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Recently, methods from the statistical physics of complex systems have been applied successfully to identify universal features in the long-range correlations (LRCs) of written texts. However, in real texts, these universal features are being intermingled with language-specific influences. This paper aims at the characterization and further understanding of the interplay between universal and language-specific effects on the LRCs in texts. To this end, we apply the language-sensitive mapping of written texts to word-length series (wls) and analyse large parallel (of same content) corpora from 10 languages classified to four families (Romanic, Germanic, Greek and Uralic). The autocorrelation functions of the wls reveal tiny but persistent LRCs decaying at large scales following a power-law with a language-independent exponent [Formula: see text]0.60–0.65. The impact of language is displayed in the amplitude of correlations where a relative standard deviation [Formula: see text]40% among the analyzed languages is observed. The classification to language families seems to play a significant role since, the Finnish and Germanic languages exhibit more correlations than the Greek and Roman families. To reveal the origins of the LRCs, we focus on the long words and perform burst and correlation analysis in their positions along the corpora. We find that the universal features are linked more to the correlations of the inter-long word distances while the language-specific aspects are related more to their distributions.

In this paper we report a low cost, simple, electrochemical method for large-area growth of single crystal ZnO nanorods. The method utilizes a metallic zinc foil as the source of the necessary zinc ions for ZnO growth on indium-doped tin oxide (ITO) glass slides. The method is thoroughly discussed and investigated varying all the parameters involved. The resulting ZnO nanorods are highly oriented along c-axis and densely packed, while their length and diameter can be tuned by varying the growth parameters. Two different types of seed layers on the ITO glass slides are tested. A seed layer made by spin coating of ZnO nanoparticles results in a twofold increase of the ZnO nanorod surface density as compared with a ZnO thin film seed layer by physical vapor deposition. Additionally, the effect of oxygen supply during electrodeposition was investigated as a crucial regulatory parameter not only for the geometrical and topological characteristics of the ZnO nano-arrays but for their physical properties as well.

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Several applications of nanotechnology are based on the surface nanopatterning of materials and the new functionalities it brings about. Not surprisingly, the novel material properties are tightly linked to nanostructure morphology and very sensitive to its geometrical characteristics. Therefore, the measurement and characterization of nanostructure morphology is very critical in order to get control of the added value of nanopatterning on material properties and functionalities. In other words, there is an emergent need for accurate and concise metrology of all kinds of nanostructures.Up to now, the main tools for imaging and measuring the nanostructures are the well-known and widely used probe microscopes (AFM, SEM, TEM). However, due to the minute size of the measured structures, the measurement results are strongly dependent on the effects of measuring device and process. In order to get as more accurate measurement as possible, it is necessary to deconvolute the true structure from the effects of measurement. This can be made through the development and implementation of mathematical modeling methods able to get control of the measurement effects on the result and aid the acquisition of the true morphology. Furthermore, novel mathematical and computational methods are required in the characterization of complex surface nanostructures created by deposition, etching, ion bombardment or laser treatment of surfaces. The mathematical and computational methods needed to aid the accurate and complete metrology of surface nanostructures are collectively defined by the term computational nanometrology.In this paper, first we shortly introduce the field of computational nanometrology and define its content. Then we focus on two specific applications to demonstrate the benefits of computational nanometrology. In the first, a new mathematical transform is proposed to enable the simultaneous characterization of both periodicity and feature width in almost periodic arrangements of nanodots on a surface. In the second, the multifractal spectrum of complex nanomorphologies is calculated to quantify their multiscale hierarchical structuring. Both methodologies are motivated and applied to the characterization of polymer surfaces after their treatment in plasma reactors.