1,721,050 research outputs found
Proposed design platform for intensive structural computational analysis
No full textESMC- 7th EUROMECH Solid Mechanics Conference – Lisbona 7-11 Settembre 2009 (Riassunto esteso
Manifold learning by a deep Gaussian process autoencoder
No full textThe paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian process autoencoder algorithm has the following two main characteristics. The former is a bottleneck structure, borrowed by variational autoencoders and the latter is based on the so-called doubly stochastic variational inference for deep Gaussian processes architecture (DSVI). The main novelties of the paper consist in DGPA algorithm and the experimental protocol for evaluating it. In fact, to the best of our knowledge, deep Gaussian processes algorithms have not been applied to manifold learning, yet. Besides, an experimental protocol is introduced, the so-called manifold learning performance protocol (MLPP), to compare quantitatively the geometric preserved properties of manifold learning projections of the proposed deep Gaussian process autoencoder with the ones of state-of-the-art manifold learning algorithms. Extensive experimental tests on eleven synthetic and five real datasets show that deep Gaussian process autoencoder compares favorably with the other manifold learning competitors
Deep Learning for Time Series Forecasting: Advances and Open Problems
No full textA time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field of machine learning, applied to time series forecasting can cope with complex and high-dimensional time series that cannot be usually handled by other machine learning techniques. The aim of the work is to provide a review of state-of-the-art deep learning architectures for time series forecasting, underline recent advances and open problems, and also pay attention to benchmark data sets. Moreover, the work presents a clear distinction between deep learning architectures that are suitable for short-term and long-term forecasting. With respect to existing literature, the major advantage of the work consists in describing the most recent architectures for time series forecasting, such as Graph Neural Networks, Deep Gaussian Processes, Generative Adversarial Networks, Diffusion Models, and Transformers
Residual Strength Evaluation for Aerospace Composite Structures with Large Notch Damage in MSC.Nastran SOL 700 Advanced Composite based on Alphastar GENOA Software
No full textLarge notch damage usually consists of failed or completely severed stiffeners or spars, or chord with failed or completely severed attached skin or web. Large notch damage results from unknown damage sources so the damage event is immediately obvious to the flight crew. The airplane must be capable of successfully completing a flight with such damage. The determinate accidental damage event is to ensure a balanced design approach in which the damage size in linked to the structural configuration. The criterion that is generally used, called the “two bay crack criterion,” states that the damage tolerant composite structure should sustain the regulatory loads with damage in skin like sharp slit or notch which extends two adjacent bays with severed center stiffener. For this damage state, any structural design must demonstrate the residual strength load capability not less than as defined in the regulations. In order to reduce costs and product lead-time, Virtual Testing (VT) can be used to reduce the scale of
physical testing necessary for structures development and certification. Virtual Testing involves an accurate simulation of physical tests using multi-scale Progressive Failure Analysis. In this paper residual strength evaluation for composite structures with large notch damage using MSC.Nastran SOL 700 advanced composite based on AlphaSTAR GENOA Software has been investigated. Accurate prediction of failure of all structural aerospace component is crucial in order to achieve the goal of weight minimization, material savings and improved performances. MSC software provides a very convenient solution to perform complex analyses of the aerospace structure as well as for nonlinear analysis including materials and large deformations and strains. Using this tool we are able to optimize configuration and complete the design in less time than have been
required using traditional design methods. In this paper an example will be shown, comparing a virtual and real testing of an aerospace composite stiffened reinforced panel
Small dense low-density lipoprotein in familial combined hyperlipidemia: Independent of metabolic syndrome and related to history of cardiovascular events.
No full text1. Atherosclerosis. 2009 Mar;203(1):320-4. Epub 2008 Jul 12. Small dense low-density lipoprotein in familial combined hyperlipidemia: Independent of metabolic syndrome and related to history of cardiovascular events. Pauciullo P, Gentile M, Marotta G, Baiano A, Ubaldi S, Jossa F, Iannuzzo G, Faccenda F, Panico S, Rubba P. Department of Clinical and Experimental Medicine, University Federico II Medical School, Nuovo Policlinico, Edificio 1, Via Pansini 5 - 80131 - Naples, Italy. [email protected] INTRODUCTION: It is unclear whether small dense low-density lipoprotein (sdLDL) are associated with familial combined hyperlipidemia (FCHL), independently of the metabolic syndrome (MS). It is also unclear whether sdLDL are related to history of cardiovascular (CVD) events in FCHL patients, independently of MS. PATIENTS AND METHODS: Serum levels of sdLDL, expressed as percentage of total LDL cholesterol (LDL score), were determined in 137 probands with FCHL and in 133 normolipidemic, normotensive, normoglycemic healthy subjects. RESULTS: In binary logistic regression age- and gender-adjusted LDL score values above the 90th and 95th percentiles of the values in the control group (10.23 and 13.11%, respectively) were found to be significant predictors of FCHL status, independently of MS diagnosis (p=0.007 and p<0.0001, respectively). Values of the LDL score above the 90th and the 95th percentile of the control group resulted to be significantly related to FCHL status, even after adjustment for the components of MS (p=0.006 and p=0.001, respectively). Among FCHL patients, values of the LDL score above 95th percentile of the values in the control group were found to be significantly related to personal and/or family history of CVD events, independently of age, gender, total cholesterol, apolipoprotein (apo) B, and MS status (p=0.016). The same significant relationship was found adjusting for all components of MS (p=0.034). CONCLUSIONS: High concentrations of sdLDL are highly specific markers of FCHL, independently of concomitant MS. In FCHL patients high levels of sdLDL are related to history of CVD events, independently of MS, total cholesterol and apo B. PMID: 18760784 [PubMed - indexed for MEDLINE
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
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
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