@article{MTMT:34824732,
	title = {Short-term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling},
	url = {https://m2.mtmt.hu/api/publication/34824732},
	author = {Bouzghiba, Houria and Mendyl, Abderrahmane and Kenza, Khomsi and Gabor, Geczi},
	doi = {10.1002/clen.202300395},
	journal-iso = {CLEAN-SOIL AIR WATER},
	journal = {CLEAN-SOIL AIR WATER},
	unique-id = {34824732},
	issn = {1863-0650},
	abstract = {Air pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short-term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO2) and particulate matter (PM10), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey-Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short-term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region.},
	keywords = {particulate matter; air pollution; Environmental Sciences; GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY; Marine & Freshwater Biology; Short-term prediction; ARIMA search grid},
	year = {2024},
	eissn = {1863-0669}
}

@{MTMT:34819531,
	title = {BIOMECHANICAL PROFILING OF THE CAPSICUM ANNUUM FRX MUTANT GENOTYPE},
	url = {https://m2.mtmt.hu/api/publication/34819531},
	author = {Pápai, Bánk and Kovács, Zsófia and Bedő, Janka and Khin, Nyein Chan and Tóth-Lencsés, Andrea Kitti and Csilléry, Gábor and Szamosi, Csaba and Timár, Zoltán and Szőke, Antal and Veres, Anikó},
	booktitle = {FIBOK 2024 6th National Conference of Young Biotechnologists},
	unique-id = {34819531},
	year = {2024},
	pages = {42-43},
	orcid-numbers = {Kovács, Zsófia/0000-0001-8902-7952}
}

@article{MTMT:34798937,
	title = {Are Hungarian Grey Cattle or Hungarian Racka Sheep the Best Choice for the Conservation of Wood-Pasture Habitats in the Pannonian Region?},
	url = {https://m2.mtmt.hu/api/publication/34798937},
	author = {Penksza, Károly and Saláta, Dénes and Fűrész, Attila and Penksza, Péter and Fuchs, Márta and Pajor, Ferenc and Sipos, László and Falusi, Eszter and Wagenhoffer, Zsombor and Szentes, Szilárd},
	doi = {10.3390/agronomy14040846},
	journal-iso = {AGRONOMY-BASEL},
	journal = {AGRONOMY (BASEL)},
	volume = {14},
	unique-id = {34798937},
	abstract = {Wood pastures have been characteristic farming types in the Pannonian biogeographical region over the centuries. In the present work, we studied wood-pastures of typical geographical locations in the North Hungarian Mountain Range of Hungary characterized by similar environmental conditions but grazed by different livestock. The sample area of Cserépfalu was grazed by Hungarian Grey Cattle, while the Erdőbénye was grazed by Hungarian Racka Sheep. Coenological records of the sites were collected from 2012 to 2021 in the main vegetation period according to the Braun-Blanquet method with the application of 2 × 2 m sampling quadrats, where the coverage estimated by percentage for each present species was also recorded. To evaluate the state of vegetation, ’ecological ordering’ distribution, diversity, and grassland management values were used. Between the two areas, the grazing pressure of the two studied livestock produced different results. Based on the diversity values, woody–shrubby–grassland mosaic diversity values were high (Shannon diversity: 2.21–2.87). Cattle grazing resulted in a variable and mosaic-like shrubby area with high cover values. Based on our results, grazing by cattle provides an adequate solution for forming and conserving wood-pasture habitats in the studied areas of Hungary. However, if the purpose is to also form valuable grassland with high grassland management values, partly sheep grazing should be suggested.},
	year = {2024},
	eissn = {2073-4395},
	pages = {846},
	orcid-numbers = {Saláta, Dénes/0000-0002-7149-0022; Fűrész, Attila/0000-0003-2287-529X; Fuchs, Márta/0000-0002-4997-8320; Pajor, Ferenc/0000-0003-1501-0968; Sipos, László/0000-0002-4584-6697; Falusi, Eszter/0000-0002-1183-3513}
}

@{MTMT:34798018,
	title = {APPLE LUTEOVIRUS P0 PROTEIN IS A SUPPRESSOR OF LOCAL AND SYSTEMIC RNA SILENCING},
	url = {https://m2.mtmt.hu/api/publication/34798018},
	author = {Jahan, Almash and Várallyay, Éva},
	booktitle = {FIBOK 2024 6th National Conference of Young Biotechnologists},
	unique-id = {34798018},
	year = {2024},
	pages = {27-27}
}

@{MTMT:34796057,
	title = {Nyílt homoki gyepek regenerációjának monitorozása az Újpesti Homoktövis Természetvédelmi Területen (2006-2021)},
	url = {https://m2.mtmt.hu/api/publication/34796057},
	author = {Fűrész, Attila and Falusi, Eszter and Bajor, Zoltán and Sipos, László and Fuchs, Márta and Penksza, Péter and Szentes, Szilárd and Wagenhoffer, Zsombor and Penksza, Károly},
	booktitle = {XIX. Kárpát-medencei Környezettudományi Konferencia. Absztrakt füzet},
	unique-id = {34796057},
	year = {2024},
	pages = {138},
	orcid-numbers = {Fűrész, Attila/0000-0003-2287-529X; Falusi, Eszter/0000-0002-1183-3513; Sipos, László/0000-0002-4584-6697; Fuchs, Márta/0000-0002-4997-8320}
}

@{MTMT:34796027,
	title = {Házi vízibivalyok által, eltérő intenzitással legeltetett gyepek és legeltetési üzemmódból kivett gyepek florisztikai felmérése},
	url = {https://m2.mtmt.hu/api/publication/34796027},
	author = {Fintha, Gabriella and Fűrész, Attila and Falusi, Eszter and Járdi, Ildikó and Penksza, Károly},
	booktitle = {XIX. Kárpát-medencei Környezettudományi Konferencia. Absztrakt füzet},
	unique-id = {34796027},
	year = {2024},
	pages = {136},
	orcid-numbers = {Fűrész, Attila/0000-0003-2287-529X; Falusi, Eszter/0000-0002-1183-3513; Járdi, Ildikó/0000-0002-1560-7863}
}

@{MTMT:34795993,
	title = {Festuca taxonok ploid vizsgálata Magyarországon},
	url = {https://m2.mtmt.hu/api/publication/34795993},
	author = {Balogh, Dániel and Fűrész, Attila and Penksza, Károly and Lantos, Csaba and Szőke, Antal},
	booktitle = {XIX. Kárpát-medencei Környezettudományi Konferencia. Absztrakt füzet},
	unique-id = {34795993},
	year = {2024},
	pages = {131},
	orcid-numbers = {Fűrész, Attila/0000-0003-2287-529X}
}

@CONFERENCE{MTMT:34788378,
	title = {Gyapjúhulladék alkalmazása a kertészetben és a talajerő-gazdálkodásban},
	url = {https://m2.mtmt.hu/api/publication/34788378},
	author = {Kovács, Flórián and Papdi , Enikő and Veres, Andrea and Szegő, Anita and Juhos, Katalin},
	booktitle = {XIX. Kárpát-medencei Környezettudományi Konferencia. Absztrakt füzet},
	unique-id = {34788378},
	year = {2024},
	pages = {9}
}

@article{MTMT:34730230,
	title = {Agronomic management response in maize ( Zea mays L.) production across three agroecological zones of Kenya},
	url = {https://m2.mtmt.hu/api/publication/34730230},
	author = {Kipkulei, Harison Kiplagat and Bellingrath‐Kimura, Sonoko Dorothea and Lana, Marcos and Ghazaryan, Gohar and Baatz, Roland and Matavel, Custodio and Boitt, Mark and Chisanga, Charles B. and Brian Rotich, Kanyongi and Moreira, Rodrigo Martins and Sieber, Stefan},
	doi = {10.1002/agg2.20478},
	journal-iso = {AGROSYSTEMS G EOSCI ENVIRON},
	journal = {AGROSYSTEMS GEOSCIENCES & ENVIRONMENT},
	volume = {7},
	unique-id = {34730230},
	abstract = {Maize ( Zea mays L.) productivity in Kenya has witnessed a decline attributed to the effects of climate change and biophysical constraints. The assessment of agronomic practices across agroecological zones (AEZs) is limited by inadequate data quality, hindering a precise evaluation of maize yield on a large scale. In this study, we employed the DSSAT‐CERES‐Maize crop model (where CERES is Crop Environment Resource Synthesis and DSSAT is Decision Support System for Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model was calibrated and evaluated with observed grain yield, biomass, leaf area index, phenology, and soil water content from 2‐year experiments. Remote sensing (RS) images derived from the Sentinel‐2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT‐CERES‐Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures at pixel scales. Evaluation of agronomic measures revealed that sowing dates and cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ II and AEZ III exhibited elevated yields when implementing combined practices of early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha −1 in AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the CERES‐Maize model and high‐resolution RS data in estimating production at larger scales. Furthermore, this integrated approach holds promise for supporting agricultural decision‐making and designing optimal strategies to enhance productivity while accounting for site‐specific conditions.},
	year = {2024},
	eissn = {2639-6696},
	orcid-numbers = {Kipkulei, Harison Kiplagat/0000-0003-0643-2077; Bellingrath‐Kimura, Sonoko Dorothea/0000-0001-7392-7796; Lana, Marcos/0000-0002-1733-1100; Ghazaryan, Gohar/0000-0003-4606-0140; Baatz, Roland/0000-0001-5481-0904; Matavel, Custodio/0000-0002-3800-7887; Boitt, Mark/0000-0003-3417-6875; Chisanga, Charles B./0000-0002-7388-5415; Moreira, Rodrigo Martins/0000-0001-6794-6026; Sieber, Stefan/0000-0002-4849-7277}
}

@article{MTMT:34727185,
	title = {A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data},
	url = {https://m2.mtmt.hu/api/publication/34727185},
	author = {M'hamdi, Oussama and Takács, Sándor and Palotás, Gábor and Ilahy, Riadh and Helyes, Lajos and Pék, Zoltán},
	doi = {10.3390/plants13050746},
	journal-iso = {PLANTS-BASEL},
	journal = {PLANTS-BASEL},
	volume = {13},
	unique-id = {34727185},
	abstract = {The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on °Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting °Brix (R² = 0.98, RMSE = 0.07) and lycopene content (R² = 0.87, RMSE = 0.61), and excelling in colour prediction (a/b ratio) with a R² of 0.93 and RMSE of 0.03. ANN lagged behind particularly in colour prediction, showing a negative R² value of −0.35. Shapley additive explanation’s (SHAP) summary plot analysis indicated that both models are effective in predicting °Brix and lycopene content in tomatoes, highlighting different aspects of the data. SHAP analysis highlighted the models’ efficiency (especially in °Brix and lycopene predictions) and underscored the significant influence of cultivar choice and environmental factors like climate and soil. These findings emphasize the importance of selecting and fine-tuning the appropriate ML model for enhancing precision agriculture, underlining XGBoost’s superiority in handling complex agronomic data for quality assessment.},
	year = {2024},
	eissn = {2223-7747},
	orcid-numbers = {Takács, Sándor/0000-0001-5401-7658; Ilahy, Riadh/0000-0001-9405-3098; Pék, Zoltán/0000-0001-9767-8800}
}