In the present work, by using a paralelepipedic thermogravitational microcolumn, the temperature gradient influence on the stability of the flow was or has been examined, emphasizing mixtures with negative Soret coefficients. Experiments and numerical analysis were conducted for DCMIX2 Toulene-Methanol binary subsystem. This binary subsystem has a broad range of negative Soret values for low concentrations of Methanol which was analysed. Two different concentrations have been studied in order to confirm existence of temporal stability windows of those mixtures. Experiments were compared with numerical simulations conducted in open source software OpenFOAM, for both cases.

In recent years, many researchers have addressed the issue of interior climate and how it affects human health. Investigations performed at schools and office buildings have found that CO2 concentrations often exceed the limit value of 1500 ppm given in Decree No. 20/2012 Coll., on technical requirements for buildings. In addition, interior space often exhibits very low relative humidity. This results in poor conditions that are detrimental to human health and not conducive to studying and work. One means of improving the interior microclimate is implementing green walls. These walls can help generate a much better climate and greatly enhance the mental well-being of the inhabitants. In addition, they greatly improve dust levels and acoustics in the room. The research compared the interior conditions in two classrooms at the Faculty of Civil Engineering, Brno University of Technology. One had a green wall installed while the other was in its original configuration. CO2 concentration, temperature, and relative humidity were measured. A survey was conducted to assess the influence of the green wall on students and teachers (mental well-being, efficiency, productivity, creativity, etc.). Results obtained thus far show that the room with the green wall provides far better interior conditions, mainly in terms of lower CO2 concentration and higher relative humidity, improving students’ and teachers’ mood and health (as confirmed by the survey as well).

In this paper we explore a way of securing a secret inside a graph by observing pieces of the secret as colors assigned to the graph vertices. If a graph allows a highly (a, b)-resistant k-multicoloring then a secret can be divided into k parts and sets of those parts distributed to the vertices of the graph so that no a attackers can steal the secret, and when a attackers and b malfuntioning vertices leave the graph, the secret is still whole in the remaining graph. In this paper we explore how many vertices a graph must have in order to allow a highly (3, 1)-resistant k-multicoloring, and what is the minimal number of colors, for graphs that do allow such multicoloring.

. Complex networks are often used to model objects and their relations. Network descriptors are graph-theoretical invariants assigned to graphs that correspond to complex networks. Transmission and betweenness centrality are well known network descriptors and networkness and network surplus have been recently analyzed. All these four descriptors are based on the unrealistic assumption about equal communication between all vertices. Here, we amend this by assuming that vertices on the distance larger then one communicate less than those that are neighbors. We analyze network descriptors for all possible values of the factor α that measures reduction in the communication of the vertices that are not neighbors. We term these descriptors one-alpha descriptors and determine their extremal values.

Networks (or graphs) appear as dominant structures in different domains, including sociology, biology, neuroscience and computing. In most cases, these graphs are directed which changes the semantics of the edges that are no longer symmetrical in the sense that the beginning vertex transfers some property or value to the end vertex, but not vice versa. Detecting community structure in complex networks is an interdisciplinary topic with many relevant areas of application. In order to detect communities in directed acyclic networks, apart from the direction of the edge, the requirement for topological ordering of the vertices should be taken into account. In other words, if the vertices are topologically order is such a way that x1 < x2 < ... < xn we are interested in dividing the network into communities C1, C2, ..., Ck in such a way that: if xi < xj , xi ∈ Ci, xj ∈ Cj then Ci < Cj or Ci = Cj We present an algorithm derived from LPA algorithms which are commonly used in network detection, mostly because of their quick computational time and fairly good results. They were originally developed for undirected networks, but have been modified for this purpose.