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Publications - 6374

A Simulation-Optimization Framework for Road Maintenance Scheduling with Adaptive Agent Behavior

Szabolcs Fischer Amir Hajimirzajan Milad Kazemian Ahmad Davari Ghezelhesar

Publication Name: Lecture Notes in Networks and Systems

Publication Date: 2026-01-01

Volume: 1768 LNNS

Issue: Unknown

Page Range: 173-189

Description:

Recently, with an increasing number of people traveling by car, there has been a growing demand for effective traffic management, reduced travel times, and improved road and street maintenance plans. Here, it is evident that drivers make a well-informed decision on which route to take by utilizing smartphone routing, traffic announcements, and advancements in navigation technology. In the present study, the authors aim to develop a road maintenance plan that incorporates a bi-level optimization and simulation framework. They focus on the upper level by optimizing the road maintenance plan; at a lower level, intelligent agents acting as savvy passengers seek to minimize driving time and wait times in traffic. To evaluate the intelligent behavior of agents in reducing travel time on blocked routes (due to road repairs) under various scenarios, the authors first calculate the agents’ behavior in finding the optimal travel demand route and then integrate the optimization of the road maintenance plan. The results of this study demonstrated the effectiveness of informing passenger agents and their intelligence in correcting routes and reducing travel time.

Open Access: Yes

DOI: 10.1007/978-3-032-13898-9_20

Intelligent Traffic Signal Control Using Rule Based Fuzzy System

Tamrat Delessa Chala Ĺaszĺo T. Ḱoczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2023-01-01

Volume: 1087

Issue: Unknown

Page Range: 347-371

Description:

Over the past decades, there has been an ever-increasing saturation of traffic networks due to the growing number of road vehicles, and due to the available limited. To solve these problems, adaptive, (semi-) intelligent traffic control has been used widely for the last decades. These systems nevertheless, have some shortages, the most obvious one being that these systems use the presence of vehicles at the lanes immediately before reaching the intersections. The real queue size cannot be taken into consideration. In the present approach, the input values are supposed to come from cameras connected with image processing systems and directed microphones. We propose a new traffic signal control system with a hierarchical structure based on similarly Mamdani control, however, containing essentially novel elements and having more intelligent features. This new model and the connected algorithmic approach allow rather complex control strategies, but only a simple case study has been implemented. Compared with existing fuzzy traffic controls, the novel approach has more adaptability and flexibility, by having the potential to differentiate an arbitrary number of traffic directions and by increasing general safety by the additional emergency vehicle handling feature. In addition, the calculation with queues, and individual vehicles weighted with the waiting time makes the system more flexible than any existing intelligent model.

Open Access: Yes

DOI: 10.1007/978-3-031-25759-9_17

Sustainable Consumption from a Domestic Food Purchasing Perspective Among Hungarian Generation Z

Laszlo Vasa Mónika Garai-Fodor Anett Popovics

Publication Name: Decision Making Applications in Management and Engineering

Publication Date: 2024-01-23

Volume: 7

Issue: 2

Page Range: 401-417

Description:

The relevance of the study is the global consumer trend towards sustainability. Sustainable consumption has positive environmental, social, and economic impacts, which makes it a key issue in the context of food consumption. Preference for regional, and domestic products can significantly support sustainable consumption. The study focused on the demographically and economically important Generation Z from the perspective of sustainable consumption. The main research objective of the study is to analyze the Hungarian food purchasing habits of Generation Z and to segment Generation Z according to the Hungarian food consumption criteria to characterize potential target groups. During the research, qualitative and quantitative data collection was carried out. The study focuses on the presentation of the results of the latter. In the quantitative survey, we conducted a pre-tested standardized questionnaire online survey. Subject recruitment was carried out using a snowball sampling method, resulting in 518 evaluable questionnaires. In a quantitative study, distinct segments of Hungarian food consumers were characterized according to their food consumption preferences. The research also demonstrated that the groups of Generation Z according to Hungarian food consumption preferences differ significantly from each other in terms of their perception of Hungarian food. The research concludes that Generation Z is a group of domestic food consumers with specific characteristics, who could be the main base for sustainable consumption. For those who have not yet developed this motivation, the main reasons are an unsophisticated preference system and a lack of education. Systematic marketing activities aimed at the first component of attitudes are therefore most needed to attract these segments.

Open Access: Yes

DOI: 10.31181/dmame7220241108

The recent advances of near-infrared spectroscopy in dairy production—a review

Tamás Tóth George Bázár Zoltan Kovacs Haruna Gado Yakubu

Publication Name: Critical Reviews in Food Science and Nutrition

Publication Date: 2022-01-01

Volume: 62

Issue: 3

Page Range: 810-831

Description:

One of the major issues confronting the dairy industry is the efficient evaluation of the quality of feed, milk and dairy products. Over the years, the use of rapid analytical methods in the dairy industry has become imperative. This is because of the documented evidence of adulteration, microbial contamination and the influence of feed on the quality of milk and dairy products. Because of the delays involved in the use of wet chemistry methods during the evaluation of these products, rapid analytical techniques such as near-infrared spectroscopy (NIRS) has gained prominence and proven to be an efficient tool, providing instant results. The technique is rapid, nondestructive, precise and cost-effective, compared with other laboratory techniques. Handheld NIRS devices are easily used on the farm to perform quality control measures on an incoming feed from suppliers, during feed preparation, milking and processing of cheese, butter and yoghurt. This ensures that quality feed, milk and other dairy products are obtained. This review considers research articles published in reputable journals which explored the possible application of NIRS in the dairy industry. Emphasis was on what quality parameters were easily measured with NIRS, and the limitations in some instances.

Open Access: Yes

DOI: 10.1080/10408398.2020.1829540

Unpacking IT-Driven Digital Transformation in Marketing 4.0 Through a Sociomaterial Gioia Lens

Ankit Mehrotra Nourah O. Alshaghdali Istvan Drotar Sushant Kumar Vishnoi

Publication Name: Journal of Global Information Management

Publication Date: 2025-01-01

Volume: 33

Issue: 1

Page Range: Unknown

Description:

The application of Artificial Intelligence (AI)-enabled technologies into the retail environment has led to the emergence of the Marketing 4.0 paradigm, which integrates customer digital and physical touch-points to deliver value. The study used sociomateriality as a theoretical lens to examine how AI-driven marketing practices were enacted, negotiated, and established in retail organizations through human-material entanglements. The crowdsourcing platform Prolific Academic was used to collect data from retail professionals through open-ended essays. Data were analyzed using Gioia's methodology, which led to the identification of five dimensions—sociomaterial entanglement, material agency, situated practices, temporal emergence, and sociomaterial identity—which aligned with sociomateriality theory, encouraging the adoption of Marketing 4.0 in the retail context. The study developed a holistic framework to visualize the relationships that emerged from the participants' responses, addressing the criticalities of the Marketing 4.0 ecosystem.

Open Access: Yes

DOI: 10.4018/JGIM.393626

Artificial neural network analysis of chemical reaction and radiation effects on MHD ternary nanofluid flow over an exponentially accelerated inclined plate

S. V.K. Varma B. T. Raju Hijaz Ahmad Waleed Mohammed Abdelfattah Charankumar Ganteda Hameed Miya

Publication Name: South African Journal of Chemical Engineering

Publication Date: 2026-07-01

Volume: 57

Issue: Unknown

Page Range: Unknown

Description:

This investigation explores the magnetohydrodynamic (MHD) free convective heat and mass transfer characteristics of a ternary nanofluid traversing an exponentially accelerated inclined plate within a porous medium. The theoretical framework integrates the complexities of internal heat generation/absorption and fluctuating wall temperatures. Analytical solutions were rigorously derived utilizing the Laplace transform technique, while a sophisticated Artificial Neural Network (ANN) was implemented to forecast and corroborate these mathematical outcomes. Heat Transfer (Nusselt Number) evaluated against the interplay of the Prandtl number, thermal radiation parameters, and temporal progression. Mass Transfer (Sherwood Number) analyzed as a function of magnetic permeability, the Schmidt number, and time. Thermal Enhancement findings indicate that an augmentation in the nanofluid volume fraction significantly bolsters thermal conductivity, thereby elevating the temperature profile. The proposed Levenberg-Marquardt Algorithm-based Backpropagation Artificial Neural Network (LMA BANN) demonstrated exceptional predictive fidelity. The model achieved a precision threshold exceeding 99.9% for the Nusselt number and near-perfect accuracy for the Sherwood number. These results are substantiated by negligible Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values, coupled with correlation coefficients (R) nearing unity, signifying a robust alignment between the analytical and predicted datasets.

Open Access: Yes

DOI: 10.1016/j.sajce.2026.100912

NVH characterization of a ladder-like welded structure using finite element analysis and experimental method

Dániel Feszty Balázs Vehovszky Attila Schweighardt

Publication Name: Advances in Acoustics Noise and Vibration 2021 Proceedings of the 27th International Congress on Sound and Vibration Icsv 2021

Publication Date: 2021-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

In vehicle industry, considering a chassis, the evaluation of the experimental modal analysis is usually done up to 80 Hz, since the modal density is increasing with frequency. In addition, the deviation between the measurement and simulation is getting more significant at higher frequencies. The purpose of this study is to extend the usable test range and to improve the accuracy of the results both in case of measurement and FE (Finite Element) simulation. Present paper introduces the vibrational characterization of a ladder-like welded structure. This test case represents a simplified model of a vehicle's frame consisting of rod-like elements with high stiffness, connected to each other in welded junctions. Although such simulational and experimental modal analysis procedures are widely used in practice, results can significantly change according to the used method and the fine-tuning of parameters. For this reason, a further goal is to find the best measurement and simulation technique for the given structure. During the FE analysis different modelling solutions and element types were compared. In order to discover the effect of the manufacturing inaccuracies, the same measurements were performed on two distinct, but theoretically identical samples. The influence of the experimental setup (e.g. excitation and fixing method) and settings were investigated as well. Finally, FE simulation and experimental results are compared using Frequency Response Functions.

Open Access: Yes

DOI: DOI not available

Development and experimental evaluation of single-port substrate integrated waveguide resonator with dual-parameter sensitivity for non-invasive blood glucose monitoring

Balázs Benyó J. Geoffrey Chase Katalin Kovács Hussam Al-Saedi Omer S. Alkhafaf Ameer B. Alsultani

Publication Name: Measurement Journal of the International Measurement Confederation

Publication Date: 2026-06-16

Volume: 278

Issue: Unknown

Page Range: Unknown

Description:

Current Blood Glucose (BG) monitoring techniques are invasive or semi-invasive and can impose financial and practical burden on patients. In this study a compact, non-invasive and single port Substrate Integrated Waveguide (SIW) loaded with X-shape has been present. The sensor resonant at 1.7 GHz, a dual parameter employed to evaluate the sensor by tracking the shift of resonance frequency in (MHz) and the reflection coefficient (dB) to detect glucose-induced changes in tissue permittivity. The device is designed using full-wave electromagnetic simulations with multilayer tissue models and validated experimentally on five human volunteers under controlled fasting and post-glucose conditions. Across the physiological range of 20–200 mg/dL, the sensor exhibits sensitivities up to 0.310 MHz per mg/dL and 0.333 dB per mg/dL, demonstrating consistent responsiveness to glucose variations. The results indicate that the proposed resonator can track glucose-related dielectric changes using a simple contact-based configuration. However, the measurements are influenced by subject-specific variability and sensor placement conditions, which currently limit generalization and repeatability. Further work is required to improve robustness, calibration, and validation on larger cohorts before practical deployment.

Open Access: Yes

DOI: 10.1016/j.measurement.2026.121635

Explainable XGBoost-based models of root-zone soil moisture profiling using coupled Sentinel-2 and IoT data in loam and silt loam soil

Tarek Alahmad A. Nyéki M. Neményi

Publication Name: Discover Applied Sciences

Publication Date: 2026-06-01

Volume: 8

Issue: 6

Page Range: Unknown

Description:

Background: Accurate prediction of soil moisture content (SMC) is crucial for sustainable irrigation management and enhancing resilience against climate change. However, in-situ sensing offers accurate point-scale measurements but lacks spatial representativeness, while satellite-based offer spatial coverage but are either too coarse at field scale or indirect and cloud -sensitive. Integrating satellite observation with ground-based monitoring and IoT meteorological data could exploit complementary strengths by linking canopy conditions and atmospheric drivers to reliable in-field reference measurements. Method: This study predicts SMC at five depths (5 to 80 cm) for two soil texture classes (loam and silt loam) using Extreme Gradient Boosting (XGBoost) by integrating Sentinel-2 vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) with Internet of Things (IoT)-derived meteorological data using two input scenarios and gravimetric SMC as reference for model training and evaluation. Results: The model trained with combined inputs achieved higher accuracy compared with using only vegetation indices as predictors in both soil textures and all depths. This was especially evident in loam soil at 5 and 20 cm depth, with R² values of 0.95 and 0.79 and RMSE values of 0.88% and 1.46%, respectively, compared to R² values of 0.70 and 0.69 and RMSE values of 2.26% and 1.77% when using vegetation indices only. The model achieved near-perfect accuracy in silt loam with R² = 0.99 at 5–20 cm and RMSE = 0.49–0.40% at same depths. SHapley Additive exPlanations (SHAP) analysis identified NDVI as the most influential predictor in surface soil layers (mean SHAP = 0.11–0.22), reflecting its strong sensitivity to canopy vigor. In contrast, solar radiation emerged as key determinant in deeper soil layers (60 and 80 cm; SHAP = 0.12–0.18), highlighting the importance of atmospheric evaporative demand in controlling subsoil moisture dynamics. Conclusions: The model’s accuracy and interpretability enable depth-specific decision support for irrigation timing and water use efficiency under variable weather conditions, while providing actionable driver insights for climate-adaptive management aligned with SDGs 6 and 13. The approach is validated for loam and silt loam textures using optical Sentinel-2 indices, which are subject to cloud cover and revisit latency; therefore, the current framework is not suitable for real-time irrigation scheduling without accounting for these delays. Future integration with SAR and gap-filling strategies would be required for operational real-time applications.

Open Access: Yes

DOI: 10.1007/s42452-026-08673-3