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  1. Article ; Online: Context of mutations within the Romanian agricultural sector$nAlecu Ioan-Niculae Alecu, Gyorgy Szabo, Nagy Caroly, Angelescu Irina

    Alecu, Ioan-Niculae / Angelescu, Irina / Nagy, Caroly / Szabo, Gyorgy

    Agrarian economy and rural development : realities and perspectives for Romania : 7th edition of the International Symposium , p. 174-181

    2016  , Page(s) 174–181

    Abstract: This paper represents an analysis of the way in which the potato-growing areas and yields obtained in Covasna County have evolved, compared to the situation existent at national level. In the introduction and in the first part of the paper, the global ... ...

    Abstract This paper represents an analysis of the way in which the potato-growing areas and yields obtained in Covasna County have evolved, compared to the situation existent at national level. In the introduction and in the first part of the paper, the global and European situation is also mentioned, taking into account the special importance of potato in the human diet in different areas of the world. The period between 2006 and 2014 is analysed, with a special focus on the situation existent in 2014 and with references to the national situation, as it was mentioned before. The evolution of potato-growing areas and yields is also analysed, in comparison with other crops in the production structure of the county, with the aim to explain the meaning of this evolution and taking into account the special importance this crop has always had in the economy of the county. Although the potato-growing areas in the Covasna County have registered, similar to the national situation, a sharp decline, potato continues to have a special significance in the agricultural economy of the county, as well as in the agricultural economy of the entire country, that continues to be ranked the third place among the potato-growing countries at the European level.
    Keywords potato ; cropped areas ; yields ; area diminishing ; climatic conditions
    Language English
    Size Online-Ressource
    Publisher Research Institute for Agricultural Economy and Rural Development
    Publishing place Bucharest
    Document type Article ; Online
    Database ECONomics Information System

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  2. Book ; Online: On the Advice Complexity of Online Unit Clustering

    Nagy-György, Judit

    2023  

    Abstract: In online unit clustering a set of n points of a metric space that arrive one by one, partition the points into clusters of diameter at most one, so that number of clusters is minimized. This paper gives linear upper and lower bounds for the advice ... ...

    Abstract In online unit clustering a set of n points of a metric space that arrive one by one, partition the points into clusters of diameter at most one, so that number of clusters is minimized. This paper gives linear upper and lower bounds for the advice complexity of 1-competitive online unit clustering algorithms in terms of number of points in $\mathbb{R}^d$ and $\mathbb{Z}^d$.
    Keywords Computer Science - Data Structures and Algorithms ; 68W27
    Publishing date 2023-09-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Comorbidities or extra-articular manifestations: time to reconsider the terminology?

    Dey, Mrinalini / Nagy, Gyorgy / Nikiphorou, Elena

    Rheumatology (Oxford, England)

    2022  Volume 61, Issue 10, Page(s) 3881–3883

    MeSH term(s) Arthritis, Rheumatoid ; Comorbidity ; Humans
    Language English
    Publishing date 2022-03-03
    Publishing country England
    Document type Editorial
    ZDB-ID 1464822-2
    ISSN 1462-0332 ; 1462-0324
    ISSN (online) 1462-0332
    ISSN 1462-0324
    DOI 10.1093/rheumatology/keac134
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  4. Article ; Online: Impact Evaluation of Score Classes and Annotation Regions in Deep Learning-Based Dairy Cow Body Condition Prediction

    Nagy, Sára Ágnes / Kilim, Oz / Csabai, István / Gábor, György / Solymosi, Norbert

    Animals. 2023 Jan. 04, v. 13, no. 2

    2023  

    Abstract: Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aims to investigate the accuracy with which supervised machine learning, specifically a deep convolutional neural network (CNN), can be used to retrieve ... ...

    Abstract Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aims to investigate the accuracy with which supervised machine learning, specifically a deep convolutional neural network (CNN), can be used to retrieve body condition score (BCS) classes estimated by an expert. We recorded images of animals’ rumps in three large-scale farms using a simple action camera. The images were annotated with classes and three different-sized bounding boxes by an expert. A CNN pretrained model was fine-tuned on 12 and 3 BCS classes. Training in 12 classes with a 0 error range, the Cohen’s kappa value yielded minimal agreement between the model predictions and ground truth. Allowing an error range of 0.25, we obtained minimum or weak agreement. With an error range of 0.5, we had strong or almost perfect agreement. The kappa values for the approach trained on three classes show that we can classify all animals into BCS categories with at least moderate agreement. Furthermore, CNNs trained on 3 BCS classes showed a remarkably higher proportion of strong agreement than those trained in 12 classes. The prediction precision when training with various annotation region sizes showed no meaningful differences. The weights of our trained CNNs are freely available, supporting similar works.
    Keywords body condition ; cameras ; dairy cows ; energy ; neural networks ; prediction
    Language English
    Dates of publication 2023-0104
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article ; Online
    ZDB-ID 2606558-7
    ISSN 2076-2615
    ISSN 2076-2615
    DOI 10.3390/ani13020194
    Database NAL-Catalogue (AGRICOLA)

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  5. Article ; Online: Viewpoint: Could better understanding of risk factors for comorbidities pave the way towards personalized therapy in rheumatoid arthritis?

    Majnik, Judit / Nagy, György

    Rheumatology (Oxford, England)

    2023  Volume 62, Issue SI3, Page(s) SI271–SI273

    Abstract: In addition to joints, several organs can be affected in rheumatoid arthritis. Coexisting conditions with different pathomechanisms all contribute to disease activity, treatment efficacy, mortality and quality of life. The wide selection of treatment ... ...

    Abstract In addition to joints, several organs can be affected in rheumatoid arthritis. Coexisting conditions with different pathomechanisms all contribute to disease activity, treatment efficacy, mortality and quality of life. The wide selection of treatment options makes it possible for rheumatologists to personalize treatment for their patients, which in present practice mainly includes the consideration of established comorbidities and contraindications. We suggest that further research can enable clinicians to take into account the individual risk of the future development of comorbidities, when making therapeutic decisions. Individual risk assessment could be mainly based on biomarkers and the better understanding of the patomechanism of different coexisting conditions, as we highlight with the examples of depression and interstitial lung disease. This biomarker-based person-centred therapy can lead not only to the treatment but ideally even the prevention of coexisting conditions, and can lead to better disease control, survival and quality of life in rheumatoid arthritis.
    MeSH term(s) Humans ; Quality of Life ; Arthritis, Rheumatoid/drug therapy ; Risk Factors ; Comorbidity ; Rheumatologists ; Biomarkers
    Chemical Substances Biomarkers
    Language English
    Publishing date 2023-10-21
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1464822-2
    ISSN 1462-0332 ; 1462-0324
    ISSN (online) 1462-0332
    ISSN 1462-0324
    DOI 10.1093/rheumatology/kead352
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  6. Article ; Online: Felnőttkori agydaganatok sebészete.

    Bagó, György Attila / Nagy, Gergő Dávid

    Magyar onkologia

    2023  Volume 68, Issue 1, Page(s) 13–25

    Abstract: Despite the advanced medical and radiation therapy, the role of surgical resection of brain neoplasms still remains indisputable. The maximal safe resection of benign brain tumors may result in complete recovery of the patient. Surgery of malignant ... ...

    Title translation Brain tumor surgery in adults.
    Abstract Despite the advanced medical and radiation therapy, the role of surgical resection of brain neoplasms still remains indisputable. The maximal safe resection of benign brain tumors may result in complete recovery of the patient. Surgery of malignant tumors can resolve mass effect, improve the neurological condition of the patient providing the possibility for further complex oncotherapy based on molecular level histopathology results. The advances in technical and multidisciplinary environment of brain tumor surgery facilitate more radical and safer resection resulting in better outcomes and preservation of quality of life, even in case of tumors which were considered inoperable until recently. In this review we present the recent technical innovations used in brain tumor surgery and discuss the surgical strategy of the most common tumor types (gliomas, meningiomas, cranial nerve tumors and brain metastases). The surgical management of complex skull base tumors, pituitary tumors, as well as neuro-endoscopic surgery and pediatric brain tumors are discussed in other papers of this special issue.
    MeSH term(s) Adult ; Humans ; Brain Neoplasms/surgery ; Meningeal Neoplasms/pathology ; Meningeal Neoplasms/surgery ; Meningioma/radiotherapy ; Meningioma/surgery ; Meningioma/pathology ; Neurosurgical Procedures/methods ; Quality of Life ; Skull Base Neoplasms/surgery ; Skull Base Neoplasms/pathology
    Language Hungarian
    Publishing date 2023-11-12
    Publishing country Hungary
    Document type Review ; English Abstract ; Journal Article
    ZDB-ID 414033-3
    ISSN 2060-0399 ; 0025-0244
    ISSN (online) 2060-0399
    ISSN 0025-0244
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  7. Article ; Online: Impact Evaluation of Score Classes and Annotation Regions in Deep Learning-Based Dairy Cow Body Condition Prediction

    Sára Ágnes Nagy / Oz Kilim / István Csabai / György Gábor / Norbert Solymosi

    Animals, Vol 13, Iss 194, p

    2023  Volume 194

    Abstract: Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aims to investigate the accuracy with which supervised machine learning, specifically a deep convolutional neural network (CNN), can be used to retrieve ... ...

    Abstract Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aims to investigate the accuracy with which supervised machine learning, specifically a deep convolutional neural network (CNN), can be used to retrieve body condition score (BCS) classes estimated by an expert. We recorded images of animals’ rumps in three large-scale farms using a simple action camera. The images were annotated with classes and three different-sized bounding boxes by an expert. A CNN pretrained model was fine-tuned on 12 and 3 BCS classes. Training in 12 classes with a 0 error range, the Cohen’s kappa value yielded minimal agreement between the model predictions and ground truth. Allowing an error range of 0.25, we obtained minimum or weak agreement. With an error range of 0.5, we had strong or almost perfect agreement. The kappa values for the approach trained on three classes show that we can classify all animals into BCS categories with at least moderate agreement. Furthermore, CNNs trained on 3 BCS classes showed a remarkably higher proportion of strong agreement than those trained in 12 classes. The prediction precision when training with various annotation region sizes showed no meaningful differences. The weights of our trained CNNs are freely available, supporting similar works.
    Keywords deep learning ; dairy cow ; body score ; prediction ; accuracy ; Veterinary medicine ; SF600-1100 ; Zoology ; QL1-991
    Subject code 006
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: Impact Evaluation of Score Classes and Annotation Regions in Deep Learning-Based Dairy Cow Body Condition Prediction.

    Nagy, Sára Ágnes / Kilim, Oz / Csabai, István / Gábor, György / Solymosi, Norbert

    Animals : an open access journal from MDPI

    2023  Volume 13, Issue 2

    Abstract: Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aims to investigate the accuracy with which supervised machine learning, specifically a deep convolutional neural network (CNN), can be used to retrieve ... ...

    Abstract Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aims to investigate the accuracy with which supervised machine learning, specifically a deep convolutional neural network (CNN), can be used to retrieve body condition score (BCS) classes estimated by an expert. We recorded images of animals' rumps in three large-scale farms using a simple action camera. The images were annotated with classes and three different-sized bounding boxes by an expert. A CNN pretrained model was fine-tuned on 12 and 3 BCS classes. Training in 12 classes with a 0 error range, the Cohen's kappa value yielded minimal agreement between the model predictions and ground truth. Allowing an error range of 0.25, we obtained minimum or weak agreement. With an error range of 0.5, we had strong or almost perfect agreement. The kappa values for the approach trained on three classes show that we can classify all animals into BCS categories with at least moderate agreement. Furthermore, CNNs trained on 3 BCS classes showed a remarkably higher proportion of strong agreement than those trained in 12 classes. The prediction precision when training with various annotation region sizes showed no meaningful differences. The weights of our trained CNNs are freely available, supporting similar works.
    Language English
    Publishing date 2023-01-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606558-7
    ISSN 2076-2615
    ISSN 2076-2615
    DOI 10.3390/ani13020194
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  9. Article ; Online: Difficult to treat rheumatoid arthritis: Sequential therapy with different personalized biological targets could be an option.

    Gremese, Elisa / Bruno, Dario / Nagy, György / Ferraccioli, Gianfranco

    European journal of internal medicine

    2024  

    Language English
    Publishing date 2024-01-31
    Publishing country Netherlands
    Document type Letter
    ZDB-ID 1038679-8
    ISSN 1879-0828 ; 0953-6205
    ISSN (online) 1879-0828
    ISSN 0953-6205
    DOI 10.1016/j.ejim.2024.01.019
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  10. Book ; Online: Prolactin

    Nagy, György M. / Nagy, György M. / Toth, Bela E.

    2013  

    Keywords Physiology ; Infertility & fertilization
    Size 1 electronic resource (246 pages)
    Publisher IntechOpen
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021050021
    ISBN 9789535170730 ; 9535170732
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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