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Found 6289 publications
The fight against financing terrorism: New challenges and developments in Hungarian law
Publication Name: Acta Juridica Hungarica
Publication Date: 2014-01-01
Volume: 55
Issue: 3
Page Range: 236-260
Description:
No description provided
Open Access: Yes
Exploring the impact of ChatGPT on education: A web mining and machine learning approach
Publication Name: International Journal of Management Education
Publication Date: 2024-03-01
Volume: 22
Issue: 1
Page Range: Unknown
Description:
ChatGPT, an artificial intelligence model, has garnered significant interest within education. This study examined public sentiment regarding ChatGPT's influence on education by utilizing web mining and natural language processing (NLP) techniques. By adopting an empirical approach and leveraging machine learning models to process 2003 web articles, the study extracts valuable insights. The results indicate that ChatGPT has emerged as a crucial educational tool, offering advantages for both students and educators. Notably, the study emphasized ChatGPT's role in enhancing students' writing abilities and fostering dynamic, interactive learning environments. ChatGPT's capacity to address a broad spectrum of questions demonstrates its versatility and adaptability, contributing to more inclusive and personalized educational experiences. However, the study also uncovered challenges tied to academic integrity, such as plagiarism and cheating, which stem from incorporating AI-driven tools like ChatGPT into education. This raises concerns regarding ethical aspects, including responsible AI usage and data privacy, and highlights the need for institutions to develop guidelines and policies for AI tool implementation in education. This study's findings hold theoretical and practical implications for integrating ChatGPT into educational settings. It is the first to employ web mining and NLP techniques to analyze public opinions on ChatGPT's impact on education comprehensively.
Open Access: Yes
Early Detection of ITSC Faults in PMSMs Using Transformer Model and Transient Time-Frequency Features
Publication Name: Energies
Publication Date: 2025-08-01
Volume: 18
Issue: 15
Page Range: Unknown
Description:
Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions.
Open Access: Yes
DOI: 10.3390/en18154048
Szekszárd-Palánk and the postglacial recolonization of the Pannonian Basin
Publication Name: Dissertationes Archaeologicae Ex Instituto Archaeologico Universitatis De Rolando Eotvos Nominatae
Publication Date: 2025-01-01
Volume: 3
Issue: 13
Page Range: 321-351
Description:
Szekszárd-Palánk, located in South Transdanubia (Hungary), was discovered in the late 1950s and has yielded several hundred archaeological finds, including lithics and faunal remains. Initially, the site was regarded as ‘the latest Palaeolithic’ site in Hungary; later, it was reclassified as an Early Mesolithic industry bridging the Palaeolithic–Mesolithic transition. More recently, the site was proposed to be evidence for the continuity of Epigravettian hunter-gatherers from the Late Glacial to the Early Holocene. However, recent findings regarding the Late Epigravettian in the Pannonian Basin suggest that these populations vanished with the onset of Greenland Interstadial 1. To address this discrepancy, the authors reassessed the lithic assemblage and archaeozoological remains, obtained new radiocarbon dates, and conducted a new excavation to re-evaluate the stratigraphy and geomorphological processes of the site. Our new absolute dates place the site firmly between 11.6–10.4 ka cal BP, and techno-typo-logical analysis attributes it to the Early Holocene Final Epigravettian. These results indicate that hunter-gatherers largely abandoned the Pannonian Basin during Greenland Interstadial 1 and Greenland Stadial 1. This population vacuum ended at the onset of the Preboreal with the arrival of hunter-gatherers from the Balkans or Adriatic coastlines. The repopulation process appears to have been influenced by palaeoecological factors, with the establishment of the pioneering Early Holocene Final Epigravettian settlement in South Transdanubia coinciding with global sea-level rise Meltwater Pulse Event 1B.
Open Access: Yes
Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation
Publication Name: Infocommunications Journal
Publication Date: 2021-01-01
Volume: 13
Issue: 3
Page Range: 14-23
Description:
Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.
Open Access: Yes
Impact of Climate Change on Electric Energy Production from Medium-Size Photovoltaic Module Systems Based on RCP Climate Scenarios
Publication Name: Energies
Publication Date: 2024-08-01
Volume: 17
Issue: 16
Page Range: Unknown
Description:
The impact of climate change is increasingly evident in various domains today and is gaining prominence in scientific inquiries. Climate change also affects the utilisation of renewable energies. The article examines the effects of 21st-century climate change on the annual electric energy production of medium-sized photovoltaic module systems. The study bases its analysis on three possible scenarios: a pessimistic (RCP 8.5), a less pessimistic (RCP 4.5), and an optimistic (RCP 2.6) scenario. The applied Representative Concentration Pathways (RCP) scenarios were developed by the Intergovernmental Panel on Climate Change (IPCC) to enhance comparability in analyses related to climate change. Compared to older linear models, an innovation utilises a more flexible and multidirectional model. One of the article’s key findings is that, for all three examined settlements, the annual yield of the studied photovoltaic systems will surpass the levels of the base year 2010 by the middle and end of the century. Another significant outcome is that, under the three scenarios analysed, the ratio of annual performance variation to annual global radiation variation shows substantial differences by the middle and end of the century compared to the 2010 baseline. In the optimistic scenario, this ratio exceeds 1, whereas in the pessimistic and less pessimistic scenarios, it falls below 1. This ratio does not directly inform about the annual production—which increases in all cases—but rather about the changes in efficiency. These efficiency changes are influenced by the rise in annual average temperatures and the fluctuation in sunny hours yearly. The third finding reveals that under the climate change pessimistic scenario (RCP 8.5), the efficiency decrease is less adverse than in the less pessimistic scenario (RCP 4.5).
Open Access: Yes
DOI: 10.3390/en17164009
Effect of n-3 polyunsaturated fatty acid feeding on the fatty acid profile and odor of milk in danbred sows
Publication Name: Journal of Applied Animal Research
Publication Date: 2021-01-01
Volume: 49
Issue: 1
Page Range: 447-459
Description:
The effects of n-6 and n-3 fatty acid supplementation on the fatty acid profile of sow milk were investigated using traditional fatty acid analysis and a novel method of the electronic nose (EN). The control group received 6.3 g of sunflower oil (SO) rich in n-6 fatty acids per kg feed, and experimental animals received the same amount of fish oil (FO) as an n-3 fatty acid source. The diets were corn- and soybean meal-based diets. Supplementation of SO enhanced the amount of linoleic acid (C18:2, n-6) (SO: 8.43 mg/mL vs. FO: 6.63 mg/mL milk), and significantly increased (p < 0.02) the amount of total polyunsaturated fatty acids (SO: 9.92 mg/mL vs. FO: 8.61 mg/mL milk) in the sow’s milk. On the contrary, FO significantly increased the amount of n-3 polyunsaturated fatty acids (FO: 1.17 mg/mL vs. SO: 0.69 mg/mL milk), especially eicosapentaenoic acid (C20:5, n-3), docosapentaenoic acid (C22:5, n-3), and docosahexaenoic acid (C22:6, n-3), in the milk (p < 0.001). FO and SO supplementation did not affect the analytical composition of milk. Milk samples collected from the differently fed individuals could be clearly separated according to the feeding groups based on the odour profile described by the EN.
Open Access: Yes
Innovative Approaches in Railway Management: Leveraging Big Data and Artificial Intelligence for Predictive Maintenance of Track Geometry
Publication Name: Tehnicki Vjesnik
Publication Date: 2024-01-01
Volume: 31
Issue: 4
Page Range: 1245-1259
Description:
This paper introduces and describes a method for extracting, processing, and analyzing large amounts of track geometrical data. It allows for a more accurate description of the orbital deterioration correlations than currently applied procedures, and it seems to be more valuable and efficient in practice. The initial data were the track geometry measurement and classification data for the whole national network provided by the Hungarian State Railways, i.e., the MÁV PLC. The MÁV provided data for the whole Hungarian railway network for 27 half-years, measured and recorded by the FMK-004 type special diesel locomotive (i.e., track geometry measuring car). The paper discusses the development of a procedure to automatically compute important condition ratings from the available data set of millions of units according to the algorithms created for railway industry colleagues, thus helping the maintenance and renewal decision-making process. Functions have been developed to classify the track geometry condition of a given railway line, to predict how long the service level can be maintained without intervention (i.e., e.g., lining, leveling, and tamping with a mechanized maintenance train), to determine the time of the necessary maintenance intervention, the time of the upgrade (rehabilitation or modernization), and to develop a track geometry prediction procedure that makes full use of the mathematical and computational possibilities of the present day.
Open Access: Yes
Analysis Of The Dynamics Of Cashless Payments In Kazakhstan In The Context Of The Covid-19 Pandemic
Publication Name: Economy Strategy and Practice
Publication Date: 2021-01-01
Volume: 16
Issue: 2
Page Range: 54-61
Description:
The COVID-19 pandemic has accelerated the development of FinTech and the transition to cashless payments of the population of various countries, including Kazakhstan. For provision of cashless payments there are created platforms which work in NFC & MFS systems, they protected by security protocols (Secure Element (SE)) and allow to store confidential user data. Changes in the business model of banks have led to the emergence of a new format of banking products and services that can be used through smartphones (the main operating systems Android and iOS). The goal of the study is to analyze the dynamics of the impact of the COVID-19 pandemic on the use of cashless payments through national payment systems in Kazakhstan. Based on the goal, a null and an alternative hypothesis were set, of which the second was confirmed in the result. Research methods used to write this article are economic and statistical analysis and synthesis, graphical method, analysis of the series of dynamics. The data for the study was taken from the NBK Statistical Bulletin: data on cashless payments through the Interbank System of Money Transfer (ISMT) and the Interbank Clearing System (ICS) for the period from 2002 to 2020. The impact of the COVID-19 pandemic on the dynamics of the transformation of cashless payments made by using financial technologies has been proved. Recommendations for further improvement of the financial system are given. The development of national payment systems makes it possible to reduce the level of fraud in the financial sector, to increase the speed of cashless transfers and level out transaction errors. Study limitation: only national payment systems ISMT and ICS were used.
Open Access: Yes
