An essential part of the ESFRI roadmap to foster the European science in the field of large laser systems, the project ELI-Beamlines is to be built in the Czech Republic. The project has been submitted by the Institute of Physics to the European Commision and it is expected to be financed from the structural funds just due. The facility, which consists of several laser beamlines delivering a very high power density (up to 1023 W/cm2) on the target in a repetitive regime, should be ready by 2015. A smaller sister-project HiLASE should support the ELI-Beamlines by providing high average power repetitive ns lasers as an intermediate pumping element of the ELI laser chains, but, at the same time, of interest for various laser assisted technologies. and Karel Rohlena.
"You cannot improve it, if you cannot measure it." Nemůžete vylepšit to, co neumíte změřit, prohlásil lord Kelvin v roce 1906 na slavnostním zasedání normalizační společnosti IEC (International Electrotechnical Commission) coby její právě jmenovaný prezident. Měřit ovšem nejde bez měřidel - a pravidel. Bez norem si také nelze představit žádnou techniku: jak byste asi vyměnili baterii, kdyby každá měla jiné napětí a jiné rozměry? Ovšem máme-li už normu pro technické prostředky (třeba ČSN IEC 60050-482). musíme mít, a to především, také normy na příslušné veličiny (např. délku, šířku,napětí,...) a jejich jednotky (metr, volt,...). and Jan Obdržálek.
Spatial tasks in rodents are commonly used to study general mechanisms of cognition. We review two groups of novel spatial tasks for rodents and discuss how they can extend our understanding of mechanisms of spatial cognition. The first group represents spatial tasks in which the subject does not locomote. Locomotion influences neural activity in brain structures important for spatial cognition. The tasks belonging to the first group make it possible to study cognitive processes without the interfering impact of locomotion. The second group represents tasks in which the subject approaches or avoids a moving object. Despite this topic is intensively studied in various animal species, little attention has been paid to it in rodents. Both groups of the tasks are powerful tools for addressing novel questions about rodent cognition., D. Klement, K. Blahna, T. Nekovářová., and Obsahuje bibliografii a bibliografické odkazy
This paper investigates the mean square stability of a class of stochastic neural networks with time-varying delays. By virtue of the stochastic analysis method and linear matrix inequality (LMI) approach, a new sufficient condition is proposed where the feasibility of the conditions can be readily checked by the Matlab LMI control toolbox. Moreover, our method has the advantage of removing the restrictions on the time varying delays, so the derived results are less conservative than the previous works. A numerical example with simulations are provided to illustrate the effectiveness of the developed results.
A novel hybrid rule network based on TS fuzzy rules is proposed to resolve the problems of fuzzy classification and prediction. The proposed model learns by using genetic algorithm and is able to cover the whole distribution regions of the samples. In the learning process: (1) fuzzy intervals of each dimension of the samples are partitioned evenly; (2) computing intervals (CIs) are established based on the even intervals; (3) linear weighted model of several normal probability distributions is used to describe the sample probability distribution on CIs; (4) membership degree of each CI is learnt to evaluate the importance of each CI, avoiding the problem that the optimal intervals are difficult to cover the original sample spaces; (5) dynamic rule selection mechanism is used to dynamically combine a small number of optimal rules linearly to achieve nonlinear approximation, reducing the computation load. Three experiments are performed: the experiments on Iris and Mackey-Glass chaotic time series show that HRN can achieve satisfactory results and is more effective in terms of generalization ability, whereas the experiment on exhaust gas temperature demonstrates that HRN can predict the EGT of aero engine effectively.
Excessive production of reactive oxygen species (ROS) are implicated in the pathogenesis of numerous disease states. However, direct measurement of in vivo ROS in humans has remained elusive due to limited access to appropriate tissue beds and the inherently short half-lives and high reactivity of ROS. Herein, we describe a novel technique by which to measure in vivo ROS in human skeletal muscle. Microdialysis probes were inserted into the vastus lateralis of eight healthy volunteers. Amplex Ultrared, a highly specific fluorogenic substrate for hydrogen peroxide (H2O2), and horseradish peroxidase (HRP), were perfused through microdialysis probes, and outflowing dialysate was collected and fluorescence was measured. Extracellular H2O2 that crossed the microdialysis membrane was measured via fluorescence of the dialysate. Superoxide dismutase (SOD) was then added to the inflowing perfusion media to convert any superoxide crossing the microdialysis membrane to H2O2 within the microdialysis probe. Fluorescence significantly increased (P=0.005) upon SOD addition. These data demonstrate the feasibility of measuring both in vivo H2O2 and superoxide in the extracellular environment of human skeletal muscle, providing a technique with a potential application to a wide range of circulatory and metabolic studies of oxidative stress., J. D. La Favor, E. J. Anderson, R. C. Hickner., and Obsahuje bibliografii
In this paper, we discuss some generalized stability of solutions to a class of nonlinear impulsive evolution equations in the certain piecewise essentially bounded functions space. Firstly, stabilization of solutions to nonlinear impulsive evolution equations are studied by means of fixed point methods at an appropriate decay rate. Secondly, stable manifolds for the associated singular perturbation problems with impulses are compared with each other. Finally, an example on initial boundary value problem for impulsive parabolic equations is illustrated to our theory results.
In this paper, we propose a new global and fast Multilayer Perceptron Neural Network (MLP-NN) which can be used to forecast the automotive price. Nowadays, the gradient-based techniques, such as back propagation, are widely used for training neural networks. These techniques have local convergence results and, therefore, can perform poorly even on simple problems when forecasting is out of sample. On the other hand, the global search algorithms, like Tabu Search (TS), suffer from low rate convergence. Motivated by these facts, a new global and fast hybrid algorithm for training MLP-NN is provided. In our new framework, a hybridization of an extended version of TS with some local techniques is constructed in order to train the connected weights of the network. The extended version of TS in the proposed scheme consists of a simple TS together with the intensification and diversification search methods, and the local search methods are based on a direct strategy of Nelder-Mead (NM) or Levenberg-Marquardt (LM) techniques. This hybridization leads us to have a global and fast trained network in order to use in some forecasting problems. To show the efficiency and effectiveness of our new proposed network, we apply our new scheme for forecasting the automotive price in Iran Khodro Company which is the biggest car manufacturer in Iran. The results are promising compared to the cases when we apply the TS and some other forecasting techniques individually. We also compare the results with the case when we employ the gradient-based optimization techniques such as LM, and global search methods such as Genetic Algorithm (GA) and hybrid of MLP-NN with GA.