CAINE: A Context-Aware Information-Centric Network Ecosystem

Information-centric networking (ICN) is an emerging networking paradigm that places content identifiers rather than host identifiers at the core of the mechanisms and protocols used to deliver content to end users. Such a paradigm allows routers enhanced with content-awareness to play a direct role in the routing and resolution of content requests from users, without any knowledge of the specific locations of hosted content. However, to facilitate good network traffic engineering and satisfactory user QoS, content routers need to exchange advanced network knowledge to assist them with their resolution decisions. In order to maintain the location-independency tenet of ICNs, such knowledge (known as context information) needs to be independent of the locations of servers. To this end, we propose CAINE - Context-Aware Information-centric Network Ecosystem - which enables context-based operations to be intrinsically supported by the underlying ICN routing and resolution functions. Our approach has been designed to maintain the location-independence philosophy of ICNs by associating context information directly to content rather than to the physical entities such as servers and network elements in the content ecosystem, while ensuring scalability. Through simulation, we show that based on such location-independent context information, CAINE is able to facilitate traffic engineering in the network, while not posing a significant control signalling burden on the network

A common ontology for multi-dimensional shapes

In recent years, digital shapes have become more and more widespread and have been made available in a plethora of online repositories. A systematic and formal approach for capturing and representing shape-related information is needed to facilitate its reuse and enable the demonstration of useful cross-domain usage scenarios. In this paper we present an ontology for digital shapes, called the Common Shape Ontology (CSO). We discuss the rationale, the requirements and the scope of this ontology, we present in detail its structure and describe the most relevant choices related to its development. Finally, we show how the CSO conceptualization is used in domain-specific application scenarios

Genetic origins of the Minoans and Mycenaeans

The origins of the Bronze Age Minoan and Mycenaean cultures have puzzled archaeologists for more than a century. We assembled genome-wide data from nineteen ancient individuals, including Minoans from Crete, Mycenaeans from mainland Greece, and their eastern neighbours from southwestern Anatolia. We show that Minoans and Mycenaeans were genetically similar, having at least three quarters of their ancestry from the first Neolithic farmers of western Anatolia and the Aegean1,2, and most of the remainder from ancient populations like those of the Caucasus3 and Iran4,5. However, the Mycenaeans differed from Minoans in deriving additional ancestry from an ultimate source related to the hunter-gatherers of eastern Europe and Siberia6–8, introduced via a proximal source related to either the inhabitants of either the Eurasian steppe1,6,9 or Armenia4,9. Modern Greeks resemble the Mycenaeans, but with some additional dilution of the early Neolithic ancestry. Our results support the idea of continuity but not isolation in the history of populations of the Aegean, before and after the time of its earliest civilizations

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales. Archives of Virology (2021) 166:3567–3579. https://doi.org/10.1007/s00705-021-05266-wIn March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.This work was supported in part through Laulima Government Solutions, LLC prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272201800013C. J.H.K. performed this work as an employee of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC under Contract No. HHSN272201800013C. This work was also supported in part with federal funds from the National Cancer Institute (NCI), National Institutes of Health (NIH), under Contract No. 75N91019D00024, Task Order No. 75N91019F00130 to I.C., who was supported by the Clinical Monitoring Research Program Directorate, Frederick National Lab for Cancer Research. This work was also funded in part by Contract No. HSHQDC-15-C-00064 awarded by DHS S&T for the management and operation of The National Biodefense Analysis and Countermeasures Center, a federally funded research and development center operated by the Battelle National Biodefense Institute (V.W.); and NIH contract HHSN272201000040I/HHSN27200004/D04 and grant R24AI120942 (N.V., R.B.T.). S.S. acknowledges partial support from the Special Research Initiative of Mississippi Agricultural and Forestry Experiment Station (MAFES), Mississippi State University, and the National Institute of Food and Agriculture, US Department of Agriculture, Hatch Project 1021494. Part of this work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001030), the UK Medical Research Council (FC001030), and the Wellcome Trust (FC001030).S

2020 taxonomic update for phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

In March 2020, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. At the genus rank, 20 new genera were added, two were deleted, one was moved, and three were renamed. At the species rank, 160 species were added, four were deleted, ten were moved and renamed, and 30 species were renamed. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

In March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV

A Real Time Decision Support System for Roadway Network Incident Response Logistics

Incident-related congestion is a serious problem of great concern for most metropolitan traffic management authorities. The high economic and social impact associated with the incident-related congestion has prompted Traffic Management Agencies world-wide to develop incident management systems (IMS). Incident response logistics (IRL) encompass all actions needed for the effective deployment of incident response resources and constitute an essential component of any IMS. The incident management decision making environment suggests that decision support systems (DSSs) can be used in order to improve the quality of the decisions and expedite the decision making process of the IRL. The objective of this paper is to develop a DSS for supporting real-time decisions related to IRL. The development of the proposed DSS is based on an extensive user-requirements survey in six European countries and integrates mathematical models, rules and algorithms in a user friendly environment in order to minimise incident response time. The proposed DSS provides the following functionalities: (i) districting, (ii) dispatching of response units (RUs), (iii) routing of the RUs, and (iv) on-scene management and it has been demonstrated successfully under real life conditions and accepted as a useful decision making tool by its users

A Methodological framework for developing a DSS for Hazardous Material Emergency Response Operations

The production, storage, and transportation of hazardous materials are processes of vital economic importance for any advanced and technologically complex society. Although the production and distribution of hazardous materials is associated with economic development, there is a significant potential danger to the natural and social environment in the event of their accidental release, a fact that prompts for the development and implementation of methods and techniques that aim to improve hazardous materials risk management decisions. The objective of this paper is to present a unified framework for developing a Decision Support System (DSS) for supporting a vital function of risk management, namely the management of emergency response operations. The proposed framework recognizes the peculiarities of the hazardous materials decision-making environment which is characterized by: (i) multiple stakeholders, i.e., persons and organizations involved in and affected by hazardous materials risk management decisions; (ii) lack of a formal management structure for monitoring and controlling in a unified manner all Emergency Response Resources; (iii) lack of clear distinction and fragmentation of responsibilities of the actors involved in risk management operations; and (iv) dynamic/real-time decisions, i.e., risk determinants change over time. The proposed framework was used in order to develop a DSS for managing emergency response operations for large scale industrial accidents in Western Attica, Greece