Search Everything

Tip: Search using "First Name + Last Name", e.g.
János Kiss instead of Kiss János.

Publications - 6374

NREM parasomnia-related behaviors and adverse childhood experiences

Mengesha Srahbzu Biresaw Vivian M. Correa József Vitrai Anna Szűcs

Publication Name: Sleep Medicine

Publication Date: 2024-09-01

Volume: 121

Issue: Unknown

Page Range: 365-369

Description:

Purpose: To assess the prevalence, types, sociodemographic factors, and reported dangerous activities of sleep-related behaviors likely representing NREM parasomnia episodes, as well as their association with adverse childhood experiences in Hungary. Methods: Cross-sectional survey of 1000 adults (aged ≥18 years) representing the Hungarian population, using a non-probability quota sampling with a random walk method and a structured face-to-face interview. A multi-criterion weighting procedure was applied to correct bias along the main sociodemographic variables to the data available. Binary logistic regression estimated the odds of NREM parasomnia-related behaviors associated with sociodemographic factors and adverse childhood experiences. Results: The prevalence of NREM parasomnia-related behaviors was 2.7 %, and self-reported sleep-eating was 0.1 % of the population (4.6 % of parasomnia-like activities). For middle-aged adults, the odds of sleep ambulation were significantly lower than for younger adults (OR 0.3; P = 0.03). A participant's family occurrence of reported parasomnia-like activity increased their odds of having it by more than 7 times (OR 7.1; P < 0.001). Nine participants out of those 27 people reporting NREM parasomnia-related behavior episodes, reported childhood adverse experiences, increasing the odds of parasomnia-related behavior by more than six times (OR 6.2; P < 0.001) compared to those not reporting it. Conclusion: This is the first population survey in Hungary on adult sleep-related behaviors likely representing NREM parasomnia episodes and the potential association with childhood traumatic events preceding them. The related dangerous behaviors call for safety measures and prevention. The significant association between adverse childhood events and NREM parasomnia-related behaviors needs further analysis.

Open Access: Yes

DOI: 10.1016/j.sleep.2024.07.027

Generative AI-driven transition to circular and responsible supply chains: Unpacking the dynamics of eco-centric design intelligence and ethical responsiveness

Eszter Lukács Jiyang Cheng Sabrine Mallek

Publication Name: Technological Forecasting and Social Change

Publication Date: 2026-04-01

Volume: 225

Issue: Unknown

Page Range: Unknown

Description:

The study focuses on understanding how the use of generative Artificial Intelligence (AI) can beneficially result in circular supply chain transformation while embedding design intelligence, ethical intelligence, and predictive intelligence within socio-technical systems. This study proposes and validates a model that integrates generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness, which collectively affect circular supply chain resilience and socio-environmental value realization, mediated by Sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To test the hypothesis, data were collected from 264 professionals in supply chain and technology-related industries in the USA. As the findings suggest, generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness significantly enhance sustainable process reconfiguration capability, which drives AI-enabled stakeholder co-creation. A serial mediation model indicates that Generative AI capabilities affect circular supply chain resilience and socio-environmental value realization via sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To our surprise, the regenerative policy ambidexterity negatively moderates the relationship between AI-enabled stakeholder co-creation and the realization of socio-environmental value. The results provide actionable advice for managers implementing generative AI in sustainable supply chains. Instead of focusing solely on algorithmic efficiency, if an organization can develop reconfiguration capability and engage stakeholders, it would generate systemic sustainability benefits.

Open Access: Yes

DOI: 10.1016/j.techfore.2025.124522

Carotenoid quantification of Cucurbita spp. by spectrophotometry, high-performance liquid chromatography and photoacoustics

Ildikó Jócsák György Végvári Noémi Kappel Ottó Dóka

Publication Name: Acta Scientiarum Polonorum Technologia Alimentaria

Publication Date: 2019-01-01

Volume: 18

Issue: 2

Page Range: 143-152

Description:

Background. Photoacoustic spectroscopy (PAS) is a tool for the rapid and non-destructive identification of materials even without contact. In recent years, there have been several works concerning the applicability of PAS in food analytical measurements. The intention of this work is to identify whether there is a correlation between total carotenoid and the β-carotene content of pumpkin and squash measured by high-performance liquid chromatography (HPLC), spectrophotometry (SP) and PAS. Material and methods. 'Crown prince F1', 'Veenas F1', 'Atlas F1' and 'Apollo F1' (SAKATA) were used as experimental materials. The samples were measured in a fresh state and in a lyophilised condition with HPLC, SP and PAS. Results. The results of SP show that total carotene content varies according to the species and variety. Lyophilisation resulted in lower, although varying carotene content compared to the raw form. Typical PA spectra of pumpkins were determined (300-550 nm), normalized to the carbon black powder. At 17 Hz the amplitude and carotene content shows direct proportionality in the range investigated. Photoacoustic (PA) signal and carotenoid content of pumpkin samples gave a linear correlation (R2 = 0.9821). Conclusion. The measurement of PA spectra gives reliable information about the total carotene content of pumpkin and squash samples. These findings may allow the use of PAS as a fast tool for the carotenoid determination in squashes and give the possibility of instead for the results to be used for the evaluation of squash varieties currently used for industrial processing in functional food development.

Open Access: Yes

DOI: 10.17306/J.AFS.2019.0639

Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles

R. Toth Erno Horváth Gergo Igneczi Krisztian Nyilas

Publication Name: Automotive Innovation

Publication Date: 2024-02-01

Volume: 7

Issue: 1

Page Range: 59-70

Description:

Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.

Open Access: Yes

DOI: 10.1007/s42154-023-00259-8

Sustainable development and common commercial policy

Balázs Horváthy

Publication Name: Acta Juridica Hungarica

Publication Date: 2012-12-01

Volume: 53

Issue: 4

Page Range: 334-344

Description:

No description provided

Open Access: Yes

DOI: 10.1556/AJur.53.2012.4.5

Maize yield prediction based on artificial intelligence using spatio-temporal data

G. Milics Bálint Daróczy C. Kerepesi A. J. Kovács A. Nyéki M. Neményi András Benczúr

Publication Name: Precision Agriculture 2019 Papers Presented at the 12th European Conference on Precision Agriculture Ecpa 2019

Publication Date: 2019-01-01

Volume: Unknown

Issue: Unknown

Page Range: 1011-1017

Description:

The aim of this study was to predict maize yield by artificial intelligence using spatio-temporal training data. Counter-propagation artificial neural networks (CP-ANNs), XY-fused networks (XY-Fs), supervised Kohonen networks (SKNs), extreme gradient boosting (XGBoost) and support-vector machine (SVM) were used for predicting maize yield in 5 vegetation periods. Input variables for modelling were: soil parameters (pH, P2O5, K2O, Zn, Clay content, ECa, draught force, Cone index), and micro-relief averages and meteorological parameters for the 63 treatment units. The best performing method (XGBoost) attained 92.1 and 95.3% of accuracy on the training and the test set.

Open Access: Yes

DOI: 10.3920/978-90-8686-888-9_124

Some regularized versions of the method of fundamental solutions

Csaba Gáspár

Publication Name: Lecture Notes in Computational Science and Engineering

Publication Date: 2013-01-21

Volume: 89 LNCSE

Issue: Unknown

Page Range: 181-198

Description:

A powerful method of the solution of homogeneous equations is considered. Using the traditional approach of the Method of Fundamental Solutions, the fundamental solution has to be shifted to external source points. This is inconvenient from computational point of view, moreover, the resulting linear system can easily become severely ill-conditioned. To overcome this difficulty, a special regularization technique is applied. In this approach, the original second-order elliptic problem (a modified Helmholtz problem in the paper) is approximated by a fourth-order multi-elliptic boundary interpolation problem. To perform this boundary interpolation, either the Method of Fundamental Solutions, or a direct multi-elliptic interpolation can be used. In the paper, a priori error estimations are deduced. A numerical example is also presented. © 2013 Springer-Verlag.

Open Access: Yes

DOI: 10.1007/978-3-642-32979-1_12

ReGAIN: a reinforcement-enhanced generative AI framework for intelligent intrusion detection in IoT networks

Nadeem Javaid Dragan Pamucar Khalid Mahmood Awan Muhammad Shafiq Imran Ahmed Aymin Javed Jin Ghoo Choi

Publication Name: Complex and Intelligent Systems

Publication Date: 2026-04-01

Volume: 12

Issue: 4

Page Range: Unknown

Description:

The advent of the Internet of Things (IoT) enables billions of devices in wide-ranging domains such as healthcare, industry, and smart cities to interconnect with each other, but these connections make the network vulnerable to advanced cyber threats too. Current intrusion detection methods have failed to provide effective detection capabilities mainly because of issues such as extremely imbalanced data distributions, low classification accuracy, or static and manually tuned hyperparameters that do not generalize well in dynamic IoT settings. These challenges are exacerbated by unique IoT constraints, including limited device resources and dynamic attack patterns, which further complicate effective detection. To address these challenges, in this study we present a Reinforcement-enhanced Generative Artificial Intelligence (ReGAIN) framework for intelligent intrusion detection in IoT networks. In this approach, we use a generative autoencoder for data balancing to generate realistic minority class instances in the latent feature space, and meanwhile to obtain stable and unbiased learning of the model. This paper introduces a novel Pointer-Attention Dual Network (PAD-Net) that employs a Dual Attention Network (DANet) and a Pointer Network (PtrNet) to enhance spatial attention and inter-feature relationships. We also propose Reinforcement-enhanced PAD-Net (RePAD-Net), which leverages reinforcement learning to automatically optimize key hyperparameters at each training step, further enhancing generalization ability and robustness. The intrusion detection task in this study is a multi-class classification problem, where different types of attacks are distinguished from each other. Experimental results demonstrate that PAD-Net and RePAD-Net achieve notable improvements of 3.79% and 8.79% in accuracy, 3.79% and 8.78% in recall, 2.79% and 9.01% in F1-score, 3.79% and 8.83% in Mathews correlation coefficient, and 3.94% and 9.11% in Cohen’s Kappa, respectively, along with significant reductions in log loss of 47.42% and 70.96% and hamming loss of 24.33% and 56.37% compared with baseline models such as naive bayes, gradient boosting, densely connected network, long short term memory, hybrid models, DANet and PtrNet. Additionally, 10-fold cross validation is applied to validate the results of proposed models. These findings confirm that our proposed ReGAIN framework, which is able to alleviate data imbalance and improve learning generalization, can dramatically enhance the reliability of detection performance under complex IoT intrusion environments.

Open Access: Yes

DOI: 10.1007/s40747-026-02241-3

Determining the dwell time constraint for switched H∞ filters

András Edelmayer Zsolt Horváth

Publication Name: Przeglad Elektrotechniczny

Publication Date: 2019-01-01

Volume: 95

Issue: 1

Page Range: 7-11

Description:

This paper presents an algorithm for determining the minimum dwell time constraint for switched linear H∞ fault detection filters. When applying switched systems, ensuring the stability is a crucial part, which can be guaranteed, when we switch slowly enough between the subsystems, more precisely the intervals between two consecutive switching, called dwell time, are large enough. The problem formulation is based on multiple Lyapunov functions and is expressed through a special form of linear matrix inequities (LMIs), which include a nonlinear term with the dwell time. This represents a multivariable time dependent optimization problem. To solve this special formulated LMIs, we propose an algorithm, called Td-iteration, which is a combination of the procedure of interval halving with an LMI solver. The results of the illustrative example suggest further benefits.

Open Access: Yes

DOI: 10.15199/48.2019.03.03