A Turke turn'd Quaker: conversion from Islam to radical dissent in early modern England

The study of the relationship between the anglophone and Islamic worlds in the seventeenth century has been the subject of increas- ing interest in recent years, and much attention has been given to the cultural anxiety surrounding “Turning Turke”, conversion from Christianity to Islam, especially by English captives on the Barbary coast. Conversion in the other direction has attracted far less scrutiny, not least because it appears to have been far less com- mon. Conversion from Islam to any form of radical dissent has attracted no scholarship whatsoever, probably because it has been assumed to be non-existent. However, the case of Bartholomew Cole provides evidence that such conversions did take place, and examining the life of this “Turke turn’d Quaker” provides an insight into the dynamics of cross-cultural conversion of an exceptional kind

PUENTES Program: An Institutional Response Claiming for Bridges in a Time of Trumpeting Walls

Book chapter in Gaulee U., Sharma S., Bista K. (eds) Rethinking Education Across Borders.Following the 2016 U.S. presidential election and the government openly anti-immigrant rhetoric threatening to deport unauthorized immigrants (including students with DACA protection), several actors in Mexico organized to launch the PUENTES program to facilitate enrollment of Mexican students living in the U.S. at a Mexican HEI to finish their degrees. In this chapter we analyze, from a policy perspective, how a country can prepare to serve their once migrant citizens, now returning students, who need to be re-enrolled into the higher education system and therefore into the society. Key findings suggest that the program has been successful in the following ways: (1) It has provided visibility to the issue of forced migration back to Mexico; (2) It helped to expedite changes in legislation that now make it easier for anyone with partial studies outside Mexico to continue with their education in an HEI in the country; and (3) It provided an alternative, not only to students who faced deportation but also to those who willingly saw an opportunity to continue with their studies at an institution in their place of birth.24 month embargo; first online 15 February 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

MVC1_GUI: A MATLAB graphical user interface for first-order multivariate calibration. An upgrade including artificial neural networks modelling

In the present report, an upgrade of a MATLAB graphical user interface (GUI) toolbox for implementing first-order multivariate calibration models is presented. The new freely available Multivariate Calibration 1 (MVC1_GUI) incorporates new models and features that make it a very versatile tool for data processing. In addition to the linear models, i.e., principal component regression (PCR) and partial least-squares 1 (PLS-1), included in the earlier software version, PLS-2 and maximum likelihood principal component regression (MLPCR) are now available, together with two non-linear models based on two different types of artificial neural networks (ANN): feed-forward multi-layer network with radial basis functions (RBF) and multi-layer back-propagation perceptron (MLP). The toolbox accepts different input data formats, and incorporates many advanced pre-processing algorithms to improve prediction capabilities. The development and validation of each model and its subsequent application to unknown samples is straightforward, since it generates many different plots regarding model performance, including outlier detection. Prediction results are produced along with analytical figures of merit and standard errors calculated by uncertainty propagation.Fil: Chiappini, Fabricio Alejandro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentin

Artificial neural network data analysis for classification of soils based on their radionuclide content

The artificial neural network (ANN) data analysis method was used to recognize and classify soils of an unknown geographic origin. A total of 103 soil samples were differentiated into classes according to the regions in Serbia and Montenegro from which they were collected. Their radionuclide (Ra-226, U-238, U-235, K-40, Cs-134, Cs-137, Th-232, and Be-7) activities detected by gamma-ray spectrometry were then used as inputs to ANN. Five different training algorithms with different numbers of samples in training sets were tested and compared in order to find the one with the minimum root mean square error (RMSE). The best predictive power for the classification of soils from the fifteen regions was achieved using a network with seven hidden layer nodes and 2500 training epochs using the online back-propagation randomized training algorithm. With the optimized ANN, most soil samples not included in the ANN training data set were correctly classified at an average rate of 92%

A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks

Data splitting is an important step in the artificial neural network (ANN) development process, whereby the available data are divided into training, testing, and validation subsets to ensure good generalization ability of the model. Considering that only one split of the data is typically used when developing ANN models, data splitting has a significant impact on model performance, depending on which data are allocated to the three subsets. Therefore, it is important to find a data splitting method that consistently results in predictive validation errors that are representative of the predictive errors obtained over the full range of the available data. This paper addresses this issue by introducing a benchmarking approach for comparing different data splitting methods in terms of (1) bias, which is the difference between the expected validation performance over the entire data set and that obtained using a particular data splitting method and (2) variability, which is the spread of the validation errors obtained by repeated implementation of that method. The utility of the proposed approach is assessed on a number of well-known data splitting methods in the context of four water resources ANN modelling problems. The results obtained indicate that the proposed approach for comparing data splitting methods is more representative than the previous approach where a value of zero is used as the predictive performance benchmark, as it can avoid the selection of an over-optimistic data splitting method that under-represents extreme data in the validation set. Key Points Development of predictive performance benchmark for estimating model bias Development of benchmarking approach for comparing data splitting methods Application of the approach to different data splitting methods and datasets ©2013. American Geophysical Union. All Rights Reserved.Wenyan Wu, Robert J. May, Holger R. Maier, and Graeme C. Dand