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Publications - 6374

Job mismatch in early career of graduates under post-communism

Péter Róbert

Publication Name: International Journal of Manpower

Publication Date: 2014-07-01

Volume: 35

Issue: 4

Page Range: 500-513

Description:

Purpose – The purpose of this paper is to investigate vertical and horizontal mismatch between education and current occupation for graduates in four post-communist societies: Hungary, Poland, Lithuania and Slovenia. In this way it contributes to the field by exploring how mechanisms, known from previous studies on western societies, affect job mismatch in emerging market economies. Design/methodology/approach – Two dependent variables are constructed: working in a nongraduate occupation as defined by the ISCO job title depicts vertical mismatch; assessment of the job from the perspective of the fields of study describes horizontal mismatch. Since the dependent variables are dichotomous ones, binary logistic regression models are fitted to the data predicting the incidence of mismatch. Explanatory variables cover mechanisms affecting job mismatch: variation by fields of studies, accumulated work experience during studies, labour market uncertainties during early career, trade off between job safety and job mismatch, persistence of “bad” labour market entry during early career, influence of parental background on school-to-work transition. Findings – The analysis reveals significant differences for study fields in association with occupational specificity of the disciplines. Only study-related work experience seems to be advantageous to find a matching job. Labour market uncertainties increase the probability of job mismatch. Job safety is more important than a matching job. Originality/value – Mismatch in first occupation has strong and long-lasting effect on the job match even five years after the graduation. The effect of parental background on job mismatch is curvilinear.

Open Access: Yes

DOI: 10.1108/IJM-05-2013-0113

What diseases and risks cause health losses in Hungary?

Virág Horváth József Vitrai

Publication Name: Orvosi Hetilap

Publication Date: 2026-02-01

Volume: 167

Issue: 6

Page Range: 232-242

Description:

Introduction: Using Global Burden of Disease 2023 data, this study examines the structure of health losses in Hungary, focusing on diseases, risk factors, and international comparisons. Objective: To identify which diseases and risk factors contribute most to Hungary’s health burden, how these relate to disability and premature mortality, and how patterns differ by gender and in comparison, with Central European countries. Method: Age-standardized values per 100,000 inhabitants, broken down by gender and disease/risk category, were analyzed for Hungary and compared with Austria, the Czech Republic, Poland, and Slovakia. Results: Cardiovascular diseases, cancers, and musculoskeletal disorders caused the largest losses. High blood pressure was the leading risk factor. Premature mortality was substantially higher in Hungary; men showed especially elevated levels due to smoking, diet, and hypertension. Morbidity-related losses were dominated by musculoskeletal and mental disorders. Discussion: Hungary’s burden stems not only from mortality but also from chronic disabling conditions. The mortality component is particularly unfavourable in international comparison. Conclusion: Improving treatment quality, timely care, and early diagnosis is essential, while reducing morbidity requires stronger long-term care and rehabilitation. Effective policy should complement lifestyle-focused prevention with better access to high-quality curative care and gender-responsive interventions. Consistent use of objective burden-of-disease data can support decision-making. A systemic approach – combining prevention, supportive environments, and a strengthened healthcare system – is needed to reduce health losses in Hungary. Orv Hetil. 2026; 167(6): 232–242.

Open Access: Yes

DOI: 10.1556/650.2026.33481

Data-Driven Planning for Casualty Evacuation and Treatment in Sustainable Humanitarian Logistics

Hasan Khademi Zare Mohammad Bagher Fakhrzad Hasan Hosseini Nasab Majid Movahedi Rad Shahla Jahangiri

Publication Name: Algorithms

Publication Date: 2026-02-01

Volume: 19

Issue: 2

Page Range: Unknown

Description:

After large-scale disasters, swift and robust humanitarian logistics are crucial to provide timely assistance to injured people and displaced individuals. This study proposes a bi-objective optimization model for humanitarian logistics network design to simultaneously consider the facility location-allocation decisions, along with the transportation operation issues under uncertainty. The framework addresses the needs of both severely and mildly injured casualties and homeless populations. A hybrid robust optimization approach is accordingly developed that incorporates scenario-based, box-type, and polyhedral uncertainty representations to handle the uncertainty of factors such as casualty volume, travel times, facility failures, and demands for resources. More recently, machine learning methods have been applied to classify casualties and displaced individuals with respect to their geographic distribution and severity, further improving demand estimates and operational efficacy. This study seeks to develop a data-driven and robust optimization framework for designing humanitarian logistics networks under uncertainty, enabling decision-makers and emergency planners to gain insights into enhancing casualty evacuation, medical treatment, and shelter allocation in disaster response operations. The case of the Kermanshah earthquake in Iran is used for assessing the applicability of the model. The computational experiments and comparative analyses conducted show that the developed model exhibits high efficiency and robustness. The results are useful for guiding disaster preparedness and strategic decisions in humanitarian logistics. Besides operational performance, the model optimizes sustainability in the area of emergency response based on cost efficiency and social fairness, as underlined by SDGs 3 and 11.

Open Access: Yes

DOI: 10.3390/a19020104

Digitalization and its tax implications: Evidence from the uk and hungary

Arief Rahman Sevinj Ahmadova

Publication Name: Studies in Systems Decision and Control

Publication Date: 2024-01-01

Volume: 525

Issue: Unknown

Page Range: 511-520

Description:

Digitalization has transformed the way businesses operate and interact with their customers, suppliers, and governments. It has also created new challenges and opportunities for tax policy and administration. By using a literature review, this paper examines the effects of digitalization on tax in three countries: The UK and Hungary. It analyzes how these countries have adapted their tax systems to cope with the digital economy and what the main issues and trade-offs are. The paper concludes that countries need to globalize their tax policies in order to protect their tax bases in the digital economy. For this, they need to work together with international organizations to develop new international tax rules and tools for the digital economy.

Open Access: Yes

DOI: 10.1007/978-3-031-54383-8_39

Analysis of the Aerodynamic Parameters of Road Vehicles Affected by Weather Conditions

I. Lakatos Brúnó Péter

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 1033-1038

Description:

Optimizing the aerodynamic parameters of road vehicles is essential due to their impact on the environment. It is a special aim of developments, including electric and non-electric vehicles. A lot of research is being conducted according to energy efficiency and range. These parts are the most significant directions of development. Aerodynamic parameters such as drag coefficient exert a significant influence on vehicle energy efficiency. The purpose of this paper is to investigate the aerodynamic parameters of the Ahmed body in rainy weather conditions. Different types of rain and vehicle speeds are studied and compared to each other to examine their effect on the drag coefficient. The examination is carried out using the Computational Fluid Dynamics (CFD) method. In the two-phase simulations, the rain is modeled as solid particles. Results can be used to obtain the most fuel- or electricity-efficient rain type-vehicle speed combinations and thereby can help to contribute a more sustainable transport. The results clearly show that rain has a measurable effect on the drag coefficient. As the rain intensity increases, the drag coefficient increases, too. However, there are uncertainties in the upward trend. As airspeed increases, the increasing trend becomes more stable.

Open Access: Yes

DOI: 10.3303/CET24114173

Artificial Aging of Ultra-low Viscosity Lubricant Samples on a Programmable Oil Aging Rig

Ibolya Zsoldos András Lajos Nagy

Publication Name: Lecture Notes in Mechanical Engineering

Publication Date: 2021-01-01

Volume: 22

Issue: Unknown

Page Range: 139-147

Description:

An artificial lubricant aging rig was developed in order to simulate aging processes of automotive lubricants. This article presents the development of the aging apparatus and its control system as well as results of artificial aging of SAE 0W-20 grade automotive lubricant with a modified thermal cycling procedure. Friction and wear measurements on a high frequency reciprocating rig were conducted to describe the lubricating properties of the artificially aged samples. Select oil samples were analyzed through FTIR spectroscopy.

Open Access: Yes

DOI: 10.1007/978-981-15-9529-5_12

Consumption Monitoring System for Demand Base Energy Supply Innovation - Prototyping EMAK (Energy Management Data Center) at ZalaZONE

Virag Meszaros Attila Balogh

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 865-870

Description:

Witnessing the importance of energy efficiency and sustainability, there is a definite need for modular, standard-component-driven, vendor-independent energy management systems. A system with real-world scenarios capable of driving practical experiences and incorporating AI enhancements would serve as a base for Energy Community platforms that can include even small energy consumers and producers. A multi-year R&D pilot project was established to create a unique Energy Community model using the industrial environment at ZalaZONE, the Hungarian Vehicle Proving Ground in Zalaegerszeg. The main novelty of the project and the study is the unique setup of parallel R&D work from the academic and practitioner's view and the unique data per second of online, real-time data collection methodology and structure. The EMAK platform uses micro transactional data processing enabled to collect, aggregate, and analyse data every second, build more efficient consumption, and balance models with machine learning enhancements. The onsite research includes three buildings (five building parts) with 200 sensors, 5 data aggregators, energy meters, internal and external heat and humidity meters, and weather stations, measuring electricity consumption, heat, humidity, wind, sun radiation, and more environmental data. That network and the backbone software set measure, aggregate, display, and monitor 867 data points, transmitting 8,600,000 data points daily. Although the project lasts till 31st December 2025, the work has already contributed to several key findings within the ecosystem.

Open Access: Yes

DOI: 10.3303/CET24114145

Feasibility of Using Statistical Forecasting Method in the Marcal Catchment Area

Katalin Bene Gábor Kerék Máté Szabó

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 1021-1026

Description:

Flooding is one of the most destructive natural disasters, posing significant risks to under-construction and existing structures. It can also compromise critical infrastructure, such as roads and railways, by weakening embankments. While infrastructure damage is severe, the foremost concern remains the population's safety, making technological advancements and timely information dissemination crucial. Flood forecasting is vital in preparing communities and enabling flood defense organizations to respond effectively. This study aimed to develop a reliable flood forecasting method for the downstream sections of the Marcal River, where population density is high, using real-time data. Accurate flood forecasting relies on a comprehensive monitoring network and precise measurements that predict water flow and other hydrological conditions over several days. Real-time data during flood events is also essential for emergency response. Key hydrological and meteorological factors, including water levels, flow rates, and precipitation, are integral to this process. The study analyzed daily water flow data from 1960 to 2018, collected from stations along the Marcal River and its tributaries, combined with precipitation data, to forecast the river's flow at its outlet in Mórichida. Multi-level regression analysis, incorporating first- and second-order polynomials, was used to predict flood peaks at this outflow. The model employed flood wave peaks and simultaneous rising or receding flows from five additional river stations. Focusing on events with peak flows exceeding 20 m3/s, the researchers identified 68 cases, with 9-20 measurements per event. Confidence and prediction intervals confirmed the model's accuracy, predicting flood peaks within ±10 m3/s, offering a reliable, less complex alternative to traditional models.

Open Access: Yes

DOI: 10.3303/CET24114171

A Novel Method for Simulation Model Generation of Production Systems Using PLC Sensor and Actuator State Monitoring

Szabolcs Fischer Norbert Szántó Gergő Monek

Publication Name: Journal of Sensor and Actuator Networks

Publication Date: 2025-06-01

Volume: 14

Issue: 3

Page Range: Unknown

Description:

This article proposes and validates a novel methodology for automated simulation model generation of production systems based on monitoring sensors and actuator states controlled by Programmable Logic Controllers during regular operations. Although conventional Discrete Event Simulation is essential for material flow analysis and digital experimentation in Industry 4.0, it remains a resource-intensive and time-consuming endeavor—especially for small and medium-sized enterprises. The approach introduced in this research eliminates the need for prior system knowledge, physical inspection, or modification of existing control logic, thereby reducing human involvement and streamlining the model development process. The results confirm that essential structural and operational parameters—such as process routing, operation durations, and resource allocation logic—can be accurately inferred from runtime data. The proposed approach addresses the challenge of simulation model obsolescence caused by evolving automation and shifting production requirements. It offers a practical and scalable solution for maintaining up-to-date digital representations of manufacturing systems and provides a foundation for further extensions into Digital Shadow and Digital Twin applications.

Open Access: Yes

DOI: 10.3390/jsan14030055