289 Forbush Decrease effects (FD) were processed, for 207 of which corresponding flares were determined. The following parameters of FDs were determined from cosmic ray recordings at the Alert station: start of FD, time of first or second minimum value, the time lag of the beginning of FD behind the source flare, the
duration of the descending phase, the decrease of FD in per cent, the duration of the whole decrease effect and the rate of decline phase. The occurrence of FDs in the years of solar cycle No. 21 and the distribution of source flares over the solar disc was demonstrated (including NS oř EW asymmetry). In the II part will be
published the statistical distribution of the individual parameters of FDs and their mutual relations. The values of the FD parameters are also related to the importance and also to the longitudinal position of the flares. Some results of studied relations were used for interpretations of the characteristics and shape of the magnetic plasma cloud.
We developed new parameters for molecular dynamics (MD) simulations, namely partial atomic charges, equilibrium bond-lengths, angles, dihedrals, atom types, and force constants of chlorophyll a (Chl) and plastoquinone (PQ), and both reduced and neutral form of primary acceptor (PHO) molecule. These parameters are essential for MD simulations that can interpret various structure functional relationships during primary processes of charge separation and stabilization in photosystem 2 reaction centres. and P. Palenčár, F. Vácha, M. Kutý.
The paper is concerned with the graph formulation of forced anisotropic mean curvature flow in the context of the heteroepitaxial growth of quantum dots. The problem is generalized by including anisotropy by means of Finsler metrics. A semi-discrete numerical scheme based on the method of lines is presented. Computational results with various anisotropy settings are shown and discussed.
Based on the dependence of the maximum relative RM-number of the add 11-year cycle on the RM of the previous even cycle it is forecast that RM should exceed 200 in the next 11-year sunspot cycle No. 23. and Published in Bull. Astron. Inst. Czechosl. 42 (1991), 157-158
An empirical model for forecasting electrie power consumption is forrnulated. The research concerns the preparation and optimal selection of characteristic variables. Prototype patteriis of eleetric power consumption over a day are described by proper by encoding the day-types and their self-organised adaptation to the data recorded in the past. In this procedure, holidays are treated by specific prototype patterns. The influence of the environmental temperature on the consumed power is accounted for by including the extrerne vahies of temperature in a day into prototype patterns. These patterns are employed as parameters of a norrnalised radial basis function neural network, which is used to forecasting the consumption process. The performance of forecasting and the applicability of various input variables is tested, based on one- and four-year-long records of electrie power consumption in Slovenia.
This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16]. It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems. Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models. The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications. The presented overview is not complete, it only reflects the authors experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector. The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources. The global solar radiation forecasting is based on numerical weather prediction models. The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.
In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a new hybrid model by combining a linear and nonlinear model for forecasting time series data. The proposed model (GRANN_ARIMA) integrates nonlinear Grey Relational Artificial Neural Network (GRANN) and linear ARIMA model, combining new features such as multivariate time series data as well as grey relational analysis to select the appropriate inputs and hybridization succession. To validate the performance of the proposed model, small and large scale data sets are used. The forecasting performance was compared with several models, and these include: individual models (ARIMA, Multiple Regression, Grey Relational Artificial Neural Network), several hybrid models (MARMA, MR_ANN, ARIMA\_ANN), and Artificial Neural Network (ANN) trained using Levenberg Marquardt algorithm. The experiments have shown that the proposed model has outperformed other models with 99.5% forecasting accuracy for small-scale data and 99.84% for large-scale data. The empirical results obtained have proved that the GRANN_ARIMA model can provide a better alternative for time series forecasting due to its promising performance and capability in handling time series data for both small and large scale data.
We contend that since what is true cannot be false, foreknowledge is transparently incompatible with free will. We argue that what is crucial to the conflict is the role of truth in foreknowledge and that the identity of the one who foreknows is irrelevant., Tvrdíme, že vzhledem k tomu, že to, co je pravdivé, nemůže být falešné, je předzvěst transparentně neslučitelná se svobodnou vůlí. Tvrdíme, že rozhodující pro konflikt je role pravdy v předzvědomí a že identita toho, kdo předvídá, je irelevantní., and Alex Blum
Internet a informační technologie jsou úzce propojeny s mnoha činnostmi reálného světa a stejně jako se v běžném světě vyskytují kriminálníci, i v elektronickém světě se pohybují různé živly, kterým jde pouze o vlastní prospěch. Při zajišťování stop a objasňování nastalé činnosti hrají klíčovou roli forenzní specialisté. Situaci v běžném světě lidem přibližují populární televizní seriály, ale obdobná pracoviště existují také pro analýzu stop zanechaných v kyberprostoru. Jedním z nich je FLAB, Forenzní LABoratoř, kterou provozuje sdružení CESNET. and Aleš Padrta.
The work described in this contribution was performed as a part of the 3AS project (Active Aeroelastic Aircraft Structures) which was funded under contract (Contract No. G4RD-CT-2002-00679) of the European Union. The paper deals with the tests of the X-DIA component aeroelastic demonstrator (the front part of the fuselage with the foreplane of the three plane jet transport aircraft) in the VZLU Prague. and Obsahuje seznam literatury