Dynamic Computational Analyses of Complete Scramjet Engine Modules

2001 ◽  
Author(s):  
D. S. McRae ◽  
Jack R. Edwards
Keyword(s):  
Scramjet Engine ◽  
Computational Analyses

Computational Analyses and Design Improvements of Graft-to-Vein Anastomoses

2000 ◽  
Vol 28 (1-2) ◽  
pp. 141-147 ◽  
Author(s):  
P. W. Longest ◽  
Clement Kleinstreuer ◽  
P. J. Andreotti
Keyword(s):  
Computational Analyses

scholarly journals Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 525 ◽  
Author(s):  
Mehdi Keshavarz-Ghorabaee ◽  
Maghsoud Amiri ◽  
Edmundas Kazimieras Zavadskas ◽  
Zenonas Turskis ◽  
Jurgita Antucheviciene
Keyword(s):  
Essential Elements ◽  
New Method ◽  
Multi Criteria Decision Making ◽  
Analysis Of Means ◽  
Simulation Based ◽  
Criteria Weights ◽  
The Stability ◽  
Computational Analyses ◽  
Weighting Methods

The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

Design and analysis of an inlet and combustion chamber of scramjet engine

2020 ◽  
Author(s):  
Karthikeyan Paramasivam ◽  
Anderson Arul Gnana Dhas ◽  
Nivin Joy ◽  
Kanimozhi Balakrishnan ◽  
Vedagiri Sri Harsha ◽  
...  
Keyword(s):  
Combustion Chamber ◽  
Scramjet Engine

scholarly journals Volatile organic compound patterns predict fungal trophic mode and lifestyle

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yuan Guo ◽  
Werner Jud ◽  
Fabian Weikl ◽  
Andrea Ghirardo ◽  
Robert R. Junker ◽  
...  
Keyword(s):  
Organic Compound ◽  
Fungal Species ◽  
Chemical Diversity ◽  
Phenotypic Integration ◽  
Ecological Studies ◽  
Volatile Organic ◽  
Ecological Importance ◽  
Fungal Interactions ◽  
Computational Analyses ◽  
Different Levels

AbstractFungi produce a wide variety of volatile organic compounds (VOCs), which play central roles in the initiation and regulation of fungal interactions. Here we introduce a global overview of fungal VOC patterns and chemical diversity across phylogenetic clades and trophic modes. The analysis is based on measurements of comprehensive VOC profiles of forty-three fungal species. Our data show that the VOC patterns can describe the phyla and the trophic mode of fungi. We show different levels of phenotypic integration (PI) for different chemical classes of VOCs within distinct functional guilds. Further computational analyses reveal that distinct VOC patterns can predict trophic modes, (non)symbiotic lifestyle, substrate-use and host-type of fungi. Thus, depending on trophic mode, either individual VOCs or more complex VOC patterns (i.e., chemical communication displays) may be ecologically important. Present results stress the ecological importance of VOCs and serve as prerequisite for more comprehensive VOCs-involving ecological studies.

Experimental and Computational Analyses of the Oxidation Mechanism of the Poly(arylsilane) Family as the Side Reaction during the Baking Process

2020 ◽  
Vol 124 (29) ◽  
pp. 16149-16158
Author(s):  
Osamu Kobayashi ◽  
Kunihiro Noda ◽  
Naohiko Ikuma ◽  
Dai Shiota ◽  
Takayoshi Ishimoto ◽  
...  
Keyword(s):  
Oxidation Mechanism ◽  
Side Reaction ◽  
Baking Process ◽  
Computational Analyses

scholarly journals FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

2021 ◽  
Author(s):  
Maxwell Adam Levinson ◽  
Justin Niestroy ◽  
Sadnan Al Manir ◽  
Karen Fairchild ◽  
Douglas E. Lake ◽  
...  
Keyword(s):  
Computational Result ◽  
Large Scale ◽  
Graph Model ◽  
Inferential Reasoning ◽  
Computational Framework ◽  
Textual Description ◽  
Evidence Graph ◽  
Computational Analyses ◽  
Processing Steps ◽  
Multiple Processing

AbstractResults of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

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