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Publications - 6674

Digital divide and digitalization in Europe: A bibliometric analysis

Nataša Kovač Rafał Kusa Jewel Kumar Roy Katarzyna Żmija Jerzy Duda

Publication Name: Equilibrium Quarterly Journal of Economics and Economic Policy

Publication Date: 2024-06-30

Volume: 19

Issue: 2

Page Range: 463-520

Description:

Research background:Digitalization and the associated digital divide are crucial issues impacting socio-economic development globally. Extensive research has examined digitalization and the digital divide in EU countries, but there is a lack of understanding regarding comparisons with studies conducted in Western Balkan countries. This study investigates digitalization trends in research from the past five years in both regions, focusing on efforts and factors contributing to the digital gap. Purpose of the article: The study analyzes research on digitalization from 2018 to 2023 in the EU and Western Balkans. It explores factors causing the digital divide and efforts in digitalization, aiming to guide future research and policy for digital inclusion and sustainable development. Methods: The study employs a meticulous data selection process, choosing Scopus as the database for its extensive coverage of diverse journals. A total of 1119 articles from EU countries and 277 from Western Balkan countries are selected for bibliometric analysis, adhering to PRISMA guidelines. Findings & value added: The research reveals a growing interest in digitalization-related issues, demonstrating the multidisciplinary nature of ongoing research. It points out the distribution of publications on digitalization in the EU and Western Balkans countries. The EU focuses on digital technologies, economic growth, and sustainability, while Western Balkan countries focus on COVID-19 impact and digitalization in education and business. The research compares digitalization efforts in the EU and Western Balkan countries presented in the literature, pointing to new dimensions of the digital divide studies. It discusses how socio-economic contexts affect digital transformation and stresses the need for tailored policy approaches for digital inclusivity. These insights are of great importance for policymakers, researchers, and practitioners working towards global digital development and bridging the digital divide. The study lays the groundwork for future research and policy considerations, considering limitations like potential bias in databases and search criteria.

Open Access: Yes

DOI: 10.24136/eq.2899

Numerical algorithm of fourth-grade nanofluid flow with heat transfer consists of aluminum alloys over a riga plate

M. M. Mosallem H. I. Elsaeedy Hijaz Ahmad Asif Ullah Hayat Hassan Ali Ghazwani

Publication Name: Journal of Thermal Analysis and Calorimetry

Publication Date: 2025-10-01

Volume: 150

Issue: 19

Page Range: 15723-15736

Description:

The MHD (magnetohydrodynamic) fourth-grade nanofluid flow consisting of aluminum alloys (Ti6Al4V) nanoparticles over a Riga plate is studied. The study of fourth-grade fluids (FGFs) improves the capacity to design systems and procedures for a variety of industries, ultimately promoting performance and the durability of the product. Ti6Al4V nanoparticles (NPs) are dissolved in water to prepare the nanofluid. The FGF flow has been analyzed under the impacts of Arrhenius activation energy, heat source/sink, and chemical reaction. The modeled equations (momentum, energy, and fluid concentration equations) are reformed into dimension-free form through similarity conversion. The transform set of ordinary differential equations (ODEs) is numerically solved by using the parametric continuation method (PCM). For accuracy of the results, the outcomes are compared to the published work. The error between the present results and the published study is -0.00028% at M = 5.0 (magnetic parameter), which ensures that the proposed methodology and model are accurate and reliable. From the graphic results, it has been noticed that the velocity field improves with the influence of fourth-grade fluid parameter, cross-viscous coefficient, and third-grade fluid parameter. The thermal profile of NF boosts with the variation in heat source parameters and the rising number of Ti6Al4V-NPs.

Open Access: Yes

DOI: 10.1007/s10973-025-14713-8

Valorization of Waste Wood Flour and Rice Husk in Poly(Lactic Acid)-Based Hybrid Biocomposites

Amar Patnaik László Lendvai Tej Singh Maria Omastova G. Dogossy

Publication Name: Journal of Polymers and the Environment

Publication Date: 2023-02-01

Volume: 31

Issue: 2

Page Range: 541-551

Description:

This study explores the possibility of developing a new class of hybrid particulate-filled biocomposites using wood flour and rice husk wastes as environmentally friendly additives to poly(lactic acid) (PLA) as matrix material. Samples were prepared with fillers of different concentrations (0, 2.5, 5, 7.5 and 10 wt %), while the ratio of wood flour and rice husk was fixed at 1:1 in all cases. The preparation of biocomposites was performed through extrusion using a twin-screw extruder. Subsequently, they were formed into specimens by injection molding. Mechanical, thermal, thermomechanical, and morphological properties were examined. The addition of natural waste particles resulted in a remarkable improvement both in tensile and flexural modulus; however at a cost of impact strength and tensile strength. Meanwhile, flexural stress at conventional strain values were barely affected by the presence of wood flour and rice husk. The SEM images confirmed that there is a limited interfacial adhesion between the components, which supports the results obtained during mechanical tests. Both the differential scanning calorimetry (DSC) and the dynamic mechanical analysis indicated that the glass transition temperature of PLA was not affected by the incorporation of filler particles; however, the crystalline structure was gradually altered with increasing filler loading according to the DSC. Additionally, the particles were observed acting as nucleating agents, thereby increasing the overall crystallinity of PLA.

Open Access: Yes

DOI: 10.1007/s10924-022-02633-9

How does intergenerational transmission affect green innovation? Evidence from Chinese family businesses

Irum Shahzadi Peijun Xie Shiyi Tang Jian Wang Irum Shahzadi Yuriy Bilan

Publication Name: Structural Change and Economic Dynamics

Publication Date: 2025-06-01

Volume: 73

Issue: Unknown

Page Range: 158-169

Description:

Green innovation in family businesses is a significant yet underexplored area of research, particularly with regard to the influence of dynamic succession characteristics on intergenerational inheritance and its impact on innovation. This study, integrating the social-emotional wealth theory (SEW) and the agency theory, examines 505 Chinese listed family firms spanning from 2011 to 2020. Employing the Difference-in-Differences (DID) method, we investigate how intergenerational inheritance affects green innovation investment over time. Our findings reveal that initially, intergenerational transmission tends to inhibit green innovation investment in family businesses; however, this effect diminishes as the intergenerational process unfolds, indicative of the maturation of the second generation. Notably, we observe that a higher education level among second-generation heirs weakens the inhibitory effect of intergenerational inheritance on green innovation investment. This study addresses a gap in green innovation research by considering intergenerational transmission dynamics in family businesses, thus enhancing our understanding of innovation behaviors within this context. By synthesizing SEW and agency theory, this research offers novel insights into the varying impacts of intergenerational inheritance on firm innovation, shedding light on approaches to reconcile the willingness-ability paradox in family business innovation and promoting effective governance of succession processes.

Open Access: Yes

DOI: 10.1016/j.strueco.2024.12.022

Constructing and sampling partite, 3-uniform hypergraphs with given degree sequence

András Hubai Tamás Róbert Mezei Ferenc Béres András Benczúr István Miklós

Publication Name: Plos One

Publication Date: 2024-05-01

Volume: 19

Issue: 5 May

Page Range: Unknown

Description:

Partite, 3-uniform hypergraphs are 3-uniform hypergraphs in which each hyperedge contains exactly one point from each of the 3 disjoint vertex classes. We consider the degree sequence problem of partite, 3-uniform hypergraphs, that is, to decide if such a hypergraph with prescribed degree sequences exists. We prove that this decision problem is NP-complete in general, and give a polynomial running time algorithm for third almost-regular degree sequences, that is, when each degree in one of the vertex classes is k or k − 1 for some fixed k, and there is no restriction for the other two vertex classes. We also consider the sampling problem, that is, to uniformly sample partite, 3-uniform hypergraphs with prescribed degree sequences. We propose a Parallel Tempering method, where the hypothetical energy of the hypergraphs measures the deviation from the prescribed degree sequence. The method has been implemented and tested on synthetic and real data. It can also be applied for χ2 testing of contingency tables. We have shown that this hypergraph-based χ2 test is more sensitive than the standard χ2 test. The extra sensitivity is especially advantageous on small data sets, where the proposed Parallel Tempering method shows promising performance.

Open Access: Yes

DOI: 10.1371/journal.pone.0303155

Ionic liquid binary mixtures: Machine learning-assisted modeling, solvent tailoring, process design, and optimization

Yang Lei Xiaodong Liang Xinyan Liu Yuqiu Chen Sulei Ma Rafiqul Gani Georgios M. Kontogeorgis

Publication Name: Aiche Journal

Publication Date: 2024-05-01

Volume: 70

Issue: 5

Page Range: Unknown

Description:

This work conducts a comprehensive modeling study on the viscosity, density, heat capacity, and surface tension of ionic liquid (IL)-IL binary mixtures by combining the group contribution (GC) method with three machine learning algorithms: artificial neural network, XGBoost, and LightGBM. A large number of experimental data from reliable open sources is exhaustively collected to train, validate, and test the proposed ML-based GC models. Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these ML-based GC models are sequentially integrated into computer-aided mixed solvent design, process design, and optimization through an industrial case study of recovering hydrogen from raw coke oven gas. Optimization results demonstrate their high computational efficiency and integrability in solvent and process design, while also highlighting the significant potential of IL-IL binary mixtures in practical applications.

Open Access: Yes

DOI: 10.1002/aic.18392

Sustainability in Public Finances Concerning Transfer Pricing in the EU

András Lapsánszky Gábor Hulkó János Kálmán

Publication Name: Chemical Engineering Transactions

Publication Date: 2023-01-01

Volume: 107

Issue: Unknown

Page Range: 523-528

Description:

Ensuring the sustainability of public finances is a crucial concern for the European Union, particularly in the context of transfer pricing, which is focused on tax base erosion and profit shifting. Transfer pricing, involving the internal transfer of goods, services, or intellectual property between related entities, can significantly impact member states' tax revenues and overall economic stability. Base erosion and profit shifting is a term used to describe tax planning strategies that multinational companies use to exploit gaps and mismatches in tax rules to artificially shift profits to low or no-tax jurisdictions, thereby reducing their overall tax liability. The scope of this study examines these two topics, mainly by using jurisprudential methods and analysis of scientific sources, as well as to research the effect of tax-based erosion and inequality among state jurisdictions. The assessment and analysis of the problems in these areas have been going on for years, and in essence, the neuralgic points are clear in terms of problem definition. Nevertheless, appropriate normative solutions have either not been developed to a full extent or are being implemented slowly. Considering the lengthy process of adopting normative rules, the main aim of this study is to make suggestions for the field of law enforcement and organs of public administration that could lead to changes in the areas of transfer-pricing, base erosion, and profit shifting. In conclusion, three key areas of action are proposed. Firstly, the promotion and everyday implementation of digital taxation contribute to the efficient exchange of data. Secondly, much closer cooperation between tax authorities on this basis can be strengthened at the Member State level in practical administration. Thirdly, the more effective safeguarding of the single market by national administrations.

Open Access: Yes

DOI: 10.3303/CET23107088

Development of an Artificial Intelligence Powered Medication Risk Score Calculator Application

Eva Aggerholm Sædder Elek Dinya Nóra Gyimesi Lóránd Erdélyi Péter Domján Viola Angyal Ádám Bertalan Gyula Király

Publication Name: Basic and Clinical Pharmacology and Toxicology

Publication Date: 2025-10-01

Volume: 137

Issue: 4

Page Range: Unknown

Description:

The publication explores the development of the Augmented Medication Risk Score (AUGMERIS) calculator, a web application supported by artificial intelligence, designed to automate the evaluation of medication therapies with the Danish Medication Risk Score (MERIS) method. It is a tool that assesses drug combinations and kidney function in estimated glomerular filtration rate (eGFR), which helps clinical pharmacists identify high-risk patients. To overcome the problem of processing unstructured electronic health records (EHRs), a hybrid text processing model was created by combining rigorous algorithms and Generative Pre-trained Transformer (GPT) technology, which was integrated into a web application along with an automated risk calculation programme. Our objective was to develop and test a globally accessible calculator application with the validation of performance on poor-quality data. Despite the validation limitations, the text processing function serves the application satisfactorily. The AUGMERIS web app is built with Python 3 and shared globally by Streamlit. Volunteer testers from eight different countries performed a total of 383 trial calculations. The application has the potential to improve global pharmacotherapy by identifying patients requiring medication reviews. Its wider adoption might enhance patient safety and optimize treatments in a variety of healthcare systems.

Open Access: Yes

DOI: 10.1111/bcpt.70109

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

Mehran Amini László T. Kóczy

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