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Authors - 10602
A. M. Csergő
15029949000
János Szlávik
6602551338
Balázs Tóth
57196930196
Khaled Alghafli
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Publications - 6674
Socio-political determinants of circular economy behavior: A cross-sectional analysis across Italy
Publication Name: Socio Economic Planning Sciences
Publication Date: 2025-08-01
Volume: 100
Issue: Unknown
Page Range: Unknown
Description:
The circular economy (CE) has emerged as a crucial alternative to the traditional linear economic model, which relies on resource extraction, production, and waste disposal, resulting in significant environmental degradation and resource depletion. In contrast, the CE emphasizes resource efficiency through practices such as reusing, repairing, refurbishing, and recycling, providing both environmental and economic benefits. This study investigates the complex interaction between socio-political factors and individual-level CE practices in Italy, addressing gaps in existing research that primarily focus on specific consumer behaviors or demographic characteristics. Particularly, utilizing probit and multivariate probit analyses on the 2021 AVQ “Aspects of Daily Life” dataset from ISTAT, the research examines how socio-political involvement, budget constraints, positive educational externalities, and demographic factors influence CE behaviors. The findings reveal that socio-political factors, particularly political trust in local governments, significantly influence circular practices, with higher trust associated with greater adoption of sustainable transportation and local products, while lower political engagement correlates with increased waste and reduced sustainability, highlighting the need for targeted educational initiatives and localized policies to promote a circular economy effectively.
Open Access: Yes
Enhancing seismic assessment and risk management of buildings: A neural network-based rapid visual screening method development
Publication Name: Engineering Structures
Publication Date: 2024-04-01
Volume: 304
Issue: Unknown
Page Range: Unknown
Description:
Some of the existing buildings are designed based on lower design standards or even without considering seismic design standards. Recent earthquakes have further highlighted the vulnerability of these buildings when subjected to severe seismic activity. Consequently, it has become imperative to conduct seismic vulnerability assessments of the existing building stock. Therefore, the assessment of the existing building stock is required through the utilization of Rapid Visual Screening (RVS) methods. However, the existing conventional RVS methods used in seismic building assessments have shown limited accuracy. Furthermore, because these methods were developed based on expert opinions and/or due to access limitations to detailed assessment-based generated data used for their development, further enhancing them is challenging. To address these limitations, a new RVS method, which leverages Neural Networks (NN) and building-specific parameters, for reinforced concrete, adobe mud, bamboo, brick, stone, and timber buildings has been proposed in this study. Unlike conventional methods that rely on site seismicity class, the developed data-driven approach incorporates building-specific parameters such as the fundamental structural period and building spectral acceleration. The developed RVS method is specifically tailored to analyze diverse types of buildings in regions with varying seismicity risks, all in preparation for an impending earthquake. In this study, the developed RVS method demonstrated a promising 68% test accuracy, effectively representing the building performance against earthquakes. These findings illustrate the potential of the developed NN based RVS method in assessing existing buildings, thereby mitigating potential loss of life and property during imminent earthquake and alleviating the associated economic burden. Furthermore, this study introduces a new RVS method that can pave the way for future advancements in the field of seismic vulnerability assessment of existing buildings.
Open Access: Yes
Prediction model of performance–energy trade-off for CFD codes on AMD-based cluster
Publication Name: Future Generation Computer Systems
Publication Date: 2025-08-01
Volume: 169
Issue: Unknown
Page Range: Unknown
Description:
This work explores the importance of performance–energy correlation for CFD codes, highlighting the need for sustainable and efficient use of clusters. The prime goal includes the optimisation of selecting and predicting the optimal number of computational nodes to reduce energy consumption and/or improve calculation time. In this work, the utilisation cost of the cluster, measured in core-hours, is used as a crucial factor in energy consumption and selecting the optimal number of computational nodes. The work is conducted on the cluster with AMD EPYC Milan-based CPUs and OpenFOAM application using the Urban Air Pollution model. In order to investigate performance–energy correlation on the cluster, the CVOPTS (Core VOlume Points per TimeStep) metric is introduced, which allows a direct comparison of the parallel efficiency for applications in modern HPC architectures. This metric becomes essential for evaluating and balancing performance with energy consumption to achieve cost-effective hardware configuration. The results were confirmed by numerous tests on a 40-node cluster, considering representative grid sizes. Based on the empirical results, a prediction model was derived that takes into account both the computational and communication costs of the simulation. The research reveals the impact of the AMD EPYC architecture on superspeedup, where performance increases superlinearly with the addition of more computational resources. This phenomenon enables a priori the prediction of performance–energy trade-offs (computing-faster or energy-save setups) for a specific application scenario, through the utilisation of varying quantities of computing nodes.
Open Access: Yes
Investigating the Factors Influencing the Strength of Cold-Formed Steel (CFS) Sections
Publication Name: Buildings
Publication Date: 2024-04-01
Volume: 14
Issue: 4
Page Range: Unknown
Description:
The utilization of cold-formed steel (CFS) sections in construction has become widespread due to their favorable attributes, including their lightweight properties, high strength, recyclability, and ease of assembly. To ensure their continued safe and efficient utilization, this review provides a comprehensive investigation into the factors influencing the strength of CFS members. This analysis encompasses design codes, prediction methodologies, material properties, and various structural configurations. This review uncovers discrepancies among existing design codes, particularly noting conservative predictions in AISI and AS/NZS standards for composite and built-up sections. Additionally, the effectiveness of prediction methods such as the direct strength method and effective width method varies based on specific structural configurations and loading conditions. Furthermore, this review delves into recent advancements aimed at enhancing fire resistance, connection design, and the composite behavior of CFS structures. The influence of factors such as eccentricity, sheathing materials, and bolt spacing on structural performance is also examined. This study underscores the crucial role of accurate prediction methods and robust design standards in ensuring the structural integrity and safety of CFS constructions. Through a comparative analysis, it is revealed that AISI and AS/NZS standards exhibit conservatism in predicting nominal buckling loads compared to experimental data. Conversely, a non-linear finite element analysis demonstrates a strong correlation with laboratory tests, offering a more accurate prediction of nominal buckling capacity. Overall, this review offers comprehensive insights aimed at optimizing CFS structural design practices. By identifying key areas for future research and development, this work contributes to the ongoing advancement of safe and efficient CFS construction applications.
Open Access: Yes
Prediction of possible tornado strike using complex m-polar fuzzy information based on Dombi operators
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-08-01
Volume: 16
Issue: 8
Page Range: Unknown
Description:
Tornados are extremely catastrophic, and the global effect of natural calamities like tornados is enormous and needs prompt and effective management. We can tackle this problem by using measures like multi-criteria decision-making (MCDM) to identify high-risk areas of a potential tornado strike. We frequently use MCDM techniques to solve the complexities and uncertainties of modern-era problems. We present a study that builds a prediction model by combining the Dombi aggregation operator with a complex m-polar fuzzy set (CmFS) to accurately guess when a tornado will hit. Our proposed model determines an expert panel, criteria, and a set of alternatives after identifying the problem. We create summed-up decision matrices using complex m-polar fuzzy Dombi aggregation operators (CmFDAO) after experts evaluate criteria and options. The algorithm then presents the best option with the help of a final decision score matrix. Our model uses a set of eight meteorological elements and eight experts to assess four possible tornado locations and pinpoint an area with a high risk of tornado strikes. The results generated by our aggregation operator set demonstrate that our proposed method for handling complex and multi-polar data is concise and efficient when compared to other sets. This early prediction highlights the potential of significant risk reduction to the environment and human life due to catastrophic events like tornados by enhancing early warning systems and effective emergency management.
Open Access: Yes
AI in medical diagnosis: AI prediction & human judgment
Publication Name: Artificial Intelligence in Medicine
Publication Date: 2024-03-01
Volume: 149
Issue: Unknown
Page Range: Unknown
Description:
AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, ‘explainability’, and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.
Open Access: Yes
Comparative Analysis of Ascaris suum and Macracanthorhynchus hirudinaceus Infections in Free-Ranging and Captive Wild Boars (Sus scrofa) in Hungary
Publication Name: Animals
Publication Date: 2024-03-01
Volume: 14
Issue: 6
Page Range: Unknown
Description:
Ascaris suum and Macracanthorhynchus hirudinaceus cause a large loss of yield in farm animals as well as in free-living and captive wild boar herds, thereby causing economic damage. This study compared A. suum and M. hirudinaceus infections in free-ranging and captive wild boars (Sus scrofa) in Hungary. The authors measured the A. suum and M. hirudinaceus infections of a 248-hectare wild boar garden and an 11,893-hectare free-living wild boar herd in the sample area. In all cases, samples were collected from shot wild boars. In total, 216 wild boars were examined from June 2015 to June 2023 in Hungary. Of the 173 dissected wild boars from the wild, 57 (32.9%) were infected with A. suum, while 30 (69.8%) of the 43 individuals from the captive area were infected. The prevalence of M. hirudinaceus in the free-living area population was 9.25% (16 wild boars), while that of the captive population was 34.89% (15 wild boars). In the case of the examined helminths, the captive herd was 36.9% more infected than the herd living in the open area.
Open Access: Yes
DOI: 10.3390/ani14060932
Effects of agro-climatic indices on wheat yield in arid, semi-arid, and sub-humid regions of Iran
Publication Name: Regional Environmental Change
Publication Date: 2024-03-01
Volume: 24
Issue: 1
Page Range: Unknown
Description:
This study aimed to analyze the impact of variations of drought-related agro-climatic indices including cumulative precipitation, cumulative potential evapotranspiration, cumulative actual evapotranspiration, cumulative crop evapotranspiration, cumulative water stress, and cumulative water deficit during nine consecutive phenological stages (emergence to physiological maturity) on wheat yield in arid, semi-arid, and sub-humid regions of Iran during 1999–2018. Principal component analysis was used to recognize the main components that largely explained the variations of agro-climatic indices during different stages of the crop growing period. Then, the relationships between the major components, retrieved from principal component analysis, and the crop yield were assessed. Wheat irrigation requirements were also calculated to investigate the regional water supply–demand patterns during the crop growing period. The findings highlighted increasing impacts of cumulative precipitation, cumulative potential evapotranspiration, cumulative crop evapotranspiration, and cumulative actual evapotranspiration and decreasing impacts of cumulative water stress and deficit on wheat yield, particularly in arid and semi-arid regions. The crop yield was more affected by variations of the agro-climatic indices during the reproductive phase than the vegetative phase. Accordingly, booting to flowering in the arid region, flowering in the sub-humid region, and stem elongation to booting in the semi-arid region were the most sensitive periods of wheat to agro-climatic indices variations. Wheat irrigation requirements in arid and semi-arid regions started earlier than in the sub-humid region. From the findings, it was concluded that adjusting the irrigation schedule based on wheat irrigation requirements during the wheat growing period could help farmers to achieve a favorable wheat yield.
Open Access: Yes
Social Sustainability Analysis in Szigetköz: A Study of Four Case Locations
Publication Name: Chemical Engineering Transactions
Publication Date: 2023-01-01
Volume: 107
Issue: Unknown
Page Range: 463-468
Description:
This paper is based on research that examined civil and community life in 34 settlements of Szigetköz in 2021 and 2022. A total of 337 NGOs and countless informal groups operate in the region. The examination is based on questionnaire research (43 fillings), interviews (25 pieces), observations (9 times) and document analysis, and one of the results is four case studies about four settlements (Dunasziget, Kimle, Mecsér and Győrladamér). The goal of this paper is to summarize the knowledge related to social sustainability and analyze its operation in these settlements. The principal finding of the research is that the four settlements coped with the challenges posed by their geographical location, economic situation and infrastructural capabilities in four ways. These roads show creative solutions using local resources, which are the key to the social sustainability of settlements.
Open Access: Yes
DOI: 10.3303/CET23107078
