VirtualMesh: An Emulation Framework for Wireless Mesh Networks in OMNeT++

Wireless Mesh Networks (WMN) have proven to be a key technology for increased network coverage of Internet infrastructures. The development process for new protocols and architectures in the area of WMN is typically split into evaluation by network simulation and testing of a prototype in a test-bed. Testing a prototype in a real test-bed is time-consuming and expensive. Irrepressible external interferences can occur which makes debugging difficult. Moreover, the test-bed usually supports only a limited number of test topologies. Finally, mobility tests are impractical. Therefore, we propose VirtualMesh as a new testing architecture which can be used before going to a real test-bed. It provides instruments to test the real communication software including the network stack inside a controlled environment. VirtualMesh is implemented by capturing real traffic through a virtual interface at the mesh nodes. The traffic is then redirected to the network simulator OMNeT++. In our experiments, VirtualMesh has proven to be scalable and introduces moderate delays. Therefore, it is suitable for predeployment testing of communication software for WMNs.

Identification of Francisella tularensis cluster in central and western Europe

We conducted a molecular analysis of Francisella tularensis strains isolated in Switzerland and identified a specific subpopulation belonging to a cluster of F. tularensis subsp. holarctica that is widely dispersed in central and western continental Europe. This subpopulation was present before the tularemia epidemics on the Iberian Peninsula.

Long-term patterns of subjective wellbeing in schizophrenia: cluster, predictors of cluster affiliation, and their relation to recovery criteria in 2842 patients followed over 3 years

To study the longitudinal patterns of subjective wellbeing in schizophrenia using cluster analysis and their relation to recovery criteria, further to examine predictors for cluster affiliation, and to evaluate the sensitivity and specificity of baseline subjective wellbeing cut-offs for cluster affiliation.

Cluster randomized clinical trials in orthodontics: design, analysis and reporting issues

Cluster randomized trials (CRTs) use as the unit of randomization clusters, which are usually defined as a collection of individuals sharing some common characteristics. Common examples of clusters include entire dental practices, hospitals, schools, school classes, villages, and towns. Additionally, several measurements (repeated measurements) taken on the same individual at different time points are also considered to be clusters. In dentistry, CRTs are applicable as patients may be treated as clusters containing several individual teeth. CRTs require certain methodological procedures during sample calculation, randomization, data analysis, and reporting, which are often ignored in dental research publications. In general, due to similarity of the observations within clusters, each individual within a cluster provides less information compared with an individual in a non-clustered trial. Therefore, clustered designs require larger sample sizes compared with non-clustered randomized designs, and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this article to highlight with relevant examples the important methodological characteristics of cluster randomized designs as they may be applied in orthodontics and to explain the problems that may arise if clustered observations are erroneously treated and analysed as independent (non-clustered).