1,722,092 research outputs found
L'ARCHIVIO DIGITALE PER I PROCESSI DI ALTO ARTIGIANATO. Ricerche a confronto in Italia e Cina
Full text linkIl contributo affronta la digitalizzazione del Patrimonio Intangibile custodito dai processi artigianali tradizionali dei distretti manifatturieri delle PMI tramite un confronto di due casi di studio. Due distinti territori produttivi – mobili e complementi in Toscana (Italia) e argenteria Miao nella Provincia di Guizhou (Cina) – sono indagati in termini di caratteristiche, strutture e capacità di accesso all’innovazione tecnologica. Dall’analisi emerge un ruolo privilegiato dell’archivio digitale, come catalizzatore di processi creativi e organizzativi, poiché da un lato favorisce la costruzione di una memoria organizzata e trasmissibile che supporta le PMI nella competitività dei mercati, dall’altro funge da base per lo sviluppo di processi di user-experience mirati alla divulgazione e al reinserimento nel contesto del design contemporaneo.This article approaches the digitisation of the Intangible Heritage preserved by the traditional craft processes of SME manufacturing districts through a comparison of two case studies. Two distinct manufacturing areas – furniture and accessories in Tuscany (Italy) and Miao silverware in Guizhou Province (China) – are investigated in terms of their characteristics, structures and capacity to access technological innovation. From the analysis emerges a privileged role of the digital archive as a catalyst of creative and organisational processes. On the one hand, it favours the construction of an organised and transmissible memory that supports SMEs in their market competitiveness. On the other, it serves as a basis for developing user-experience processes aimed at dissemination and reintegration in the context of contemporary design
A Framework for Physics-Informed Deep Learning Over Freeform Domains
No full textDeep learning is a popular approach for approximating the solutions to partial differential equations (PDEs) over different material parameters and boundary conditions. However, no work has yet been reported on learning PDE solutions over changing shapes of the underlying domain. We present a framework to train neural networks (NN) and physics-informed neural networks (PINNs) to learn the solutions to PDEs defined over varying freeform domains. This is made possible through our adoption of a parametric non-uniform rational B-Spline (NURBS) representation of the underlying physical shape. Distinct physical domains are mapped to a common parametric space via NURBS parameterization. In our approach, we formulate NNs and PINNs that learn the solutions to PDEs as a function of the shape of the domain itself through shape parameters. Under this formulation, the loss function is based on an unchanging parametric domain that maps to a variable physical domain. Residual computation in PINNs is made possible through the Jacobian of the mapping. Numerical results show that our networks can be trained to predict the solutions to a PDE defined over an entire set of shapes. We focus on the linear elasticity PDE and show how we can build a surrogate model that is able to predict displacements and stresses over a variety of freeform domains. To assess the efficacy of all networks in this work, data efficiency, network accuracy, and the capacity of networks to extrapolate are considered and compared between NNs and PINNs. The comparison includes cases where little training data is available. Transfer learning and applications to shape optimization are analyzed as well. A selection of the used codes and datasets is provided at https://github.com/fmezzadri/shape_parameterized.git
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The restricted promoter activity of the liver transcription factor hepatocyte nuclear factor 3β involves a cell-specific factor and positive autoactivation
No full textThe transcription factor hepatocyte nuclear factor 3 (HNF-3) is involved in the coordinate expression of several liver genes. HNF-3 DNA binding activity is composed of three different liver proteins which recognize the same DNA site. The HNF-3 proteins (designated α, β, and γ) possess homology in the DNA binding domain and in several additional regions. To understand the cell-type-specific expression of HNF-3β, we have defined the regulatory sequences that elicit hepatoma-specific expression. Promoter activity requires -134 bp of HNF-3β proximal sequences and binds four nuclear proteins, including two ubiquitous factors. One of these promoter sites interacts with a novel cell-specific factor, LF-H3β, whose binding activity correlates with the HNF-3β tissue expression pattern. Furthermore, there is a binding site for the HNF-3 protein within its own promoter, suggesting that an autoactivation mechanism is involved in the establishment of HNF-3β expression. We propose that both the LF-H3β and HNF-3 sites play an important role in the cell-type-specific expression of the HNF-3β transcription factor
Physics-informed neural network based topology optimization through continuous adjoint
No full textIn this paper, we introduce a Physics-Informed Neural Networks (PINNs)-based Topology optimization method that is free from the usual finite element analysis and is applicable for both self-adjoint and non-self-adjoint problems. This approach leverages the continuous formulation of TO along with the continuous adjoint method to obtain sensitivity. Within this approach, the Deep Energy Method (DEM)-a variant of PINN-completely supersedes traditional PDE solution procedures such as a finite-element method (FEM) based solution process. We demonstrate the efficacy of the DEM-based TO framework through three benchmark TO problems: the design of a conduction-based heat sink, a compliant displacement inverter, and a compliant gripper. The results indicate that the DEM-based TO can generate optimal designs comparable to those produced by traditional FEM-based TO methods. Notably, our DEM-based TO process does not rely on FEM discretization for either state solution or sensitivity analysis. During DEM training, we obtain spatial derivatives based on Automatic Differentiation (AD) and dynamic sampling of collocation points, as opposed to the interpolated spatial derivatives from finite element shape functions or a static collocation point set. We demonstrate that, for the DEM method, when using AD to obtain spatial derivatives, an integration point set of fixed positions causes the energy loss function to be not lower-bounded. However, using a dynamically changing integration point set can resolve this issue. Additionally, we explore the impact of incorporating Fourier Feature input embedding to enhance the accuracy of DEM-based state analysis within the TO context. The source codes related to this study are available in the GitHub repository: https://github.com/xzhao399/DEM_TO.git
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
