487 research outputs found

    The metamorphosis of the far right in Hungary: the case of Jobbik

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    One of the most successful far-right parties in Hungary and among the Visegrad Four states, Jobbik, has moderated and transformed into centrist people's party. This thesis analyzes the incentives and effects of repositioning the far-right party on mainstream politics in Hungary. The metamorphosis of the Movement for a Better Hungary has entailed overriding changes, starting from the emergence of breakaway extreme-right groups to the rhetorical shifts of the ruling party. The study fosters an understanding of the strategy shift of the far-right and enhances the paucity of data regarding the outlined phenomenon by virtue of expert interviews with party members and employing primary data derived from the analysis of party electoral manifesto and declarations. To advance understanding of the far-right strategy shift, demand- and supply-side factors, and its reverberations, the thesis embraces various facets of the populist radical right party family, gauging populism, ethnonationalism, nativism, and copious determinants that have stipulated this shift. Apart from electoral opportunism, the Hungarian tradition of the far-right, socio-economic and extrinsic factors are analyzed. In the course of changing political and economic situation emanated by the Russo-Ukrainian war, moderation of fringe politics has entailed mainstreaming extremism and the more rigid rhetoric of Fidesz. The 2022 Hungarian National Assembly elections paved the way for these changes after unprecedented unanimity of the opposition when former far-right Jobbik cooperated with leftist and conservative parties

    Detection of experimental ERP effects in combined EEG-fMRI: evaluating the benefits of interleaved acquisition and Independent Component Analysis

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    Copyright ยฉ 2011 Elsevier. NOTICE: this is the authorโ€™s version of a work that was accepted for publication in Clinical Neurophysiology . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Clinical Neurophysiology, 2011 Vol. 122 Issue 2, pp. 267-77 DOI: http://dx.doi.org/10.1016/j.clinph.2010.06.033Objective The present study examined the benefit of rapid alternation of EEG and fMRI (a common strategy for avoiding artifact caused by rapid switching of MRI gradients) for detecting experimental modulations of ERPs in combined EEGโ€“fMRI. The study also assessed the advantages of aiding the extraction of specific ERP components by means of signal decomposition using Independent Component Analysis (ICA). Methods โ€˜Goโ€“nogoโ€™ task stimuli were presented either during fMRI scanning or in the gaps between fMRI scans, resulting in โ€˜gradientโ€™ and โ€˜no-gradientโ€™ ERPs. โ€˜Goโ€“nogoโ€™ differences in the N2 and P3 components were subjected to conventional ERP analysis, as well as single-trial and reliability analyses. Results Comparable N2 and P3 enhancement on โ€˜nogoโ€™ trials was found in the โ€˜gradientโ€™ and โ€˜no-gradientโ€™ ERPs. ICA-based signal decomposition resulted in better validity (as indicated by topography), greater stability and lower measurement error of the predicted ERP effects. Conclusions While there was little or no benefit of acquiring ERPs in the gaps between fMRI scans, ICA decomposition did improve the detection of experimental ERP modulations. Significance Simultaneous and continuous EEGโ€“fMRI acquisition is preferable to interleaved protocols. ICA-based decomposition is useful not only for artifact cancellation, but also for the extraction of specific ERP components

    แƒ‘แƒ˜แƒš แƒ™แƒšแƒ˜แƒœแƒขแƒแƒœแƒ˜แƒก แƒฏแƒแƒœแƒ“แƒแƒชแƒ•แƒ˜แƒก แƒ แƒ”แƒคแƒแƒ แƒ›แƒ”แƒ‘แƒ˜: แƒฌแƒแƒ แƒฃแƒ›แƒแƒขแƒ”แƒ‘แƒšแƒแƒ‘แƒ˜แƒก แƒ›แƒ˜แƒ–แƒ”แƒ–แƒ”แƒ‘แƒ˜

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    President of the United States of America Bill Clinton has stated his presidency with the health care reform. Despite huge efforts and support he couldnโ€™t succeed. The research address key reasons for failure of the reform: difficulties to regulate private insurance marketplace, difficulties to control expenditures for fulfillment the reform, the amount of expenditure itself. Big part of population was not ready for the radical reform of insurance systems, the business sector was against the reform, because employers were imposed huge responsibility to the employees in regard to their insurance.แƒแƒ›แƒ”แƒ แƒ˜แƒ™แƒ˜แƒก แƒจแƒ”แƒ”แƒ แƒ—แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒจแƒขแƒแƒขแƒ”แƒ‘แƒ˜แƒก แƒžแƒ แƒ”แƒ–แƒ˜แƒ“แƒ”แƒœแƒขแƒ›แƒ แƒ‘แƒ˜แƒš แƒ™แƒšแƒ˜แƒœแƒขแƒแƒœแƒ›แƒ แƒ—แƒแƒ•แƒ˜แƒกแƒ˜ แƒ›แƒ›แƒแƒ แƒ—แƒ•แƒ”แƒšแƒแƒ‘แƒ แƒฏแƒแƒœแƒ“แƒแƒชแƒ•แƒ˜แƒก แƒกแƒ˜แƒกแƒขแƒ”แƒ›แƒ˜แƒก แƒ แƒ”แƒคแƒแƒ แƒ›แƒ˜แƒ— แƒ“แƒแƒ˜แƒฌแƒงแƒ, แƒ—แƒฃแƒ›แƒชแƒ แƒ“แƒ˜แƒ“แƒ˜ แƒซแƒแƒšแƒ˜แƒกแƒฎแƒ›แƒ”แƒ•แƒ˜แƒก แƒ“แƒ แƒ›แƒฎแƒแƒ แƒ“แƒแƒญแƒ”แƒ แƒ˜แƒก แƒ›แƒ˜แƒฃแƒฎแƒ”แƒ“แƒแƒ•แƒแƒ“ แƒ•แƒ”แƒ  แƒจแƒ”แƒซแƒšแƒ แƒ›แƒ˜แƒกแƒ˜ แƒ’แƒแƒขแƒแƒ แƒ”แƒ‘แƒ. แƒ™แƒ•แƒšแƒ”แƒ•แƒแƒจแƒ˜ แƒ’แƒแƒœแƒฎแƒ˜แƒšแƒฃแƒšแƒ˜แƒ แƒ™แƒšแƒ˜แƒœแƒขแƒแƒœแƒ˜แƒก แƒฏแƒแƒœแƒ“แƒแƒชแƒ•แƒ˜แƒก แƒ แƒ”แƒคแƒแƒ แƒ›แƒ˜แƒก แƒฌแƒแƒ แƒฃแƒ›แƒแƒขแƒ”แƒ‘แƒšแƒแƒ‘แƒ˜แƒก แƒซแƒ˜แƒ แƒ˜แƒ—แƒแƒ“แƒ˜ แƒ›แƒ˜แƒ–แƒ”แƒ–แƒ”แƒ‘แƒ˜: แƒ™แƒ”แƒ แƒซแƒ แƒกแƒแƒ“แƒแƒ–แƒฆแƒ•แƒ”แƒ แƒ‘แƒแƒ–แƒ แƒ˜แƒก แƒ แƒ”แƒ’แƒฃแƒšแƒ˜แƒ แƒ”แƒ‘แƒ˜แƒก แƒกแƒ˜แƒ แƒ—แƒฃแƒšแƒ”, แƒ แƒ”แƒคแƒแƒ แƒ›แƒ˜แƒก แƒ’แƒแƒœแƒฎแƒแƒ แƒชแƒ˜แƒ”แƒšแƒ”แƒ‘แƒ˜แƒกแƒแƒ—แƒ•แƒ˜แƒก แƒกแƒแƒญแƒ˜แƒ แƒ แƒฎแƒแƒ แƒฏแƒ”แƒ‘แƒ–แƒ” แƒ™แƒแƒœแƒขแƒ แƒแƒšแƒ˜แƒก แƒกแƒ˜แƒ แƒ—แƒฃแƒšแƒ”, แƒฎแƒแƒ แƒฏแƒ”แƒ‘แƒ˜แƒก แƒ“แƒ˜แƒ“แƒ˜ แƒ›แƒแƒชแƒฃแƒšแƒแƒ‘แƒ, แƒ›แƒแƒกแƒแƒฎแƒšแƒ”แƒแƒ‘แƒ˜แƒก แƒ“แƒ˜แƒ“แƒ˜ แƒœแƒแƒฌแƒ˜แƒšแƒ˜ แƒแƒ  แƒแƒฆแƒ›แƒแƒฉแƒœแƒ“แƒ แƒ›แƒ–แƒแƒ“ แƒฏแƒแƒœแƒ“แƒแƒชแƒ•แƒ˜แƒก แƒกแƒ˜แƒกแƒขแƒ”แƒ›แƒ˜แƒก แƒ แƒแƒ“แƒ˜แƒ™แƒแƒšแƒฃแƒ แƒ˜ แƒ แƒ”แƒคแƒแƒ แƒ›แƒ˜แƒ แƒ”แƒ‘แƒ˜แƒกแƒแƒ—แƒ•แƒ˜แƒก, แƒ‘แƒ˜แƒ–แƒœแƒ”แƒก แƒกแƒ”แƒฅแƒขแƒแƒ แƒ›แƒ แƒงแƒ•แƒ”แƒšแƒแƒ–แƒ” แƒ“แƒ˜แƒ“แƒ˜ แƒฌแƒ˜แƒœแƒแƒแƒฆแƒ›แƒ“แƒ”แƒ’แƒแƒ‘แƒ แƒ’แƒแƒฃแƒฌแƒ˜แƒ แƒ แƒ”แƒคแƒแƒ แƒ›แƒแƒก, แƒ แƒแƒ“แƒ’แƒแƒœ แƒ แƒ”แƒคแƒแƒ แƒ›แƒ แƒ“แƒแƒ›แƒกแƒแƒฅแƒ›แƒ”แƒ‘แƒšแƒ”แƒ‘แƒก แƒฃแƒ“แƒ˜แƒ“แƒ”แƒก แƒžแƒแƒกแƒฃแƒฎแƒ˜แƒกแƒ›แƒ’แƒ”แƒ‘แƒšแƒแƒ‘แƒแƒก แƒ”แƒ™แƒ˜แƒกแƒ แƒ”แƒ‘แƒ“แƒ แƒ—แƒ˜แƒ—แƒ”แƒฃแƒšแƒ˜ แƒ“แƒแƒกแƒแƒฅแƒ›แƒ”แƒ‘แƒฃแƒšแƒ˜แƒก แƒ“แƒแƒ–แƒฆแƒ•แƒ”แƒ•แƒ˜แƒกแƒ—แƒ•แƒแƒšแƒกแƒแƒ–แƒ แƒ˜แƒกแƒ˜แƒ—
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