A koncessziós szerződés helye a honi kötelmi jog rendszerében

The main goal of this study is to place the concession contract in the Hungarian contract law system. In order to be able to place the concession contract in the contract law system, it is necessary to review public (administrative) law contracts, private law (atypical) contracts and mixed law contracts (i.e. containing both private and public law elements). Accordingly, we take into account the most significant authors dealing with public (administrative) law contracts, private law (atypical) contracts, and concession contracts and their works on the above mentioned subject. The study examines not only the literary and dogmatic concepts of each contract, but the legal concept as well. It is important to emphasize that we approach administrative legal contracts from the direction of theory of administrative legal act which is one of the foundations of administrative legal dogmatics. Finally, we also examine the transformation of the legal environment for concession contracts (e.g. establishment of a new regulatory authory concerning concessions)

Populism unrestrained: Policy responses of the Orbán regime to the pandemic in 2020–2021

The paper provides a case study on how the Orban regime in Hungary has dealt with coronavirus disease 2019 (COVID-19) in 2020-2021. Despite having led worldwide rankings in pandemic-related death rates since the second part of 2020, the government was not politically shaken by COVID-19. Institutionally unrestrained, the governing majority periodically renewed emergency legal regimes to control public discourses and curtail the financial resources of opposition-led local governments. The policy conduct of the regime is discussed in the context of authoritarian populism, which is conceptualized along a strategy-based approach to populism. In this, authoritarian populism is seen to generate democratic legitimacy for dismantling the institutional foundations of liberal democracy and the rule of law. This had been happening in Hungary well before COVID-19 kicked in, but the pandemic provided enhanced opportunities for this strategy. Meanwhile, fiscal policies became increasingly expansionary, signalling a partial return to the practice of preelection overspending

Poly(N-vinylimidazole)-l-poly(propylene glycol) amphiphilic conetworks and gels: molecularly forced blends of incompatible polymers with single glass transition temperatures of unusual dependence on the composition

New molecularly forced blends of incompatible poly(N-vinylimidazole) and poly(propylene glycol) polymers with single glass transition temperatures.</p

Deep comparisons of Neural Networks from the EEGNet family

Most of the Brain-Computer Interface (BCI) publications, which propose artificial neural networks for Motor Imagery (MI) Electroencephalography (EEG) signal classification, are presented using one of the BCI Competition datasets. However, these databases contain MI EEG data from less than or equal to 10 subjects . In addition, these algorithms usually include only bandpass filtering to reduce noise and increase signal quality. In this article, we compared 5 well-known neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, MI-EEGNet) using open-access databases with many subjects next to the BCI Competition 4 2a dataset to acquire statistically significant results. We removed artifacts from the EEG using the FASTER algorithm as a signal processing step. Moreover, we investigated whether transfer learning can further improve the classification results on artifact filtered data. We aimed to rank the neural networks; therefore, next to the classification accuracy, we introduced two additional metrics: the accuracy improvement from chance level and the effect of transfer learning. The former can be used with different class-numbered databases, while the latter can highlight neural networks with sufficient generalization abilities. Our metrics showed that the researchers should not avoid Shallow ConvNet and Deep ConvNet because they can perform better than the later published ones from the EEGNet family