Geopolitics of Monetary Innovation in the Longue Durée. Financialization, Digitalization and the Crisis of the Global Hegemony

Since the beginning of the new millennium, a great variety of experiences of monetary innovation has taken place worldwide. Actually, very assorted types of social agents, at very different levels of interaction and with very diverse purposes and results, are creating a plethora of global, macro-regional, local or deterritorialized currencies seriously defying both the hegemonic role of the US dollar, and the traditional agents, methods and criteria related to money's creation and circulation. Given such a picture, the main aim of this essay is to characterize and valuate the principle features of the ongoing process of monetary innovation, in light of its modern and contemporary history. We adopt an interdisciplinary theoretical frame, based on the ground of the political sociology and the world system theory, and a genealogical method focused on the monetary history of the United States. The main result here presented is the centrality of the dialectic between innovation and regulation, and of their social and institutional forces. Actually, it can help the comprehension about where, when and why new forms of money appear, as well as the individuation of the main traits characterizing the current stage of global crisis and of its risky implications

The single individual in medicine: how to escape from the probability theory trap

Doctors and their patients are always concerned with the likely outcome of an existing disease and the risk of future diseases, but there are many problems in interpreting for the individual data derived from populations. Yet recent developments in mathematics and science should allow us to do much better

The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Cellular Automata are usually considered the most efficient technology to understand the spatial logic of urban dynamics: they are inherently spatial, they are simple and computationally efficient and are able to represent a wide range of pattern and situations. Nevertheless the implementation of a CA requires the formulation of explicit spatial rules which represents the greatest limit of this approach. Whatever rich and complex the rules are, they don`t are able to capture satisfactorily the variety of the real processes. Recent developments in natural algorithms, and particularly in Artificial Neural Networks (ANN), allow to reverse the approach by learning the rules and the behaviours in urban land use dynamics directly from the Data Base, following a bottom-up process. The basic problem is to discover how and in to what extent the land use change of each cell i at time t+1 is determined by the neighbouring conditions (CA assumptions) or by other social, environmental, territorial features (i.e. political maps, planning rules) which where holding at the previous time t. Once the NN has learned the rules, it is able to predict the changes at time t+2 and following. In this paper we show and discuss the prediction capability of different architectures of supervised and unsupervised ANN. The Case study and Data Base concern the land use dynamics, between two temporal thresholds, in the South metropolitan area of Milan. The records have been randomly split in two sets which have been alternatively used in Training and in Testing phase in each ANN. The different ANNs performances have been evaluated with Statistical Functions. Finally, for the prediction, we have used the average of the prediction values of the 10 ANNs, and tested the results through the usual Statistical Functions.

Capacités morphogènes des cellules d’éponges dissociées

The reconstitution of functional sponges from aggregates, some built from non-fractionated suspensions, others from purified archaeocytes, has been studied using electron microscopy in process of time.The reorganization of aggregates made from complete suspensions mainly consists in a gathering of cells keeping their initial differentiation into functional structures. During restructuration, cellular debris resulting from dissociation and surnumerary healthy cells are phagocytized by archaeocytes.The evolution of archaeocyte aggregates points out the totipotency of these cells, since they appear to be able to differentiate into all sponge cell types. Nevertheless, the anomalies appearing during the sponge reconstitution, which mainly consist in a cell type population ratio desequilibrium, suggest that some morphogenetic regulation mechanisms are lost

Toward a precision, complexity-informed cultural policy design: Structural bottlenecks to culture-led development in Skaraborg, Sweden

We analyze the spatial-temporal dynamics of cultural vibrancy in the Swedish sub-region of Skaraborg. Our database consists of 4170 geo-localized cultural activities and facilities, mapped between October 2013 and March 2014. We make use of the TWC methodology for the dynamic simulation of the evolution of geo-localized activity starting from an observed distribution of events, and of the AutoCM ANN architecture to understand how cultural variables are related to the rest of the Skaraborg socio-economy. We find that cultural vibrancy in Skaraborg is likely characterized by a 'flaring' pattern of initial, widespread activity followed by a re-concentration into the main local urban hubs. The deep reason behind this unsuccessful developmental trajectory is the lack of centrality of cultural production in the local socio-economy, and of integration across cultural production sectors. This is in turn due also to structural bottlenecks of a non-cultural nature such as insufficient access of women to higher education. We make a case for the necessity to develop a new precision cultural policy design approach founded upon the science of complexity for both policy design and assessment, and we provide and illustrate a first technical toolkit to this purpose.(c) 2022 Elsevier B.V. All rights reserved

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study

<p>Abstract</p> <p>Background</p> <p>Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old.</p> <p>The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis</p> <p>Methods</p> <p>The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts.</p> <p>Results</p> <p>By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same.</p> <p>Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus.</p> <p>Conclusion</p> <p>The results of this study suggest that: a) cortical NFT represent the key variable in AD neuropathology; b) the neuropathologic profile of AD subjects is complex, however, c) ANNs can analyze neuropathologic features and differentiate AD cases from controls.</p