Marta Kraszewska

Road traffic accidents remain a critical public health concern worldwide, with Poland consistently experiencing high fatality rates—52 deaths per million inhabitants in 2023, compared to the EU average of 46. To investigate the underlying factors contributing to these accidents, we developed a multifactorial dataset integrating 250,000 accident records from 2015 to 2023 with spatially interpolated weather data and sociodemographic indicators. We employed Kriging interpolation to convert point-based weather station data into continuous surfaces, enabling the attribution of location-specific weather conditions to each accident. Following comprehensive preprocessing and spatial analysis, we generated visualizations—including heatmaps and choropleth maps—that revealed distinct regional patterns at the county level. Our preliminary findings suggest that accident occurrence and severity are driven by different underlying factors: while temperature and vehicle counts strongly correlate with total accident numbers, humidity, precipitation, and road infrastructure quality show stronger associations with fatal outcomes. This integrated dataset provides a robust foundation for Bayesian and time-series modeling, supporting the development of evidence-based road safety strategies.

W artykule zaprezentowano funkcjonalności oraz możliwości zastosowania platformy narzędziowej SELF. Omówiono cztery zdefiniowane warstwy platformy oraz przedstawiono proces wdrożenia rozwiązania oraz integrację poszczególnych warstw. Przedstawiono...

W pracy zaprezentowano podejście do zarządzania wiedzą w organizacji z wykorzystaniem nowoczesnych technologii informatycznych. Przedstawiono istotę procesu zarządzania wiedzą w sytuacji ważnych zmian w organizacji, jakimi może być wprowadzenie nowych metod organizacji pracy. Zagadnienie zostało omówione na przykładzie projektu wdrożeniowego platformy narzędziowej SELF w firmie Soneta, będącej producentem i dostawcą oprogramowania ERP enova365.

The paper presents the possibilities of using artificial neural networks for the valuation of option contracts on stock index. Selected papers were discussed, in which the author attempted to use neural networks in option valuation of the most important stock exchanges in the world. Analytical methods of option valuation were presented and a neural network model for the valuation of option contracts was proposed. The presented experiments for the put option on the WIG20 index of the Warsaw Stock Exchange for various time periods, in particular for situations in which there are sudden fluctuations on the market, were prepared using the Statistica application. The paper presents an analysis of the results received for the proposed models using artificial neural networks and Merton model.