Study of microbiological background of herbal ingredients and dairy-vegetable compositions

The rates of microbiological safety of powdery vegetables, vegetable-milk compositions, compound desserts have been studied. No pathogenic germs (incl. salmonella), Escherichia coli, yeast, nonspore-forming bacteria B cereus have been detected in powdery vegetable samples. The number of mesophilic aerobic and facultative anaerobic microorganisms as well as amount of molds does not exceed safety index normalized by the legislation. Proteolytic microorganisms compose the basic microflora of powdery vegetables. Microbiological background of vegetable and milk basis is characterized by the presence of microorganisms differed by different resistance to the medium conditions – рН value, presence of oxygen and high temperatures impact. Enrichment of milk base by vegetable components necessitates to adjust the thermal effect regimes prescribed for milk treatment without additional ingredients. Introduction of vegetable ingredients into milk base is accompanied by polysemantic effect of high temperatures on microorganisms of polycomponent milk – vegetable base. On the one hand introduction of vegetable raw material into milk enhances inhibitory temperature effect on microbial cells due to transition of the medium рН into sour side; on the other hand presence of vegetable raw material particles protects microorganisms against sensitive effect of high temperature. Microflora of vegetable-milk compositions after heat treatment as well as ready-made desserts on their base was presented by spore-forming bacillus the number of which is correlated by their number in the initial raw material. In order to choose the optimal regime of heat treatment all processes running during heat treatment and particularly microbiological and physical-chemical degradation of polysaccharides of vegetables cell structures

Quantifying Chaos by Various Computational Methods. Part 1: Simple Systems

The aim of the paper was to analyze the given nonlinear problem by different methods of computation of the Lyapunov exponents (Wolf method, Rosenstein method, Kantz method, the method based on the modification of a neural network, and the synchronization method) for the classical problems governed by difference and differential equations (Hénon map, hyperchaotic Hénon map, logistic map, Rössler attractor, Lorenz attractor) and with the use of both Fourier spectra and Gauss wavelets. It has been shown that a modification of the neural network method makes it possible to compute a spectrum of Lyapunov exponents, and then to detect a transition of the system regular dynamics into chaos, hyperchaos, and others. The aim of the comparison was to evaluate the considered algorithms, study their convergence, and also identify the most suitable algorithms for specific system types and objectives. Moreover, an algorithm of calculation of the spectrum of Lyapunov exponents based on a trained neural network has been proposed. It has been proven that the developed method yields good results for different types of systems and does not require a priori knowledge of the system equations

Quantifying Chaos by Various Computational Methods. Part 2: Vibrations of the Bernoulli–Euler Beam Subjected to Periodic and Colored Noise

In this part of the paper, the theory of nonlinear dynamics of flexible Euler–Bernoulli beams (the kinematic model of the first-order approximation) under transverse harmonic load and colored noise has been proposed. It has been shown that the introduced concept of phase transition allows for further generalization of the problem. The concept has been extended to a so-called noise-induced transition, which is a novel transition type exhibited by nonequilibrium systems embedded in a stochastic fluctuated medium, the properties of which depend on time and are influenced by external noise. Colored noise excitation of a structural system treated as a system with an infinite number of degrees of freedom has been studied

EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy)

This paper analyzes a case with the patient having focal structural epilepsy by processing electroencephalogram (EEG) fragments containing the “sharp wave” pattern of brain activity. EEG signals were recorded using 21 channels. Based on the fact that EEG signals are time series, an approach has been developed for their analysis using nonlinear dynamics tools: calculating the Lyapunov exponent’s spectrum, multiscale entropy, and Lempel–Ziv complexity. The calculation of the first Lyapunov exponent is carried out by three methods: Wolf, Rosenstein, and Sano–Sawada, to obtain reliable results. The seven Lyapunov exponent spectra are calculated by the Sano–Sawada method. For the observed patient, studies showed that with medical treatment, his condition did not improve, and as a result, it was recommended to switch from conservative treatment to surgical. The obtained results of the patient’s EEG study using the indicated nonlinear dynamics methods are in good agreement with the medical report and MRI data. The approach developed for the analysis of EEG signals by nonlinear dynamics methods can be applied for early detection of structural changes