication costs and response time. The second approach is focuses on answering top-k queries and proposing a distributed algorithm namely TDUD. Its aim is to efficiently answer top-k queries over distributed uncertain categorical data in single round of communication. For that purpose, we enrich the global uncertain index provided in the first approach with richer summarizing information from the local indexes, and use it to minimize the amount of communication needed to answer a top-k query. Moreover, the approach maintains the mean sum dispersion of the probability distribution on each site which are then merged at the coordinator site. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method in terms of communication costs and response time. We show empirically that the related algorithm is near-optimal in that it can typically retrieve the top-k query answers by communicating few k tuples in a single round

Efficient rectenna design incorporating new circularly polarized antenna array for wireless power transmission at 2.45GHz

International audienceWireless power transmission (WPT) is a promising technology to remotely energizing low power electronic devices in wireless communication circuits. In this paper we present an efficient rectenna (rectifier+antenna) design which is the crucial part in a microwave power transmission system. The developed design contains a new 3×3 antenna array having an operating frequency of 2.45 GHz with circular polarization and high gain, the developed structure has been modeled and carried out by using CST Microwave Studio, this antenna array is associated with an RF-to-DC microstrip rectifier which provides a high conversion efficiency of 65.8% and a DC output voltage of 7.02V which can be reached for a given input power level of 20dBm and an optimum load resistor of 750Ω