1
|
Bashir S, Almanjahie IM, Ramzan M, Cheema AN, Akhtar M, Alshahrani F. Impact of induced magnetic field on Darcy-Forchheimer nanofluid flows comprising carbon nanotubes with homogeneous-heterogeneous reactions. Heliyon 2024; 10:e24718. [PMID: 38317883 PMCID: PMC10838730 DOI: 10.1016/j.heliyon.2024.e24718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 12/14/2023] [Accepted: 01/12/2024] [Indexed: 02/07/2024] Open
Abstract
The appealing traits of carbon nanotubes (CNTs) encompassing mechanical and chemical steadiness, exceptional electrical and thermal conductivities, lightweight, and physiochemical reliability make them desired materials in engineering gadgets. Considering such stimulating characteristics of carbon nanotubes, our goal in the current study is to scrutinize the comparative analysis of Darcy-Forchheimer nanofluid flows containing CNTs of both types of multi and single-wall carbon nanotubes (MWCNTs, SWCNTs) immersed into two different base fluids over a stretched surface. The originality of the model being presented is the implementation of the induced magnetic field that triggers the electric conductivity of carbon nanotubes. Moreover, the envisioned model is also analyzed with homogeneous-heterogeneous (h-h) chemical reactions and heat source/sink. The second-order slip constraint is assumed at the boundary of the surface. The transmuted high-nonlinearity ordinary differential equations (ODEs) are attained from the governing set of equations via similarity transformations. The bvp4c scheme is engaged to get the numerical results. The influence of different parameters is depicted via graphs. For both CNTs, the rate of heat flux and the surface drag coefficient are calculated using tables. It is highlighted that an increase in liquid velocity is witnessed for a varied counts volume fraction of nanoparticles. Also, Single-wall water-based carbon nanotube fluid has comparatively stronger effects on concentration than the multi-walled carbon nanotubes in water-based liquid. The analysis also indicates that the rate of heat flux and the surface drag coefficient are augmented for both SWCNTs and MWCNTs for different physical parameters. The said model is also validated by comparing it with a published result.
Collapse
Affiliation(s)
- Seemab Bashir
- Department of Mathematics, Air University, 44000, Islamabad, Pakistan
| | - Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha 62223, Saudi Arabia
| | - Muhammad Ramzan
- Department of Computer Science, Bahria University, Islamabad, 44000, Pakistan
| | | | - Muhammad Akhtar
- Fast School of Management, National University of Computer & Emerging Sciences, A.K. Brohi Road, H-11/4, Islamabad, Pakistan
| | - Fatimah Alshahrani
- Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| |
Collapse
|
2
|
ul Haq E, Ahmad I, Hussain A, Almanjahie IM. Mixture distribution based real-coded crossover: A hybrid probabilistic approach for global optimization. IFS 2022. [DOI: 10.3233/jifs-210886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In the present simulation-based study, a novel parent-centric real-coded crossover operator is introduced with a unique probabilistic aspect of the mixture distribution. Moreover, the mixture distribution is a co-integration of double Pareto and Laplace probability distributions with various parameters. The key objective of the newly proposed methodology is to obtained optimal solutions for complex multimodal optimization problems. Hence, for its global comparison, the newly proposed mixture distribution crossover operator (MDX) is compared with double Pareto (DPX), Laplace (LX), and simulated binary (SBX) crossover operators within the conjunction of three mutation operators (MTPM, PM, and NUM). After a descriptive comparison, a Quade multiple comparison test is also administered to examine its statistical significance. Furthermore, the performance of the genetic algorithm (GA) is also examined on a set of twenty-one unconstraint benchmark functions with diverse features. The empirical results of the simulation-based study reveal that the mixture-based crossover operator obtained a substantial dominance over all considered crossover operators in terms of computational complexity, robustness, scalability, and capability of exploration and exploitation. Moreover, the Quade multiple comparison test also showed a significant superiority with graphical authentication of the performance index (PI).
Collapse
Affiliation(s)
- Ehtasham ul Haq
- Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan
| | - Ishfaq Ahmad
- Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan
| | - Abid Hussain
- Department of Statistics, Faculty of Natural Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Kingdom of Saudi Arabia
- Statistical Research and Study Support Unit, King Khalid University, Abha, Kingdom of Saudi Arabia
| |
Collapse
|
3
|
Affiliation(s)
- Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| | - Salim Bouzebda
- Université de Technologie de Compiègne, L.M.A.C., Compiègne, France
| | - Zoulikha Kaid
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| | - Ali Laksaci
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| |
Collapse
|
4
|
Almanjahie IM, Attouch MK, Kaid Z, Louhab H. Robust equivariant non parametric regression estimators for functional ergodic data. COMMUN STAT-THEOR M 2021. [DOI: 10.1080/03610926.2019.1705980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Mohammed Kadi Attouch
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Laboratoire de Probabilités Statistique Processus Stochastiques, Université Djillali Liabès, Sidi Bel-Abbès, Algérie
| | - Zoulikha Kaid
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Hayat Louhab
- Laboratoire de Probabilités Statistique Processus Stochastiques, Université Djillali Liabès, Sidi Bel-Abbès, Algérie
| |
Collapse
|
5
|
Almanjahie IM, Kaid Z, Laksaci A, Rachdi M. Predicting temperature curve based on fast kNN local linear estimation of the conditional distribution function. PeerJ 2021; 9:e11719. [PMID: 34285835 PMCID: PMC8274495 DOI: 10.7717/peerj.11719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/13/2021] [Indexed: 11/26/2022] Open
Abstract
Predicting the yearly curve of the temperature, based on meteorological data, is essential for understanding the impact of climate change on humans and the environment. The standard statistical models based on the big data discretization in the finite grid suffer from certain drawbacks such as dimensionality when the size of the data is large. We consider, in this paper, the predictive region problem in functional time series analysis. We study the prediction by the shortest conditional modal interval constructed by the local linear estimation of the cumulative function of \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}$Y$\end{document}Y given functional input variable \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}$X$\end{document}X. More precisely, we combine the \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}$k$\end{document}k-Nearest Neighbors procedure to the local linear algorithm to construct two estimators of the conditional distribution function. The main purpose of this paper is to compare, by a simulation study, the efficiency of the two estimators concerning the level of dependence. The feasibility of these estimators in the functional times series prediction is examined at the end of this paper. More precisely, we compare the shortest conditional modal interval predictive regions of both estimators using real meteorological data.
Collapse
Affiliation(s)
- Ibrahim M Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, South Region, Saudi Arabia.,Statistical Research and Studies Support Unit, King Khalid University, Abha, South Region, Saudi Arabia
| | - Zoulikha Kaid
- Department of Mathematics, College of Science, King Khalid University, Abha, South Region, Saudi Arabia.,Statistical Research and Studies Support Unit, King Khalid University, Abha, South Region, Saudi Arabia
| | - Ali Laksaci
- Department of Mathematics, College of Science, King Khalid University, Abha, South Region, Saudi Arabia.,Statistical Research and Studies Support Unit, King Khalid University, Abha, South Region, Saudi Arabia
| | - Mustapha Rachdi
- AGIM Team, Laboratoire AGEIS, Universite' Grenoble Alpes (France), Universit'e Grenoble Alpes, Grenoble, France
| |
Collapse
|
6
|
Shahzad U, Ahmad I, Almanjahie IM, Al-Noor NH, Hanif M. A novel family of variance estimators based on L-moments and calibration approach under stratified random sampling. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1945629] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Usman Shahzad
- Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan
- Department of Mathematics and Statistics, PMAS-Arid Agriculture University, Rawalpindi, Pakistan
| | - Ishfaq Ahmad
- Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan
| | - Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| | - Nadia H. Al-Noor
- Department of Mathematics, College of Science, Mustansiriyah University, Baghdad, Iraq
| | - Muhammad Hanif
- Department of Mathematics and Statistics, PMAS-Arid Agriculture University, Rawalpindi, Pakistan
| |
Collapse
|
7
|
Almanjahie IM, Alahmari WM, Laksaci A, Rachdi M. Computational aspects of the kNN local linear smoothing for some conditional models in high dimensional statistics. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1923745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Wafa Mesfer Alahmari
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Ali Laksaci
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Mustapha Rachdi
- Laboratoire AGEIS EA 7407, UFR SHS, University of Grenoble, Grenoble Cedex 09, France
| |
Collapse
|
8
|
Al-Omari AI, Almanjahie IM, Dar JG. Acceptance sampling plans under two-parameter Quasi Shanker distribution assuring mean life with an application to manufacturing data. Sci Prog 2021; 104:368504211014350. [PMID: 33950756 PMCID: PMC10305827 DOI: 10.1177/00368504211014350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The acceptance sampling plan (ASP) is a statistical tool used in industry for quality control to determine the quality of products by selecting a specified number for testing in order to accept or reject the lot. The main objective is to develop a new ASP based on truncated life tests assuming that the lifetime follows the two parameters Quasi Shanker distribution, since this distribution showed its superiority in providing a better model for some applications than the exponential distribution. The ASP steps are carried out to find the minimum sample sizes needed to assert the certain life mean that are calculated under a given customer's risk. The operating characteristic values of the sampling plan and the producer risk values are obtained. The efficiency of the suggested plans is analyzed based on real data that is fitted to the Quasi Shanker distribution. For various values of the Quasi Shanker distribution parameters, numerical examples are presented for illustrative purposes. The results indicate that the suggested ASP provides smaller sample sizes than other competitors considered in this study. The suggested ASP has been found to provide a substantial sampling economy in terms of reducing the sample. Hence, it is recommended that the ASP can be used in industry and for future research works as double and group ASP.
Collapse
Affiliation(s)
- Amer Ibrahim Al-Omari
- Department of Mathematics, Faculty of
Science, Al al-Bayt University, Mafraq, Jordan
| | - Ibrahim M Almanjahie
- Department of Mathematics, College of
Science, King Khalid University, Abha, South Region, Saudi Arabia
- Statistical Research and Studies
Support Unit, King Khalid University, Abha, Saudi Arabia
| | - Javid Gani Dar
- Department of Mathematical Sciences,
Islamic University of Science and Technology, Awantipora, Kashmir, India
| |
Collapse
|
9
|
Almanjahie IM, Chikr Elmezouar Z, Bachir BA, Kaid Z. Spatial local linear estimation of the L1-conditional quantiles for functional regressors. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2019.1620781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Zouaoui Chikr Elmezouar
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Department de Mathematique, Universit Tahri Mohammed, Bchar, Algeria
| | - Bachir Ahmed Bachir
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Zoulikha Kaid
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| |
Collapse
|
10
|
Almanjahie IM, Aissiri KA, Laksaci A, Chikr Elmezouar Z. The k nearest neighbors smoothing of the relative-error regression with functional regressor. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2020.1811870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| | - Khlood A. Aissiri
- Department of Mathematics, College of Sciences and Arts, King Khalid University, Muhail Asir, Saudi Arabia
| | - Ali Laksaci
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| | - Zouaoui Chikr Elmezouar
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
- Department de Mathematique, Université Tahri Mohammed, Béchar, Algeria
| |
Collapse
|
11
|
Aijaz M, Almanjahie IM, Dar JG. Mathematical estimation of fluid concentration in human skin during water immersion. J Adv Res 2020; 28:1-6. [PMID: 33364039 PMCID: PMC7753959 DOI: 10.1016/j.jare.2020.05.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 05/01/2020] [Accepted: 05/02/2020] [Indexed: 11/28/2022] Open
Abstract
Introduction The concentration of fluid and its analysis in human skin is innately a challenge due to its continuous movement and involvement in maximum life processes. The concentration of the fluid gets affected by the diffusion of fluids through the skin, which acts as the main barrier between the human body and the external environment. Therefore, it becomes imperative to study the process and impact of the diffusion of fluids through the skin. The problem becomes more interesting when the human body is immersed in water. Objectives The present paper studies the change in the fluid distribution of human skin during its immersion in water of different temperatures. The application part of the paper visualizes various impaired vascular function and muscle soreness by water immersion during the physiotherapy treatment. Methods A mathematical model based on the two-dimensional diffusion equation, along with appropriate boundary conditions, has been formulated. The maximum of the relevant parameters, such as fluid regulation, transfer coefficient, evaporation rate, etc., influencing the fluid distribution, have been incorporated. The model has been solved by variational finite element method, and numerical results have been obtained by the Crank-Nicholson scheme. Results The increase in fluid concentration due to treatment with cold and acute hot water immersion has been noted, and the role of water immersion in enhancing the recovery in exercise-induced muscular damage has been analyzed. Conclusions The paper addressed the issue of rate of water diffusion through human skin, which otherwise couldn't be drawn from the analogy of gas diffusion through the membrane due to the variation in permeabilities of the two processes. The paper has applications in water immersion therapies and other activities like monitoring swimming induced pulmonary edema, etc.
Collapse
Affiliation(s)
- Mir Aijaz
- Department of Mathematics, Govt. Degree College Kilam, Higher Education, J & K, India
| | - Ibrahim M Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha 62529, Saudi Arabia.,Statistical Research and Studies Support Unit, King Khalid University, Abha 62529, Saudi Arabia
| | - Javid Gani Dar
- Department of Mathematical Sciences, Islamic University of Science and Technology, J & K, India
| |
Collapse
|
12
|
Rachdi M, Laksaci A, Almanjahie IM, Chikr-Elmezouar Z. FDA: theoretical and practical efficiency of the local linear estimation based on the kNN smoothing of the conditional distribution when there are missing data. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1732378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
| | - Ali Laksaci
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| | - Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| | - Zouaoui Chikr-Elmezouar
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
- Statistical Research and Studies Support Unit, King Khalid University, Abha, Saudi Arabia
| |
Collapse
|
13
|
Nazir HM, Hussain I, Ahmad I, Faisal M, Almanjahie IM. An improved framework to predict river flow time series data. PeerJ 2019; 7:e7183. [PMID: 31304058 PMCID: PMC6610541 DOI: 10.7717/peerj.7183] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 05/24/2019] [Indexed: 11/20/2022] Open
Abstract
Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.
Collapse
Affiliation(s)
| | - Ijaz Hussain
- Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
| | - Ishfaq Ahmad
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Muhammad Faisal
- Faculty of Health Studies, University of Bradford, Bradford, United Kingdom.,Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Bradford, Bradford, United Kingdom
| | - Ibrahim M Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| |
Collapse
|
14
|
Almanjahie IM, Khan RN, Milne RK, Nomura T, Martinac B. Moving average filtering with deconvolution (MAD) for hidden Markov model with filtering and correlated noise. Eur Biophys J 2019; 48:383-393. [PMID: 31028435 DOI: 10.1007/s00249-019-01368-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Revised: 02/14/2019] [Accepted: 04/22/2019] [Indexed: 11/28/2022]
Abstract
Ion channel data recorded using the patch clamp technique are low-pass filtered to remove high-frequency noise. Almanjahie et al. (Eur Biophys J 44:545-556, 2015) based statistical analysis of such data on a hidden Markov model (HMM) with a moving average adjustment for the filter but without correlated noise, and used the EM algorithm for parameter estimation. In this paper, we extend their model to include correlated noise, using signal processing methods and deconvolution to pre-whiten the noise. The resulting data can be modelled as a standard HMM and parameter estimates are again obtained using the EM algorithm. We evaluate this approach using simulated data and also apply it to real data obtained from the mechanosensitive channel of large conductance (MscL) in Escherichia coli. Estimates of mean conductances are comparable to literature values. The key advantages of this method are that it is much simpler and computationally considerably more efficient than currently used HMM methods that include filtering and correlated noise.
Collapse
Affiliation(s)
- Ibrahim M Almanjahie
- Department of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia.,Department of Mathematics, King Khalid University, Abha, 61413, Saudi Arabia
| | - Ramzan Nazim Khan
- Department of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia.
| | - Robin K Milne
- Department of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia
| | - Takeshi Nomura
- Department of Rehabilitation, Kyushu Nutrition Welfare University, Kitakyushu, 800-029, Japan
| | - Boris Martinac
- Mechanosensory Biophysics Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia
| |
Collapse
|
15
|
Chikr-Elmezouar Z, Almanjahie IM, Laksaci A, Rachdi M. FDA: strong consistency of the kNN local linear estimation of the functional conditional density and mode. J Nonparametr Stat 2018. [DOI: 10.1080/10485252.2018.1538450] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
| | - Ibrahim M. Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Ali Laksaci
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Mustapha Rachdi
- Laboratoire AGEIS EA 7407, TIMB Team, UFR SHS, Univ. Grenoble Alpes, Grenoble Cedex 09, France
| |
Collapse
|