1,720,982 research outputs found
Fragility Curves for Wide-Flange Steel Columns and Implications on Building-Specific Earthquake-Induced Loss Assessment
Get PDFBuilding-specific loss assessment methodologies utilize component fragility curves to compute the expected losses in the aftermath of earthquakes. Such curves are not available for steel columns assuming they remain elastic due to capacity design considerations. Nonetheless, first-story steel columns in moment-resisting frames (MRFs) are expected to experience damage, through flexural yielding and formation of geometric instabilities. This paper utilizes an experimental database that was recently assembled to develop two sets of univariate drift-based column fragility curves that consider the influence of loading history. Ordinal logistic regression is also employed to develop multivariate fragility curves that capture geometric and loading parameters that affect the column performance. The implications of the proposed fragility curves on building-specific loss assessment is demonstrated using a case of an 8-story office building with steel MRFs. It is shown that structural repair costs in this case may increase by 10%, regardless of the seismic intensity, when column damage is considered. Similarly, the contribution of structural component repairs to expected annual losses may double over the building lifespan.RESSLA
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
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
Modèles basés sur des données pour l'évaluation des dommages sismiques des bâtiments : utilisation de l'apprentissage automatique à l'échelle régionale et de la surveillance structurelle in situ à l'échelle du bâtiment
No full textDecision-makers and stakeholders must have quick assessment of potential damage from earthquakes and its distribution to plan for successful emergency response. There are several methods for estimating building damage from earthquakes, but they involve many assumptions associated to a large set of parameters that controls the building response during earthquake, that can add uncertainty. Using real-world data from full-scale observations can help better understand the physical processes involved during seismic loading in building and identify sources of uncertainty in damage estimation. There is now access to a large amount of information describing the real estate building portfolios, collected through post-earthquake damage surveys at regional level and in-situ measurements in building. The objective of this study is to test the effectiveness and relevance of supervised machine learning methods for analyzing spatially distributed seismic damage after an earthquake at the regional scale. Additionally, we aim to quantify uncertainties in building damage assessment at the building-level, using in-situ earthquake data recorded at the top and the bottom floors of buildings.Les décideurs et les parties prenantes doivent disposer d'une évaluation rapide des dommages potentiels causés par les tremblements de terre et de leur distribution afin de planifier une réponse d'urgence efficace. Il existe plusieurs méthodes pour estimer les dommages causés aux bâtiments par les tremblements de terre, mais elles impliquent de nombreuses hypothèses associées à un large ensemble de paramètres qui contrôlent la réponse du bâtiment pendant le tremblement de terre, ce qui peut ajouter de l'incertitude. L'utilisation de données réelles provenant d'observations à grande échelle peut aider à mieux comprendre les processus physiques impliqués dans la charge sismique des bâtiments et à identifier les sources d'incertitude dans l'estimation des dommages. Il est maintenant possible d'accéder à une grande quantité d'informations décrivant les portefeuilles de bâtiments immobiliers, recueillies par des enquêtes sur les dommages post-séisme au niveau régional et des mesures in situ dans les bâtiments. L'objectif de cette étude est de tester l'efficacité et la pertinence des méthodes d'apprentissage automatique supervisé pour analyser les dommages sismiques spatialement distribués après un tremblement de terre à l'échelle régionale. En outre, nous visons à quantifier les incertitudes dans l'évaluation des dommages au niveau des bâtiments, en utilisant des données sismiques in-situ enregistrées aux étages supérieurs et inférieurs des bâtiments
Modèles basés sur des données pour l'évaluation des dommages sismiques des bâtiments : utilisation de l'apprentissage automatique à l'échelle régionale et de la surveillance structurelle in situ à l'échelle du bâtiment
No full textDecision-makers and stakeholders must have quick assessment of potential damage from earthquakes and its distribution to plan for successful emergency response. There are several methods for estimating building damage from earthquakes, but they involve many assumptions associated to a large set of parameters that controls the building response during earthquake, that can add uncertainty. Using real-world data from full-scale observations can help better understand the physical processes involved during seismic loading in building and identify sources of uncertainty in damage estimation. There is now access to a large amount of information describing the real estate building portfolios, collected through post-earthquake damage surveys at regional level and in-situ measurements in building. The objective of this study is to test the effectiveness and relevance of supervised machine learning methods for analyzing spatially distributed seismic damage after an earthquake at the regional scale. Additionally, we aim to quantify uncertainties in building damage assessment at the building-level, using in-situ earthquake data recorded at the top and the bottom floors of buildings.Les décideurs et les parties prenantes doivent disposer d'une évaluation rapide des dommages potentiels causés par les tremblements de terre et de leur distribution afin de planifier une réponse d'urgence efficace. Il existe plusieurs méthodes pour estimer les dommages causés aux bâtiments par les tremblements de terre, mais elles impliquent de nombreuses hypothèses associées à un large ensemble de paramètres qui contrôlent la réponse du bâtiment pendant le tremblement de terre, ce qui peut ajouter de l'incertitude. L'utilisation de données réelles provenant d'observations à grande échelle peut aider à mieux comprendre les processus physiques impliqués dans la charge sismique des bâtiments et à identifier les sources d'incertitude dans l'estimation des dommages. Il est maintenant possible d'accéder à une grande quantité d'informations décrivant les portefeuilles de bâtiments immobiliers, recueillies par des enquêtes sur les dommages post-séisme au niveau régional et des mesures in situ dans les bâtiments. L'objectif de cette étude est de tester l'efficacité et la pertinence des méthodes d'apprentissage automatique supervisé pour analyser les dommages sismiques spatialement distribués après un tremblement de terre à l'échelle régionale. En outre, nous visons à quantifier les incertitudes dans l'évaluation des dommages au niveau des bâtiments, en utilisant des données sismiques in-situ enregistrées aux étages supérieurs et inférieurs des bâtiments
Genome-Wide Analysis Of Peg3 Downstream Genes
Get PDFPaternally expressed gene 3 (Peg3) is an imprinted gene encoding a DNA-binding protein that is a predicted transcriptional repressor. The two studies presented here demonstrate the repressive functions of PEG3 in regulating different cellular processes and pathways, which were observed by analyzing the ChIP-seq results systematically using both bioinformatics tools and manual inspection of significant peaks. In the first part of the work, genome-wide features of ChIP-seq peaks were characterized and the potential de novo binding motif was reevaluated. Results suggested the transcriptional repressor role of PEG3 by binding to the conserved sequence motif of the target genes. The results also revealed the unusually higher percentage of distal intergenic PEG3 peaks, suggesting the potential role of PEG3 by binding to enhancers.
Previous studies have shown that the mutant phenotypes of Peg3 are associated with the over-expression of genes involved in lipid metabolism. In addition, the initial inspection identified Acly, Fasn, Idh1, and Hmgcr as potential downstream genes. Therefore, the second part of this study was focused on assessing the functional role of PEG3 in affecting lipogenesis since these four genes are the critical genes in the lipogenesis pathway. In vivo binding of PEG3 to the promoter region of these four key genes was confirmed through individual ChIP experiments. The opposite response of Acly expression levels against the variable gene dosages of Peg3, involving 0x, 1x, and 2x Peg3 was observed. This confirmed the transcriptional repressor role of Peg3 in the expression levels of Acly. Another set of analyses showed a sex-biased response in the expression levels of Acly, Fasn, and Idh1 against 0x Peg3 with higher levels in female and lower levels in male mammary glands. These results highlighted that Peg3 may be involved in regulating the expression levels of several key genes in adipogenesis.
Overall, this study presented in this dissertation contributes to further our understanding of the regulation and functional aspects of Peg3 and other imprinted genes
Modèles basés sur des données pour l'évaluation des dommages sismiques des bâtiments : utilisation de l'apprentissage automatique à l'échelle régionale et de la surveillance structurelle in situ à l'échelle du bâtiment
No full textDecision-makers and stakeholders must have quick assessment of potential damage from earthquakes and its distribution to plan for successful emergency response. There are several methods for estimating building damage from earthquakes, but they involve many assumptions associated to a large set of parameters that controls the building response during earthquake, that can add uncertainty. Using real-world data from full-scale observations can help better understand the physical processes involved during seismic loading in building and identify sources of uncertainty in damage estimation. There is now access to a large amount of information describing the real estate building portfolios, collected through post-earthquake damage surveys at regional level and in-situ measurements in building. The objective of this study is to test the effectiveness and relevance of supervised machine learning methods for analyzing spatially distributed seismic damage after an earthquake at the regional scale. Additionally, we aim to quantify uncertainties in building damage assessment at the building-level, using in-situ earthquake data recorded at the top and the bottom floors of buildings.Les décideurs et les parties prenantes doivent disposer d'une évaluation rapide des dommages potentiels causés par les tremblements de terre et de leur distribution afin de planifier une réponse d'urgence efficace. Il existe plusieurs méthodes pour estimer les dommages causés aux bâtiments par les tremblements de terre, mais elles impliquent de nombreuses hypothèses associées à un large ensemble de paramètres qui contrôlent la réponse du bâtiment pendant le tremblement de terre, ce qui peut ajouter de l'incertitude. L'utilisation de données réelles provenant d'observations à grande échelle peut aider à mieux comprendre les processus physiques impliqués dans la charge sismique des bâtiments et à identifier les sources d'incertitude dans l'estimation des dommages. Il est maintenant possible d'accéder à une grande quantité d'informations décrivant les portefeuilles de bâtiments immobiliers, recueillies par des enquêtes sur les dommages post-séisme au niveau régional et des mesures in situ dans les bâtiments. L'objectif de cette étude est de tester l'efficacité et la pertinence des méthodes d'apprentissage automatique supervisé pour analyser les dommages sismiques spatialement distribués après un tremblement de terre à l'échelle régionale. En outre, nous visons à quantifier les incertitudes dans l'évaluation des dommages au niveau des bâtiments, en utilisant des données sismiques in-situ enregistrées aux étages supérieurs et inférieurs des bâtiments
Modèles basés sur des données pour l'évaluation des dommages sismiques des bâtiments : utilisation de l'apprentissage automatique à l'échelle régionale et de la surveillance structurelle in situ à l'échelle du bâtiment
No full textDecision-makers and stakeholders must have quick assessment of potential damage from earthquakes and its distribution to plan for successful emergency response. There are several methods for estimating building damage from earthquakes, but they involve many assumptions associated to a large set of parameters that controls the building response during earthquake, that can add uncertainty. Using real-world data from full-scale observations can help better understand the physical processes involved during seismic loading in building and identify sources of uncertainty in damage estimation. There is now access to a large amount of information describing the real estate building portfolios, collected through post-earthquake damage surveys at regional level and in-situ measurements in building. The objective of this study is to test the effectiveness and relevance of supervised machine learning methods for analyzing spatially distributed seismic damage after an earthquake at the regional scale. Additionally, we aim to quantify uncertainties in building damage assessment at the building-level, using in-situ earthquake data recorded at the top and the bottom floors of buildings.Les décideurs et les parties prenantes doivent disposer d'une évaluation rapide des dommages potentiels causés par les tremblements de terre et de leur distribution afin de planifier une réponse d'urgence efficace. Il existe plusieurs méthodes pour estimer les dommages causés aux bâtiments par les tremblements de terre, mais elles impliquent de nombreuses hypothèses associées à un large ensemble de paramètres qui contrôlent la réponse du bâtiment pendant le tremblement de terre, ce qui peut ajouter de l'incertitude. L'utilisation de données réelles provenant d'observations à grande échelle peut aider à mieux comprendre les processus physiques impliqués dans la charge sismique des bâtiments et à identifier les sources d'incertitude dans l'estimation des dommages. Il est maintenant possible d'accéder à une grande quantité d'informations décrivant les portefeuilles de bâtiments immobiliers, recueillies par des enquêtes sur les dommages post-séisme au niveau régional et des mesures in situ dans les bâtiments. L'objectif de cette étude est de tester l'efficacité et la pertinence des méthodes d'apprentissage automatique supervisé pour analyser les dommages sismiques spatialement distribués après un tremblement de terre à l'échelle régionale. En outre, nous visons à quantifier les incertitudes dans l'évaluation des dommages au niveau des bâtiments, en utilisant des données sismiques in-situ enregistrées aux étages supérieurs et inférieurs des bâtiments
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