@article{MTMT:34860898,
	title = {Application of machine learning methods for spent fuel characterization based on gamma spectrometry measurements},
	url = {https://m2.mtmt.hu/api/publication/34860898},
	author = {Kirchknopf, Péter and Batki, B. and Völgyesi, P. and Kató, Z. and Szalóki, I.},
	doi = {10.1016/j.anucene.2024.110601},
	journal-iso = {ANN NUCL ENERGY},
	journal = {ANNALS OF NUCLEAR ENERGY},
	volume = {205},
	unique-id = {34860898},
	issn = {0306-4549},
	abstract = {Machine learning models have been developed to predict properties of spent nuclear fuel assemblies from Paks NPP, such as burnup, cooling time, initial enrichment, and Pu-239 content. Measured gamma spectra are processed, and activity ratios of fission products are calculated to serve as input features for Support Vector Regression, Random Forest, and Multi-Layer Perceptron models. Data uncertainty is considered during inference to produce prediction intervals, and input features are ranked using the Gini importance of Random Forest models. A deep learning approach using Convolutional Neural Networks has also been developed to predict the spent fuel parameters from the measured spectra directly. The new models can predict spent fuel parameters with great precision, outperforming earlier approaches that rely on nonlinear regression using a single feature to predict burnup and cooling time and can estimate initial enrichment and Pu-239 content. © 2024 The Author(s)},
	keywords = {Learning systems; Forecasting; machine learning; SPECTROMETRY; Radioactivity; Fission products; Forestry; Machine-learning; gamma spectrometry; gamma spectrometry; Gamma ray spectrometers; Paks NPP; Convolution; Machine learning methods; Burn up; Spent fuels; Deep neural networks; Convolutional neural network; Convolutional neural network; Convolutional neural networks; spent nuclear fuel; cooling time; Input features; Spent nuclear fuels; Fuel parameter; Pak NPP},
	year = {2024},
	eissn = {1873-2100}
}

@article{MTMT:34857976,
	title = {Vacancy-related color centers in two-dimensional silicon carbide monolayers},
	url = {https://m2.mtmt.hu/api/publication/34857976},
	author = {Mohseni, Meysam and Sarsari, I.A. and Karbasizadeh, S. and Udvarhelyi, Péter and Hassanzada, Q. and Ala-Nissila, T. and Gali, Ádám},
	doi = {10.1103/PhysRevMaterials.8.056201},
	journal-iso = {PHYS REV MAT},
	journal = {PHYSICAL REVIEW MATERIALS},
	volume = {8},
	unique-id = {34857976},
	issn = {2475-9953},
	year = {2024},
	eissn = {2475-9953},
	orcid-numbers = {Udvarhelyi, Péter/0000-0002-7073-1664; Gali, Ádám/0000-0002-3339-5470}
}

@article{MTMT:34845147,
	title = {UV imaging for the rapid at-line content determination of different colourless APIs in their tablets with artificial neural networks},
	url = {https://m2.mtmt.hu/api/publication/34845147},
	author = {Ficzere, Máté and Mészáros, Lilla Alexandra and Diószegi, Anna and Bánrévi, Zoltán and Farkas, Attila and Lenk, Sándor and Galata, Dorián László and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.ijpharm.2024.124174},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {657},
	unique-id = {34845147},
	issn = {0378-5173},
	year = {2024},
	eissn = {1873-3476},
	orcid-numbers = {Ficzere, Máté/0000-0002-0024-7375; Farkas, Attila/0000-0002-8877-2587; Lenk, Sándor/0000-0002-7207-0329; Galata, Dorián László/0000-0003-4760-2124}
}

@article{MTMT:34844719,
	title = {George Green és a Green-függvény},
	url = {https://m2.mtmt.hu/api/publication/34844719},
	author = {László, István},
	journal-iso = {FIZIKAI SZEMLE},
	journal = {FIZIKAI SZEMLE},
	volume = {74},
	unique-id = {34844719},
	issn = {0015-3257},
	year = {2024},
	pages = {160-165}
}

@article{MTMT:34836200,
	title = {A study of turbulent filaments in the edge plasma of the Wendelstein 7-X stellarator},
	url = {https://m2.mtmt.hu/api/publication/34836200},
	author = {Buzás, Attila and Kocsis, G. and Biedermann, C. and Cseh, G. and Szepesi, T. and Szucs, M.},
	doi = {10.1088/1741-4326/ad365e},
	journal-iso = {NUCL FUSION},
	journal = {NUCLEAR FUSION},
	volume = {64},
	unique-id = {34836200},
	issn = {0029-5515},
	abstract = {Filaments are studied by examining fast camera images on the Wendelstein 7-X stellarator. Fast cameras offer a unique perspective, revealing the complex 3D structure of filaments in the entire poloidal cross-section of the plasma. By correlating individual pixels, their location, shape, and movement are analyzed in standard and high-iota configurations. The presence of filaments is not uniform poloidally around. The number of active areas matches the number of magnetic islands in both configurations. Filaments are found to extend to multiple toroidal turns in standard configuration. No time delay is observed between the different toroidal sections. Such behavior is not seen in high-iota configuration. Filaments are observed within and without the edge shear layer, indicated by the direction of their poloidal rotation. Inside the shear layer, their velocity scatters around 1.25 km s-1, accompanied by a lifetime between 80 and 120 mu s. Outside, their velocity shows greater absolute values and variance, but still in a few km s-1 range. The similarities and differences between the two configurations are discussed and compared to previous results.},
	keywords = {PLASMA; FUSION; turbulence; Stellarator; W7X},
	year = {2024},
	eissn = {1741-4326}
}

@article{MTMT:34832941,
	title = {First-principles calculations of defects and electron-phonon interactions: Seminal contributions of Audrius Alkauskas to the understanding of recombination processes},
	url = {https://m2.mtmt.hu/api/publication/34832941},
	author = {Zhang, X. and Turiansky, M.E. and Razinkovas, L. and Maciaszek, M. and Broqvist, P. and Yan, Q. and Lyons, J.L. and Dreyer, C.E. and Wickramaratne, D. and Gali, Ádám and Pasquarello, A. and Van de Walle, C.G.},
	doi = {10.1063/5.0205525},
	journal-iso = {J APPL PHYS},
	journal = {JOURNAL OF APPLIED PHYSICS},
	volume = {135},
	unique-id = {34832941},
	issn = {0021-8979},
	abstract = {First-principles calculations of defects and electron-phonon interactions play a critical role in the design and optimization of materials for electronic and optoelectronic devices. The late Audrius Alkauskas made seminal contributions to developing rigorous first-principles methodologies for the computation of defects and electron-phonon interactions, especially in the context of understanding the fundamental mechanisms of carrier recombination in semiconductors. Alkauskas was also a pioneer in the field of quantum defects, helping to build a first-principles understanding of the prototype nitrogen-vacancy center in diamond, as well as identifying novel defects. Here, we describe the important contributions made by Alkauskas and his collaborators and outline fruitful research directions that Alkauskas would have been keen to pursue. Audrius Alkauskas’ scientific achievements and insights highlighted in this article will inspire and guide future developments and advances in the field. © 2024 Author(s).},
	year = {2024},
	eissn = {1089-7550},
	orcid-numbers = {Gali, Ádám/0000-0002-3339-5470}
}

@article{MTMT:34832886,
	title = {Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretization of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures},
	url = {https://m2.mtmt.hu/api/publication/34832886},
	author = {Wilson, S.G. and Eaton, M.D. and Kópházi, József},
	doi = {10.1080/23324309.2024.2334277},
	journal-iso = {J COMPUT THEOR TRANSP},
	journal = {JOURNAL OF COMPUTATIONAL AND THEORETICAL TRANSPORT},
	unique-id = {34832886},
	issn = {2332-4309},
	abstract = {Energy-dependent self-adaptive mesh refinement algorithms are developed for a symmetric interior-penalty scheme for a discontinuous Galerkin spatial discretization of the multi-group neutron diffusion equation using NURBS-based isogeometric analysis (IGA). The spatially self-adaptive algorithms employ both mesh (h) and polynomial degree (p) refinement. The discretized system becomes increasingly ill-conditioned for increasingly large penalty parameters; and there is no gain in accuracy for over penalization. Therefore, optimized penalty parameters are rigorously calculated, for general element types, from a coercivity analysis of the bilinear form. Local mesh refinement allows for a better allocation of computational resources; and thus, more accuracy per degree of freedom. Two a posteriori interpolation-based error measures are proposed. The first heuristically minimizes local contributions to the discretization error, which becomes competitive for global quantities of interest (QoIs). However, for localized QoIs, over energy-dependent meshes, certain multi-group components may become under-resolved. The second employs duality arguments to minimize important error contributions, which consistently and reliably reduces the error in the QoI. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.},
	keywords = {Discontinuous Galerkin; Isogeometric analysis; dual-weighted residual error measures; energy-dependent self-adaptive mesh h(p)-refinement; Multi-group neutron diffusion equation; interior-penalty scheme},
	year = {2024},
	eissn = {2332-4325}
}

@article{MTMT:34832872,
	title = {Automated group constant parameterization for low sample sizes using different Machine learning approaches},
	url = {https://m2.mtmt.hu/api/publication/34832872},
	author = {Sebestény, Dániel István and Panka, István and Batki, Bálint},
	doi = {10.1016/j.anucene.2024.110560},
	journal-iso = {ANN NUCL ENERGY},
	journal = {ANNALS OF NUCLEAR ENERGY},
	volume = {204},
	unique-id = {34832872},
	issn = {0306-4549},
	abstract = {This paper deals with group constant parameterization, a necessary step to utilize the results of assembly-level neutronics calculations at the full-core level. The focus is on low sample size problems when the commonly used linear interpolation approach is inadequate, a typical situation of using Monte Carlo codes for group constant generation. This work presents a newly developed code package for automated group constant parameterization. It implements several machine learning regression models − including a novel polynomial regression algorithm − performs hyperparameter optimization and selects the best model based on a detailed evaluation. The applicability of the new code package is demonstrated in a case study for a VVER-1200 fuel assembly covering both normal operation and transient conditions. In this example, the novel polynomial regression model provides a 73 pcm average error in kinf that leads to reactivity coefficients well within the desired precision. © 2024 The Authors},
	keywords = {Regression Analysis; machine learning; sampling; Parameterization; Sample sizes; Machine learning methods; Machine learning methods; Parametrizations; Fuel assembly; Hyper-parameter optimizations; Machine learning approaches; hyperparameter optimization; Group constant parametrization; Vver-1200 fuel assembly; Assembly levels; Group constant; Group constant parametrization; Vve-1200 fuel assembly},
	year = {2024},
	eissn = {1873-2100}
}

@article{MTMT:34830134,
	title = {Periodic Precipitation in a Confined Liquid Layer},
	url = {https://m2.mtmt.hu/api/publication/34830134},
	author = {Itatani, Masaki and Onishi, Yuhei and Suematsu, Nobuhiko J. and Lagzi, István László},
	doi = {10.1021/acs.jpclett.4c00832},
	journal-iso = {J PHYS CHEM LETT},
	journal = {JOURNAL OF PHYSICAL CHEMISTRY LETTERS},
	volume = {15},
	unique-id = {34830134},
	issn = {1948-7185},
	year = {2024},
	pages = {4948-4957},
	orcid-numbers = {Itatani, Masaki/0000-0003-1025-0452; Suematsu, Nobuhiko J./0000-0001-5860-4147; Lagzi, István László/0000-0002-2303-5965}
}

@article{MTMT:34829054,
	title = {The purity locus of matrix Kloosterman sums},
	url = {https://m2.mtmt.hu/api/publication/34829054},
	author = {Erdélyi, Márton Kristóf and Sawin, Will and Tóth, Árpád},
	doi = {10.1090/tran/9149},
	journal-iso = {T AM MATH SOC},
	journal = {TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY},
	unique-id = {34829054},
	issn = {0002-9947},
	abstract = {We construct a perverse sheaf related to the the matrix exponential sums investigated by Erdélyi and Tóth [ Matrix Kloosterman sums , 2021, arXiv:2109.00762]. As this sheaf appears as a summand of certain tensor product of Kloosterman sheaves, we can establish the exact structure of the cohomology attached to the sums by relating it to the Springer correspondence and using the recursion formula of Erdélyi and Tóth.},
	year = {2024},
	eissn = {1088-6850}
}