The hierarchy structure in directed and undirected signed networks

The concept of social stratification and hierarchy among human dates is back to the origin of human race. Presently, the growing reputation of social networks has given us with an opportunity to analyze these well-studied phenomena over different networks at different scales. Generally, a social network could be defined as a collection of actors and their interactions. In this work, we concern ourselves with a particular type of social networks, known as trust networks. In this type of networks, there is an explicit show of trust (positive interaction) or distrust (negative interaction) among the actors. In a social network, actors tend to connect with each other on the basis of their perceived social hierarchy. The emergence of such a hierarchy within a social community shows the manner in which authority manifests in the community. In the case of signed networks, the concept of social hierarchy can be interpreted as the emergence of a tree-like structure comprising of actors in a top-down fashion in the order of their ranks, describing a specific parent-child relationship, viz. child trusts parent. However, owing to the presence of positive as well as negative interactions in signed networks, deriving such “trust hierarchies” is a non-trivial challenge. We argue that traditional notions (of unsigned networks) are insufficient to derive hierarchies that are latent within signed networks

Analyzing large- scale smart card data to investigate public transport travel behaviour using big data analytics

In urban public transport, Smart card data have been used more and more in order to collect fare automatically. They allowed passengers to access almost all type of public transportation system modes (bus, train, tram, funiculars, LRT, metro, and ferryboats) with a single card that is valid for the complete journey. Although Smart card major concentration is in revenue collection, they also generate massive amounts of passive data from the technological devices installed to control the operation of them. Generated data could be beneficial to transit planners which could rise the better understanding of passengers’ behavioral patterns for short and long term service planning. However, one of the major challenges is the fact that traditional infrastructures and methods are inefficient when processing or analyzing a large volume of data. Thus, as an alternative, big data technology could be employed to enhance collecting, storing, processing, and analyzing the data. Moreover, the main motivation would be cost-efficiency of this methodology as the cost of processing and analyzing large-scale data is huge. This experience demonstrates that a combination of planning knowledge, big data, and data mining tool allows to produce travel behaviors indicators, public transport policies, operational performance, and fare policies

A hybrid approach based on numerical, statistical and intelligent techniques for optimization of tube drawing process to produced squared section from round tube

In the tube drawing process, there are a bunch of parameters which play key role in process performance. Thus, finding the optimized parameters is a controversial issue. Current study aimed to produce a squared section of round tube by tube sinking process. To simulate the process finite element method (FEM) was used. Then, to find a meaningful kinship between process input and output parameters the developed FE model was associated with the design of experiment based response surface methodology (RSM). The sufficiency of each model was checked by analysis of variances. Further, the SA (simulated annealing) was associated with RSM models to find the optimal solution regarding maximum thickness distributions and minimum force and dimensional error. Hereafter, for performing accurate optimization, the principal component analysis was used to find the appropriate weight factor of each response. The obtained results were in right agreement with those derived from simulation and confirmatory experiment