A new paradigm for deep sustainability: biourbanism

Biourbanism introduces new conceptual and planning models for a new kind of city, valuing social and economical regeneration of the built environment through developing and healthy communities. Thus, it combines technical aspects, such as zero-emission, energy efficiency, information technology, etc. and the promotion of social sustainability and human well being. In effect, this new paradigm endorses principles of geometrical coherence, Biophilic design, BioArchitecture, Biomimesis, etc. in practices of design and also new urban policies and, especially Biopolitics to promote urban revitalization by ensuring that man-made changes do not have harmful effects to humans. Green city standards start inside the designs of each building and continue either in unbuilt spaces surrounding buildings or inside complex infrastructural networks, connecting buildings and people. The proposed presentation should illustrate how new exciting developments recently, such as fractals, complexity theory, evolutionary biology and artificial intelligence are interrelated and constantly stimulate interaction between human beings and the surrounding environment. New Biophilic solutions in designs of buildings have been proved as attractive opportunities for new markets of housing. Thus, some new infrastructural projects start embracing Biophilic advanced solutions which finally aim at energy efficiency and optimal performance. As parallel activity we can now see emerging new innovative monitoring systems of building health not only in small scale, but also in large scale buildings, such as rail stations, for example, and commercial centres or even sometimes entire educational complexes integrated to new infrastructural projects. Some important case studies are going to be presented; they have been analysed and evaluated by Biourbanism and Biophilia principles and applied methods of design

Horizontal visibility graphs transformed from fractional Brownian motions: Topological properties versus Hurst index

Nonlinear time series analysis aims at understanding the dynamics of stochastic or chaotic processes. In recent years, quite a few methods have been proposed to transform a single time series to a complex network so that the dynamics of the process can be understood by investigating the topological properties of the network. We study the topological properties of horizontal visibility graphs constructed from fractional Brownian motions with different Hurst index $H\in(0,1)$. Special attention has been paid to the impact of Hurst index on the topological properties. It is found that the clustering coefficient $C$ decreases when $H$ increases. We also found that the mean length $L$ of the shortest paths increases exponentially with $H$ for fixed length $N$ of the original time series. In addition, $L$ increases linearly with respect to $N$ when $H$ is close to 1 and in a logarithmic form when $H$ is close to 0. Although the occurrence of different motifs changes with $H$, the motif rank pattern remains unchanged for different $H$. Adopting the node-covering box-counting method, the horizontal visibility graphs are found to be fractals and the fractal dimension $d_B$ decreases with $H$. Furthermore, the Pearson coefficients of the networks are positive and the degree-degree correlations increase with the degree, which indicate that the horizontal visibility graphs are assortative. With the increase of $H$, the Pearson coefficient decreases first and then increases, in which the turning point is around $H=0.6$. The presence of both fractality and assortativity in the horizontal visibility graphs converted from fractional Brownian motions is different from many cases where fractal networks are usually disassortative.Comment: 12 pages, 8 figure

Assessment of long-range correlation in animal behaviour time series: the temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus)

The aim of this study was to evaluate the performance of a classical method of fractal analysis, Detrended Fluctuation Analysis (DFA), in the analysis of the dynamics of animal behavior time series. In order to correctly use DFA to assess the presence of long-range correlation, previous authors using statistical model systems have stated that different aspects should be taken into account such as: 1) the establishment by hypothesis testing of the absence of short term correlation, 2) an accurate estimation of a straight line in the log-log plot of the fluctuation function, 3) the elimination of artificial crossovers in the fluctuation function, and 4) the length of the time series. Taking into consideration these factors, herein we evaluated the presence of long-range correlation in the temporal pattern of locomotor activity of Japanese quail ({\sl Coturnix coturnix}) and mosquito larva ({\sl Culex quinquefasciatus}). In our study, modeling the data with the general ARFIMA model, we rejected the hypothesis of short range correlations (d=0) in all cases. We also observed that DFA was able to distinguish between the artificial crossover observed in the temporal pattern of locomotion of Japanese quail, and the crossovers in the correlation behavior observed in mosquito larvae locomotion. Although the test duration can slightly influence the parameter estimation, no qualitative differences were observed between different test durations