Evaluation of complex engineering models using model quality analysis

2012 ◽  
Vol 42 ◽  
pp. 410-419 ◽  
Author(s):  
Markus Reuter ◽  
Frank Werner
Keyword(s):  
Quality Analysis ◽  
Model Quality ◽  
Engineering Models ◽  
Complex Engineering

scholarly journals PSICA: a fast and accurate web service for protein model quality analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W443-W450 ◽  
Author(s):  
Wenbo Wang ◽  
Zhaoyu Li ◽  
Junlin Wang ◽  
Dong Xu ◽  
Yi Shang
Keyword(s):  
Web Service ◽  
Structural Information ◽  
Protein Structures ◽  
Quality Analysis ◽  
Consensus Method ◽  
Model Quality ◽  
Protein Model ◽  
Local Quality ◽  
The Difference

Abstract This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.

Hybrid multiplicative dimension reduction method for uncertainty analysis of engineering structures

2020 ◽  
pp. 1748006X2092997
Author(s):  
Haihe Li ◽  
Pan Wang ◽  
Qi Chang ◽  
Changcong Zhou ◽  
Zhufeng Yue
Keyword(s):  
Uncertainty Analysis ◽  
Dimension Reduction ◽  
Reduction Method ◽  
High Dimensional ◽  
Sensitivity Index ◽  
Dimension Reduction Method ◽  
Engineering Structures ◽  
High Order Derivative ◽  
Engineering Models ◽  
Complex Engineering

For uncertainty analysis of high-dimensional complex engineering problems, this article proposes a hybrid multiplicative dimension reduction method based on the existent multiplicative dimension reduction method. It uses the multiplicative dimension reduction method to approximate the original high-dimensional performance function which is sufficiently smooth and has a small high-order derivative as the product of a series of one-dimensional functions, and then uses this approximation to calculate the statistical moments of the function. Then the variance-based global sensitivity index is employed to identify the important variables, and the identified important variables are subjected to bivariate decomposition approximation. Combined with the univariate multiplicative dimension reduction method, the hybrid decomposition approximation is obtained. Compared with the existing method, the proposed method is more accurate than the univariate decomposition approximation when used for uncertainty analysis of engineering models and needs less computational efforts than the bivariate decomposition. In the end, a numerical example and two engineering applications are tested to verify the effectiveness of the proposed method.

scholarly journals Applying Monte Carlo Simulation in New Tech

2020 ◽  
Author(s):  
Levent Yilmaz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analysis Tool ◽  
Hydrogen Energy ◽  
Energy Potential ◽  
Highly Nonlinear ◽  
Statistical Analysis Tool ◽  
Engineering Models ◽  
Complex Engineering ◽  
Physical Phenomena

Monte Carlo in Monaco is given to the theory for mathematics, whose simulation process involves generating chance variables and exhibiting random behaviours in nature. This simulation is a powerful statistical analysis tool and widely used in both non-engineering fields and engineering fields for new perspectives. This simulation has been applied to diverse problems ranging from the simulation of complex physical phenomena such as atom collisions, to the simulation of river boundary layers as meanders and Dow Jones forecasting. It can deal with many random variables, various distribution types and highly nonlinear engineering models, while Monte Carlo is also suitable for solving complex engineering problems in two areas which are varying randomly. Monte Carlo simulation is given as an application for hydrogen energy potential determination.

scholarly journals Development of OCIPSE Learning Model to Increase Students’ Scientific Creativity in Natural Science Learning

2020 ◽  
Vol 1 (1) ◽  
pp. 1-18
Author(s):  
Rizqi ◽  
Prabowo ◽  
Tjandra Kirana
Keyword(s):  
High School Students ◽  
Junior High School ◽  
Natural Science ◽  
Science Learning ◽  
Learning Model ◽  
Assessment Instrument ◽  
Quality Analysis ◽  
Quality Data ◽  
Model Quality ◽  
Scientific Creativity

This Research & Development (R & D) has the main goal to develop and produce OCIPSE learning model. The main product of this research is the OCIPSE learning model with five phases, they are 1) Orient and organize the students for study; 2) Collaborative Investigation; 3) Presentation and discussion; 4) Strengthening of scientific creativity; and 5) Evaluate and provide recognition. The OCIPSE learning model’ quality data is obtained through an expert validation process by using the OCIPSE learning model Qualification Assessment Instrument. The OCIPSE learning model quality analysis used an average validity score, single measures ICC, and Cronbach's coefficient alpha. The result of the research shows OCIPSE learning model with average content validity (3.69), construct validity (3.69), with the validity of each aspect statistically in (rα = .92) and reliability in (α = .87).  The results of this study indicate that the developed OCIPSE learning model was declared qualified by experts. The research implication is that a qualified OCIPSE learning model can be used to enhance the scientific creativity of junior high school students in natural science learning. 

Effectiveness Of Life Cycle Management Of Projects Using Information Modeling

2019 ◽  
pp. 24-29 ◽  
Keyword(s):  
Life Cycle ◽  
Construction Projects ◽  
Life Cycle Management ◽  
Information Modeling ◽  
Engineering Systems ◽  
Basic Principles ◽  
Complex Engineering ◽  
Domestic Practices ◽  
Capital Construction ◽  
Complex Engineering Systems

Improving the efficiency of life cycle management of capital construction projects using information modeling technologies is one of the important tasks of the construction industry. The paper presents an analysis of accumulated domestic practices, including the legal and regulatory framework, assessing the effectiveness of managing the implementation of investment construction projects and of complex and serial capital construction projects, as well as the life cycle management of especially dangerous technically complex and unique capital construction projects using information modeling technologies, especially capital construction projects, as well as their supporting and using systems, primarily in the nuclear and transport sectors. A review of modern approaches to assessing the effectiveness of life cycle management systems of complex engineering systems in relation to capital construction projects is carried out. The presented material will make it possible to formulate the basic principles and prospects of applying approaches to assessing the effectiveness of the life cycle management system of a capital construction project using information modeling technologies.

Engineering-Geological Studies Of The Territory, Soils Of Foundation And Foundations Of The Architectural Monument Of The "Melnikov’s House" In Moscow

2019 ◽  
pp. 41-51
Keyword(s):  
Working Condition ◽  
Overlying Water ◽  
North East ◽  
The North ◽  
Geological Processes ◽  
Architectural Monument ◽  
Geological Conditions ◽  
Zone Of Influence ◽  
Complex Engineering ◽  
Building Engineering

On the basis of engineering and design surveys of the building, engineering-geological and geophysical studies of the soils of the territory conducted by the article authors, as well as with due regard for the results of studies conducted on this territory by other authors, the features of the foundations, soils of their foundation and engineering-geological conditions of the territory of the Melnikov House are established. It is shown that the Melnikov house is located under complex engineering-geological conditions on the territory of high geological risk, in the zone of influence of tectonic disturbance. To the North of the area there is a zone of intersection of the observed disturbance with a larger disturbance that can have an impact on geological processes. To the North-East of the site of the Melnikov House, a sharp immersion of the roof of carbon deposits was revealed. It promotes groundwater seepage into limestone of the carbonate strata from overlying water-bearing sands and activation of processes of suffusion removal and sinkhole phenomena of the soil. The surveyed area is assessed as potentially karst-hazardous and adjacent to it from the North-East territory as karst-dangerous. In this regard any construction on the adjacent territory can provoke activation of sinkhole phenomena on the surface. The foundations of the building are basically in working condition. Existing defects can be eliminated during repair. The foundation soils mainly have sufficient bearing capacity. Areas of the base with bulk soil can be reinforced. However, when developing a project for the reconstruction of the building and its territory, it should be taken into account that the design of the Melnikov House does not provide for its operation on the loads at the formation of sinkholes.

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