Dive into heritage: a digital documentation platform of world heritage properties in the Arab states region

The world celebrated the 50 years of the World Heritage Convention. With more than 1000 cultural and natural sites, the 1972 World Heritage Convention is the most widely recognized. It has provided a framework for identifying, documenting, protecting, and managing the world's cultural and natural heritage with Outstanding Universal Value (OUV). The theme to mark this anniversary is: “The Next 50: World Heritage as a Source of Resilience, Humanity and Innovation”. These are the topics that have inspired the World Heritage Centre to develop, together with the Member States in the Arab Region, an online platform that leverages digital technologies to safeguard and promote the UNESCO World Heritage sites and its related intangible heritage, and transmit them to future generations. This paper discusses the current state of digital documentation of cultural heritage and the related projects/initiatives in the Arab States region. It presents the UNESCO Dive into Heritage initiative and its first outcomes. It concludes with lessons learned and future steps for the next phases of the project. First outcomes have revealed the big challenge of 3D data integration and the need to accompany the implementation stages of the project with capacity building

Dive into heritage: a digital documentation platform of World Heritage properties in the Arab states region

The world celebrated the 50 years of the World Heritage Convention. With more than 1000 cultural and natural sites, the 1972 World Heritage Convention is the most widely recognized. It has provided a framework for identifying, documenting, protecting, and managing the world's cultural and natural heritage with Outstanding Universal Value (OUV). The theme to mark this anniversary is: "The Next 50: World Heritage as a Source of Resilience, Humanity and Innovation". These are the topics that have inspired the World Heritage Centre to develop, together with the Member States in the Arab Region, an online platform that leverages digital technologies to safeguard and promote the UNESCO World Heritage sites and its related intangible heritage, and transmit them to future generations. This paper discusses the current state of digital documentation of cultural heritage and the related projects/initiatives in the Arab States region. It presents the UNESCO Dive into Heritage initiative and its first outcomes. It concludes with lessons learned and future steps for the next phases of the project. First outcomes have revealed the big challenge of 3D data integration and the need to accompany the implementation stages of the project with capacity building

MODELISATION ET ANALYSE DE LA SERIE CHRONOLOGIQUE DE PRODUCTION D’EAU DE CONSOMMATION PAR LISSAGE EXPONENTIEL

L'analyse des séries temporelles et plus particulièrement la prévision à court et moyen terme, a connu un développement important. La prévision est fondamentale dans la mesure où elle est à la base de la prise de décision optimale. Prévoir le comportement futur d'une série chronologique, nécessite l'utilisation de plusieurs méthodes de prévision, car nous avons constaté que la fiabilité d'une méthode de prévision ne dépend pas seulement de sa complexité théorique, mais aussi des données, de l'information disponible et du champs d'application. L’objectif de la modélisation de la série temporelle observée est de prédire son comportement futur à travers la détermination d’un lisseur approprié de la série des données observées. Dans cette étude Le problème de lissage est traité de façon non paramétrique en utilisant les lisseurs exponentiels qui permettent d’accorder un poids plus important aux valeurs récentes de la chronique. Un modèle de prévision de la production d’eau de consommation à partir des champs captant de la ville de Biskra est proposé. Les données hebdomadaires proviennent de l’Algérienne Des Eaux (ADE), couvrent la période de janvier 2009 à Décembre 2011. L’ensemble des résultats a permis de faire la prévision à un horizon donnée après le calage et validation de modèle et de fournir quelques éléments de réponse sur la forme de la série à long terme et sur la série des résidus obtenue (série observée – série lissée). The time series analysis and forecasting especially the short and medium term, has been an important development. Forecasting is fundamental to the extent that it is based on the optimal decision making. Predict the future behavior of a time series requires the use of several methods of forecasting, because we found the reliability of a prediction method depends not only on its theoretical complexity, but also data, the available information and application fields. The objective of modeling the observed time series is to predict its future behavior by the determination of an appropriate smoothing of the observed series data. In this study the problem of smoothing is treated using exponential smoothing which allows giving more weight to recent values of the time series. A predictive model for the production of drinking water from fields capturing of Biskra city is proposed. Weekly data from the Public authority (ADE) cover the period from January 2009 to December 2011. The results led to the forecast horizon given after calibration and validation model and provide some answers in the form of long-term series and residual series obtained (observed series - smoothed series)

DIVE INTO HERITAGE: A DIGITAL DOCUMENTATION PLATFORM OF WORLD HERITAGE PROPERTIES IN THE ARAB STATES REGION

The world celebrated the 50 years of the World Heritage Convention. With more than 1000 cultural and natural sites, the 1972 World Heritage Convention is the most widely recognized. It has provided a framework for identifying, documenting, protecting, and managing the world's cultural and natural heritage with Outstanding Universal Value (OUV). The theme to mark this anniversary is: “The Next 50: World Heritage as a Source of Resilience, Humanity and Innovation”. These are the topics that have inspired the World Heritage Centre to develop, together with the Member States in the Arab Region, an online platform that leverages digital technologies to safeguard and promote the UNESCO World Heritage sites and its related intangible heritage, and transmit them to future generations. This paper discusses the current state of digital documentation of cultural heritage and the related projects/initiatives in the Arab States region. It presents the UNESCO Dive into Heritage initiative and its first outcomes. It concludes with lessons learned and future steps for the next phases of the project. First outcomes have revealed the big challenge of 3D data integration and the need to accompany the implementation stages of the project with capacity building

Temporal context and conditional associative learning

<p>Abstract</p> <p>Background</p> <p>We investigated how temporal context affects the learning of arbitrary visuo-motor associations. Human observers viewed highly distinguishable, fractal objects and learned to choose for each object the one motor response (of four) that was rewarded. Some objects were consistently preceded by specific other objects, while other objects lacked this task-irrelevant but predictive context.</p> <p>Results</p> <p>The results of five experiments showed that predictive context consistently and significantly accelerated associative learning. A simple model of reinforcement learning, in which three successive objects informed response selection, reproduced our behavioral results.</p> <p>Conclusions</p> <p>Our results imply that not just the representation of a current event, but also the representations of past events, are reinforced during conditional associative learning. In addition, these findings are broadly consistent with the prediction of attractor network models of associative learning and their prophecy of a persistent representation of past objects.</p

Genomic Characterization of the Taylorella Genus

The Taylorella genus comprises two species: Taylorella equigenitalis, which causes contagious equine metritis, and Taylorella asinigenitalis, a closely-related species mainly found in donkeys. We herein report on the first genome sequence of T. asinigenitalis, analyzing and comparing it with the recently-sequenced T. equigenitalis genome. The T. asinigenitalis genome contains a single circular chromosome of 1,638,559 bp with a 38.3% GC content and 1,534 coding sequences (CDS). While 212 CDSs were T. asinigenitalis-specific, 1,322 had orthologs in T. equigenitalis. Two hundred and thirty-four T. equigenitalis CDSs had no orthologs in T. asinigenitalis. Analysis of the basic nutrition metabolism of both Taylorella species showed that malate, glutamate and alpha-ketoglutarate may be their main carbon and energy sources. For both species, we identified four different secretion systems and several proteins potentially involved in binding and colonization of host cells, suggesting a strong potential for interaction with their host. T. equigenitalis seems better-equipped than T. asinigenitalis in terms of virulence since we identified numerous proteins potentially involved in pathogenicity, including hemagluttinin-related proteins, a type IV secretion system, TonB-dependent lactoferrin and transferrin receptors, and YadA and Hep_Hag domains containing proteins. This is the first molecular characterization of Taylorella genus members, and the first molecular identification of factors potentially involved in T. asinigenitalis and T. equigenitalis pathogenicity and host colonization. This study facilitates a genetic understanding of growth phenotypes, animal host preference and pathogenic capacity, paving the way for future functional investigations into this largely unknown genus

Effects of Wind Speed and Direction on Monthly Fluctuations of Cladosporium Conidia Concentration in the Air

This study determined the relationship between airborne concentration of Cladosporium spp. spores and wind speed and direction using real data (local wind measured by weather station) and modelled data (air mass flow computed with the aid of HYbrid Single Particle Lagrangian Trajectory model). Air samples containing fungal conidia were taken at an urban site (Worcester, UK) for a period of five consecutive years using a spore trap of the Hirst design. A threshold of ≥6000 s m−3 (double the clinical value) was applied in order to select high spore concentration days, when airborne transport of conidia at a regional scale was more likely to occur. Collected data were then examined using geospatial and statistical tools, including circular statistics. Obtained results showed that the greatest numbers of spore concentrations were detected in July and August, when C. herbarum, C. cladosporioides and C. macrocarpum sporulate. The circular correlation test was found to be more sensitive than Spearman’s rank test. The dominance of either local wind or the air mass on Cladosporium spore distributions varied between examined months. Source areas of this pathogen had an origin within the UK territory. Very high daily mean concentrations of Cladosporium spores were observed when daily mean local wind speed was vs ≤ 2.5 m s−1 indicating warm days with a light breeze

Intelligent control and security of fog resources in healthcare systems via a cognitive fog model

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this paper, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN and DT) using a standard security dataset (NSL-KDD)