Search Everything

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

Publications - 6374

Bringing Vicia villosa, V. pannonica, V. sativa, Trifolium incarnatum and T. alexandrinum into cultivation in Hungary: a historical review

G. Pinke Attila G.L. Németh Marcell Lócsi

Publication Name: Botanikai Kozlemenyek

Publication Date: 2025-01-01

Volume: 112

Issue: 1

Page Range: 61-86

Description:

We review the history of arable naturalization and initial cropping of five legume species in Hungary in the period between the 19th and the first half of the 20th century. Nowadays, these species have an increasing importance as green manure and seed production. The cultivation of Vicia villosa Roth, an old established plant in the Carpathian Basin of Near Eastern origin, was started for green forage, mainly owing to encouraging experiences in Germany. It produced good yields even among unfavourable weather and edaphic conditions. Although Vicia pannonica Crantz is likely native to Hungary, it was brought into cultivation due to reports of satisfactory farming experiences from the USA. It had a good seed production capacity and also provided tasty forage in Hungary as well. Vicia sativa L. was probably cultivated already in the Neolithic, Bronze and Iron Ages in the Carpathian Basin. Later, in the transitional period between the three-field system and crop rotation, its foreign cultivars were re-naturalised and sowed into the fallow as a forage crop. Trifolium incarnatum L., a plant of Mediterranean origin, was first cultivated in Hungary as a stubble–sowed crop, or for clover replacement, but later it became a relevant seed–export item. The seeds of Trifolium alexandrinum L. for its first Hungarian field experiments probably came directly from Africa. Mostly, it was cropped as a secondary sowed forage in irrigated fields and as a shift crop in rice growing areas.

Open Access: Yes

DOI: 10.17716/BotKozlem.2025.112.1.61

Heat Integrated Water Regeneration Network Synthesis via Graph-Theoretic Sequential Method

Ákos Orosz Bing Shen How Sin Yong Teng Juin Yau Lim Ferenc Friedler

Publication Name: Chemical Engineering Transactions

Publication Date: 2021-01-01

Volume: 88

Issue: Unknown

Page Range: 49-54

Description:

The integration of multiple resources conservation networks is necessary to attain the ever-stringent sustainable goals. This work takes initiatives to develop a heat integrated water network via a proposed P-graph-based sequential methodology. In the first step, a set of feasible water regeneration networks is generated using the conventional P-graph framework. Then, the obtained feasible networks will be used as the inputs in the second stage which aims to generate various sets of feasible heat exchanger networks. It is worth noting that the second model is solved by an extended P-graph framework (P-HENS) for combinatorial process network optimization. The solutions are then ranked based on the total network cost. To demonstrate the effectiveness of the proposed method, a typical water regeneration network (three sources and three sinks) with multi-contaminants is used. The results show a total of 103 feasible water network structures (water network cost ranging from 0.76 M$/y to 1.18 M$/y). Thereafter, a list of feasible HIWRN can be determined using P-HENS. The top four HIWRNs which offer similar total network cost (~1.639 M$/y) are demonstrated. This proposed method provides valuable insights that allow decision-makers to further select the optimal solution which may be more beneficial as compared to the one obtained via conventional methods.

Open Access: Yes

DOI: 10.3303/CET2188008

The limits of restrictions on free competition in the state of emergency—the Hungarian fuel and food retail price maximisation in the light of the Hungarian constitutional court’s, the Strasbourg court’s and the Luxembourg court’s jurisprudence

Csaba Erdos László Knapp Viktória Verebélyi

Publication Name: Frontiers in Political Science

Publication Date: 2025-01-01

Volume: 7

Issue: Unknown

Page Range: Unknown

Description:

Since March 2020, Hungary has almost continuously been under a type of special legal order, the state of emergency, which was first introduced to better protect against the COVID-19 epidemic and then in May 2022—following the amendment of the Fundamental Law—due to the Russian-Ukrainian war. Both the crises caused by the epidemic and the armed conflict in the neighbouring country were de facto limited not only to the health and migration-humanitarian fields, but the Government made use of the exceptional legislative powers of the special legal order in almost all areas of life. Economic regulation was no exception: in 2021, the Government capped the retail price of fuel, and from February 2022 onwards, the retail price of several basic foodstuffs (including flour, sugar, milk, chicken breast and other meats, and later eggs and potatoes). The aim of this paper is to show the limits of one of the most powerful state interventions in the economy: the price maximisation. This can basically be determined on the basis of the relevant case law of three fora of legal protection—the Hungarian Constitutional Court, the European Court of Human Rights in Strasbourg and the Court of Justice of the European Union. A comparison of the case law of the above-mentioned three courts also shows which legal protection mechanism is most effective against legislation restricting the free competition—at least in a period of special legal order.

Open Access: Yes

DOI: 10.3389/fpos.2025.1542096

The Development of Mood Repair Response Repertories: I. Age-Related Changes Among 7- to 14-Year-Old Depressed and Control Children and Adolescents

Krisztina Kapornai László Mayer Nestor L. Lopez-Duran Maria Kovacs ldiko Baji Ágnes Vetró Enikö Kiss Maria Kovacs Charles George

Publication Name: Journal of Clinical Child and Adolescent Psychology

Publication Date: 2019-01-02

Volume: 48

Issue: 1

Page Range: 143-152

Description:

The purpose of this study was to test developmentally informed hypotheses about regulatory responses to sadness that attenuate versus exacerbate it (adaptive versus maladaptive mood repair responses, respectively) across late childhood, early adolescence, and mid-adolescence. In a multi-site study in Hungary, clinic-based, 7- to 14-year-olds with Diagnostic and Statistical Manual of Mental Disorders’ (4th ed., text rev.) depressive disorders (N = 697; 55% male) and age/sex matched (at 1:2) nondepressed, school-based controls (N = 1,394) reported on their usual responses to sadness/dysphoria; parental reports were obtained separately. Adaptive and maladaptive response repertoire scores were compared across ages within and across subject groups, and by informant, controlling for confounds. Contrary to Hypothesis 1, older (vs. younger) youths in both groups reported fewer adaptive regulatory responses. Maladaptive response repertoires were unrelated to age among controls but significantly increased with age among depressed youths, particularly the girls. Partially supporting Hypothesis 2, subject groups differed in age-related trajectories of mood repair repertories, but not as expected (e.g., younger depressed children reported larger adaptive response repertoires than did controls). Parental reports revealed no developmental changes in offspring’s mood repair repertories. Parent-offspring reports were most discordant for younger (vs. older) offspring, tended to converge around age 11, and were consistently and significantly larger in the depressed sample. Self-reported adaptive mood repair repertories appear to have been laid down by late childhood and then undergo “trimming” across ages 7–14 years. The extensive maladaptive mood repair response repertoires of depressed youths, which increased with age, distinguish them primarily from controls. Therefore, reducing maladaptive regulatory responses to sadness should be a priority when treating depressed youths.

Open Access: Yes

DOI: 10.1080/15374416.2017.1399399

Addressing the Impact of Resolution Scaling on YOLO Performance for Brain Tumor Detection Through Optimized Network Depth/Width Adjustments

Ferenc Lilik Ahmed Al-Ashoor Szilvia Nagy

Publication Name: Applied Sciences Switzerland

Publication Date: 2026-05-01

Volume: 16

Issue: 9

Page Range: Unknown

Description:

Deep learning-based object detectors, particularly You Only Look Once (YOLO) architectures, have demonstrated strong performance in automated brain tumor detection. However, the impact of resolution scaling on tumor localization accuracy remains underexplored, especially under conditions where image resolution is reduced. This study aims to investigate how lowering the input resolution from 640 × 640 to 480 × 480 affects detection performance and whether optimized depth/width scaling and hyperparameter tuning can compensate for the expected loss of spatial detail. In this work, we propose an optimized YOLO-based framework for brain tumor detection and localization in MRI scans, building upon the method “Addressing the Impact of Resolution Scaling on YOLO Performance for Brain Tumor Detection through Optimized Network Depth/Width Adjustments.” Our model, an enhanced variant of the BGF-YOLO architecture, is specifically tailored for the challenges of medical imaging. The proposed network features both architectural and training-level optimizations. We used a publicly available dataset from Kaggle that consists of 500 training images, 201 validation images, and 100 test images. Experimental analysis demonstrates that while reducing input resolution alone degrades performance, integrating targeted modifications specifically increases network depth and width. In addition, advanced training strategies such as MixUp augmentation, dropout regularization, AdamW optimization, cosine learning rate scheduling, and finely tuned learning rate ranges lead to substantial performance gains. The optimized model achieves a precision of up to 0.858, a recall of 0.943, mAP50 of 0.946, and mAP50–95 of 0.672. These results not only outperform the reduced-resolution baseline but also approach, and in some cases surpass, the original high-resolution BGF-YOLO setup.

Open Access: Yes

DOI: 10.3390/app16094320

Influence of the Tensor Product Model Representation Of QLPV Models on The Feasibility of Linear Matrix Inequality

Alexandra Szollosi Péter Baranyi

Publication Name: Asian Journal of Control

Publication Date: 2016-07-01

Volume: 18

Issue: 4

Page Range: 1328-1342

Description:

The present paper proves that the vertexes of the tensor product (TP) model type polytopic representation of a given quasi linear parameter varying (qLPV) state-space model strongly interfere with the feasibility regions of linear matrix inequality (LMI)-based control design methods. Furthermore this is valid both for the LMI-based feasibility of the controller and the observer design, but the influence differs for the controller and the observer system components. More specifically, the factors influencing the feasibility regions of the LMI-based control design include: (i) the manipulation of the vertexes' position; and (ii) the size and complexity of the TP model type polytopic representation, i.e. the number of the vertexes contained in the TP model representation. The proof is based on a complex control design example, where the influence of these factors stated above can be easily and clearly indicated. Furthermore the paper shows via the example that the maximal parameter space of the controller and observer also depends on these factors. The example model consists of the complex Nonlinear Aeroelastic Test Apparatus (NATA) model of the three degree of freedom aeroelastic wing section model including Stribeck friction and the control design method is based on the relaxed TP model transformation-based control design framework that supports the flexible manipulation of these factors.

Open Access: Yes

DOI: 10.1002/asjc.1238

Edge AI Benchmarking: Tools, Methodologies, and Optimization Strategies, a review

Sándor Répás Mustafa Abdulkadhim

Publication Name: International Conference on Electrical Computer and Energy Technologies Icecet 2025

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Edge computing has emerged as an important paradigm to address the evolving demands of latency-sensitive applications and the adaptation of Internet of Things (IoT) devices, offering near edge data processing to increase performance, reduce bandwidth usage, and increase data privacy. However, benchmarking these heterogeneous edge environments remains a challenge due to their distributed nature and diverse hardware configurations. This paper proposes "Scalable Heterogeneous Edge Automation Benchmarking"(SHEAB), a novel containerized and automated framework designed to evaluate edge computing systems comprehensively. SHEAB integrates containerization for portability, automation for efficiency, and a multi-layered security architecture - including firewalls, VPNs, and secure shell connections - to ensure robust data integrity across varied edge servers. This research advances the field by providing a scalable, secure, and adaptable benchmarking solution, with future directions aimed at researching hardware capability assessments and increasing AI-driven edge-computing testing and benchmarking.

Open Access: Yes

DOI: 10.1109/ICECET63943.2025.11472323

Energy Integration of Vertical Farms for Higher Efficiency and Sustainability

Jean Pimentel Ferenc Pongrácz György Ágoston Zoltán Sejpes Zsolt Bukodi István Kenyeres Ferenc Friedler

Publication Name: Chemical Engineering Transactions

Publication Date: 2021-01-01

Volume: 88

Issue: Unknown

Page Range: 727-732

Description:

As a result of the increasing human population, the availability of resources per capita has been vastly diminished in the last decades. Naturally, the depletion of valuable environmental assets such as water and arable land, poses a threat to mankind’s sustainable development. In this regard, various novel ideas have been proposed for processing agricultural products ecologically and sustainably; one of such ideas is vertical farming (VF). VF is a novel production technology that aims at enhancing both the yield and the product quality, by growing them in highly packed, high energy-density systems with high mass-flow rates and in a controlled environment. The technologies required for VF have been developed and successfully tested, thereby producing crops that meet the requirements of food safety, adequate nutrient content, and maximum yield. However, the extremely high biomass densities and high turnover rates employed to give rise to challenges regarding to energy efficiency and homogeneity patterns. In this work, a P-graph model is presented for the integration of VF systems. The algorithmic approach is employed to evaluate options for process integration and intensification of VF with plausible synergetic production processes into a dense urban environment. As a result, 115 integrated process alternatives are identified for the base case, with the best structure exhibiting a total cost of 41,920 EUR/y, thereby yielding reductions up to 11% for the total cost of the integrated network. The pareto front of economic performance and CO2 emission is presented to illustrate the potential benefits of integration, and the capability of the methodology to evaluate alternative designs.

Open Access: Yes

DOI: 10.3303/CET2188121

TURNING THE TRIPLE BURDEN OF UKRAINIAN DEPOPULATION INTO A QUADRUPLE BURDEN: THE RESULTS OF A SURVEY AMONG UKRAINIAN REFUGEE WOMEN

Sándor Remsei Éva Berde

Publication Name: Economics and Sociology

Publication Date: 2025-01-01

Volume: 18

Issue: 1

Page Range: 296-312

Description:

The effects of the Russian-Ukrainian war on Ukraine's demographic landscape are immense. One key consideration is whether Ukrainian refugee women intend to return to their country after the war ends. If the return is planned, the question of whether they would wish to have children is also relevant. This study explored these issues by surveying women who fled to Hungary and the Netherlands. Among those surveyed, 42% did not plan to return under any circumstances, and only 12% intended to return even if their home area came under Russian control. Logistic regression was used to identify factors influencing the intention to return, with reluctance to have additional children and income earned through employment emerging as the strongest explanatory factors. However, we found only modest associations between the intention to return and other variables. Our findings suggest that deeply rooted personal preferences shape these women’s plans.

Open Access: Yes

DOI: 10.14254/2071-789X.2025/18-1/16