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In spite of millions of quasi-formal workers in the European Union (EU), there is still limited understanding of what motivates workers to participate in these detrimental employment schemes, and why certain groups of workers exhibit higher inclination towards it. This article takes a novel approach by putting prospective envelope wage earners in the centre of this analysis.

Data from the 2019 Special Eurobarometer on undeclared work are used, and two-level random intercept cumulative logit modelling is applied.

One in seven fully declared EU workers would have nothing against receiving one part of their wages off-the-books. Manual workers and individuals whose job assumes travelling are the most willing to accept such kind of remuneration, and the same applies to workers with low tax morale and those who perceive the risk of being detected and persecuted as very small. On the other hand, women, older individuals, married persons and employees from large enterprises express the smallest inclination towards envelope wages. The environment in which an individual operates also plays a non-negligible role as the quality of the pension system and the strength of social contract were also identified as significant determinants of workers' readiness to accept envelope wages.

This article fills in the gap in the literature by analysing what workers think about wage under-reporting and what factors drive their willingness to accept envelope wages.

The aim of this paper is to develop the accurate computer method of predicting the average PM10 pollution for the next day on the basis of some measured atmospheric parameters, like temperature, humidity, wind, etc. This method should be universal and applicable for any place under consideration.

The paper presents the new approach to the accurate forecasting of the daily average concentration of PM10. It is based on the application of the ensemble of neural networks and wavelet transformation of the time series, representing PM10 pollution.

On the basis of numerical experiments, the paper finds that application of many neural predictors cooperating with each other can significantly improve the quality of results. The paper shows that the developed forecasting system checked on the data of PM10 pollution in Warsaw generated good overall accuracy of prediction in terms of root mean squared error, mean absolute error and mean absolute percentage error.

The main novelty of the proposed approach is the application of the wavelet transformation and many neural networks organized in the form of ensemble. The individual neural predictors are integrated into one forecasting system using different forms of integrations, including the blind source separation method and neural‐based integration.

The purpose of this paper is to develop the new method of estimation of the short‐term largest Lyapunov exponent of electroencephalogram (EEG) waveforms for the detection and prediction of the epileptic seizure.

The paper proposed the modifications concerned with the way of selection of the segments of EEG waveforms taking part in estimation of Lyapunov exponent, as well as determination of the distances between two time series. The proposed method is based on Kolmogorov‐Smirnov test of similarity of two vectors. Through the application of this test more accurate and less parameterized approach to the estimation of the short‐term largest Lyapunov exponent of EEG waveforms has been obtained.

The results of performed experiments have shown that in most cases our modified method has outperformed the classical procedure, leading to more stable results, closer to the neurologist indications. The analysis of the data has proved that the change of the largest Lyapunov exponent provides a lot of information regarding the epileptic seizure. The minimum value of Lyapunov exponent indicates fairly well the seizure moment. The T_index applied for few different electrode sites can provide good advanced prediction of the incoming epileptic seizure.

After additional experiments this method may find practical application for supporting the medical diagnosis of the epilepsy.

The proposed modification of the estimation of the short‐term largest Lyapunov exponent of the EEG waveforms eliminates some arbitrarily chosen parameters tuned by the user and leads to more accurate estimate. Such estimation results are better suited for the characterization of the epileptic activity.

The paper presents the application of neural network to the classification of the closed contours forming different shapes. The shape is represented by ‐ samples of complex numbers z_k = x_k + jy_k where x_k and y_k are the samples in the x‐y plane and j is the complex operator. The same shapes may vary in scale, be rotated and translated in arbitrary proportion and be distorted by the noise. To obtain the classification invariant to all these factors the preprocessing techniques based on the application of Fourier transformation of the samples have been applied. The Fourier coefficients form the input data to the neural classifier. Different shapes have been checked in numerical experiments and the results have proved good performance of the developed neural classifier and its relative insensitivity to the noise.