Search Results (3)

The emergence of zoonotic viral diseases, notably exemplified by the recent coronavirus disease pandemic in 2019 (COVID-19), underscores the critical need to understand the dynamics of viruses circulating in wildlife populations. This study aimed to investigate the diversity of coronaviruses in bat populations from northeastern Brazil, particularly in the state of Ceará, where little research on bat pathogens has been conducted previously. Bat sampling was performed between March 2021 and March 2022 across three municipalities, resulting in the collection of oral and rectal swabs from 298 captured individuals. Molecular analyses revealed alphacoronaviruses in multiple bat species. Additionally, a novel Betacoronavirus was identified in Artibeus planirostris, which did not fall within an established subgenus. Phylogenetic placement of these new coronavirus sequences suggests that closely related coronavirus lineages can infect a wide range of bat species sampled in distantly related Brazilian states and biomes. No SARS-CoV-2 and influenza A viruses were found in the sampled bats. These findings expand our understanding of coronavirus diversity in Brazilian bats. The detection of coronaviruses in various bat species underscores the importance of bats as reservoirs for these viruses. The absence of SARS-CoV-2 in the sampled bats indicates a lack of spillback events from human or environmental sources. However, the potential for future transmission events underscores the importance of ongoing surveillance and transmission mitigation protocols in wildlife management practices. Full article

Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data (vehicles, pedestrians, etc.). The LDM should have a common and well-defined input system in order to be interoperable across multiple data sources such as sensor detections or V2X communications. In this work, we present an interoperable graph-based LDM (iLDM) using Neo4j as our database engine and OpenLABEL as a common data format. An analysis on data insertion and querying time to the iLDM is reported, including a vehicle discovery service function in order to test the capabilities of our work and a comparative analysis with other LDM implementations showing that our proposed iLDM outperformed in several relevant features, furthering its practical utilisation in advanced driver assistance system development. Full article

Many bandwidth-intensive applications (such as online live, online games, etc.) are more suitable for using multicast to transmit information. Due to the advantages in scalability, Shared Tree (ST) is more suitable for large-scale deployment than Source-Based Tree (SBT). However, in ST-based multicast, all multicast sources need to send multicast data to a center node called a core, which will lead to core overload and traffic concentration. Besides, most existing multicast protocols use the shortest path between the source or the core and each receiver to construct the multicast tree, which will result in traffic overload on some links. In this paper, we propose an Identifier and Locator Decoupled Multicast approach (ILDM) based on Information-Centric Networking (ICN). ILDM uses globally unique names to identify multicast services. For each multicast service, the mapping between the multicast service name and the addresses of multicast tree nodes is stored in the Name Resolution System (NRS). To avoid core overload and traffic aggregation, we presented a dynamic core management and selection mechanism, which can dynamically select a low-load core for each multicast service. Furthermore, we designed a path state-aware multicast tree node selection mechanism to achieve traffic load balancing by using low-load links more effectively. Experimental results showed that our proposed multicast approach outperformed some other multicast methods in terms of core load, number of join requests, link load, traffic concentration, and routing state. Full article