VI. Perspectives

[934] Sans acquérir de significations fondamentalement nouvelles, les notions en elles-mêmes « amorphes » et neutres de Macht et de Gewalt recouvrent plus que jamais, au XXe siècle, un large spectre sémantique dans les champs de la terminologie des sciences politiques et sociales et du langage politico-idéologique, qui se recoupent. Au gré des innombrables théories de la puissance et du pouvoir, la discussion scientifique a largement suivi, en partie en les approuvant ou en les modifiant, en..

III. La fonction systémique de la « puissance » et du « pouvoir » au Moyen Âge

1. Remarque préliminaire sur l’évolution du vocabulaire et de la terminologieLe substantif Gewalt (giwalt en vieux haut allemand) vient du verbe walten, qui constitue une extension de la racine indo-européenne *ual et signifie à l’origine « avoir de la force », « disposer de », « dominer ». Dès le départ, le terme posséda une pluralité de significations, qui se regroupèrent sous la forme d’expressions fixes ou plus souples, dans le prolongement des traditions antiques associées aux champs sé..

Testing the Potential of Deep Learning in Earthquake Forecasting

Reliable earthquake forecasting methods have long been sought after, and so the rise of modern data science techniques raises a new question: does deep learning have the potential to learn this pattern? In this study, we leverage the large amount of earthquakes reported via good seismic station coverage in the subduction zone of Japan. We pose earthquake forecasting as a classification problem and train a Deep Learning Network to decide, whether a timeseries of length greater than 2 years will end in an earthquake on the following day with magnitude greater than 5 or not. Our method is based on spatiotemporal b value data, on which we train an autoencoder to learn the normal seismic behaviour. We then take the pixel by pixel reconstruction error as input for a Convolutional Dilated Network classifier, whose model output could serve for earthquake forecasting. We develop a special progressive training method for this model to mimic real life use. The trained network is then evaluated over the actual dataseries of Japan from 2002 to 2020 to simulate a real life application scenario. The overall accuracy of the model is 72.3 percent. The accuracy of this classification is significantly above the baseline and can likely be improved with more data in the futur

Experimental Infection of Rabbits with Newly Excysted Metacercariae of Japanese Fasciola Sp. and American Fasciola Hepatica by Portal Vein Route

日本産およびアメリカ産脱嚢幼肝蛭を家兎の門脈内に移入・感染させ,各種変化について検索した。幼肝蛭は感染後3時間で肝実質内に認められ,その後感染経過に伴い肝病変は漸次増大した。アメリカ産肝蛭では胆管への定着と虫卵排出も認められた。また,日本産肝蛭についても感染後虫体は漸次成長することが認められた。沈降抗体の出現時期は,感染後21-28日であった。幼肝蛭は血行によって肝臓に達してもよく発育し,感染が成立することが明らかとなった。 / Rabbits were experimentally infected with newly excysted metacercariae of the Japanese Fasciola sp. and the American Fasciola hepatica by portal vein route. At early stages of the infection, histopathological changes of the liver of rabbits infected with either F. sp. or F. hepatica were tract lesions characterized by haemorrhages, necrosis, cellular infiltration, and so on. The tract lesions grew larger and more numerous with the progress of the infection. At the 77th day of infection, increases of connective tissue around the intrahepatic bile ducts and in thickness of the wall of the bile ducts were noted. Precipitating antibodies were first detected at the 28th and 21st days of infection in sera of the rabbits infected with F. sp. and F. hepatica, respectively. Fluke eggs were first detected at the 63rd day in feces of the rabbits infected with F. hepatica, and were not detected even at the 77th day in the case of F. sp. Fluke eggs recovered from the infected rabbits were measured the maximum length of 15.9mm and 16.2mm, for F. sp. and F. hepatica, respectively. The infection of rabbits with F. sp. and F. hepatica by the vein route was successful

SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning

Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open source Python package specifically developed for fast data processing by implementing deep learning. SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitude estimation, seismic phase picking, and polarity identification. We introduce upgraded versions of previously published models such as CREIMERT capable of identifying earthquakes with an accuracy above 99.8 percent and a root mean squared error of 0.38 unit in magnitude estimation. These upgraded models outperform state of the art approaches like the Vision Transformer network. SAIPy provides an API that simplifies the integration of these advanced models, including CREIMERT, DynaPickerv2, and PolarCAP, along with benchmark datasets. The package has the potential to be used for real time earthquake monitoring to enable timely actions to mitigate the impact of seismic events. Ongoing development efforts aim to enhance the performance of SAIPy and incorporate additional features that enhance exploration efforts, and it also would be interesting to approach the retraining of the whole package as a multi-task learning problem