Sustainable Development and Technologies National Programme of the Hungarian Academy of Sciences
Sustainable
Technologies
Subprogram
After thorough preparation and independent evaluation of board, the Sustainable Technologies sub-programme of the Sustainable Development and Technologies National Program of the Hungarian Academy of Sciences (MTA) started operating on December 1, 2022.
In January 2020, Tamás Freund, the president of the Hungarian Academy ofSciences, initiated the launch of the Sustainable Development and Technologies National Program. Following successful negotiations with the Government, the opportunity to implement the program opened up in 2022 with a budget support of HUF 900 million per year provided to the MTA.
Academician László Bozó was entrusted by the President of the Hungarian Academy of Sciences with the professional development and management of the research program. The four-year research program will be implemented within the framework of a consortium.
The main organizational principle of the consortium was research excellence,
because only the best scientific workshops are capable of achieving outstanding results that significantly contribute to the acquisition of knowledge indispensable for the nation. Only institutions that meet at least one of the following conditions could be included in the consortium: MTA Excellent Research Site certification, successful participation in the Hungarian Water Science Programme of the MTA, or an active ELKH-University Research Group. Of course, the research and development planned in the FTA cannot be part of activities previously awarded and financed from other sources, but the recognized high-quality work of the research centers and the professionalism of the managers provide a guarantee that their R&D activity will be directed towards the goals set out in the FTA by the Subprogram expand using provided resources. In addition to what is listed above, the ability and ability of the individual scientific workshops to cooperate with each other played an important role in the selection: the Subprogramme will operate in a rather broad multidisciplinary field, from water sciences to agricultural sciences and the development of new generation environmental monitoring systems to energy, but these areas will only be covered by the it is possible to research by exploring and understanding existing mutual and multi-layered interdependencies.
Implementing consortium: Ecological Research Center (ÖK) (consortium leader), Budapest University of Technology and Economics (BME), Agricultural Research Center (ATK), Balaton Limnological Research Institute (BLKI), University of Miskolc (ME), National Meteorological Service (OMSZ), Pannon University (PE), University of Szeged (SZTE). Duration of operation of the FFTNP FTA: December 1, 2022 – November 30, 2026 (48 months). The total support (for four years) is HUF 3,626,835,882.
Multi-step modeling of well logging data combining unsupervised and deep learning algorithms for enhanced characterization of the Quaternary aquifer system in Debrecen area, Hungary
Discover the recent research within the Sustainable Development and Technologies project, where we conducted a multi-step modeling approach integrating unsupervised and deep learning algorithms to improve the interpretation of geophysical well-logging data for a more thorough characterization of the Quaternary aquifer system in the Debrecen area, Hungary.
Assessing heterogeneous groundwater systems: Geostatistical interpretation of well logging data for estimating essential hydrogeological parameters
In the framework of the Sustainable Development and Technologies project, this research focuses on employing an unsupervised learning approach to interpret well-log data, aiming to characterize the primary hydrostratigraphical units within the Quaternary aquifer system in the Debrecen area of Eastern Hungary.
Determination of the ammonia loss from croplands
Half of the applied fertilizers are not used globally and instead pollute the environment. Determining the degree of loss, which is crucial for prevention, is accomplished using models validated by measurements.