4,304 research outputs found
Dataflow Programming and Acceleration of Computationally-Intensive Algorithms
The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en BiotecnologĂa, IngenierĂa y TecnologĂa QuĂmicaLĂnea de InvestigaciĂłn: IngenierĂa, Ciencia de Datos y BioinformáticaClave Programa: DBICĂłdigo LĂnea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
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Gender Policy-as-Practice with Young Children: The Politics of Gender-Justice in Early Childhood Education
Trans and queer children are experiencing discrimination starting in the earliest years of schooling. In a paradoxical era of increased support for transgender and queer children on the one hand, and persistent gender violence on the other, this study examines how the New York City Department of Education (NYCDOE) gender policy is taken up in Early Childhood Education practice.
In particular, I ask: (a) What are early childhood teachers’ understanding of NYCDOE’s policy? (b) How do the larger social and material contexts, shape teachers’ enactments of the policy? (c) What do teachers’ understandings and enactments of NYC gender policy look like in their everyday classroom practices?
I use a critical policy-as-practice conceptual framework that does not take policy for granted but understands that embedded in all the policy processes, there is always a great deal of negotiation of power, where some stakeholders are empowered and other perspectives are silenced. Through semi-structured interviews with district policymakers, school administrators, and early childhood teachers, this study unveils how different actors took up NYCDOE’s gender policy in their practice, in accordance with their own ideas, motivations, and broader social and material contexts.
Findings indicate that the policy formation processes excluded the knowledge and perspectives of school communities and grassroots trans activist movements. Principals and teachers had little knowledge of the Guidelines on Gender and resources available, while several policy content and procedures reproduced gender and racial violence. Moreover, the sediment construct of childhood innocence shaped early childhood teachers’ gender-justice practices. Shifting understandings of gender, without revising understandings of childhood, this study concludes, hinders the possibility of transformative change
Towards a General Complex Systems Model of Economic Sanctions with Some Results Outlining Consequences of Sanctions on the Russian Economy and the World
The main purpose of this paper is to present a complex nonlinear modelling approach to analyzing mixed capitalist economic systems. An application of a more elaborate version of this model is to explore the consequences of sanctions on the Russian economy and evaluate the model’s predictive successes or failures. Furthermore, the formal expanded nonlinear model presented in the appendix may be seen as an initial step to put the analysis of economic sanctions within a formal complex socio-economic systems framework. The results obtained from this structural complex multisectoral model so far seem fairly accurate in terms of agreement with measured values of observable economic variables. The political consequences are uncertain and are to be explored separately in a companion paper and ultimately in a book length treatment.
Methodologically, the paper also presents the case for using Social Accounting Matrix (SAM)-based models for understanding problems of analyzing sanctions in an economywide context. Linear as well as Nonlinear models are presented in the appendix. The nonlinear modelling approach might prove to be especially relevant for studying the properties of multiple equilibria and complex dynamics
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