Hasil untuk "Hungary"

J. Collet, H. Thiele, E. Barbato et al.

Authors/Task Force Members: Jean-Philippe Collet * (Chairperson) (France), Holger Thiele * (Chairperson) (Germany), Emanuele Barbato (Italy), Olivier Barthélémy (France), Johann Bauersachs (Germany), Deepak L. Bhatt (United States of America), Paul Dendale (Belgium), Maria Dorobantu (Romania), Thor Edvardsen (Norway), Thierry Folliguet (France), Chris P. Gale (United Kingdom), Martine Gilard (France), Alexander Jobs (Germany), Peter Jüni (Canada), Ekaterini Lambrinou (Cyprus), Basil S. Lewis (Israel), Julinda Mehilli (Germany), Emanuele Meliga (Italy), Béla Merkely (Hungary), Christian Mueller (Switzerland), Marco Roffi (Switzerland), Frans H. Rutten (Netherlands), Dirk Sibbing (Germany), George C.M. Siontis (Switzerland)

J. Fitzmaurice

D. Clarke, C. Mohtadi, P. Tuffs

Michelle T. Cirunay, Bálint Hartmann, Tímea Erdei et al.

Electric power distribution networks serve as the final and essential stage in power delivery, bridging transmission infrastructure and end users. The structural configuration of these networks plays a critical role in determining system reliability, fault tolerance, and operational efficiency. Although the design of distribution systems is influenced by various regional factors, such as geography, customer density, and planning standards, the extent to which consistent structural characteristics emerge across different networks remains an open question. In this study, we perform a detailed spatial and topological analysis of five MV distribution networks in Hungary. Despite notable differences in geographic layout and consumer distribution, we identify statistically consistent patterns across several key metrics, including degree, BC, and powerline length. These findings suggest the influence of common underlying design principles or optimization constraints, potentially indicating universal structural tendencies in MV network design. The results provide insight into the organization of real-world distribution systems and offer a basis for improved planning, risk mitigation, and system optimization in future grid developments.

Evans Kiptoo Korir, Zsolt Vizi

Understanding how age-specific social contact patterns and susceptibility influence infectious disease transmission is crucial for accurate epidemic modeling. This study presents an eigenvector-based sensitivity analysis framework to quantify the impact of age-structured interactions on disease spread. By applying perturbation analysis to the Next Generation Matrix, we reformulate the basic reproduction number, $\mathcal{R}_0$, as a generalized eigenproblem, enabling the identification of key age group interactions that drive transmission. Using real-world COVID-19 contact data from Hungary, we demonstrate the framework's ability to highlight critical transmission pathways. We compare these findings with results obtained earlier using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC), validating the effectiveness of our approach. Additionally, we extend the analysis to contact structures in the UK and British Columbia, Canada, providing broader epidemiological insights. This work enhances our understanding of demographic interactions in epidemic propagation and offers a robust methodological foundation for improving infectious disease modeling and informing public health interventions.

Emre Akusta

This study analyzes OECD countries in the context of the energy trilemma index and clusters countries with similar characteristics. In the study, the k-means clustering technique is used. The optimum number of clusters was determined using the Elbow method in combination with the Silhouette Index. Moreover, all results are visualized to enhance comprehensibility. The results show that countries such as Austria, Canada, Finland, and Denmark are in the high energy trilemma group with index scores of 82.2, 82.3, 82.7, and 83.3, respectively. Countries in the high group have achieved a high level of balance between energy security, energy equity, and environmental sustainability. In addition, countries such as Belgium, Hungary, Australia, the Czech Republic, and Estonia are in the medium energy trilemma group with index scores of 76.4, 76.6, 77.1, 77.6, and 78.7, respectively. Countries in the medium group have made progress in balancing the dimensions of the energy trilemma but have not yet reached excellence. However, countries such as Mexico, Türkiye, Colombia, and Costa Rica are in the low energy trilemma group with index scores of 63.1, 64.1, 64.8, and 69.3, respectively. These low energy trilemma group countries face significant challenges in balancing energy security, energy equity, and environmental sustainability and need to make improvements in these areas.

Lesya Kolinets, Vygintas Gontis

The integration of Central and Eastern European (CEE) countries into the European Economic Area serves as a valuable experiment for the regional economic development theory. The long-lasting convergence of these economies with more advanced Western Europe exhibits a few standard features and varying policies implemented. Even the Baltic countries, which started from very similar starting positions, demonstrate their unique trajectories of development. We employ a panel data regression model that allows coefficients to vary over time to compare the contributions of a few macroeconomic factors to the GDP growth of CEE countries. In particular, we regress the annual change of GDP per capita in PPP terms as a function of achieved GDP, price, trade, investment, and debt levels. Time-varying common slope coefficients in this approach describe the external economic environment in which countries implement their own policies. The panel consists of 11 Central and Eastern European countries (Bulgaria, Czechia, Estonia, Croatia, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia, and Slovakia), which have been observed annually from 1995 to 2024. While the main selected factors of this investigation contribute to economic growth, in agreement with previous findings, the role of private debt appears vital in determining the pace of economic growth.

Ghenwa Chamouni, Filippo Lococo, Carolina Sassorossi et al.

IntroductionArtificial intelligence (AI) is increasingly integrating into the healthcare field, particularly in lung cancer care, including screening, diagnosis, treatment, and prognosis. While these applications offer promising advancements, they also raise complex challenges that must be addressed to ensure responsible implementation in clinical practice. This scoping review explores the ethical and legal aspects of AI applications in lung cancer.MethodsA search was conducted across PubMed, Scopus, Web of Science, Cochrane Library, PROSPERO, OAIster, and CABI. A total of 581 records were initially retrieved, of which 20 met the eligibility criteria and were included in the review. The PRISMA guidelines were followed.ResultsThe most frequently reported ethical concern was data privacy. Other recurrent issues included informed consent, no harm to patients, algorithmic bias and fairness, transparency, equity in AI access and use, and trust. The most frequently raised legal concerns were data protection and privacy, although issues relating to cybersecurity, liability, safety and effectiveness, the lack of appropriate regulation, and intellectual property law were also noted. Solutions proposed ranged from technical approaches to calls for regulatory and policy development. However, many studies lacked comprehensive legal analysis, and most included papers originated from high-income countries. This highlights the need for a broader global perspective.DiscussionThis review found that data privacy and protection are the most prominent ethical and legal concerns in AI applications for lung cancer care. Deep Learning (DL) applications, especially in diagnostic imaging, are closely tied to data privacy, lack of transparency, and algorithmic bias. Hybrid and multimodal AI systems raise additional concerns regarding informed consent and the lack of proper regulations. Ethical issues were more frequently addressed than legal ones, with limited consideration for global applicability, particularly in low- and lower middle-income countries. Although technical and policy solutions have been proposed, these remain largely unvalidated and fragmented, with limited real-world feasibility or scalability.

Feten Chihi, Gabriella Varga, Katalin Kopecskó

This experimental study examines the effects of landfill leachate contamination on soil hydraulic conductivity over a 12-month period, addressing the current lack of long-term experimental data in this field. Laboratory permeability tests were performed on sandy clayey silt samples contaminated with leachate at concentrations ranging from 5% to 25%. Microstructural and mineralogical analyses were conducted using SEM and XRD to identify the mechanisms behind observed changes. The results identify a critical threshold at 15% contamination, where soil behavior transitions from granular to cohesive characteristics. Hydraulic conductivity increases at low contamination levels (5–10%, up to 1.2 × 10⁻⁷ m/s) but decreases significantly at higher levels (4.172 × 10⁻⁸ m/s at 15%, 8.545 × 10⁻⁹ m/s at 20%). These changes are controlled by contamination level rather than exposure time, with values remaining stable throughout the 12-month period. The study provides essential parameters for landfill design and contamination assessment, demonstrating how leachate concentration affects long-term soil hydraulic properties through mineral formation and structural modification.

Musaab A.A. Mohammed, Ladislav Tometz, Norbert P. Szabó et al.

The Sobranecké Spa (“Salus per Aquam”) is historically known for its therapeutic mineral waters and recognized as a heritage site for its cultural significance. Despite its rich tradition and well-documented therapeutic effects, the spa ceased operations in 2004 and now remains in disrepair. However, renewed interest from the Košice self-governing region has prompted efforts to restore its activity. To support this initiative, a hydrogeological study was commissioned by the Technical University of Košice to provide a comprehensive assessment of the physical, chemical, and microbial properties of the mineral water and evaluate health risks related to dermal exposure. The study integrates hydrochemical classification, microbial assessment, and probabilistic risk analysis using Monte Carlo simulation and Sobol sensitivity analysis to evaluate dermal absorption dose (DAD), dermal hazard quotient (HQ) and hazard index (HI) for both adults and children. Hydrochemical results indicated a Na-Cl-type highly mineralized water, shaped by mineral dissolution and ion exchange processes. The microbial analysis focused on coliforms, Escherichia coli, and heterotrophic bacteria to assess potential biological risks. The results showed individual hazard quotients below 1 for most parameters, but H2S drove cumulative hazard index values to 9.5 for adults and 12.1 for children, with children facing 28 % higher risk and persistent dermal health concerns across all scenarios. However, the findings confirm that the mineral waters meet Slovak and European standards for therapeutic use. Due to the study’s single sampling event, long-term seasonal monitoring is recommended to ensure water quality stability and safety for future spa use.

Gergely Ódor, Márton Karsai

Behavior-disease models suggest that pandemics can be contained cost-effectively if individuals take preventive actions when disease prevalence rises among their close contacts. However, assessing local awareness behavior in real-world datasets remains a challenge. Through the analysis of mutation patterns in clinical genetic sequence data, we propose an efficient approach to quantify the impact of local awareness by identifying superspreading events and assigning containment scores to them. We validate the proposed containment score as a proxy for local awareness in simulation experiments, and find that it was correlated positively with policy stringency during the COVID-19 pandemic. Finally, we observe a temporary drop in the containment score during the Omicron wave in the United Kingdom, matching a survey experiment we carried out in Hungary during the corresponding period of the pandemic. Our findings bring important insight into the field of awareness modeling through the analysis of large-scale genetic sequence data, one of the most promising data sources in epidemics research.

Mario Padilla Rodriguez, Mohamed Nafea

Cardiovascular diseases are a leading cause of mortality worldwide, highlighting the need for accurate diagnostic methods. This study benchmarks centralized and federated machine learning algorithms for heart disease classification using the UCI dataset which includes 920 patient records from four hospitals in the USA, Hungary and Switzerland. Our benchmark is supported by Shapley-value interpretability analysis to quantify features' importance for classification. In the centralized setup, various binary classification algorithms are trained on pooled data, with a support vector machine (SVM) achieving the highest testing accuracy of 83.3\%, surpassing the established benchmark of 78.7\% with logistic regression. Additionally, federated learning algorithms with four clients (hospitals) are explored, leveraging the dataset's natural partition to enhance privacy without sacrificing accuracy. Federated SVM, an uncommon approach in the literature, achieves a top testing accuracy of 73.8\%. Our interpretability analysis aligns with existing medical knowledge of heart disease indicators. Overall, this study establishes a benchmark for efficient and interpretable pre-screening tools for heart disease while maintaining patients' privacy. This work is available at https://github.com/padillma1/Heart-Disease-Classification-on-UCI-dataset-and-Shapley-Interpretability-Analysis.

Mo Houtti, Abhishek Roy, Venkata Narsi Reddy Gangula et al.

Scams are a widespread issue with severe consequences for both victims and perpetrators, but existing data collection is fragmented, precluding global and comparative local understanding. The present study addresses this gap through a nationally representative survey (n = 8,369) on scam exposure, victimization, types, vectors, and reporting in 12 countries: Belgium, Egypt, France, Hungary, Indonesia, Mexico, Romania, Slovakia, South Africa, South Korea, Sweden, and the United Kingdom. We analyze 6 survey questions to build a detailed quantitative picture of the scams landscape in each country, and compare across countries to identify global patterns. We find, first, that residents of less affluent countries suffer financial loss from scams more often. Second, we find that the internet plays a key role in scams across the globe, and that GNI per-capita is strongly associated with specific scam types and contact vectors. Third, we find widespread under-reporting, with residents of less affluent countries being less likely to know how to report a scam. Our findings contribute valuable insights for researchers, practitioners, and policymakers in the online fraud and scam prevention space.

Balint Bicski, Adrian Pekar

This paper presents a comprehensive study on the identification and analysis of Service Degradation (SD) events within a university dormitory network, leveraging LAN data to develop a robust methodology applicable to diverse networking environments. Employing statistical techniques, such as Interquartile Range (IQR) and Z-score analyses, we detect significant deviations in network performance—specifically, extreme delays and jitter—that indicate potential SD. The methodology was rigorously validated in various settings, demonstrating minimal deviations in results and reinforcing the approach’s consistency and reliability. Initial tests conducted in a university dormitory environment suggest the model’s potential applicability in both residential and enterprise networks, thus broadening its utility. By refining the detection and understanding of SD indicators, this research contributes systematic methodological applications and a valuable annotated dataset to the field. This groundwork enables network administrators to enhance service quality preemptively, offering significant implications for future research and practical applications in network management.

Jihan Ghanim, Maha Issa, Mariette Awad

Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. We also apply a density-based spatial clustering of applications with noise (DBSCAN) anomaly detection approach, prior to the load forecasting task, to remove any present oultiers. Considering the effect of weather and social factors, seasonality splitting is performed on the three considered datasets from France, Germany, and Hungary containing hourly power consumption, weather, and calendar features. Root-mean-square error (RMSE) results show that removing the anomalies efficiently reduces the underestimation and overestimation errors in all the seasonal datasets. Additionally, asymmetric loss functions and seasonality splitting effectively minimize underestimations despite increasing the overestimation error to some degree. Reducing underpredictions of electricity consumption is essential to prevent power outages that can be damaging to the community.

Helena Březinová, Jana Lainto

In our contribution, we explore the Czech-speaking discourse related to Georg Brandes in the Bohemian Lands in the late nineteenth and the early twentieth century, which means before and shortly after Czechs gained their independence from Austria-Hungary in 1918. Our research of archival sources, especially periodicals and private letters, enables us to confidently claim that the impact of Brandes’s criticism on the Czech arts was rather insignificant. At the same time, the sources give a clear picture that the Czech-speaking intelligentsia were interested in using Brandes’s symbolic capital to promote their struggle for Czech cultural autonomy. Thus, it was not Brandes’s works that can be considered influential in the Czech context but his persona. This strategy of using Brandes’s symbolic capital mirrors his own efforts to be viewed as an international intermediary. Finally, we explore the East-West dynamics in Brandes’s relationship with Czechs and vice versa, and here, we identify a considerable asymmetry

Fabian P. Stangl, Laila Schneidewind, Bernhard Kiss et al.

Introduction: Patients with neurogenic lower urinary tract dysfunction (NLUTD) reliant on intermittent self-catheterization for bladder emptying are at an increased risk of recurrent urinary tract infections (rUTI). So far, the most common practice in the prevention of rUTIs is long-term low-dose antibiotic prophylaxis, phytotherapy, and immunomodulation, whereby antibiotic prophylaxis inevitably leads to the emergence of drug-resistant pathogens and difficulty in treating infections. Therefore, non-antibiotic alternatives in the prevention of rUTIs are urgently required. We aim to identify the comparative clinical effectiveness of a non-antibiotic prophylaxis regimen in the prevention of recurrent urinary tract infections in patients with neurogenic bladder dysfunction who practice intermittent self-catheterization. Methods and analysis: In this multi-centre, prospective longitudinal multi-arm observational study, a total of 785 patients practising intermittent self-catheterisation due to NLUTD will be included. After inclusion, non-antibiotic prophylaxis regimens will be instilled with either UroVaxom^® (OM-89) standard regimen, StroVac^® (bacterial lysate vaccine) standard regimen, Angocin^®, D-mannose (oral dose 2 g), bladder irrigation with saline (once per day). The management protocols will be pre-defined, but the selection of the protocol will be at the clinicians’ discretion. Patients will be followed for 12 months from the onset of the prophylaxis protocol. The primary outcome is to identify the incidence of breakthrough infections. The secondary outcomes are adverse events associated with the prophylaxis regimens and the severity of breakthrough infections. Other outcomes include the exploration of change in susceptibility pattern via the optional rectal and perineal swab, as well as health-related quality of life over time (HRQoL), which will be measured in a random subgroup of 30 patients. Ethics and dissemination: Ethical approval for this study has been granted by the ethical review board of the University Medical Centre Rostock (A 2021-0238 from 28 October 2021). The results will be published in a peer-reviewed journal and presented at relevant meetings. Study registration number: German Clinical Trials Register: Number DRKS00029142.

Mhd Ayham Darwich, Katreen Ebrahem, Maysaa Shash et al.

This study aims to develop an integrated approach for 3D lumbar vertebral biomodel design and analysis, specifically targeting unilevel disc degeneration and the replacement of lumbar artificial discs. Key objectives include improving existing design methods through 3D techniques, inverse modeling, and an engineering biomodel preparation protocol. Additionally, the study evaluates mechanical properties in the implantation area and between disc components to gauge the effectiveness of artificial discs in restoring functional movement within the studied biological model. The construction of a biological model representing the L3–L4 functional spinal unit was based on measurements from radiographic images and computed tomography data obtained from the study sample. The 3D finite element method in Ansys software (v. 19.2, ANSYS, Inc., Canonsburg, PA, USA) was used to monitor the distribution of equivalent stress values within the core of the two artificial discs and the behavior of vertebral bone components in the model. This approach enabled the creation of personalized digital models tailored to the specific implantation requirements of each patient. Stress analysis identified critical areas within the disc cores, suggesting potential design modifications to optimize artificial disc performance, such as selectively increasing core thickness in specific regions and considering adjustments during implantation. For example, preserving part of the lateral annulus fibrosus from the degenerative disc and maintaining the anterior and posterior longitudinal ligaments may play a crucial role in balancing the forces and moments experienced by the lumbar section. This study provides valuable insights into the development of patient-specific solutions for lumbar disc degeneration cases, with the potential for enhancing artificial disc design and implantation techniques for improved functional outcomes.

Wajid Umar, János Balogh, Muhammad Khalid Hameed et al.

Overuse of chemical fertilizers in agroecosystems leads to the increased economic burden, low crop production in terms of input and environmental pollution. Due to its improved nutrient management and degrading properties, synthetic slow release fertilizers have become a significant advancement in the fertilizer sector. In this study we evaluated the effect of slow release urea on nitrous oxide (N2O) emission, crop growth and crop nutrient contents. Measurements were carried out in two different texture soils (sandy loam and silty clay) under two different conditions (bare soil and planted). The N2O emission was measured for 15 days from bare soils and 48 days from planted soil. Plant fresh weight, dry weight, chlorophyll contents, N and Zn were measured in the end of the experiment. The results showed that N2O emission was reduced 33–39 % from coated urea as compared to conventional urea in bare soil. In planted soil, the coated urea reduced the N2O emission 29–33 %. The deep placement of urea in silty clay soil reduced the N2O emission up to 22.8 % as compared to surface placement. Plant fresh matter, dry matter, N and Zn contents were significantly (p ≤ 0.05) higher with coated urea as compared to conventional urea. It is concluded that the coating of urea with hydrophobic materials like stearic acid, along with Zn sources i.e. Zn fortified nano-bentonite or the ZnO nanoparticles (NPs) presents opportunities to overcome the environmental pollution and increasing the crop production and quality.

Sándor Juhász, Gergő Pintér, Ádám Kovács et al.

Cities host diverse people and their mixing is the engine of prosperity. In turn, segregation and inequalities are common features of most cities and locations that enable the meeting of people with different socio-economic status are key for urban inclusion. In this study, we adopt the concept of economic complexity to quantify the sophistication of amenity supply at urban locations. We propose that neighborhood complexity and amenity complexity are connected to the ability of locations to attract diverse visitors from various socio-economic backgrounds across the city. We construct the measures of amenity complexity based on the local portfolio of diverse and non-ubiquitous amenities in Budapest, Hungary. Socio-economic mixing at visited third places is investigated by tracing the daily mobility of individuals and by characterizing their status by the real-estate price of their home locations. Results suggest that measures of ubiquity and diversity of amenities do not, but neighborhood complexity and amenity complexity are correlated with the urban centrality of locations. Urban centrality is a strong predictor of socio-economic mixing, but both neighborhood complexity and amenity complexity add further explanatory power to our models. Our work combines urban mobility data with economic complexity thinking to show that the diversity of non-ubiquitous amenities, central locations, and the potentials for socio-economic mixing are interrelated.