In the text, a polemic is undertaken against the model of the child expected in Polish institutions of early childhood education, and which appropriates the rationalities producing social practices. The source of this model is in the logic of standardization whose cognitive and effects on identity are criticized by the author. Identifying the sources of validation of the practices normalizing some children and stigmatizing others, who do not meet the requirements of the cognitively rigid and morally trivialized standards, the text points to developmental psychology as a discipline which potentially triggers this form of oppression. In conclusion, the author describes briefly a number of examples of educational solutions in which an attempt has been made to move beyond the discourse of standardized quality in child education.
A novel magneto-optical current sensor (MOCS) with two sensing arms is proposed to improve the temperature stability. One of the arms, with a highly stable permanent magnet attached and orthogonal to the other one, is designed to provide a reference that follows the temperature characteristics of the sensing material. By a normalization operation between two arms, the temperature drift is compensated adaptively and a sensing output proportional to the measured current can be reached. A dual-input and dual-output structure is specially designed for the reference sensing arm to demodulate the DC Faraday rotation angle. This scheme compensates simultaneously two main temperature influence factors, the Verdet constant and linear birefringence. Validation tests were carried out and are discussed.
The accuracy of vehicle speed measured by a speedometer is analysed. The stress on the application of skew normal distribution is laid. The accuracy of measured vehicle speed depends on many error sources: construction of speedometer, measurement method, model inadequacy to real physical process, transferring information signal, external conditions, production process technology etc. The errors of speedometer are analysed in a complex relation to errors of the speed control gauges, whose functionality is based on the Doppler effect. Parameters of the normal distribution and skew normal distribution were applied in the errors analysis. It is shown that the application of maximum permissible errors to control the measuring results of vehicle speed gives paradoxical results when, in the case of skew normal distribution, the standard deviations of higher vehicle speeds are smaller than the standard deviations of lower speeds. In the case of normal distribution a higher speed has a greater standard deviation. For the speed measurements by Doppler speed gauges it is suggested to calculate the vehicle weighted average speed instead of the arithmetic average speed, what will correspond to most real dynamic changes of the vehicle speed parameters.
The subject of the paper is the analysis of factors determining the value of multi-entity organizations in the energy sector and their ranking according to the degree of impact on this value. For this purpose, statistical methods were used, which are best suited to determine the order of diagnostic features according to a specific criterion. The survey covered companies from the Polish energy sector, while the process itself is based on aggregated data, which represents the financial data of capital groups currently operating in the Polish energy sector. The first part of the article presents a short description of the Polish energy sector, paying particular attention to the organizational structure of the sector, i.e. companies operating on the domestic energy market. The nature of a multi-entity enterprise as a typical economic unit in the sector is described. The second part of the article describes the assumptions of multidimensional comparative analysis (MCA) as a tool for comparing multifunctional units. The MCA makes it possible to find the most important parameters or indicators having the greatest impact on the value of a multi-entity organization, i.e. a capital group. The survey covered four companies from the Polish energy sector: TAURON Polska Energia SA, ENEA SA, ENERGA SA and PGE Polska Grupa Energetyczna SA. The study with the use of MCA was conducted in three stages: - in the first stage, on the basis of information contained in the financial statements, a matrix of diagnostic features was created, describing the financial condition of the examined entity, - in the second stage, the values of diagnostic variables were normalized/unified; two methods of normalization were applied: the method of standardization and zero unitization, - in the third stage, the diagnostic variables were grouped using two methods: the model measure of Hellwig’s development and the non-standard measure of development. The results of the analysis are illustrated by tables and figures.
In recent years, deep learning and especially deep neural networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the convolutional neural networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good generalization abilities. Therefore, a number of methods have been proposed by the researchers to deal with these problems. In this paper, we present the results of applying different, recently developed methods to improve deep neural network training and operating. We decided to focus on the most popular CNN structures, namely on VGG based neural networks: VGG16, VGG11 and proposed by us VGG8. The tests were conducted on a real and very important problem of skin cancer detection. A publicly available dataset of skin lesions was used as a benchmark. We analyzed the influence of applying: dropout, batch normalization, model ensembling, and transfer learning. Moreover, the influence of the type of activation function was checked. In order to increase the objectivity of the results, each of the tested models was trained 6 times and their results were averaged. In addition, in order to mitigate the impact of the selection of learning, test and validation sets, k-fold validation was applied.