1,721,340 research outputs found
Simulation systems of: "Free energies of membrane stalk formation from a lipidomics perspective"
No full textSimulation systems of:
Free energies of membrane stalk formation from a lipidomics perspective
Chetan S. Poojari, Katharina C. Scherer, Jochen S. Hub
Nature Communications, 12, 6594 (2021), https://doi.org/10.1038/s41467-021-26924-2
First published as a preprint manuscript in BioRxiv as:
Free energies of stalk formation in the lipidomics era
Chetan S. Poojari, Katharina C. Scherer, Jochen S. Hub,
BioRxiv, https://www.biorxiv.org/content/10.1101/2021.06.02.446700v1, 2021
The archive contains
starting conformations of double-membrane systems
topologies
MD parameter files
Running the simulations requires a modified version of GROMACS, which implements the chain coordinate available at GitLab:
https://gitlab.com/cbjh/gromacs-chain-coordinat
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Experimental validation of multi-sensor data fusion model for railway wheel defect identification
No full textWheel defects are detrimental for railway train and track components and should be detected and identified as early as possible. Wheel Impact Load Detector (WILD) is a commercial condition monitoring system used for detecting the defective wheels. This system usually measures the rail strain at different points by multiple sensors. WILD converts the measured strains to the force and uses the peak force, dynamic force, and ratio of the peak force to the static force to estimate the condition of the in-service wheels. These methods are useful for detecting the severe defects contributing to the contact force to the extent that exceed a predetermined threshold. Therefore, in the prior research a fusion method has been developed to reconstruct a new informative pattern from the data collected by the multiple sensors. The reconstructed pattern provides a comprehensive description of the wheel condition. This paper validates the fusion method using a set of lab tests to investigate the applicability of the proposed method. For this purpose, a test rig has been built consisting of a circular rail, a rotating arm, and a wheel. Six strain sensors have been installed under the rail in the symmetric locations over the rail circle with 60 degree intervals. The fusion method used to reconstruct a signal from the bending strain signals measured by the multiple sensors. Different wheel defects including the flat and out-of-round wheels have been tested and the results validated the fusion method by providing informative patterns.Transport Engineering and Logistic
Unsupervised Physics-Informed Health Indicator Discovery for Complex Systems
No full textDiscovering health indicators (HI) is essential for prognostics and health management of complex systems, as an HI enables timely interventions and effective maintenance strategies. However, most of the existing methodologies for HI discovery rely on labeled data which is expensive and complicated to obtain in the real world. In this paper, we propose a novel, unsupervised physics-informed model structured after expert knowledge in the form of a graphical representation of the expected relationships between sensor readings, operating conditions, and degradation. In addition, a soft constraint is used to guide the representation of the HI according to generally available expert knowledge about degradation. We evaluated the model on a turbofan engine dataset and conducted four experiments by manipulating the original data to create realistic real-world scenarios. The proposed method discovers an HI that exhibits better intrinsic qualities than the current state-of-the-art methodologies, leading to enhanced prognostic performance. Notably, in situations where the initial health state of each system varies, the proposed method achieves an average prognostic performance improvement of approximately 20% compared to existing state-of-the-art methods.Air Transport & Operation
Generic Hybrid Models for Prognostics of Complex Systems
No full textHybrid models combining physical knowledge and machine learning show promise for obtaining accurate and robust prognostic models. However, despite the increased interest in hybrid models in recent years, the proposed solutions tend to be domain-specific. As a result, there is no compelling strategy of what, where, and how physics-derived knowledge can be integrated into deep learning models depending on the available representation of physical knowledge and the quality of data for the development of prognostic models for complex systems. This Ph.D. project aims to develop a general strategy for hybridizing prognostic models by exploring multiple methods to incorporate physical knowledge at various stages of the learning algorithm. The project will prioritize general expert knowledge as the primary source of information, while domain-specific knowledge will serve as an additional feature when applicable.Air Transport & Operation
Variations on the Author
Get PDF“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
Get PDFWe 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
Adaptive Prognostics: A reliable RUL approach
No full textIn the past decade, data-driven methodologies have gained increasing popularity, offering a foundation for predicting the remaining useful life (RUL) of engineering systems and structures using condition monitoring (CM) data. A particularly intriguing challenge lies in accurately predicting the RUL of systems that exhibit exceptional performance, whether underperforming or overperforming, owing to unforeseen phenomena occurring during their operational life. These unique systems, often referred to as outliers, pose a formidable challenge for RUL prediction. This research addresses this challenge by introducing a novel data-driven model, which is known as the Similarity Learning Hidden Semi-Markov Model (SLHSMM) and extends the capabilities of the Non-Homogeneous Hidden Semi-Markov Model (NHHSMM). The training dataset comprises strain data obtained from open-hole carbon-epoxy specimens exposed solely to fatigue loading. In contrast, the validation-testing dataset includes strain data from two specimens subjected to both fatigue and in-situ impact loading, representing an unexpected and previously unseen event in the training data. The study compares RUL estimations generated by the SLHSMM and NHHSMM. The results indicate that the SLHSMM outperforms the NHHSMM, offering superior accuracy in predicting outliers' RUL. This underscores its capability to adapt to unexpected phenomena and seamlessly incorporate unforeseen data into prognostics.Structural Integrity & Composite
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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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