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Found 6334 publications

A comprehensive analysis of European Union funds for higher education institutions in Hungary

Mónika Rajcsányi-Molnár Marta Konczosne Szombathelyi Nóra Mária Halmai

Publication Name: Journal of Infrastructure Policy and Development

Publication Date: 2024-01-01

Volume: 8

Issue: 13

Page Range: Unknown

Description:

This study aims to examine the evolution of the system of support sources in Hungary, focusing on the specific goals supporting higher education in the development programs Széchenyi 2020 (2014–2020) and Széchenyi Plan Plus (2021–2027). The study provides insights into development program evolution and changes, aiming to inform EU funding opportunities for Hungarian higher education institutions over a nearly 10-year period. By focusing on the operational programs that are the basis for the upcoming tenders, the study will display the target system of EU funds that can be utilized to bolster higher education institutions in Hungary. The study is based on document analysis, examining the Hungarian policy tools of the development programs and the operational program strategies of the ten-year time period from 2014 to 2024. By analyzing the support landscape for higher education institutions in Hungary, this study contributes to a better understanding of how the key objectives and criteria of strategic programs have evolved. It also examines the aspects and elements defined in two different development programs over the last ten years. The result of the study can contribute to anticipate the types of funding opportunities that may be available in the future and inform future decision-making processes.

Open Access: Yes

DOI: 10.24294/jipd9069

The Impact of Workplace Narratives on Organizational Behaviour – A Systematic Literature Review

László Buics Anikó Makkos Veronika Nagy

Publication Name: International Scientific Business Conference Limen Leadership Innovation Management and Economics Integrated Politics of Research

Publication Date: 2024-01-01

Volume: 2024

Issue: Unknown

Page Range: Unknown

Description:

Narratives and the behaviours they support play a key role in shaping the workplace climate. The positive effects — for example, building team cohesion through leadership narratives — increase employee morale and performance, while the negative effects — for example, caused by ineffective leadership communication — decrease loyalty and work performance. The purpose of this article is to examine the impact of narratives, leadership communication, and communities of practice (CoPs) on organisational climate. The importance of CoPs is highlighted: these formal or informal communities are a breeding ground for organisational innovation and positive behaviours. Trust as a key factor is also examined, as it is particularly important in vertical and horizontal communication. The article applies the PRISMA methodology and the PEO framework to conduct a systematic literature review in the Scopus database to collect data and present the currently available knowledge in the field.

Open Access: Yes

DOI: 10.31410/LIMEN.S.P.2024.1

Investigation of Lubrication Capability of Zinc Oxide-Reinforced Nanolubricants in Automotive Applications †

D. Tóth I. Szabó

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

This article aims to introduce the tribological investigation of nanoscale zinc oxide particles as friction and wear reduction additives in the automotive industry and to present the results of the measurements. The surface-activated nanoparticles were homogenized into a neat Group-III-type base oil at five different concentrations, and their tribological properties were tested using a simplified ball-on-disc tribosystem. The arising wear scar images were investigated, and the occurred wear volume values were also calculated using a confocal microscope. The evaluation presented excellent friction and wear reduction properties, especially at higher concentrations (0.4 and 0.5 wt%). The authors would like to highlight the tribological decreasing potentials provided by such nanoparticles. Nanoparticle-reinforced lubricants can be one of the future solutions to developing operating machines with an achievable maximum energy efficiency.

Open Access: Yes

DOI: 10.3390/engproc2024079087

Calculation of Heating of Reinforced Concrete Tunnel Wall During Fire

Éva Lublóy Zoltán Major János Szép

Publication Name: Advances in Transdisciplinary Engineering

Publication Date: 2024-01-01

Volume: 59

Issue: Unknown

Page Range: 366-374

Description:

In this article, we present the thermal parameters of reinforced concrete tunnel lining materials and their changes during fire exposure. After describing the material properties, we present a test method to investigate the heating of reinforced concrete tunnel linings. As the presented method can only be considered as partially standard, we validate it on the basis of the available literature and our Excel program based on the presented theory. During the validation, it has been demonstrated that the method is suitable for solving practical professional tasks and that it is able to provide sufficiently accurate results. Since the results presented can be used not only for design purposes but also as an initial step in fire diagnostics to determine the extent of damage in fire-loaded tunnel walls, we also construct novel curves for the analysis of reinforced concrete walls, which can be used effectively for fire curves with cooling phase, where the accumulated temperature inside the wall further heats the zones inside and further residual strength loss may occur due to chemical processes in the zones. Based on the results of the model presented in this paper, designers can take into account the changes in the temperature distribution of the reinforced concrete tunnel wall, which have a decisive influence on the evolution of the internal forces due to external influences, and can calculate the magnitude of the stresses due to inhibited thermal expansion by using approximate models.

Open Access: Yes

DOI: 10.3233/ATDE240568

Comparative Analysis of Machine Learning Algorithms in Traffic Mainstream Control on Freeway Networks

Mehran Amini Ĺaszĺo T. Ḱoczy

Publication Name: Ines 2024 28th IEEE International Conference on Intelligent Engineering Systems 2024 Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 37-41

Description:

Efficient management of mainstream traffic flow on freeway networks is a critical challenge in urban transportation, with significant implications for congestion mitigation and environmental sustainability. The purpose of this study is to address the problem of predicting traffic volumes and maintaining flow rates below critical densities, thereby preventing the onset of congestion on interconnected freeway systems. Motivated by the need for real-Time traffic control strategies, this research employs machine learning algorithms to forecast traffic volumes, leveraging a comprehensive dataset of traffic patterns on freeways. In our approach, we conducted a comparative analysis of two advanced machine learning algorithms: Long Short-Term Memory (LSTM) networks, which are adept at modeling time-series data with long-range temporal dependencies, and Random Forest regression, known for its robust performance across diverse datasets. We enriched the traffic data through feature engineering, incorporating temporal variables, vehicular counts, and a calculated measure of proximity to critical density for the targeted freeway. Our findings indicate a markedly disparate performance between the algorithms. The LSTM model showed a moderate ability to capture the variance in traffic flow, with an R2 score of 0.619. In contrast, the Random Forest model demonstrated exceptional predictive accuracy, achieving an R2 of 0.998, and substantially outperforming the LSTM model in terms of both Mean Squared Error and Root Mean Squared Error.

Open Access: Yes

DOI: 10.1109/INES63318.2024.10629114

TESTING THE FRAGMENTATION OF RAILWAY BALLAST MATERIAL BY LABORATORY METHODS USING PROCTOR COMPACTOR

Szabolcs Fischer Mykola Sysyn Attila Németh Jianxing Liu Erika Juhasz Gusztáv Baranyai

Publication Name: Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu

Publication Date: 2024-01-01

Volume: Unknown

Issue: 1

Page Range: 58-68

Description:

The physical classification of crushed stone and gravel used in railway construction is based on their strength and endurance and is performed by a laboratory test method using a rotating drum or a mortar method. The values of fracture resistance calculated using the Los Angeles method and abrasion calculated using the Micro-Deval method show a corresponding correlation and require further investigation. Purpose. The development of a new method for measuring rock material fracture that is consistent with widely used standards while also being more comparable to real-world railway operating conditions. Certainly, both standard tests are essential for ensuring product homogeneity during production, so the new recommended method is only a supplement. Methodology. The Proctor device was used to induce so-called shock loads from above, similar to railway loading conditions. Unlike the standard method, the andesite material was placed in a standard cylinder in these tests. The samples were pre-screened and sorted; the specified weight was approximately 1,300 g, and the specified sizes of the individual particles were 6.3, 8.0 and 11.2 mm. Only prewashed and dried materials of NZ (fine crushed stone) or KZ (special crushed stone) from four different quarries (Tállya, Szob, Nógrádkövesd, Recsk) with different rock physics characteristics were considered. The Proctor compactor machine was used because of its calculable labor (19.86 J/impact) and the crushing effect of the calculable impacts (64, 128, 256 and 1,028 blows). Even after loading different numbers of impacts, homogeneous samples from different quarries were sieved to measure the masses of fragments per fraction. Findings. The set of measurements made it possible to establish a series of fragmentation and degradation curves for each of the three repeated measurements based on the composition of the material and the number of blows, which showed the degradation of samples with different physical and mechanical properties of the rock material and particle sizes. With an increasing number of impacts, the amount of crushed material in the sample increased, but the distribution of crushed material did not decrease evenly and proportionally as the number of impacts increased. Parameters and indices were also computed to identify various correlations (i. e., FV, d < 22.4, d < 0.5, d < 0.063 mm, CU, M ratio, λ ratio). Some of them (e. g., FV) needed to be changed, but they were predefined due to the nature of the tests. Originality. While many standard and alternative railway track ballast fragmentation test methods and measurement tools are available, this paper proposes a new laboratory method and demonstrates the specific measurement and application effectiveness. Practical value. In addition to standard tests that are already widely used, the new method for measuring the fractional composition of railway ballast can help simulate real-world operating conditions of a railroad track in the laboratory. This method will improve the safety of railway operations.

Open Access: Yes

DOI: 10.33271/nvngu/2024-1/058

Modeling the Stiffening Behavior of Sand Subjected to Dynamic Loading

Majd Ahmad R. P. Ray

Publication Name: Geosciences Switzerland

Publication Date: 2024-01-01

Volume: 14

Issue: 1

Page Range: Unknown

Description:

In geotechnical engineering, dynamic soil models are used to predict soil behavior under different loading conditions. This is crucial for many dynamic geotechnical problems related to earthquakes, train loading and machine foundation design. Researchers agree that under dry or drained conditions, cohesionless soils increase in stiffness with each loading cycle. Soil models that simulate the dynamic behaviors of soils are often coupled with the Masing criteria. Such models neglect the impact of stiffening during cyclic loading, leading to an underestimation in the shear modulus (G). This study investigates the stiffening behavior by conducting laboratory tests on three types of Danube sands using the Resonant Column-Torsional Simple Shear device (RC-TOSS). The increase in the dynamic shear modulus with an increasing number of cycles is substantial, especially for samples with low density. Sometimes, the dynamic shear modulus doubles when loaded at high stress levels for more than 50 cycles. A new model is introduced to simulate the stiffening behavior of dry sand when subjected to cyclic torsional loading. Modifications are proposed for the Ramberg–Osgood and Hardin–Drnevich models and for the Masing criteria to overcome the limitations that accompany these models due to the influence of stiffening caused by repetitive loading being ignored. This model can be implemented in finite element and finite difference software to solve dynamic geotechnical problems.

Open Access: Yes

DOI: 10.3390/geosciences14010026

Automated Assessment of Engine Performance During Dynamometer Testing †

Cs Tóth-Nagy Tamás Koller

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

The ever-increasing number of novel functions in modern vehicles continuously expands with the application of cognitive information technology, creating a new need for testing during market introduction. As the virtual test environment evolves, the need for real tests conducted on the road continuously decreases, saving time and cost while maximizing quality indicators. This article presents a new type of automatic monitoring system created in a fully virtual test environment. The automated assessment during dynamometer testing (ADT) method automatically evaluates the values measured on the engine dynamometer at predefined intervals, compares them to reference data, and provides feedback on the correctness of the current test. The present paper discusses the monitoring methodology and its application on an engine dynamometer, and it presents the results of the method applied during a real engine test.

Open Access: Yes

DOI: 10.3390/engproc2024079028

Predicting Natural Frequencies of a Cantilever Using Machine Learning

Ambrus Zelei Krisztián Horváth

Publication Name: Advances in Transdisciplinary Engineering

Publication Date: 2024-01-01

Volume: 59

Issue: Unknown

Page Range: 105-111

Description:

In the context of structural analysis and design, natural frequencies play a vital role, and their prediction is essential in machine and vehicle design processes. The simulations related to the modal parameters are computationally intensive for systems with large complexity. This paper demonstrates on an illustrative academic example that natural frequencies can be successfully predicted using ML models. This paper aims to develop a model based on machine learning (ML) to predict a simple cantilever's natural frequencies based on the physical parameters of the beam. The independent variables X are the geometric parameters including width, length, and thickness, while the dependent variable Y is the natural frequency. The study is framed using a systematic methodology that covers the stages of data collection, ML model selection, model training and validation. The validation process proves the effectiveness of ML as a computationally cheap replacement for traditional methods of prediction. The current research contributes to the investigation of the usage of commercially available ML tools in structural engineering. We report that the ODYSSEE A-Eye software is capable of natural frequency prediction with a varying geometry structure with less than 4% error for an 80-member training set of cantilever beam with various dimensions. Further developments will include considerations of noise, vibration, and harshness (NVH) to enhance system performance and improve user comfort.

Open Access: Yes

DOI: 10.3233/ATDE240533