In this study, a new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The computed Lyapunov exponents of the EEG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg-Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of training performance and classification accuracies. Receiver operating characteristic (ROC) curves were used to assess the performance of the detection process. The results confirmed that the proposed MLPNN trained with the Levenberg-Marquardt algorithm has potentiality in detecting the electroencephalographic changes.
Water is one of the most important components of the environment, having a direct effect on the maintenance of life on the Earth. In this paper, analysis of groundwater level variations, water balance and all the parameters included in these quantities, i.e. precipitation, evapotranspiration, surface run-off and subsurface run-off, were performed in the area of the Sudety Mountains for the period of November 2002 - October 2015. The groundwater level variations were computed on the basis of the mean Terrestrial Water Storage (TWS) values determined from Gravity Recovery and Climate Experiment (GRACE) observations and Global Land Data Assimilation System (GLD AS). TWS data have been determined with a spatial resolution of one degree and temporal resolution of one month. According to the results, groundwater level variation can be approximately determined by water balance changes (with reverse sign). Specifically, for the Sudety area a high average stability of total water storage over the period of past 13 years and decline in groundwater level by about 13 cm (approximately 1 cm/year) was detected., Zofia Rzepecka, Monika Birylo, Joanna Kuczynska-Siehien, Jolanta Nastula and Katarzyna Pajak., and Obsahuje bibliografické odkazy
This article aims to share the results of research conducted in the Fergana chemical plant of furan compounds (FCPFC) in Uzbekistan.19 workers of the Furan compounds plant, in Fergana, Uzbekistan, were tested. By neutron activation analysis method, we have studied microelement composition of saliva, blood, dental hard tissue, and the level of Ca, Zn, Fe, and Ag in these subjects. We have detected that the level of chemical elements in dental hard tissue, blood, and saliva of these workers was subject to negative changes as compared to the analysis results from those in the control group. The research results have practical value for the prophylaxis, treatment, and health resumption of the people living in rugged ecological environment and workers who are engaged with harmful substances in chemical industry. Furthermore,this research also provides recommendations for treatment of dental diseases related to common conditions of pathophysiological processes carried out by living organisms., Sunnatillo Gaffarov, Salim Sharipov, and Literatura
We investigate Solutions provided by the finite-context predictive model called neural prediction machine (NPM) built on the recurrent layer of two types of recurrent neural networks (RNNs). One type is the first-order Elman’s simple recurrent network (SRN) trained for the next symbol prediction by the technique of extended Kalman filter (EKF). The other type of RNN is an interesting unsupervised counterpart to the “claissical” SRN, that is a recurrent version of the Bienenstock, Cooper, Munro (BCM) network that performs a kind of time-conditional projection pursuit. As experimental data we chose a complex symbolic sequence with both long and short memory structures. We compared the Solutions achieved by both types of the RNNs with Markov models to find out whether training can improve initial Solutions reached by random network dynamics that can be interpreted as an iterated function system (IFS). The results of our simulations indicate that SRN trained by EKF achieves better next symbol prediction than its unsupervised counterpart. Recurrent BCM network can provide only the Markovian solution that is not able to cover long memory structures in sequence and thus beat SRN.
This paper presents some structural properties of a generalized Petri net (PN) with an algorithm to determine the (partial) conservativeness and (partial) consistency of the net. A product incidence matrix A=CCT or A~=CTC is defined and used to further improve the relations among PNs, linear inequalities and matrix analysis. Thus, based on Cramer's Rule, a new approach for the study of the solution of a linear system is given in terms of certain sub-determinants of the coefficient matrix and an efficient algorithm is proposed to compute these sub-determinants. The paper extends the common necessary and/or sufficient conditions for conservativeness and consistency in previous papers and some examples are designed to explain the conclusions finally.
Toxoplasma gondii (Nicolle et Manceaux, 1908) is an obligate intracellular apicomplexan parasite and can infect warmblooded animals and humans all over the world. Development of effective vaccines is considered the only ideal way to control infection with T. gondii. However, only one live vaccine is commercially available for use in sheep and goats. Thus more effective antigenic proteins are searched for. In the present study we report a novel protein by secreted T. gondii termed Myc regulation 1 (MYR1). The physical and chemical characteristics, epitopes, hydrophilicity and functional sites of MYR1 were analysed by multiple bioinformatic approaches. The 3D models of MYR1 proteins were constructed and analysed. Furthermore, liner B-cell epitopes and T-cell epitopes of MYR1 protein and SAG1 were predicted. Compared to SAG1, MYR1 with good B-cell epitopes and T-cell epitopes had a potentiality to become a more successful vaccine against T. gondii. The bioinformatics analysis of MYR1 proteins could laid the foundation for further studies of its biological function experimentally and provide valuable information necessary for a better prevention and treatment of toxoplasmosis., Jian Zhou, Gang Lu, Shenyi He., and Obsahuje bibliografii
Judging the influence of private law enforcement on the success of the leniency programs is a veryinteresting and current topic. When dealing with it, it is necessary to find an answer to following question:How should be handled the initiative of the European Commission to strengthen the private law pillar ofthe cartel law enforcement not to weaken the public law pillar at the same time? It is possible to assume thatthe support of private enforcement of damages discourages cartel participants to report the existence of acartel and avoid the fine imposed by the state, because by admitting a cartel they potentially face an evenlarger financial burden than in the case of a fine. However, the other point of view is, taking into account thevery low effort of the impaired parties (mainly consumers) to enforce their claims through private means,that the support of private enforcement should not be perceived as a danger to the functionality of leniencyprograms, but only as their suitable complement, which can exert sufficient pressure on the cartel participantsto perform their activities in accordance with law. The article strives to show that at the moment the supportof private enforcement of cartel law does not pose any danger to leniency programs and that the implementationof legal institutions proposed by the European Commission, which would emphasize the role of privateenforcement, is desirable.
Over a large range of the pressure, one cannot ignore the fact that the viscosity grows significantly (even exponentially) with increasing pressure. This paper concerns long-time and large-data existence results for a generalization of the Navier-Stokes fluid whose viscosity depends on the shear rate and the pressure. The novelty of this result stems from the fact that we allow the viscosity to be an unbounded function of pressure as it becomes infinite. In order to include a large class of viscosities and in order to explain the main idea in as simple a manner as possible, we restrict ourselves to a discussion of the spatially periodic problem.