How the choice of a computational model could rule the chemical interpretation: The Ni(II) catalyzed ethylene dimerization as a case study

2009 ◽  
pp. NA-NA
Author(s):  
Vincent Tognetti ◽  
Pascal Le Floch ◽  
Carlo Adamo
Keyword(s):  
Computational Model ◽  
Ethylene Dimerization

Numerical Simulation of Inner Support for Excavation Based on FEM Software ABAQUS

2012 ◽  
Vol 236-237 ◽  
pp. 632-635
Author(s):  
Yue Sun ◽  
Yue Nan Chen ◽  
Zhi Yun Wang
Keyword(s):  
Numerical Simulation ◽  
Finite Element ◽  
Computational Model ◽  
Dimensional Space ◽  
General Purpose ◽  
Two Dimensional ◽  
Clay Layer ◽  
Finite Element Software ◽  
Spring Element

In two-dimensional space, an elasto-plastic finite element computational model was established to simulate inner support for excavation on the basis of the general-purpose finite element software ABAQUS. The soil was assumed to be a uniform and normally consolidated clay layer and strut was discreted by spring element. Compared with published case study, it can be concluded that FEM software AQAQUS can present one reliable simulation progress of inner support for excavation.

scholarly journals A Cognitive Computational Model Inspired by the Immune System Response

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Mohamed Abdo Abd Al-Hady ◽  
Amr Ahmed Badr ◽  
Mostafa Abd Al-Azim Mostafa
Keyword(s):  
Immune System ◽  
Computational Model ◽  
Cognitive Architecture ◽  
Intelligent Agent ◽  
System Response ◽  
Danger Theory ◽  
Proposed Model ◽  
Immune System Response ◽  
Fuzzy State

The immune system has a cognitive ability to differentiate between healthy and unhealthy cells. The immune system response (ISR) is stimulated by a disorder in the temporary fuzzy state that is oscillating between the healthy and unhealthy states. However, modeling the immune system is an enormous challenge; the paper introduces an extensive summary of how the immune system response functions, as an overview of a complex topic, to present the immune system as a cognitive intelligent agent. The homogeneity and perfection of the natural immune system have been always standing out as the sought-after model we attempted to imitate while building our proposed model of cognitive architecture. The paper divides the ISR into four logical phases: setting a computational architectural diagram for each phase, proceeding from functional perspectives (input, process, and output), and their consequences. The proposed architecture components are defined by matching biological operations with computational functions and hence with the framework of the paper. On the other hand, the architecture focuses on the interoperability of main theoretical immunological perspectives (classic, cognitive, and danger theory), as related to computer science terminologies. The paper presents a descriptive model of immune system, to figure out the nature of response, deemed to be intrinsic for building a hybrid computational model based on a cognitive intelligent agent perspective and inspired by the natural biology. To that end, this paper highlights the ISR phases as applied to a case study on hepatitis C virus, meanwhile illustrating our proposed architecture perspective.

scholarly journals A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yubin Xiao ◽  
Zheng Xiao ◽  
Xiang Feng ◽  
Zhiping Chen ◽  
Linai Kuang ◽  
...  
Keyword(s):  
Computational Model ◽  
Cross Validation ◽  
State Of The Art ◽  
Prediction Methods ◽  
Good Prediction ◽  
Average Case ◽  
Comparison Results ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

scholarly journals A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

2020 ◽  
Author(s):  
Yubin Xiao ◽  
Zheng Xiao ◽  
Xiang Feng ◽  
Zhiping Chen ◽  
Linai Kuang ◽  
...  
Keyword(s):  
Computational Model ◽  
Cross Validation ◽  
State Of The Art ◽  
Prediction Methods ◽  
Good Prediction ◽  
Average Case ◽  
Comparison Results ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.Results: In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (5-fold CV), 10-Fold Cross Validation (10-fold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in 5-fold CV, 10-fold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.Conclusion: The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

scholarly journals Computational Simulation Model of Milk Production Process, Case Study: Dairy Plant FCP-ESPOCH

2018 ◽  
Vol 1 (2) ◽  
pp. 179
Author(s):  
Iván Menes Camejo ◽  
Gladys Lorena Aguirre Sailema ◽  
Katherine Maribel Gallegos Carrillo ◽  
Jorge Ariel Menéndez Verdecia
Keyword(s):  
Computational Model ◽  
Simulation Model ◽  
Production Process ◽  
Milk Production ◽  
Queuing Theory ◽  
Computational Simulation ◽  
Production Environments ◽  
Dairy Plant ◽  
Future Experimentation

Abstract. Computational simulation is a powerful tool that allows the experimentation of variants in production environments, which made on real scenarios, would entail heavy costs for the company. For this, it is necessary to correctly define the model that represents the actual processes involved. This paper presents the development of a computational simulation model, developed with "Siman" programming language and "Arena" software, based on queuing theory for the processes of milk production process in the Dairy Plant FCP-ESPOCH. We sought to determine the efficiency of the computational model using the scientific method, techniques of descriptive statistics and hypothesis demonstration. The results indicate that the data of the model are similar to the real ones in the processes of Daily Crude Milk Reception and Production Daily Pasteurized Milk, concluding that the computational model is valid for future experimentation.

Development of a Computational Model to Evaluate Steam Surface Condenser Tubing Modifications

2007 ◽  
Author(s):  
Richard E. Pearce ◽  
Bryan R. Becker
Keyword(s):  
Heat Exchanger ◽  
Computational Model ◽  
Operating Cost ◽  
Heat Rate ◽  
Summation Method ◽  
Condenser Tube ◽  
The Impact ◽  
Fuel Costs ◽  
The Ideal

The objective was to provide a useful computational tool for assessing the impact of condenser tube modifications on power plant condenser performance and unit heat rate, which directly affect the operating cost. To achieve this, a methodology was utilized to evaluate the economics of condenser modifications based upon design and economic information. The numerical model of condenser performance was developed using the Heat Exchanger Institute method and the Resistance Summation method. The software calculates results based on both methods but this paper will only discuss the results from the Heat Exchanger Institute method for the case study. Since condenser performance has a direct impact upon fuel costs, heat rate, power production and emissions, this computational model can be used to assess the economic impact of a proposed condenser tubing replacement over a specified service life. A case study will be discussed concerning a condenser tube replacement project that was analyzed to determine the ideal replacement tube material for the relevant parameters associated with this particular unit.

scholarly journals Technical Notes: Computational Model for Housing Drainage System Case Study: Kharismatama Permai Housing in Padang

2012 ◽  
Vol 19 (1) ◽  
pp. 83
Author(s):  
Mas Mera ◽  
Yessi Puspita Dewi ◽  
Deri Saputra ◽  
Zelfa Lonna Monica
Keyword(s):  
Computational Model ◽  
Drainage System

A Computational Model for Wells’ Performance Analysis

2021 ◽  
Author(s):  
Okon Edet Ita ◽  
Dulu Appah
Keyword(s):  
Computational Model ◽  
Niger Delta ◽  
Operating Cost ◽  
Production Operation ◽  
Analysis Method ◽  
Well Performance ◽  
Nodal Analysis ◽  
Field Development ◽  
Reduce Operating Cost

Abstract The ability to identify underperforming wells and recover the remaining oil in place is a cornerstone for effective reservoir management and field development strategies. As advancement in computing programming capabilities continuous to grow, Python has become an attractive method to build complicated statistical models that predicts, diagnose or analyze well performance, efficiently and accurately. The aim of this study is to develop a computational model that will allows us to diagnose and analyze well performance using nodal analysis with the help of python. In this study, python was used to compute Nodal analysis method using Darcy and Vogel Equations. A case study was carried out using the data obtained from a field operating in the Niger Delta. Again, sensitivity of tubing size was conducted using python. The results obtained showed that a computational model with python has the ability to visualize, model and analyze wells performances. This technique will petroleum engineers to better monitor evaluate and enhance their production operation without the need for expensive softwares. This will reduce operating cost increases revenue.

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