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International Conference on Inventive Computation Technologies

作者： Aman Singh Babita Pandey Computer Science and Engineering Lovely Professional University Punjab India Compter Application Department Lovely Professional University Punjab India

ISBN: (纸本)9781509012862

Liver is one of the vital organs of human body. It performs number of metabolic functions that are essential for living a healthy life. Early diagnosis of liver disease is a difficult task because the symptoms are more visible in later stages of the damage. Appropriate evaluation of patients becomes a challenge for clinicians which eventually make the disease more alarming. This study according aims to construct an intelligent computing method for classifying various degree of liver damage. Correct identification of degree of liver damage will help the physicians to give appropriate amount of dose to liver patients. For implementation, linear discriminant analysis (LDA), diagonal linear discriminant analysis (DLDA), quadratic discriminant analysis (QDA), diagonal quadratic discriminant analysis (DQDA), classification and regression tree (CART) and k-nearest neighbors (KNN) are deployed. Attained simulation results demonstrated that euclidean distance metric based KNN computational method outperforms all and has given remarkably good results with a prediction accuracy of 92.53%.

关键词： Liver disorders k-nearest neighbor computational method classification algorithms

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Design of Potential RNAi (miRNA and siRNA) Molecules for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Gene Silencing by computational method

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INTERDISCIPLINARY SCIENCES-computational LIFE SCIENCES 2015年第3期7卷 257-265页

作者： Nur, Suza Mohammad Hasan, Md. Anayet Al Amin, Mohammad Hossain, Mehjabeen Sharmin, Tahmina Univ Chittagong Fac Biol Sci Dept Genet Engn & Biotechnol Chittagong 4331 Bangladesh Mawlana Bhashani Sci & Technol Univ Dept Biotechnol & Genet Engn Santosh 1902 Tangail Bangladesh

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF1ab replicase polyprotein may be used as a suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence-specific manner. However, there is a genetic inconsistency in different viral isolates;it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In the current study, four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.

关键词： MERS-CoV RNAi Antiviral Gene silencing computational method

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A New computational method of the Friction Power Loss of Spiral Bevel Gears of a Helicopter Reducer 5

A New Computational Method of the Friction Power Loss of Spi...

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5th International Conference on Transportation Engineering, ICTE 2015

作者： Li, Yuan Yuan, Jiehong Song, Lijun Mei, Guiming Zhang, Kunlun Lin, Zejin Department of Traffic and Transportation Engineering College of Basic Education National University of Defense Technology Changsha China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle Engineering Hunan University Changsha Hunan China State Key Laboratory of Traction Power Southwest Jiaotong University Chengdu China

ISBN: (数字)9780784479384

ISBN: (纸本)9780784479384

The objective of this paper is to develop a new computational method of friction power loss of the spiral bevel gears transmission system of the helicopter tail reducer. At first, the velocity and force of the meshing point were analyzed during meshing process. Then a computational model of friction power loss for spiral bevel gear was established based on the principle of meshing and tribology theory, considering the effects of actual contact ratio and time-variant factors. This model considered the effects of actual contact ratio and time-variant factors including mesh point relative sliding velocity of mesh point, normal load, friction coefficient, etc. © ASCE.

关键词： computational method Friction power loss Spiral bevel gears spiral bevel gears computational methods power loss point of engagement contact ratio friction power BEVEL GEAR engaging

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The reverse-DADI method: Computation of frequency-dependent atomic polarizabilities for carbon and hydrogen atoms in hydrocarbon structures

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JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER 2024年 329卷

作者： Brosseau-Habert, N. Miradji, F. Picaud, S. Devel, M. Univ Franche Comte Inst FEMTO ST UMR 6274 SUPMICROTECH Besancon France Univ Franche Comte Inst UTINAM UMR 6213 CNRS Besancon France

A specific method, combining some ingredients of the well-known DDA and PDI approaches, has been developed in our group since many years to calculate the absorption cross-sections of carbonaceous nanoparticles based on their atomistic details. This method, here named the Dynamic Atomic Dipole Interaction (DADI) model, requires the knowledge of the position and frequency-dependent polarizability of each atom constituting the nanoparticles. While the atomic positions can be quite easily obtained, for example as the results of molecular dynamics simulations, obtaining the frequency-dependent atomic polarizabilities is a trickier task. Here, a fitting procedure, named the reverse-DADI method, has been applied to calculate the frequency-dependent atomic polarizability values for carbon and hydrogen atoms involved in aromatic cycles or in aliphatic chains, on the basis of frequency-dependent molecular polarizabilities of various PAH and alkane molecules, calculated with the TD-DFT theory, in the UV-Visible range. Then, using these frequency-dependent atomic polarizabilities as input parameters in the DADI model has been shown to lead to an accurate representation of the absorption cross-sections of various PAH and alkane molecules with respect to the corresponding values obtained at the TD-DFT level, with however the great advantage of a much shorter time of calculations. Furthermore, these results are indications of a good transferability of the frequency-dependent atomic polarizability values obtained here to any C or H atom of any PAH or alkane molecule. This opens the way for building large databases of optical properties for carbonaceous species of atmospheric or astrophysical interests.

关键词： Atomic polarizability Hydrocarbons Absorption cross section computational method Astrophysics

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Green's function molecular dynamics method for contact mechanics with a viscoelastic layer

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MATERIALS PHYSICS AND MECHANICS 2024年第1期52卷 60-68页

作者： Petrenko, V. G. Solovyov, I. U. Antonov, E. V. Dorogin, L. M. Murugesan, Y. ITMO Univ St Petersburg Russia Univ Padua Padua Italy

Advanced technological and engineering solutions involve usage of complex tribological systems. There is a demand for precise and computationally efficient methods to describe such systems. The aim of this study is to develop a computationally efficient method to solve the problem of deformation of a two -layer system using the Green's function molecular dynamics (GFMD) technique. We consider a viscoelastic layer attached to an elastic halfspace and derive a constitutive equation in Fourier space from the corresponding elastic solution. This thirdorder equation is numerically integrated by the backward Euler method, and a quasi -static solution is found through the fast inertial relaxation engine (FIRE) optimization algorithm. The method is illustrated with a simple model of indentation by a rigid cylinder. Using this method, contact area and pressure were calculated as a function of time for various shear modulus values.

关键词： boundary element method viscoelasticity contact mechanics computational method contact area

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Advanced computational methods in Lithium-Sulfur Batteries

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ADVANCED FUNCTIONAL MATERIALS 2024年第46期34卷

作者： Chen, Ruoxi Zhou, Yucheng He, Jiajun Li, Xiaodong Univ Virginia Dept Mech & Aerosp Engn 122 Engineers Way Charlottesville VA 22904 USA

Lithium-sulfur (Li-S) batteries, as one of the most promising "post-Li-ion" energy storage devices, encounter several intrinsic challenges: polysulfide dissolution and shuttle effect, poor sulfur utilization, lithiation-induced sulfur expansion, and lithium dendritic growth. These challenges must be resolved, and the associated mechanisms must be completely understood before the practical applications of Li-S batteries. Despite significant progress in enhancing battery capacities, experimental studies on the working mechanisms of Li-S batteries remain challenging. Alternatively, computational methods are proven useful for understanding Li-S electrochemistry and designing high-performance Li-S batteries. This review presents recent advances in computational methods (density functional theory, molecular dynamics simulations, and finite element analysis) for Li-S batteries, compares their advantages, and summarizes their favorable applications in addressing the challenges of Li-S batteries. computational methods should find more applications in the development of Li-S batteries. Many challenges need to be addressed to facilitate the practical use of lithium-sulfur (Li-S) batteries. computational methods are proven useful for understanding Li-S electrochemistry and designing high-performance Li-S batteries. This review presents state-of-the-art computational methods (density functional theory, molecular dynamics simulations, and finite element analysis), compares their advantages, and summarizes their favorable applications in tackling the challenges of Li-S batteries. image

关键词： computational method density functional theory lithium-sulfur battery molecular dynamics multiphysics finite element

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A computational method in analyzing of delivery time uncertainty for highly complex supply networks

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MEASUREMENT 2014年 55卷 549-563页

作者： Safaei, Mehdi Mehrsai, Afshin Thoben, Klaus Dieter Univ Bremen Fac Prod Engn Int Grad Sch D-28359 Bremen Germany BIBA Bremer Inst Prod & Logist GmbH IKAP Bremen Germany

Today, business models are invariably part of complex networks of suppliers, manufacturers and distributors. Uncertainty is recognized as an inevitable characteristic of supply networks and managers need to be aware of its specifications and consequences of that. Therefore, understanding, acknowledgment, and moderation of the causes and effect of uncertainty is crucial. Under-controlled uncertainty leads to the improvement of networks' performances and reliable networks. This paper complies with uncertain complex supply networks with their fundamental types. By defining critical routes in PERT networks, a combination of stochastic and mathematical models calculates the delivery time uncertainty in supply networks. This approach can be used as a tool for managers to control and monitor uncertainty in complex networks. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Complex supply network Delivery time uncertainty Probability density function computational method Dynamics in supply network

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Predicting disease-associated genes: computational methods, databases, and evaluations

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WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY 2021年第2期11卷 e1383-e1383页

作者： Luo, Ping Chen, Bolin Liao, Bo Wu, Fang-Xiang Univ Saskatchewan Div Biomed Engn Saskatoon SK Canada Univ Hlth Network Princess Margaret Canc Ctr Toronto ON Canada Northwestern Polytech Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China Hainan Normal Univ Sch Math & Stat Haikou Hainan Peoples R China Univ Saskatchewan Dept Mech Engn Saskatoon SK S7N 5C9 Canada Univ Saskatchewan Dept Comp Sci Saskatoon SK S7N 5C9 Canada

Complex diseases are associated with a set of genes (called disease genes), the identification of which can help scientists uncover the mechanisms of diseases and develop new drugs and treatment strategies. Due to the huge cost and time of experimental identification techniques, many computational algorithms have been proposed to predict disease genes. Although several review publications in recent years have discussed many computational methods, some of them focus on cancer driver genes while others focus on biomolecular networks, which only cover a specific aspect of existing methods. In this review, we summarize existing methods and classify them into three categories based on their rationales. Then, the algorithms, biological data, and evaluation methods used in the computational prediction are discussed. Finally, we highlight the limitations of existing methods and point out some future directions for improving these algorithms. This review could help investigators understand the principles of existing methods, and thus develop new methods to advance the computational prediction of disease genes. This article is categorized under: Technologies > Machine Learning Technologies > Prediction Algorithmic Development > Biological Data Mining

关键词： computational method disease gene prediction disease-related database machine learning random walk

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Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on computational methods

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Computer Modeling in Engineering & Sciences 2021年第10期129卷 31-45页

作者： Lejun Gong Xingxing Zhang Li Zhang Zhihong Gao Jiangsu Key Laboratory of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China

Novel coronavirus disease 2019(COVID-19)is an ongoing health *** studies are related to ***,its molecular mechanism remains *** rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational *** paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine *** method obtains seed genes based on prior *** genes are mined from biomedical *** candidate genes are scored by machine learning based on the similarities measured between the seed and candidate ***,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of *** results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

关键词： COVID-19 SARS-CoV-2 computational method bioinformatics genotype machine learning

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computational methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

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MOLECULAR THERAPY-NUCLEIC ACIDS 2020年 21卷 156-171页

作者： Yan, Congcong Zhang, Zicheng Bao, Siqi Hou, Ping Zhou, Meng Xu, Chongyong Sun, Jie Wenzhou Med Univ Sch Biomed Engn Sch Ophthalmol & Optometry Wenzhou 325027 Peoples R China Wenzhou Med Univ Eye Hosp Wenzhou 325027 Peoples R China Wenzhou Med Univ Affiliated Hosp 2 Dept Radiol Wenzhou 325027 Peoples R China

Long non-coding RNAs (lncRNAs) have been recognized as critical components of a broad genomic regulatory network and play pivotal roles in physiological and pathological processes. Identification of disease-associated lncRNAs is becoming increasingly crucial for fundamentally improving our understanding of molecular mechanisms of disease and developing novel biomarkers and therapeutic targets. Considering lower efficiency and higher time and labor cost of biological experiments, computer-aided inference of disease-associated RNAs has become a promising avenue for facilitating the study of lncRNA functions and provides complementary value for experimental studies. In this study, we first summarize data and knowledge resources publicly available for the study of lncRNA-disease associations. Then, we present an updated systematic overview of dozens of computational methods and models for inferring lncRNA-disease associations proposed in recent years. Finally, we explore the perspectives and challenges for further studies. Our study provides a guide for biologists and medical scientists to look for dedicated resources and more competent tools for accelerating the unraveling of disease-associated lncRNAs.

关键词： long non-coding RNAs disease lncRNA-disease association computational method bioinformatics

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