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The new progressive smart technologies announced in the fourth industrial revolution in aviation—Aviation 4.0—represent new possibilities and big challenges in aircraft maintenance processes. The main benefit of these technologies is the possibility to monitor, transfer, store, and analyze huge datasets. Based on analysis outputs, there is a possibility to improve current preventive maintenance processes and implement predictive maintenance processes. These solutions lower the downtime, save manpower, and extend the components’ lifetime; thus, the maximum effectivity and safety is achieved. The article deals with the possible implementation of an unmanned aerial vehicle (UAV) with an infrared camera and Radio Frequency Identification (RFID) as two of the smart hangar technologies for airframe condition monitoring. The presented implementations of smart technologies follow up the specific results of a case study focused on trainer aircraft failure monitoring and its impact on maintenance strategy changes. The case study failure indexes show the critical parts of aircraft that are subjected to damage the most. The aim of the article was to justify the need for thorough monitoring of critical parts of the aircraft and then analyze and propose a more effective and the most suitable form of technical condition monitoring of aircraft critical parts. The article describes the whole process of visual inspection performed by an unmanned aerial vehicle (UAV) with an IR camera and its related processes; in addition, it covers the possible usage of RFID tags as a labeling tool supporting the visual inspection. The implementations criteria apply to the repair and overhaul small aircraft maintenance organization, and later, it can also increase operational efficiency. The final suggestions describe the possible usage of proposed solutions, their main benefits, and also the limitations of their implementations in maintenance of trainer aircraft.

Research background: Business profit and its stable development are key performance indicators. Many enterprises performed earnings manipulation, either upward or downward, according to the current business and macroeconomic situation, as well as time. These activities may interrupt the stationarity of time series. This article focuses on the transport enterprises, and the assessment of bonds in their earnings. Purpose of the article: The target of the article was to identify the occurrence of non-stationary and its unit root in the EBITDA of transport enterprises for each country in V4 during the period of 2010?2019. Methods: The stationarity and unit roots in time series were tested by the Kwiatkowski, Phillips, Schmidt, and Shin tests and the Augmented Dickey-Fuller based on the samples of 470 Slovak, 405 Czech, 774 Polish, and 1,056 Hungarian. The behavior of earnings manipulation (the first cause of non-stationarity) was indicated by the Modified Jones model. Additional causes for non-stationarity were confirmed by the regression analysis, including factors such as the GDP, unemployment rate, average monthly gross wage, and the Ease of doing business index. Findings & value added: The non-stationarity in the time series of EBITDA was disclosed for each country in the V4 region. Earnings management was discovered to be the cause of this erratic development. Thus, the value-added for the authorities and auditors is to show the association between non-stationary and creative accounting. In addition, purposeful downward manipulation in the transport sector occurs, not upward, which is typical in general. The methodology used in the study may be applied cross-sectorally in emerging countries. The labelling of specific macroeconomic variables depending on the country offers enterprises the opportunity to focus on factors with a crucial influence on their existence and activities.

A conflict is an infringement of minimum separation between at least two aircraft. The model is based on these assumptions: aircraft fly on level straight line routes, only an infringement of the lateral separation is considered, deviations are excluded, aircraft at the same flight level fly the same average speed, and aircraft fly towards an intersection and may change direction after intersection. Hence, conflicts mainly occur owing to a loss of minimum separation between aircraft flying at the same flight level. Calculation of average number of potential conflicts is designated for long time interval; hence, aircraft velocity deviations are negligible. The mathematical model in this paper is intended to compare different alternatives of intersection configuration of air traffic services routes. The comparison is based on the results: an average number of potential conflicts per hour on intersection of routes, index of conflicts intensity, and intersection capacity.

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