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Publications - 6383

Road patterns of housing estates in Hungary

Pál Hegyi

Publication Name: Pollack Periodica

Publication Date: 2015-04-01

Volume: 10

Issue: 1

Page Range: 83-92

Description:

This paper deals with the road patterns of housing estates in eight Hungarian cities. The investigation was directed to the characteristic of street networks using geographic information system. The different types of housing estates were compared using the following indicators: ratio of T and X junctions, ratio of the dead ends, average distance between nodes, connectivity index, and road density. It is concluded that according to their street network the housing estates have three periods.

Open Access: Yes

DOI: 10.1556/Pollack.10.2015.1.8

Biogeography-Based Optimization of Machine Learning Models for Accurate Penetration Rate Prediction Using Rock Texture Coefficient

Reza Mikaeil Dragan Pamucar Akbar Esmaeilzadeh Sina Shaffiee Haghshenas Shahrokh Khosravimanesh Sina Shaffiee Haghshenas Vladimir Simic

Publication Name: International Journal of Computational Intelligence Systems

Publication Date: 2025-12-01

Volume: 18

Issue: 1

Page Range: Unknown

Description:

Predicting drill penetration rate (PR) in rock environments remains a significant challenge due to the complex interplay between rock texture, drilling fluid properties, and operational parameters. Traditional empirical models often lack generalizability and are based on inconsistent datasets, limiting their reliability. To address these limitations, this study develops a comprehensive experimental dataset using rock samples collected from various mines in Iran, tested under controlled laboratory conditions with different drilling fluids, bit loads, and rotational speeds. Texture coefficient (TC), electrical conductivity (EC), load on bit (LOB), and bit rotational velocity (BRV) were selected as input features. Four machine learning models—support vector regression (SVR), stochastic gradient descent (SGD), K-nearest neighbors (KNN), and decision tree (DT)—were trained to predict PR. A biogeography-based optimization (BBO) algorithm was employed to fine-tune hyperparameters and enhance model accuracy. Additionally, a novel hybrid error index (HEI) was introduced to comprehensively evaluate model performance. Among all models, the DT achieved the best accuracy with an HEI of 0.3753, followed by KNN, SVR, and SGD. These findings demonstrate the potential of the DT model, combined with optimized learning and a robust dataset, to reliably predict penetration rate in rock-based engineering projects.

Open Access: Yes

DOI: 10.1007/s44196-025-00973-7

Food safety risk analysis utilising K-lexicographic-max product of neutrosophic graph

Dragan Pamucar Nasreen Kausar M. Kaviyarasu Nasser El-Kanj M. Rajeshwari Nasser El-Kanj Vladimir Simic

Publication Name: Ain Shams Engineering Journal

Publication Date: 2025-12-01

Volume: 16

Issue: 12

Page Range: Unknown

Description:

In this study, we introduce the concept of the K-Lexicographic Max Product (K−LMP) of neutrosophic graphs and explore its associated degree structure to enhance decision-making frameworks in food safety applications related to risk assessment, including freshness, contamination, and spoilage. Neutrosophic graphs, capable of handling indeterminacy, inconsistency, and incompleteness, provide a flexible mathematical foundation for modelling complex systems. By incorporating the K−LMP into neutrosophic graphs, we offer a novel approach to comparing and ranking food safety scenarios where multiple attributes and uncertain information coexist. We present example graphs and theorems related to K−LMP and further define the K-Lexicographic degree to quantify node significance within the context of neutrosophic graphs. To validate the practical utility of this approach, a food safety analysis is implemented, demonstrating how the model identifies critical control points and supports more robust, transparent decision-making under uncertainty. This work contributes to the advancement of neutrosophic graph theory and its interdisciplinary application in food quality and safety management.

Open Access: Yes

DOI: 10.1016/j.asej.2025.103761

Real-time monitoring of ammonia emissions from cereal crops using LoRaWAN-based sensing technology

Endre Harsányi Sana Arshad Safwan Mohammed Morad Mirzaei Main Al-Dalahmeh Neményi Miklos Nyéki Anikó Alahmad Tarek Gombkötő Nóra Szabó Szilárd A. Nyéki

Publication Name: Scientific Reports

Publication Date: 2026-12-01

Volume: 16

Issue: 1

Page Range: Unknown

Description:

This study presents a LoRaWAN-based IoT system developed for real-time monitoring of ammonia (NH₃) emissions in cereal crop fields. Sustainable agriculture increasingly demands on-farm greenhouse gas (GHG) tracking linked to environmental variables. IoT offers efficient real-time monitoring of soil NH₃ emissions and associated factors. Our research introduces a unique Field Monitoring Laboratory: a LoRaWAN-connected IoT system integrating soil, crop, and microclimate sensors to observe NH₃⁺, air temperature, rainfall, humidity, soil temperature, and moisture content. The system comprises a field lab, data server, and custom dashboard with analytics capabilities. NH₃ fluxes were measured in autumn-sown cereals across three growing seasons (2020–2023). Tukey’s Kramer test revealed significant (p < 0.05, p < 0.001) differences in NH₃ emissions and environmental variables between years. Highest NH₃ emissions (1.94 ppm in 2020, 1.71 ppm in 2021) coincided with elevated air (25–31 °C) and soil (21–23 °C) temperatures, and higher mean and peak rainfall (0.40–0.48 mm average; max 9–31.6 mm). Principal Component Analysis showed 65.8% variance explained by PC1 and PC2, with high loadings from temperature and soil moisture. Spearman’s correlation indicated moderate positive associations (r = 0.38–0.4, p < 0.05) of NH₃ with soil moisture at 20 cm and 40 cm of soil depth, and a weak negative correlation (r = -0.16 and − 0.17) with soil temperature at 20 cm and 40 cm. The study underscores the potential of IoT technology using calibrated gas sensors and LoRaWAN for real-time NH₃ and environmental monitoring, enabling informed decision-making in smart agriculture.

Open Access: Yes

DOI: 10.1038/s41598-025-31661-3

Predictor set optimization for collaborative filtering

Orsolya Horváth Gabor Takacs

Publication Name: Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2011

Publication Date: 2011-12-12

Volume: 1

Issue: Unknown

Page Range: 404-407

Description:

One of the most efficient approaches to create a recommender system is collaborative filtering (CF). CF does not require metadata about users and items, but only interactions between users and items (e.g. ratings), therefore it can be applied in many problem domains. Experience shows that for achieving high accuracy, it is worthwhile to use a blended solution, consisting of many predictors. This paper presents an algorithm for constructing a set of CF predictors so that the overall accuracy of the set is high. The algorithm was tested on the Netflix Prize dataset that contains 100 million ratings. © 2011 IEEE.

Open Access: Yes

DOI: 10.1109/IDAACS.2011.6072784

Truck Load Positions Effect on Dynamic Behavior of Fractured Steel Box Girder Bridge

Rajmund Kuti M. Brinissat Richard P. Ray

Publication Name: Advances in Transdisciplinary Engineering

Publication Date: 2024-01-01

Volume: 59

Issue: Unknown

Page Range: 67-74

Description:

The harsh climate and environmental hazards contribute to the structural damage of steel bridges. Substantial dynamic loading from heavy trucks can worsen existing cracks. This paper investigates the dynamic behavior of a steel box girder bridge, the Szapáry bridge, with a fractured girder subjected to moving truck loads. Initially, a finite element model simulates the seven-span continuous bridge behavior during static load testing. The model also accurately simulated the dynamic load tests performed. A series of hypothetical damage (fractured girder) and dynamic loading scenarios reveal the effects of truck positions on the damaged bridge's dynamic response. Dynamic displacement induced due to traffic loading helps evaluate a bridge's structural health. Results of the parametric analysis highlight that several factors, including truck velocity and position, bridge span length, and truck lateral spacing, significantly affect the dynamic vibration of the fractured bridge. The results offer insight into the effectiveness of dynamic response analysis for conditioned-based maintenance and damage detection.

Open Access: Yes

DOI: 10.3233/ATDE240528

Seismic Vulnerability Assessment of an Unanchored Circular Storage Tank Against Elephant’s Foot Buckling

Nurullah Bektaş Engin Aktaş

Publication Name: Journal of Vibration Engineering and Technologies

Publication Date: 2023-06-01

Volume: 11

Issue: 4

Page Range: 1661-1678

Description:

Purpose: Seismic vulnerability assessment of liquid containing storage tanks is the most vital relevance for industrial plants and society safety to endure damage during impending earthquakes. Because such systems also play an essential role in the public lifeline and also ensure continued use in emergencies. Furthermore, considering that the material contained in individual plants could be hazardous, requisite precautions have paramount importance against undesired leakage. The high internal pressure and axial forces exerted by the liquid in the steel tanks near the tank wall bottom produce elastic–plastic buckling, also known as Elephant’s Foot Buckling (EFB). As far as the authors are aware, no study has been carried out that involves a critical assessment and comparison of IDA and truncated IDA-based EFB failure criterion. This study provides insight into incremental dynamic analysis (IDA) and truncated IDA-based seismic evaluation of cylindrical unanchored steel storage tanks by employing a developed pressure-based surrogate modeling approach. For this purpose, probability-based seismic assessment of a representative sample is considered based on IDA and truncated IDA approaches to identify the potential of the EFB failure and to explore potential enhancements in the sophisticated structural analysis model to prevent the hazardous effects of impending earthquakes. Methods: Due to the significance of industrial plants for public safety and benefit, the structural response evaluation methods for different types of storage tanks have been widely reported. In the literature, the most comprehensive analytical assessment methodology is the IDA approach, in which nonlinear time-history analyses are considered in the finite element analysis model to assess the structural model’s seismic performance. Results: To generate fragility curves, both IDA approaches are employed, taking into consideration and ignoring uncertainty of material properties. The values of the two methods-based fragility curves approach each other as the magnitude of dispersion increases. Conclusion: The two fragility curves give the probability of failures close to each other as the dispersion amount increases while considering the uncertainty of the material properties. In addition, fragility curves generated based on the truncated IDA have been found to give a higher probability of failure, up to 32.5 percent. When compared to the IDA-based fragility curves, the truncated IDA-based fragility curves were found to be on the conservative side.

Open Access: Yes

DOI: 10.1007/s42417-022-00663-0

Position sensorless extended and unscented Kalman filters with permanent magnet flux linkage and load torque estimation for surface-mounted PMSM

Krisztián Horváth

Publication Name: Automatika

Publication Date: 2024-01-01

Volume: 65

Issue: 3

Page Range: 1201-1212

Description:

In this paper, novel position sensorless state estimators with improved robustness to permanent magnet (PM) flux linkage variations in permanent magnet synchronous machines (PMSMs) are presented. Unlike state estimators using conventional infinite inertia or electromechanical models, the estimators presented here can also estimate the PM flux linkage, so they are not sensitive to its uncertainty. For each models used for state estimation, a detailed observability study is presented. Due to the nonlinear models, extended and unscented Kalman filter algorithms are used for the implementation. To compare the sensitivity of conventional and proposed state estimators to uncertainty in electrical parameters, numerical simulations are carried out. In addition, the computational burden of the estimators is compared by real-time execution.

Open Access: Yes

DOI: 10.1080/00051144.2024.2354643

Response to Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice

Istvan Racz András Horváth Zoltán Horváth

Publication Name: Clinical Endoscopy

Publication Date: 2022-09-01

Volume: 55

Issue: 5

Page Range: 701-702

Description:

No description provided

Open Access: Yes

DOI: 10.5946/ce.2022.123.1