1H spin-lattice relaxation experiments have been performed for water–Bovine Serum Albumin (BSA) mixtures, including 20%wt and 40%wt of BSA. The experiments have been carried out in a frequency range encompassing three orders of magnitude, from 10 kHz to 10 MHz, versus temperature. The relaxation data have been thoroughly analyzed in terms of several relaxation models with the purpose of revealing the mechanisms of water motion. For this purpose, four relaxation models have been used: the data have been decomposed into relaxation contributions expressed in terms of Lorentzian spectral densities, then three-dimensional translation diffusion has been assumed, next two-dimensional surface diffusion has been considered, and eventually, a model of surface diffusion mediated by acts of adsorption to the surface has been employed. In this way, it has been demonstrated that the last concept is the most plausible. Parameters describing the dynamics in a quantitative manner have been determined and discussed.

Professor Zadeh in his works proposed the idea of grouping similar objects on the basis of certain similarity measures, thus initiating the paradigm of granular computing. He made the assumption that similar objects may have similar decisions. This natural assumption, operates in other scientific methodologies, e.g. methods based on k nearest neighbours, in reasoning by analogy and in rough set theory. The above assumption implies the existence of grouped information nodes (granules) and has potential applications in reducing the size of decision systems. The hypothesis has guided" the creation of granulation techniques based on the use of rough inclusions (introduced by Polkowski and Skowron) -according to the scheme proposed by Polkowski. In their work, the possibility of a large reduction of the size of decision systems while maintaining the classification efficiency was verified in experimental works.In this paper, we investigate the possibility of using random sampling in the approximation of decision systems -as part of dealing with Big Data sets. We use concept-dependent granulation as a reference approximation method. Experiments on selected real-world data have shown a common regularity that gives a hint on how to apply random sampling for fast and effective size reduction of decision systems.

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Pseudo-random number generation techniques are an essential tool to correctly test machine learning processes. The methodologies are many, but also the possibilities to combine them in a new way are plenty. Thus, there is a chance to create mechanisms potentially useful in new and better generators. In this paper, we present a new pseudo-random number generator based on a hybrid of two existing generators - a linear congruential method and a delayed Fibonacci technique. We demonstrate the implementation of the generator by checking its correctness and properties using chi-square, Kolmogorov and TestU01.1.2.3 tests and we apply the Monte Carlo Cross Validation method in classification context to test the performance of the generator in practice.