Liberal internationalism and the decline of the state: A comparative analysis of the thought of Richard Cobden, David Mitrany, and Kenichi Ohmae.

The purpose of the thesis is to provide a critical analysis of the liberal idea of the decline of the state based on a historical comparison. It takes special note of the implications of state failure for international relations. The author identifies three acknowledged proponents of the theme. They are Richard Cobden (1804-1865), David Mitrany (1888-1975), and Kenichi Ohmae (b. 1943). The dissertation analyses how Cobden, Mitrany, and Ohmae view the state and its role in their respective periods. It elucidates similarities and differences between their conceptions with the aim of shedding light on the status of the state in their systems of political and economic thought. It also puts the three thinkers into context by exposing the influence of their historical and social environments. A supplementary objective is to infuse caution into future prophesies about the state's imminent decline. The text is divided into three sections. The first analyses Cobden, Mitrany, and Ohmae's empirical claims. The second focuses on their normative judgements. Finally, the third directs the attention to their predictive assertions. The discussion is organised according to the distinction between the state as a country in its entirety, and the state as an institution of government separate from the society which it rules. The central question of the dissertation asks what we can learn from a study of the history of the liberal idea of the decline of the state. The thesis emphasises, in particular, five lessons and concludes that Cobden, Mitrany, and Ohmae primarily propose normative arguments for less state involvement in economic and international relations, but conceal them partly in empirical and predictive assertions. The liberal idea of the decline of the state is more of an ideological statement in response to contemporary political, social, and economic trends than an objective observation of an empirically verifiable fact

Distinct Cholesterol Localization in Glioblastoma Multiforme Revealed by Mass Spectrometry Imaging

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor in adults and is highly resistant to chemo- and radiotherapies. GBM has been associated with alterations in lipid contents, but lipid metabolism reprogramming in tumor cells is not fully elucidated. One of the key hurdles is to localize the lipid species that are correlated with tumor growth and invasion. A better understanding of the localization of abnormal lipid metabolism and its vulnerabilities may open up to novel therapeutic approaches. Here, we use time-of-flight secondary ion mass spectrometry (ToF-SIMS) to spatially probe the lipid composition in a GBM biopsy from two regions with different histopathologies: one region with most cells of uniform size and shape, the homogeneous part, and the other with cells showing a great variation in size and shape, the heterogeneous part. Our results reveal elevated levels of cholesterol, diacylglycerols, and some phosphatidylethanolamine in the homogeneous part, while the heterogeneous part was dominated by a variety of fatty acids, phosphatidylcholine, and phosphatidylinositol species. We also observed a high expression of cholesterol in the homogeneous tumor region to be associated with large cells but not with macrophages. Our findings suggest that ToF-SIMS can distinguish in lipid distribution between parts within a human GBM tumor, which can be linked to different molecular mechanisms

Detektering av Barkborre Angripningar med satellit Bilder

Sveaskog is Swedens largest forest owner, owning 14 percent of the Swedish forest lands. Recently, due to warmer and drier summers as a consequence of climate change, spruce bark beetles have caused damages at a massive scale. In 2019 Sogeti developed a promising first product for monitoring the vitality of large areas through the use of Sentinel-2 data by comparing images from the same month between two years, and the results from this first product where promising. To take the detection of bark beetle infestations to the next stage of development, supervised learning was used. Models where trained with a time-series of Sentinel-2 data in conjunction with national landcover data, ground level humidity data, and height data to predict segmentations that represented infestations. Target segmentations where created by clustering GPS points and tresholding a rasterized representation of the generated clusters. In total four different model architectures where tested (LSTM, GRU, 3D convolutional, and logistic regression) and then evaluated both quantitatively and qualitatively on a test set. It was found that the GRU based model was best able to identify bark beetle infestations.Sveaskog är Sveriges största skogsägare, med en total landareal på 14 procent av den svenska skogsmarken. Nyligen, p.g.a. varmare och torrare somrar som en konsekvens av klimatförändringar har granbarkborren orsakat skada till skogen på en enorm skala. År 2019 utvecklade Sogeti ett lovande första resultat för övervakning av skogen på stor skala genom att jämföra vitaliteten i skogen från Sentinel-2 data i samma månad mellan två år. Resultaten från denna första produkt var lovande. För att kunna vidareutveckla produkten till nästa steg användes övervakad inlärning. Modeller tränades på en tidserie av Sentinel-2 data i kombination med nationella marktäckedata, markfuktighetsdata, och höjddata för att kunna prediktera segmenteringar som representerar skog som har angripits av granbarkborre. Målsegementeringarna som modellerna tränades mot skapades genom att klustra skördardata och den rastroiserade representationen av de generarade klustrarna filtrerades. Totalt tränades fyra olika modeller (LSTM, GRU, 3D faltning, och logistisk regression), som sedan evaluerades på ett sista test set både kvantitativt och kvalitativt. Från detta drogs slutsatsen att den GRU baserade modellen var bäst på att identifiera skog som har angripits av granbarkborre

Detektering av Barkborre Angripningar med satellit Bilder

Sveaskog is Swedens largest forest owner, owning 14 percent of the Swedish forest lands. Recently, due to warmer and drier summers as a consequence of climate change, spruce bark beetles have caused damages at a massive scale. In 2019 Sogeti developed a promising first product for monitoring the vitality of large areas through the use of Sentinel-2 data by comparing images from the same month between two years, and the results from this first product where promising. To take the detection of bark beetle infestations to the next stage of development, supervised learning was used. Models where trained with a time-series of Sentinel-2 data in conjunction with national landcover data, ground level humidity data, and height data to predict segmentations that represented infestations. Target segmentations where created by clustering GPS points and tresholding a rasterized representation of the generated clusters. In total four different model architectures where tested (LSTM, GRU, 3D convolutional, and logistic regression) and then evaluated both quantitatively and qualitatively on a test set. It was found that the GRU based model was best able to identify bark beetle infestations.Sveaskog är Sveriges största skogsägare, med en total landareal på 14 procent av den svenska skogsmarken. Nyligen, p.g.a. varmare och torrare somrar som en konsekvens av klimatförändringar har granbarkborren orsakat skada till skogen på en enorm skala. År 2019 utvecklade Sogeti ett lovande första resultat för övervakning av skogen på stor skala genom att jämföra vitaliteten i skogen från Sentinel-2 data i samma månad mellan två år. Resultaten från denna första produkt var lovande. För att kunna vidareutveckla produkten till nästa steg användes övervakad inlärning. Modeller tränades på en tidserie av Sentinel-2 data i kombination med nationella marktäckedata, markfuktighetsdata, och höjddata för att kunna prediktera segmenteringar som representerar skog som har angripits av granbarkborre. Målsegementeringarna som modellerna tränades mot skapades genom att klustra skördardata och den rastroiserade representationen av de generarade klustrarna filtrerades. Totalt tränades fyra olika modeller (LSTM, GRU, 3D faltning, och logistisk regression), som sedan evaluerades på ett sista test set både kvantitativt och kvalitativt. Från detta drogs slutsatsen att den GRU baserade modellen var bäst på att identifiera skog som har angripits av granbarkborre