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Publications - 6374

Machine Learning Prediction of Pavement Macrotexture from 3D Laser-Scanning Data

Mohammad Fahad Nagy Richard Kristof Gyorgy Nagy

Publication Name: Applied Sciences Switzerland

Publication Date: 2026-01-01

Volume: 16

Issue: 1

Page Range: Unknown

Description:

Featured Applications: Pavement texture evaluation using a traditional sand patch method, 3D laser scanning, and machine learning algorithms. Pavement macrotexture, quantified by mean texture depth (MTD) and mean profile depth (MPD), is a critical parameter for road safety and performance. The traditional sand patch test is labor-intensive and slow, creating a bottleneck for modern pavement management systems. Accurately translating the rich point cloud data into reliable MTD values using the 3D scanning method remains a challenge, with current methods often relying on oversimplified correlations. This research addresses this gap by developing and validating a novel machine learning framework to predict MTD and MPD directly from high-resolution 3D laser scans. A comprehensive dataset of 127 pavement samples was created, combining traditional sand patch measurements with detailed 3D point clouds. From these point clouds, 27 distinct surface features spanning statistical, spatial, spectral, and geometric domains were developed. Six machine learning algorithms, consisting of Random Forest, Gradient Boosting, Support Vector Regression, k-Nearest Neighbor, Artificial Neural Networks, and Linear Regression, were implemented. The results demonstrate that the ensemble-based Random Forest model achieved superior performance, predicting MTD with an R2 of 0.941 and a mean absolute error (MAE) of 0.067 mm, representing a 56% improvement in accuracy over traditional digital correlation methods. Model interpretation via SHAP analysis identified root mean square height (Sq) and surface skewness (Ssk) as the most influential features.

Open Access: Yes

DOI: 10.3390/app16010500

Modeling the Stiffening Behavior of Sand Subjected to Dynamic Loading

Majd Ahmad R. P. Ray

Publication Name: Geosciences Switzerland

Publication Date: 2024-01-01

Volume: 14

Issue: 1

Page Range: Unknown

Description:

In geotechnical engineering, dynamic soil models are used to predict soil behavior under different loading conditions. This is crucial for many dynamic geotechnical problems related to earthquakes, train loading and machine foundation design. Researchers agree that under dry or drained conditions, cohesionless soils increase in stiffness with each loading cycle. Soil models that simulate the dynamic behaviors of soils are often coupled with the Masing criteria. Such models neglect the impact of stiffening during cyclic loading, leading to an underestimation in the shear modulus (G). This study investigates the stiffening behavior by conducting laboratory tests on three types of Danube sands using the Resonant Column-Torsional Simple Shear device (RC-TOSS). The increase in the dynamic shear modulus with an increasing number of cycles is substantial, especially for samples with low density. Sometimes, the dynamic shear modulus doubles when loaded at high stress levels for more than 50 cycles. A new model is introduced to simulate the stiffening behavior of dry sand when subjected to cyclic torsional loading. Modifications are proposed for the Ramberg–Osgood and Hardin–Drnevich models and for the Masing criteria to overcome the limitations that accompany these models due to the influence of stiffening caused by repetitive loading being ignored. This model can be implemented in finite element and finite difference software to solve dynamic geotechnical problems.

Open Access: Yes

DOI: 10.3390/geosciences14010026

Intended benefits and challenges of cooperation between FinTechs and commercial banks

Ádám Kerényi Dóra Horváth Roland Zsolt Szabó

Publication Name: Acta Oeconomica

Publication Date: 2022-09-30

Volume: 72

Issue: 3

Page Range: 289-308

Description:

The financial industry has undergone several changes in recent years. One of these changes is the emergence of financial technology (FinTech) companies that are radically transforming the industry, posing a significant challenge to traditional commercial banks. In this study, we examined the responses of the Hungarian banks to the emergence of innovative FinTech startups and explored the benefits and barriers of the FinTech accelerator programs launched by banks. We conducted 27 semi-structured interviews with top executives of banks, FinTech startups and scaleups, investors and regulators to identify the potential benefits and barriers during the cooperation between banks and FinTechs. The most important results of our research show that during the partnership, several advantages can be gained by both parties. Still, the realization of these benefits is significantly hindered by the excessive exploitation focus of banks. Ambidextrous internal champions or suppliers of the banks are needed for successful cooperation between FinTechs and banks.

Open Access: Yes

DOI: 10.1556/032.2022.00023

Managing the resolution of simulation models

László Muka Gábor Lencse

Publication Name: Esm 2008 2008 European Simulation and Modelling Conference Modelling and Simulation 2008

Publication Date: 2008-01-01

Volume: Unknown

Issue: Unknown

Page Range: 38-42

Description:

A novel approach based on inflation and deflation is proposed for managing the resolution of simulation models. Different methods are proposed for manual or automatic deflation. An example is given how a topology description language can be extended to support the inflation/deflation concept. Dynamic management of the model resolution is introduced using the method called inflate-the-next and also two of its possible improvements. © 2008 EUROSIS-ETI.

Open Access: Yes

DOI: DOI not available

Comparison of supply chain management (SCM) adoption at small and medium-sized enterprises (SMEs): A review from Hungary and Indonesia

Santi Setyaningsih Peter Kelle

Publication Name: Journal of International Studies

Publication Date: 2021-01-01

Volume: 14

Issue: 3

Page Range: 26-42

Description:

Large enterprises recognized first the importance of Supply Chain Management (SCM) strategy to achieve competitive advantage and process efficiency. Small and Medium-Sized Enterprises (SMEs) have specific challenges in adaptation. The authors conjectured that geographical and supply chain differences have a major effect on the adaptation level of SCM strategy and methods, especially for SMEs. To investigate it, this paper compares two countries, Hungary, and Indonesia. The research focus is on SMEs, based on a cross-sectional survey of 274 Hungarian and 110 Indonesian enterprises with informants mainly related to top management. The data indicated that in Indonesia, with a larger, more complex geographical structure and more advanced SCM capabilities, the SMEs have a higher implementation level of SCM strategy in their organization strategy compared to Hungary. However, the sample indicates that the tendencies are similar in both countries interpreting the inter-enterprise value chain and in utilizing SCM methods for cooperation with other parties, mostly using Vendor Managed Inventory (VMI) and Just in Time (JIT).

Open Access: Yes

DOI: 10.14254/2071-8330.20211/14-3/2

Global burden of cancer in children and adolescents aged 0–19 years, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023

Jasvinder Singh Bhatti Sayeh Ezzikouri Ali Hasanpour- Dehkordi Takeshi Fukumoto Seyyed Shamsadin Athari Hala Rashad Elhabashy Aleksandr Y. Aravkin Paul Narh Doku Dariush Haghmorad Theophilus I. Emeto Adeniyi Francis Fagbamigbe Nermin Ghith Anis Ahmad Chaudhary Mahwish Arooj Hamidreza Hasani Robert Kaba Alhassan Salahdein Aburuz Lucas Guimarães Abreu Saeid Anvari Muhammad Sohail Afzal Jonathan M. Kocarnik Mosab Arafat Morenike Oluwatoyin Folayan Hanadi Al Hamad Ayesha Fahim Mohammad Farahmand Lisa M. Force Adewale Oluwaseun Fadaka Nadia M. Hamdy Demelash Areda Veer Bala Gupta Maha Moh'd Wahbi Atout Natalie Pritchett Souad Bouaoud Ayman Ahmed Aso Mohammad Darwesh Cem Bilgin Dong Woo Choi Wafa A. Aldhaleei Awais Altaf Ferrán Catalá-López Danish Ahmad Bashir Dabo Rakhi Dandona Mohammed Albashtawy Mohamed Abouzid Omotayo Francis Fagbule Shirin Barati Soham Bandyopadhyay Ahmed Y. Azzam Abdulfatai Aremu Teferi Gebru Gebremeskel Arvin Haj-Mirzaian Catherine Bisignano Aragaw Tesfaw Desale Benedetta Armocida Hasan Aalruz Kayleigh Bhangdia Isaac Sunday Chukwu Md Kamrul Hasan Promit Ananyo Chakraborty Louise Penberthy Maryam Bemanalizadeh Robert Kokou Dowou Giulia Carreras Xiaochen Dai Maysaa El Sayed Zaki Johannes Haubold Mohammad Asghari-Jafarabadi Fatemeh Afrashteh John Dube Ali Hasanpour- Dehkordi Shahkaar Aziz Logan M. Glasstetter Genanew K. Getahun Sri Harsha Boppana Alistair Acheson Chiranjib Chakraborty Saroja Devi Geetha Razieh Bahreini Yohannes Habtegiorgis Abate Sabah Al-Marwani Mohammad Mahdi Bastan Samuel Demissie Darcho Thao Huynh Phuong Do Miglas Welay Gebregergis Lee Deitesfeld Abdel Rahman E'mar Mohammed Elshaer Lemessa Assefa A. Ayana Chadi Eltaha Awoke Derbie Habteyohannes Abid Ali Safwat Aly Nguyen Hoang Anh Andrew Crist Miranda L. May Maha Moh d.Wahbi Atout Hasan Aalruz Syed Anees Ahmed Demelash Areda Lalit Dandona Karem H. Alzoubi Yasser Bustanji

Publication Name: Lancet

Publication Date: 2026-04-04

Volume: 407

Issue: 10536

Page Range: 1360-1373

Description:

Background Information on childhood cancer burden is crucial for effective cancer policy planning. Unfortunately, observed paediatric cancer data are not available in every country, and previous global burden estimates have not discretely reported several common cancers of childhood. We aimed to inform efforts to address childhood cancer burden globally by analysing results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, which now include nine additional cancer causes compared with previous GBD analyses. Methods GBD 2023 data sources for cancer estimation included population-based cancer registries, vital registration systems, and verbal autopsies. For childhood cancers (defined as those occurring at ages 0–19 years), mortality was estimated using cancer-specific ensemble models and incidence was estimated using mortality estimates and modelled mortality-to-incidence ratios (MIRs). Years of life lost (YLLs) were estimated by multiplying age-specific cancer deaths by the standard life expectancy at the age of death. Prevalence was estimated using survival estimates modelled from MIRs and multiplied by sequelae-specific disability weights to estimate years lived with disability (YLDs). Disability-adjusted life-years (DALYs) were estimated as the sum of YLLs and YLDs. Estimates are presented globally and by geographical and resource groupings, and all estimates are presented with 95% uncertainty intervals (UIs). Findings Globally, in 2023, there were an estimated 377 000 incident childhood cancer cases (95% UI 288 000–489 000), 144 000 deaths (131 000–162 000), and 11·7 million (10·7–13·2) DALYs due to childhood cancer. Deaths due to childhood cancer decreased by 27·0% (15·5–36·1) globally, from 197 000 (173 000–218 000) in 1990, but increased in the WHO African region by 55·6% (25·5–92·4), from 31 500 (24 900–38 500) to 49 000 (42 600–58 200) between 1990 and 2023. In 2023, age-standardised YLLs due to childhood cancer were inversely correlated with country-level Socio-demographic Index. Childhood cancer was the eighth-leading cause of childhood deaths and the ninth-leading cause of DALYs among all cancers in 2023. The percentage of DALYs due to uncategorised childhood cancers was reduced from 26·5% (26·5–26·5) in GBD 2017 to 10·5% (8·1–13·1) with the addition of the nine new cancer causes. Target cancers for the WHO Global Initiative for Childhood Cancer (GICC) comprised 47·3% (42·2–52·0) of global childhood cancer deaths in 2023. Interpretation Global childhood cancer burden remains a substantial contributor to global childhood disease and cancer burden and is disproportionately weighted towards resource-limited settings. The estimation of additional cancer types relevant in childhood provides a step towards alignment with WHO GICC targets. Efforts to decrease global childhood cancer burden should focus on addressing the inequities in burden worldwide and support comprehensive improvements along the childhood cancer diagnosis and care continuum. Funding St Jude Children's Research Hospital, Gates Foundation, and St Baldrick's Foundation.

Open Access: Yes

DOI: 10.1016/S0140-6736(26)00200-X

On the sensitivity of the weighted relevance aggregation operator and its application to fuzzy signatures

Istvan A. Harmati Laszlo T. Koczy

Publication Name: Communications in Computer and Information Science

Publication Date: 2016-01-01

Volume: 611

Issue: Unknown

Page Range: 798-808

Description:

The weighted relevance aggregation operator is a modified, flexible version of the general power mean. In this paper we discuss the sensitivity of this operator, namely we give bounds on the change of the output in terms of vector norms of the change of the input variables. We apply these results to characterize to sensitivity of fuzzy signatures which are equipped with these operators in its nodes.

Open Access: Yes

DOI: 10.1007/978-3-319-40581-0_65

Capsule Network based 3D Object Orientation Estimation

Claudiu Radu Pozna Áron Ballagi Janos Hollosi

Publication Name: International Conference on Electrical Computer Communications and Mechatronics Engineering Iceccme 2023

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Convolutional neural networks have proven to be one of the most efficient methods for processing visual data. Due to the popularity of the field, there is a growing interest in the reliability of intelligent systems. It has been shown that convolutional neural networks can be fooled by extreme inputs or noisy inputs. To overcome the current problems of convolutional neural networks, the theory of capsule networks was introduced by Geoffrey Hinton and his research team. In this work we want to investigate the theory of capsule networks for orientation recognition of 3-dimensional objects. We consider the case when the data are noise loaded by various adversarial attacking methods. We compare our results with the efficiency of convolutional neural network based solutions, highlighting the difference between the two theories. We investigate the efficiency reduction that can be observed using different adversarial attacking methods. Our results will show how much more efficient the capsule network is compared to the neural networks.

Open Access: Yes

DOI: 10.1109/ICECCME57830.2023.10252762

Drone Applications in Logistics and Supply Chain Management: A Systematic Review Using Latent Dirichlet Allocation

Imen Zrelli Mohammad Iranmanesh Abderahman Rejeb Karim Rejeb Rawan Abusulaiman Raghad AlSahafi

Publication Name: Arabian Journal for Science and Engineering

Publication Date: 2024-09-01

Volume: 49

Issue: 9

Page Range: 12411-12430

Description:

As an emergent technology, drones have grown increasingly integral to the logistics and supply chain management (SCM) sectors. This review paper seeks to address this gap by analyzing a carefully curated dataset of 96 journal articles, sourced from Scopus and Web of Science, concerning drone applications in logistics and SCM. Our study reveals a rising trend in drone-related logistics and SCM research and highlights the growing significance of this technology. Using latent Dirichlet allocation (LDA), we distilled the corpus into ten distinct topics and provided a comprehensive overview of the field’s thematic landscape. The most prominently studied themes were drones for humanitarian logistics and SCM and optimization of last-mile delivery using drones and trucks in urban logistics. These areas underscore the promise of drones in addressing humanitarian challenges and urban logistics optimization, respectively. Conversely, the least explored topics were drones safety and utility and drone delivery and consumer behavior in food logistics. To our knowledge, this is the first paper applying LDA to examine drone applications in logistics and SCM, offering novel insights into the current state of the literature. Moreover, based on our findings, we propose a future research agenda to encourage further exploration into under-studied areas and to sustain the momentum of this exciting field.

Open Access: Yes

DOI: 10.1007/s13369-023-08681-0