Short term prediction of E greater than or equal to 10 MeV proton fluxes from solar flares

Both the anisotropic and isotropic diffusion theories can be used to extrapolate proton fluxes for E greater than or equal to 10 meV for over 50% of the particle events. The isotropic diffusion theory uses a diffusion coefficient: D = Mr sup beta. It was found that M and beta tended to be functions of flare position on the solar disk. A measurement of the interplanetary flux in near earth space gives a good indication of the polar cap fluxes. It was found that the 30 MHz absorption over the poles during a PCA is proportional to the square root of the integral proton flux E greater than or equal to 11 meV in interplanetary space, J = KA squared, with K = 8 plus or minus 2 and J in protons/sq cm-sec-ster

Dunsany and mythology in modern drama ..

Typewritten sheets in cover. Incorrectly paged, no p. 164-166. Thesis (M.A.)--Boston University Bibliography: p. 167-180

Renewed Latvia. A Case Study of the Transnational Fascism Model

This article examines the lesser-known authoritarian regime of Kārlis Ulmanis, the Vadonis [Leader] of Latvia from 1934-1940, as a case study of transnational fascism. Specifically, by investigating the nature of Mazpulki [Latvian 4-H] – an agricultural youth organization modeled on American 4-H which became during the Ulmanis regime a sort of unofficial ‘Ulmanis Youth’ institution – and its international connections, and particularly with Italy, the article contends that we should view the Ulmanis regime as having been part of the transnational fascist wave that swept over Europe in the period between the two world wars. The article also makes the historiographical point that the transnational fascism model offers key analytical methods for interpreting fascism’s syncretic nature, especially in the case of those regimes which had some recognizable features of ‘generic’ fascism but which have previously been categorized as merely authoritarian. Future studies of such regimes will expand our understanding of the nature of and links between the many varied manifestations of interwar fascism. DOI: 10.1163/22116257-0020200

The Relationship between Stress and Dementia: An Investigation of Physiological and Psychological Connections across the Lifespan

While many studies have examined the relationship between stress and dementia, there is no general consensus on what the relationship is, or even whether such a relationship exists at all (Fountoulakis, 2011; Wang, 2012). This literature review consequently considers a broad range of hypotheses on the topic. The central focus of this review was to identify what aspects of stress – throughout life – increase the likelihood of dementia in the elderly. The available research indicates that there is indeed a relationship between stress and dementia, and that stress interacts with dementia risk in multiple ways. There is, first of all, significant evidence to support the hypothesis that chronic stress leads to excessive cortisol release, and that excessive cortisol release damages the brain in ways that can lead to dementia. There is, secondly, significant correlation evidence to suggest that stress interacts with low intellectual stimulation, and that both stress and low intellectual stimulation predict the onset of dementia. Both of these pathways to dementia present multiple opportunities for clinical intervention. This literature review therefore directs some focus toward what aspects of stress would seem to be conducive to treatment. It was found that there are a number of stress interventions that could and should be implemented in large segments of the population. This review, though, fails to completely isolate the effects of stress, and consequently can only suggest a few general interventions. There is still much research that could be done in the area and more specific interventions to be discovered

Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity

The clustering of data into physically meaningful subsets often requires assumptions regarding the number, size, or shape of the subgroups. Here, we present a new method, simultaneous coherent structure coloring (sCSC), which accomplishes the task of unsupervised clustering without a priori guidance regarding the underlying structure of the data. sCSC performs a sequence of binary splittings on the dataset such that the most dissimilar data points are required to be in separate clusters. To achieve this, we obtain a set of orthogonal coordinates along which dissimilarity in the dataset is maximized from a generalized eigenvalue problem based on the pairwise dissimilarity between the data points to be clustered. This sequence of bifurcations produces a binary tree representation of the system, from which the number of clusters in the data and their interrelationships naturally emerge. To illustrate the effectiveness of the method in the absence of a priori assumptions, we apply it to three exemplary problems in fluid dynamics. Then, we illustrate its capacity for interpretability using a high-dimensional protein folding simulation dataset. While we restrict our examples to dynamical physical systems in this work, we anticipate straightforward translation to other fields where existing analysis tools require ad hoc assumptions on the data structure, lack the interpretability of the present method, or in which the underlying processes are less accessible, such as genomics and neuroscience

The relaxation method for learning in artificial neural networks

A new mathematical approach for deriving learning algorithms for various neural network models including the Hopfield model, Bidirectional Associative Memory, Dynamic Heteroassociative Neural Memory, and Radial Basis Function Networks is presented. The mathematical approach is based on the relaxation method for solving systems of linear inequalities. The newly developed learning algorithms are fast and they guarantee convergence to a solution in a finite number of steps. The new algorithms are highly insensitive to choice of parameters and the initial set of weights. They also exhibit high scalability on binary random patterns. Rigorous mathematical foundations for the new algorithms and their simulation studies are included