Search Everything

Tip: Search using "First Name + Last Name", e.g.
János Kiss instead of Kiss János.

Publications - 6374

Examination of the Effect of Wind on Vehicle Drag Coefficient from Aerodynamics Point of View

I. Lakatos Brúnó Péter Péter Kardos

Publication Name: Periodica Polytechnica Transportation Engineering

Publication Date: 2026-11-28

Volume: 54

Issue: 1

Page Range: 34-40

Description:

The aim of the paper is to investigate the wind effect on vehicle aerodynamic properties. Two-dimensional Ahmed body is examined with Computational Fluid Dynamics (CFD) method. During the analysis, two different studies were carried out. First, the simplified vehicle shape was examined in standing position, only the force from the wind velocity affected the body. The drag coefficient change was examined in case of different wind velocities and angles of attack. After that, the effect of wind while driving was investigated. The body was defined as a moving object and at another inlet the wind was defined. Different travelling and wind velocities with different angles of attack were studied. Based on the results of the simulations, a comprehensive impact of the wind can be measured on the drag coefficient. This proves that the wind has a measurable effect on vehicle aerodynamic properties and must be taken into account when investigating the effect of weather conditions on vehicle aerodynamic properties.

Open Access: Yes

DOI: 10.3311/PPtr.40035

Data-Driven Pavement Performance: Machine Learning-Based Predictive Models

Nurullah Bektaş Mohammad Fahad

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-04-01

Volume: 15

Issue: 7

Page Range: Unknown

Description:

Featured Application: This research provides effective methodology for pavement performance predictions using the data obtained from finite element analysis and merging it with machine learning algorithms. Traditional methods for predicting pavement performance rely on complex finite element modelling and empirical equations, which are computationally expensive and time-consuming. However, machine learning models offer a time-efficient solution for predicting pavement performance. This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. The input variables include axle load, truck load, traffic speed, lateral wander modes, asphalt layer thickness, traffic lane width and tire types, while the output variables consist of number of passes to fatigue damage, number of passes to rutting damage, fatigue life reduction in number of years and rut depth at 1.3 million passes. A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. In contrast, linear regression and KNN demonstrated the lowest performance, with MSE values up to 188% higher than CatBoost. This study concludes that integrating machine learning with finite element analysis provides further improvements in pavement performance predictions.

Open Access: Yes

DOI: 10.3390/app15073889

Remarks to the topological characterization of cellular systems

Ibolya Zsoldos Tamás Réti

Publication Name: Journal of Geometry and Physics

Publication Date: 2011-08-01

Volume: 61

Issue: 8

Page Range: 1476-1478

Description:

In this note we discuss and solve an open problem (a conjecture) posed in a paper "On the combinatorial characterization of quasicrystals" published earlier in the Journal of Geometry and Physics. The conjecture proved is valid for the majority of space filling cellular systems (polycrystals, nanotubes, and fullerenes). © 2011 Elsevier B.V.

Open Access: Yes

DOI: 10.1016/j.geomphys.2011.03.016

Regulatory governance and AI innovation quality: A cross-country evaluation of technology, institutions, and ESG outcomes

Irum Shahzadi Wei Chen Meijie Yao Sabrine Mallek

Publication Name: Technological Forecasting and Social Change

Publication Date: 2026-09-01

Volume: 230

Issue: Unknown

Page Range: Unknown

Description:

This study examines how AI innovation quality shapes national ESG performance, including its governance dimensions, across 82 countries from 2000 to 2023. AI quality is measured as citation intensity per capita, reflecting scientific influence and technological sophistication validated in prior macro studies rather than patent volume, as citations better capture deployable impact for policy applications like regulatory compliance monitoring and transparent decision systems. Building on resource-resilient world theory, we assess direct AI effects and conditional interactions with financial institutions, globalization exposure, human capital, corruption, economic complexity, and clean-energy transitions using robust multi-model econometrics including IV-GMM to address endogeneity. The findings reveal advanced AI innovation quality significantly improves composite ESG performance, with stronger effects in developed economies where institutional capacity amplifies technological capabilities. AI citation intensity translates scientific breakthroughs into practical governance tools, enhancing regulatory transparency (G pillar), emissions tracking (E pillar), and social compliance (S pillar), though developing nations face implementation barriers. The findings imply that high-quality AI innovation serves as a critical technological shock absorber, enabling nations to enhance regulatory transparency and manage resources more resiliently against external shocks. However, the divergence between developed and developing nations suggests that technological sophistication must be paired with institutional reforms and human capital development to avoid value destruction. Ultimately, strategically governing AI innovation quality is essential for aligning global technological trajectories with long-term ESG performance and sustainable development goals.

Open Access: Yes

DOI: 10.1016/j.techfore.2026.124733

Development of High-Entropy Alloy Coating by Additive Technology

L. K. Varga B. Kocsis Gábor Gulyás

Publication Name: Frontiers in Materials

Publication Date: 2022-01-18

Volume: 8

Issue: Unknown

Page Range: Unknown

Description:

In this research study, various additively fabricated coatings and bulk 3D-printed parts were prepared, using the mechanical and corrosion-resistant properties of CrMnFeCoNi (Cantor alloy) and AlCrMnFeNi high-entropy alloys (HEAs). The coatings were applied to an EN 1.0038 carbon steel substrate using direct metal laser sintering. We attempted to optimize the 3D printing parameters of HEA alloys. The effect of volumetric energy density (VED) on the microstructure was investigated by scanning electron microscopy. We also examined the change of relative concentration of alloys in the direction of 3D printing (z-axis) as well as the volumetric failures (cracks and gaps). Standard salt spray tests were performed to test the corrosion resistance of various coatings after 3D printing. The use of both raw materials applied as thick films was successful; they retained their corrosion-resistant properties even with a change in their composition. Regarding the crystal structure, no difference was found between the base material and the material applied as a coating on the basis of X-ray diffraction investigations. Bulk HEA printing experiments need further optimization concerning their structural integrity and density in the case of the Cantor alloy. Bulk 3D printing experiments of the AlCrMnFeNi alloy did not yield satisfactory results because of the formation of dendritic microstructure and brittle BCC phase, and the residual internal stress resulted in part distortion and improper printing.

Open Access: Yes

DOI: 10.3389/fmats.2021.802076

Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries

Dávid Fekete Usman Ghani Peter Toth

Publication Name: Sustainability Switzerland

Publication Date: 2023-05-01

Volume: 15

Issue: 10

Page Range: Unknown

Description:

Geographical mapping has revolutionized data analysis with the help of analytical tools in the fields of social and economic studies, whereby representing statistical research variables of interest as geographic characteristics presents visual insights. This study employed the QGIS mapping tool to create predicted choropleth maps of Visegrád Group countries based on crime rate. The forecast of the crime rate was generated by time series analysis using the ARIMA (autoregressive integrated moving averages) model in SPSS. The literature suggests that many variables influence crime rates, including unemployment. There is always a need for the integration of widespread data insights into unified analyses and/or platforms. For that reason, we have taken the unemployment rate as a predictor series to predict the future rates of crime in a comparative setting. This study can be extended to several other predictors, broadening the scope of the findings. Predictive data-based choropleth maps contribute to informed decision making and proactive resource allocation in public safety and security administration, including police patrol operations. This study addresses how effectively we can utilize raw crime rate statistics in time series forecasting. Moreover, a visual assessment of safety and security situations using ARIMA models in SPSS based on predictor time-series data was performed, resulting in predictive crime mapping.

Open Access: Yes

DOI: 10.3390/su15108088

Phase portraits and bifurcations induced by static and dynamic friction models

Mate Antali Balazs J. Bekesi G. Csernák

Publication Name: Nonlinear Dynamics

Publication Date: 2025-07-01

Volume: 113

Issue: 13

Page Range: 15863-15899

Description:

The paper discusses the phase-space structure of six variants of a simple mechanical system that differ in the applied friction model. It is shown that many properties of the Coulomb and the Stribeck models, such as the number of equilibria and their stability, are inherited by the Dahl and the LuGre dynamic friction models, respectively. Exploiting similar relationships, a Coulomb-based and a Stribeck-based version of the Generalized Maxwell-Slip model are also introduced. The detailed analysis of these models reveals a surprisingly rich variety of equilibrium types and bifurcations. Moreover, it is highlighted that the most frequently used values of the Stribeck exponent may lead to atypical results such that even a small deviation from these values changes the bifurcation scenario.

Open Access: Yes

DOI: 10.1007/s11071-025-10974-y

Neuro-computing analysis of MHD bioconvective ternary nanofluid flow over a curved stretching surface

Mohamed R. Eid Syed M. Hussain Shazia Kalsoom Wasim Jamshed S. Bilal Hijaz Ahmad Sawera Batool Noor Zeb Khan Kamel Guedri Asad Ullah

Publication Name: Results in Surfaces and Interfaces

Publication Date: 2026-08-01

Volume: 24

Issue: Unknown

Page Range: Unknown

Description:

Objective Magnetically influenced bioconvective flow of ternary nanofluid induced by the expansion of curved surface by incorporating thermophoresis, Brownian motion, chemical species, activation energy and motile microbes to elucidate complex thermal fluid transport phenomena. Method ology: The mathematical model describing flow mechanism was formulated in sense of PDEs (partial differential equations), which are converted in ODEs (ordinary differential equations) by employing similar set of variables. Numerical technique by integrating the shooting method and RK-4 approach is employed to obtain the outcomes of study. Afterwards, neuro-computing model is designed to forecast Nusselt number for mono, hybrid and ternary nanoparticles comparatively. Key findings Findings of the analysis indicate that velocity of fluid intensifies by uplifting curvature factor while thermal profile goes down. Thermophoretic and Brownian diffusion factors cause the temperature of the fluid to rise but lower the associated flux. Higher curvature and activation energy factors elevate concentration distribution, whereas microbe density depreciates versus Peclet and bioconvective Lewis numbers. The MSE values obtained during training (2.79e-08, 7.63e-08, and 1.55e-07) demonstrate the model's robustness. Applications It is concluded that heat and mass transportation phenomenon is superior with the induction of ternary nanoparticles as compared to mono and hybrid, giving valuable insights for the design of improved thermal energy storage and bioconvective transference mechanism in engineering and biomedicine utilizations.

Open Access: Yes

DOI: 10.1016/j.rsurfi.2026.100853

Modular code generation for creating IoT applications

Gábor Paller Gábor Élo

Publication Name: International Conference on Electrical Computer Communications and Mechatronics Engineering Iceccme 2022

Publication Date: 2022-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

This paper builds on an earlier work that aims to generate code for connected digital systems (IoT, Internet of Things). Orcc-IoT was the adaptation of the Orcc dataflow design environment and code generator to the IoT problem area. IoT systems are characterized by heterogeneity of both the software and hardware environments therefore code generators targeting them must be able to handle embedded, client and server enviroments too. The first version of Orcc-IoT provided simplistic code generators. The extensions presented in this paper add embedded and server-side modules and more specialized wireless communication options to test Orcc-IoT's modular architecture.

Open Access: Yes

DOI: 10.1109/ICECCME55909.2022.9987980