2019 ◽  
pp. 181-210
Author(s):  
M. Y. Osokin

The article is an excerpt from the biography of the Russian writer, historian and collector of curiosities F. Dmitriev-Mamonov, to be published by B.S.G.-Press. The fragment considers three hitherto undisclosed episodes of his life: the 1770 criminal investigation of Mamonov’s attempted poisoning by the writer and former lecturer of the Land Gentry Cadet Corps Johann Fonberg, who had worked as his personal librarian for two months; followed by problems with his mental health in the 1780s, when he began suspecting that his closest family were plotting to kill him and began to subject his serfs to harsh punishments; and, finally, his donations to Moscow University in May 1770, in February 1772 and, probably, in November 1779, which consisted of a collection of medals, copies of P. Lippert’s engraved gems, and the portrait of field marshal P. Saltykov. All three instances appear connected: the donations coincide with three major incidents in Mamonov’s life (the attempted poisoning, a bad wound sustained in Chudov monastery during the suppression of the Plague revolt, and official proceedings against him for cruel treatment of serfs), which forced him to contemplate his mortality and the need to plan for the future of his collection.


2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.


2021 ◽  
pp. 205030322098698
Author(s):  
Peter Lambertz

The Japanese “new religions” ( Shin Shūkyō) active in Kinshasa (DR Congo) nearly all perform healing through the channeling of invisible divine light. In the case of Sekai Kyūseikyō (Church of World Messianity), the light of Johrei cannot be visually apprehended, but is worn as an invisible aura on the practitioner’s body. This article discusses the trans-cultural resonances between Japan and Central Africa regarding the ontology of spiritual force, regimes of subjectivity, and the gradual embodiment of Johrei divine light as a protection against (suspicions of) witchcraft. Meanwhile, I argue that religious multiplicity in urban Africa encourages cultural reflexivity about concepts of health and healing, self-responsibility, and Pentecostal suspicion-mongering of occult sciences. Thus, Johrei divine light not only feeds into a longstanding local tradition of spiritual healing; within the religiously multiple city, it is also a discursive space for, and an experience and performance of, emic critique.


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