Aberystwyth et son amour: talking to locals in the UK’s most Europhile town

Is Aberystwyth, a small town in West Wales, really as Europhile as the recent YouGov polling suggests? Sarah Trotter and Nick Morgan, two PhD researchers who grew up there, returned to the town to ask locals about the prospect of a Brexit

Exploring the global and local networks of national information system integration initiatives : a case study of national cultural collection integration from an actor-network perspective

Australian Museums Online (AMOL) was the earliest attempt to make Australia&rsquo;s distributed cultural collections accessible from a single online resource. Despite early successes, significant achievements and the considerable value it offered certain groups, the project ran into operational difficulties and was eventually discontinued. By using Actor-Network Theory and analysing the global and local actor-networks, it is revealed that although the project originated from large, state museums, buy-in was restricted to individuals, rather than institutions and the most significant value was for smaller, regional institutions. Furthermore, although the global networks that governed the project could translate their visions through the local production networks, because the network&rsquo;s underlying weaknesses were never addressed, over time this destablised the global networks. This case study offers advice for projects attempting to consolidate data sources from disparate sources, and highlights the importance of individual actors in championing the project.<br /

Management of digital map data using a relational database model

Special issue (CISRG - Cartographic Information Systems Research Group) ;

Política e identidad cultural en la desesperanza de José Donoso

Este trabajo analiza la importancia política de la construcción de la identidad nacional en la novela La dececperanza, del escritor chileno losé Donoso. Aunque gran parte de la crítica actual subraya el aparente realismo de esta novela, el presente estudio se enfoca en la manera como la novela construye un conflicto discursivo entre varias concepciones de lo chileno. La tesis central del artículo es que la fuerza crítica de La dececperanza reside en su exposición irónica de los valores imperantes de la clase media chilena, los cuales tienen su origen en un racismo íntimamente relacionado con los prejuicios clasistas que han estructurado la sociedad chilena desde la época de la Colonia.This article considers the political significance of the construction of national identity in losé Donoso’s La dececperanza. Although much contemporary criticism has regarded this work as a realist novel, this analysis examines its staging of the discursive conflict between competing ideas of what it means to be Chilean. The central argument is that the novel’s critical force derives from its ironic exploration of the values of the Chilean middle classes which are unconsciously based on a racism that is closely linked to the class prejudices that have structured Chilean society since colonial times

On the detection of myocardial scar based on ECG/VCG analysis

In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%)

Bedford Stuyvesant Restoration Corporations Farm to Early Care Program: A Foundation for Healthier, Stronger Central Brooklyn Families and Communities

Across New York City and the nation, low-income and working families with young children endeavor to raise strong, healthy children; maintain their family's health; find and keep decent jobs and affordable housing; create safe communities; and claim a voice in shaping their neighborhoods. At the same time, within these communities, resilient families and children, skilled and experienced leaders, and many established civic organizations with a history of organizing to improve their neighborhoods have shown the power of local action to promote health, equity and community development.In this policy brief, we describe one effort to mobilize community assets to develop a comprehensive and integrated approach to supporting well-being, prosperity, increased community power and pathways out of poverty. For the past five years, Bedford Stuyvesant Restoration Corporation (Restoration) has used its farm to early care program as one foundation for an integrated approach to community development. In telling that story, this policy brief seeks to inform efforts to develop the next stage of farm to early care in Brooklyn, inspire others to adapt this approach to their own communities and cities, and share the lessons Restoration has learned from this work. These experiences can also inform initiatives to use improvements in institutional food programs as a starting point for transforming other systems such as senior centers, afterschool programs, and health care centers

Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation of many images. Other simulation methods based on linear imaging models, such as the convolution method, are much faster but are too inaccurate to be used in application. In this paper, we explore deep learning models that attempt to translate a STEM image produced by the convolution method to a prediction of the high accuracy multislice image. We then compare our results to those of regression methods. We find that using the deep learning model Generative Adversarial Network (GAN) provides us with the best results and performs at a similar accuracy level to previous regression models on the same dataset. Codes and data for this project can be found in this GitHub repository, https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice