Specificity of Aircraft Crash Compared to Other Missile Impacts

2008 ◽  
Author(s):  
Pierre Kœchlin ◽  
Serguei¨ Potapov
Keyword(s):  
A Priori ◽  
Structural Response ◽  
Bending Moment ◽  
Experimental Tests ◽  
Research Field ◽  
Quantitative Classification ◽  
Stress Resultant ◽  
Physical Phenomena ◽  
Aircraft Crash

Before modeling an aircraft crash on a shield building, it is very important to understand the physical phenomena and the structural behavior associated with this kind of impact. In the scientific literature, aircraft crash is classified as a soft impact, or as an impact of deformable missile. Nevertheless the existing classifications are not precise enough to be able to predict the structural response mode. In this paper, the author proposes a quantitative classification of soft and hard impacts, based on structural considerations, and in accordance with existing definitions and moreover with intuition. The experimental tests carried out during the last thirty years in the research field of aircraft crash are reviewed in the light of the new classification. It shows that this characterization has a real physical meaning: it gives the limit between two kinds of failure. Furthermore, since it is on one hand an a priori classification and on the other hand expressed in terms of non-dimension variables, it is very helpful to calibrate new experimental tests for aircraft crash. Finally, using this classification, the paper explains that during an aircraft crash, the perforation process of a concrete shield building is the result of structural waves (bending and shear waves). It opens the way to a prediction of aircraft crash perforation based on a criterion expressed in terms of stress resultant variables (combined bending moment, shear force and membrane force).

scholarly journals Quantitative classification of carbonate aquifers based on hydrodynamic behaviour

2021 ◽  
Vol 29 (1) ◽  
pp. 33-52
Author(s):  
Attila Kovács
Keyword(s):  
A Priori ◽  
Base Flow ◽  
Quantitative Classification ◽  
Carbonate Aquifers ◽  
Functional Relationships ◽  
Hydrodynamic Behaviour ◽  
Crucial Information ◽  
Karst Systems ◽  
Hydrogeological Systems

AbstractA quantitative classification of carbonate aquifers based on hydrodynamic behaviour is introduced. This type of classification is necessary to understand the physical functioning of carbonate hydrogeological systems and to provide a realistic interpretation of field data. Carbonate aquifers are generally considered as karst systems; however, geomorphology and aquifer geology alone are insufficient for determining hydrodynamic behaviour. Analysis of spring and well hydrographs based on analytical solutions is applied to establishing a quantitative classification. A base-flow recession coefficient is used as an indicator of hydrodynamic behaviour. Detailed numerical analyses suggest that carbonate systems can be classified into two distinct groups based on hydrodynamic behaviour. The physical processes depend on a combination of hydraulic and geometric parameters, and their functional relationships can be quantitatively determined. The proposed classification methodology involves making an assumption about aquifer type, estimating aquifer properties from hydrograph data, and comparing the results with field observations. The proposed classification methodology was applied to aquifers representing the two groups of carbonate systems. In both cases, the applied methods revealed crucial information about hydrodynamic functioning of the investigated systems. While the studied limestone aquifer showed karstic hydrodynamic behaviour, the investigation of a dolomite aquifer disproves a priori assumptions on karstic flow conditions. Dolomite aquifers represent an ambiguous group of carbonates and require caution in the selection of investigation tools and interpretation of hydrogeological data. The introduced methodology provides a reliable means of determining the hydrodynamic functioning of an aquifer and supports the quantitative classification of carbonate hydrogeological systems.

Modeling Affordances and Functioning for Personalized Robotic Assistance

2020 ◽  
Author(s):  
Alessandro Umbrico ◽  
Gabriella Cortellessa ◽  
Andrea Orlandini ◽  
Amedeo Cesta
Keyword(s):  
Domain Knowledge ◽  
Experimental Tests ◽  
International Classification Of Functioning ◽  
Knowledge Representation And Reasoning ◽  
Health State ◽  
Knowledge Reasoning ◽  
Cognitive Assistance ◽  
Ontological Model ◽  
And Function

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.

scholarly journals Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gabriel A. Colozza-Gama ◽  
Fabiano Callegari ◽  
Nikola Bešič ◽  
Ana C. de J. Paviza ◽  
Janete M. Cerutti
Keyword(s):  
Machine Learning ◽  
Sanger Sequencing ◽  
Learning Algorithm ◽  
Absolute Quantification ◽  
Braf V600e Mutation ◽  
Braf V600e ◽  
Driver Genes ◽  
Quantitative Classification ◽  
Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

A Priori Typology-Based Prediction of Benthic Macroinvertebrate Fauna for Ecological Classification of Rivers

2008 ◽  
Vol 42 (5) ◽  
pp. 894-906 ◽  
Author(s):  
Jukka Aroviita ◽  
Esa Koskenniemi ◽  
Juho Kotanen ◽  
Heikki Hämäläinen
Keyword(s):  
A Priori ◽  
Benthic Macroinvertebrate ◽  
Ecological Classification ◽  
Macroinvertebrate Fauna

Quantitative classification of near-fault ground motions selected by energy indicators

Structures ◽  
2022 ◽  
Vol 35 ◽  
pp. 780-791
Author(s):  
Dahai Zhao ◽  
Huiwei Wang ◽  
Ding Wang ◽  
Ruiguang Zhu ◽  
Jinghui Zhang
Keyword(s):  
Ground Motions ◽  
Quantitative Classification ◽  
Near Fault ◽  
Energy Indicators

The relevance of oral language skills to early literacy: A multivariate analysis

1999 ◽  
Vol 20 (2) ◽  
pp. 167-190 ◽  
Author(s):  
DEBORAH L. SPEECE ◽  
FROMA P. ROTH ◽  
DAVID H. COOPER ◽  
SUSAN DE LA PAZ
Keyword(s):  
Early Literacy ◽  
Oral Language ◽  
Language Skills ◽  
A Priori ◽  
Kindergarten Children ◽  
Language And Literacy ◽  
Direct Role ◽  
Oral Language Skills ◽  
Subtype Differences

This study examined relationships between oral language and literacy in a two-year, multivariate design. Through empirical cluster analysis of a sample of 88 kindergarten children, four oral language subtypes were identified based on measures of semantics, syntax, metalinguistics, and oral narration. Validation efforts included (a) concurrent and predictive analyses of subtype differences on reading, spelling, and listening comprehension measures based on a priori hypotheses and (b) a comparison of the teacher classification of the children with the empirical classification. The subtypes represented high average, low average, high narrative, and low overall patterns of oral language skill. The high average subtype received the most consistent evidence for validation. The pattern of validation results indicates that the relationship between oral language and literacy is not uniform and suggests a modification of the assumption that oral language skills have a direct role in reading acquisition.

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