Generolo Jono Žemaičio Lietuvos karo akademija

Effective use of Big Data can significantly support the development of smart cities and the new digital economy. The aim of the article is a multi-criteria evaluation of IT systems in terms of Big Data processing, taking into account the support for the development of smart cities. The article includes theoretical and empirical research. The adopted criteria for assessing the architecture of IT systems relate to barriers to the implementation of the digital economy in smart cities and the guidelines of international data strategies. The evaluation covered, among other things, cybersecurity and the effectiveness of organizing, storing, and producing new information. The research results allowed us to identify the key factors of Big Data processing efficiency. Based on the research results, an effective model of Big Data processing in organizations was developed. In particular, various data models were analyzed as one of the main elements of software architecture of information systems. The research also focused on data processing techniques such as data warehousing, machine learning, and distributed computing. The efficiency factors of IT systems identified in the research reduce barriers to developing global data strategies and smart cities.

Natural conditions and traditions of agriculture production are challenging by sustainable development in Slovakia. The significant portion of arable land is devoted to the cultivation of crops, especially cereals, feed crops and industry crop. There is a high representation of mountains areas in country, therefore the fertile lowlands in west-south and east part are the primary locations of almost all agricultural production. The main objective of this work is to integrate the bio-physical crop data on yields with the economic data enabling to calculate net returns, in order to identify alternative costs of agricultural management practices for cultivation of 13 most important crops in selected west-south regions of Slovakia. We use bottom up optimization model which is based on linear programming. Management practices are differentiated according to nutrient input and irrigation application in order to evaluate yields as well as environmental factor – water use efficiency. Results suggest that in terms of net returns the management practices with high and medium nutrient input without irrigation are the most profitable but can represent a potential environmental pressure for soil. Such kind of optimization targets to create a better condition to reach environmental and economic balance of crop production.

The purpose of this paper is to investigate how learning in collaboration can support the development of sustainability competence in the military. The concept of sustainability in the military is controversial; nevertheless, requirements for more conscious decisions regarding economics, environment and society issues enter military practice and require rethinking the processes of military education and development. The application of computer-supported collaborative learning brings new opportunities in overcoming controversy of sustainability in the military and at the same time puts forward the solutions and skills for implementations of sustainability in the military.