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Publications - 6289

Periodicity in radix systems with base 3

Attila Kovács Izabella Ingrid Farkas Gabor Kallos

Publication Name: Publicationes Mathematicae Debrecen

Publication Date: 2025-01-01

Volume: 106

Issue: 3-4

Page Range: 475-489

Description:

In this paper, we examine the base 3 number expansions in the ring of integers with digits {0, a, −b}, a, b ∈ N and a ≡ b (mod 3). Among others, we show that for arbitrary natural numbers n and k, there is a system where n has a periodic expansion with length k. We specify an infinite number of radix systems for a given valid signature.

Open Access: Yes

DOI: 10.5486/PMD.2025.10042

Estimating "level of safety" in traffic modeling using human error

Zsuzsanna Toth-Szabo K. Kovacs

Publication Name: 15th World Congress on Intelligent Transport Systems and ITS America Annual Meeting 2008

Publication Date: 2008-12-01

Volume: 4

Issue: Unknown

Page Range: 2562-2573

Description:

The aim of the paper is to present the results of a study about involving the human error into traffic modeling. The analysis of driver's behavior in a selected traffic situation (drivers do or do not stop at red light) provided us to define the meaning human error in the context. Based on Bayesian theorem, the probability of bad decision is one of the results of the first part of this paper. Having built the junctions in VISSIM environment, and after a detailed investigation of output parameters (in simulated traffic there is no car passing at red), a software module was developed for having the bad decision probabilities using VISSIM. Grouping drivers by driving style (aggressive, normal, slow) gives opportunity (defining numbers of driver passing at red) to find the missing link. The trial version of this software now is able to define the probability of bad decision using VISSIM outputs.

Open Access: Yes

DOI: DOI not available

Dem-driven investigation and AutoML-Enhanced prediction of Macroscopic behavior in cementitious composites with Variable frictional parameters

Majid Movahedi Rad Vahid Shafaie

Publication Name: Materials and Design

Publication Date: 2025-06-01

Volume: 254

Issue: Unknown

Page Range: Unknown

Description:

This study presents a numerical investigation and predictive modeling framework to evaluate the influence of microscale frictional parameters on the mechanical behavior and failure mechanisms of cementitious composites. In the first phase, discrete element modeling (DEM) was employed to analyze the effects of bonded friction angle and non-bonded friction coefficient on the stress–strain response, failure evolution, and macro-scale properties. The results revealed a distinct transition from tensile to shear-dominated failure modes beyond a critical friction angle, accompanied by notable changes in compressive strength and deformation characteristics. Additionally, the role of non-bonded friction coefficient in post-failure behavior was identified, emphasizing its influence on load-redistribution. In the second phase, an AutoML-driven artificial neural network (ANN) was optimized via grid search, selecting an optimal four-layer model to predict macroparameters from microscale DEM inputs. The proposed ANN demonstrated high predictive accuracy, effectively capturing nonlinear dependencies while significantly reducing the need for additional numerical simulations. This integration of DEM and AI-based predictive modeling provides a computationally efficient, scalable solution for material characterization, enabling faster, data-driven insights into cementitious composite behavior without reliance on extensive simulation campaigns.

Open Access: Yes

DOI: 10.1016/j.matdes.2025.114069

Chatbot assistant based on Large-Language Models for University students

Csaba Hajdu K. Kovacs Ami Otsuka

Publication Name: Ines 2025 29th IEEE International Conference on Intelligent Engineering Systems 2025 Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 77-82

Description:

Large-language models (LLMs) have recently gained significant traction in natural language processing (NLP) by accurately modeling and imitating human-like conversations. One standout application area involves chatbots, which leverage LLMs to provide context-aware, natural language interactions. However, existing solutions often target English and rely on external cloud-based platforms, raising concerns about data privacy and language coverage. In contrast, this paper presents a locally deployable, Hungarian-language chatbot developed to assist university students with education and examination regulations. The proposed system ensures in-house deployment, facilitating compliance with institutional data policies and offering cost-effective scalability. Beyond offering straightforward answers on deadlines and academic rules, our chatbot is designed to handle more nuanced student inquiries, enhancing user experience and administrative efficiency. Preliminary testing demonstrates robust performance in Hungarian context. Future plans include extending the chatbot's domain to more complex subjects, broader document sets, and additional institutions, as well as integrating high-performance computing resources for large-scale deployments.

Open Access: Yes

DOI: 10.1109/INES67149.2025.11078205

On Metrics Used in Colonoscopy Image Processing for Detection of Colorectal Polyps

Raneem Ismail Szilvia Nagy

Publication Name: Smart Innovation Systems and Technologies

Publication Date: 2021-01-01

Volume: 216

Issue: Unknown

Page Range: 137-151

Description:

Colorectal cancer is nowadays the fourth cause of cancer death worldwide. Prevention of colorectal cancer by detection and removal of early stage lesions is of essential importance and has become a public health challenge worldwide. As the screening is carried out mainly by some sort of endoscope, and the endoscopic image processing is an important area of research and development, it is essential to know what kind of measures are used in determining whether polyp finding hit rates or miss rates are acceptable. It is rather natural to match the hit rate measures to the method itself; thus, in this contribution, the most typical polyp detecting methods are summarized shortly together with the metrics they use for evaluation of their results. However, in computer-aided diagnostics, the measure that is used by the medical community might differ from the measures typical in image processing researches. Also, the output of such polyp detecting methods is tested as inputs for active contour methods.

Open Access: Yes

DOI: 10.1007/978-981-33-4676-5_10

Detecting New Hijacked Journals by Using a List of Known Hijacked Journals and the Diagnosis of Web Domain Data

Lóránt Dávid Raghu Raman Mehdi Dadkhah Mihály Hegedűs Marilyn H. Oermann Lóránt Dénes Dávid

Publication Name: Serials Review

Publication Date: 2024-01-01

Volume: 50

Issue: 3-4

Page Range: 91-96

Description:

Academia has faced the challenge of emerging hijacked journals, which create a fake website for a legitimate journal by copying its name, ISSN, and other metadata. Authors submitting a manuscript to a hijacked journal would not recognize that the journal website was for a hijacked journal instead of the legitimate one. There are various methods for detecting hijacked journals that are usable by information technology savvy researchers. Detected hijacked journals through use of these methods are usually then added to hijacked journals blacklists. This paper presents a new method for hijacked journals detection that uses the web domain data and list of known hijacked journals to identify new ones. By implementing this method, nine new hijacked journals were identified. This method can be used for detecting new hijacked journals and preventing additional victims–authors who submit papers to the hijacked instead of the legitimate journal.

Open Access: Yes

DOI: 10.1080/00987913.2024.2411664

Key Performance Indicators for Evaluating Electric Buses in Public Transport Operations

Agoston Winkler Balázs Horváth Xiao Li

Publication Name: Vehicles

Publication Date: 2025-06-01

Volume: 7

Issue: 2

Page Range: Unknown

Description:

The evaluation of electric buses used in public transportation operations encompasses several critical factors that directly influence the operational efficiency, as well as the economic viability, environmental advantages, and user experience. Energy consumption is a critical metric for assessing the energy efficiency of electric buses. It facilitates a better understanding of vehicle performance across varying road conditions and advances the implementation of energy-saving solutions. The passenger demand model is a tool used to assess the quality and experience of electric buses, with the assessment being based on real usage. The operational mileage is defined as the driving distance of electric buses on a single charge. This parameter has a significant impact on both urban coverage and route optimization. The article under consideration identifies evaluation indicators for electric buses. These indicators are derived from a set of 100 questionnaire responses, which were collected in Győr, Hungary. The classification of the indicators into three segments—mechanical, operational and bus transportation system—is proposed, with the underlying rationale and significance of each indicator’s selection being elucidated. The findings indicate that this component is essential for developing a comprehensive evaluation system for electric buses and serves as a solid foundation for more intricate future studies.

Open Access: Yes

DOI: 10.3390/vehicles7020058

Exploring entrepreneurial phases with machine learning models: Evidence from Hungary

Áron Szennay Judit Csákné Filep Melinda Krankovits

Publication Name: Entrepreneurial Business and Economics Review

Publication Date: 2025-06-01

Volume: 13

Issue: 2

Page Range: 101-122

Description:

Objective: The article aims to explore the potential differences between the two phases of entrepreneurship, i.e., total early-stage entrepreneurial activity and established business, as defined by the Global Entrepreneurship Monitor (GEM). The study aimed to classify entrepreneurs using various machine learning models and to evaluate their classification performance comparatively. Research Design & Methods: Using the Hungarian GEM datasets from 2021 to 2023, we analysed a subsample of 964 entrepreneurs. Due to inconsistent results from traditional analyses (e.g., correlations, regressions, principal component analyses), we employed machine learning approaches (supervised learning classification methods) to uncover latent relationships between variables. Findings: The study utilized seven machine learning classification methods to examine the feasibility of grouping companies within the sample using Hungarian GEM data. Findings indicate that machine learning techniques are particularly effective for classifying businesses, although the performance of each method varies significantly. Implications & Recommendations: These results provide valuable insights for researchers in selecting methodologies to identify various business phases. Moreover, they offer practical benefits for market research professionals, suggesting that machine learning techniques can enhance the classification and understanding of entrepreneurial phases. Contribution & Value Added: The study adds to the existing body of knowledge by demonstrating the effectiveness of machine learning methods in classifying business phases. It highlights the variability in performance across different machine learning techniques, thereby guiding future research and practical applications in market research and entrepreneurship studies.

Open Access: Yes

DOI: 10.15678/EBER.2025.130206

Direct Writing of Quasi-Sinusoidal and Blazed Surface Relief Optical Transmission Gratings in Bi12GeO20, Er: LiNbO3 and Er: Fe: LiNbO3 Crystals by Nitrogen Ion Microbeams of 5 MeV and 10.5 MeV Energy

Gyula Nagy István Bányász György Kármán Ágnes Nagyné Szokol István Rajta Vladimir Havránek István Bányász Robert Magnusson Maria Cinta Pujol

Publication Name: Sensors

Publication Date: 2025-02-01

Volume: 25

Issue: 3

Page Range: Unknown

Description:

High diffraction efficiency optical transmission gratings with quasi-sinusoidal and saw-tooth surface relief profiles were fabricated in Bi12GeO20, Er: LiNbO3 and Er: Fe: LiNbO3 crystals by ion beam implantation. The gratings were directly written by nitrogen ion microbeams at energies of 5 MeV and 10.5 MeV. The finest grating constant was 4 μm. Grating constants for the majority of the gratings were 16 μm. The highest amplitudes of the gratings reached 1600 nm. The highest first-order diffraction efficiency obtained in a sinusoidal grating was 25%, close to the theoretical maximum of 33%. The highest first-order diffraction efficiency of a blazed grating was also 25%, without Littrow optimization. Such gratings can be incorporated into integrated optical biosensors.

Open Access: Yes

DOI: 10.3390/s25030804