SVEN OLOV LINDHOLM AND THE LITERARY INSPIRATIONS OF SWEDISH FASCISM

Very little research has been done into the leader of the most prominent Swedish fascist party of the interwar period, the leader of the Nationalsocialistiska Arbetarepartiet, Sven Olov Lindholm, in spite of extensive source material in his personal archive. This article explores the literary influences on his politics, which Lindholm cited in his private documents and interviews, both contemporary and post-war. The immediate impact of notable Swedish writers, poets especially, such as Verner von Heidenstam, Viktor Rydberg, Esaias Tegnér, and Bertel Gripenberg, is demonstrated. These authors, largely of the Swedish Romantic tradition, are shown to be parts of one major Scandinavian cultural current in particular, namely Gothicism (göticism), manifested through a centuries-long interest in the Old Nordic heritage. In Sweden, the influence of new far-Right ideas that made their way into the country in the 1920s intersected with Gothicism in unique ways, which gave Swedish fascists a peculiar relationship to both fascism and their national heritage. Ultimately, these literary Gothicist influences allowed a particular naturalizing codification of Swedish fascism in the 1930s. Under the influence of, above all, contemporary Finno-Swedish health specialist Are Waerland, Lindholm is shown to have actively shaped Swedish fascism in line with his literary exemplars

Den lärda fru Lyche och professor Sjuskägg - Om ett bortglömt akademikerpar från frihetstiden

Biografiskt dubbelporträtt av den danska skaldinnan Lyche Sophia Friis (c:a 1699-1747) och hennes make, sedermera professorn i teologi, Gustaf Ernst von Bildstein (1703-1769)

A hybrid feature pool-based emotional stress state detection algorithm using EEG signals.

Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain‐specific information pool to develop an effective machine learning model. In this study, a multi‐domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet‐based bandwidth specific feature analysis from the time‐frequency domain. Then, a wrapper‐based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the nonredundant features. Finally, the k‐nearest neighbor (k‐NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non‐linear dimensionality reduction techniques, as well as those without feature ranking