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Religioni E Societa-Rivista Di Scienze Sociali Della Religione ; 37(103):41-49, 2022.
Article in English | Web of Science | ID: covidwho-2307195

ABSTRACT

Anthroposophy is the spiritual and esoteric current conceived by the Austrian philosopher Rudolf Steiner (1861-1925) after his experience in the Theosophical Society established by Helena Petrovna Blavatski (1831-1891). This paper focuses on the digital ethnography and the content analysis of a sample of independent anthroposophical social network production, analysing the 2021 Facebook posts of ten users selected on the basis of the cohesion and pre-eminence principle. Moving from these data and from the theoretical premises of independent Italian anthroposophy, I aim to discuss the epistemological status of its peculiar epidemiological counter-narrative, its socio-political implications and the role of conspiracy narratives within the contemporary spirituality frame. The specific feature emerging from the analysis is that of a narrative in which political power - mediumistic in its very nature - would be nothing more than an automaton acting for global economic elites, in their turn being tools in the hands of supernatural hindering entities. In this perspective, the social network production conveys a complex set of paradigmatic narratives in which the dynamics of re-sharing and amplification feed not only the internal semantic circuit, but also the offline social environment the actors belong to.

2.
Computers and Industrial Engineering ; 179, 2023.
Article in English | Scopus | ID: covidwho-2298995

ABSTRACT

Aiming at the problem of low accuracy of two-dimensional preference information aggregation, this paper takes two-dimensional interval grey numbers as an example to define its preference information mapping rules. This rule maps preference information to preference points on a two-dimensional plane. Based on the theory of plane Steiner-Weber point, we construct a two-dimensional optimal model, and prove the optimality of the model theoretically. Then, adopt plant growth simulation algorithm (PGSA) to solve the proposed model. The obtained optimal aggregation point that can represent the comprehensive opinions. Finally, by analyzing the selection problem of Fangcang shelter hospital and comparing it with the particle swarm optimization (PSO) method, we conclude that the sum of weighted Euclidean distance obtained by our method is minimal. The aggregation precision of our method is higher than that of other aggregation method to a certain extent. © 2023 Elsevier Ltd

3.
IEEE Open J Signal Process ; 2: 248-264, 2021.
Article in English | MEDLINE | ID: covidwho-1304065

ABSTRACT

We propose 'Tapestry', a single-round pooled testing method with application to COVID-19 testing using quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits, at clinically acceptable false positive or false negative rates. Tapestry combines ideas from compressed sensing and combinatorial group testing to create a new kind of algorithm that is very effective in deconvoluting pooled tests. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. For guaranteed recovery of [Formula: see text] infected samples out of [Formula: see text] being tested, Tapestry needs only [Formula: see text] tests with high probability, using random binary pooling matrices. However, we propose deterministic binary pooling matrices based on combinatorial design ideas of Kirkman Triple Systems, which balance between good reconstruction properties and matrix sparsity for ease of pooling while requiring fewer tests in practice. This enables large savings using Tapestry at low prevalence rates while maintaining viability at prevalence rates as high as 9.5%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We evaluate Tapestry in simulations with synthetic data obtained using a novel noise model for RT-PCR, and validate it in wet lab experiments with oligomers in quantitative RT-PCR assays. Lastly, we describe use-case scenarios for deployment.

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