Meteorological conditions significantly affect air traffic safety and can also affect a pre-planned flight plan. Difficult meteorological conditions are particularly hazardous during take-off and landing procedures. Still, they can also cause disruptions to air traffic by causing, for example, delays to air traffic or diversion of aircraft to other airports. From the airlines’ point of view, such situations are not beneficial if flights are diverted to other airports due to reduced visibility at the airport caused by fog and haze. For flight operations, a popular navigation system with a precision approach is the ILS, which has several categories enabling an approach even in adverse meteorological conditions. However, not every airport has a high-category ILS, and setting up such navigation equipment is lengthy and costly. The main objective of this article is to analyze the impact of meteorological conditions, particularly visibility, on the possibility of performing flight procedures with different ILS categories. The study was designed to quantify the limitations associated with meteorological conditions with specific ILS equipment at a given airport. The research questions for this study include the following: What were the meteorological conditions in terms of visibility? What impact did the visibility parameter have on the performance of landing operations at the airport under study? Can an indication of the probability of stopping landing operations be important in recommendations for scheduling airline flights to avoid delays? Three airports were selected for the analysis: Warsaw Chopin Airport, Warsaw Modlin Mazowiecki Airport, and Krakow John Paul II Airport. The analysis was based on approximately 52,000 METAR dispatches in 2019 and 2022. The research indicated during which periods landing procedures were most frequently halted and calculated such a change with a different category of ILS. For the Kraków Airport, the probability of stopping landing procedures in any month was calculated, along with recommendations for flight schedule planning for this airport. The research results can be used to better plan airline flight schedules, avoiding hours with a high probability of reduced visibility, which may result in rerouting flights to another airport. Long-term low clouds and reduced visibility affect the safety of operations but also cause delays.

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

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.

Spoofing, alongside jamming of the Global Navigation Satellite System signal, remains a significant hazard during general aviation or Unmanned Aerial Vehicle operations. As aircraft utilize various support systems for navigation, such as INS, an insufficient Global Navigation Satellite System signal renders Unmanned Aerial Vehicles nearly uncontrollable, thereby posing increased danger to operations within airspace and to individuals on the ground. This paper primarily focuses on assessing the impact of the budget friendly Software-Defined Radio, HackRF One 1.0, on the safety of Unmanned Aerial Vehicles operations. Considering the widespread use of Software-Defined Radio devices today, with some being reasonably inexpensive, understanding their influence on Unmanned Aerial Vehicles safety is crucial. The generation of artificial interference capable of posing a potential threat in expanding Unmanned Aerial Vehicles airspace is deemed unacceptable.

The paper focuses on the area of economic evaluation and reliability of ergatic base complex operator. The estimation of operation quality, mainly with in-built complex systems, is evaluated with the use of various methods. The selected method presumes the use of efficiency indicator of performed tests in order to prove implementation tests reliability of new complex systems into ergatic base complex. The outputs create numeral characteristics according to which it is possible to estimate the level of conformity of costs on their operation itself. Therefore, the optimization of financial costs on maintaining the reliability of the above mentioned systems with the assumption of estimated level of the optimization of unexpected costs for the operator, e.g. of airport base system, is the decisive parameter [1]. The paper describes the possibilities of MATLAB environment applicability while observing effective use of resources to sustain high reliability of airport base system and its mutual relationship with current complex systems.