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Correia JP. Multifractal analysis of maize and soybean DNA. Sci Rep 2024; 14:10687. [PMID: 38724570 PMCID: PMC11082218 DOI: 10.1038/s41598-024-60722-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
This paper investigates the complexity of DNA sequences in maize and soybean using the multifractal detrended fluctuation analysis (MF-DFA) method, chaos game representation (CGR), and the complexity-entropy plane approach. The study aims to understand the patterns and structures of these DNA sequences, which can provide insights into their genetic makeup and improve crop yield and quality. The results show that maize and soybean DNA sequences exhibit fractal properties, indicating a complex and self-organizing structure. We observe the persistence trend between sequences of base pairs, which indicates long-range correlations between base pairs. We also identified the stochastic nature of the DNA sequences of both species.
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Affiliation(s)
- J P Correia
- Departamento de Física, Universidade Federal do Rio Grande do Norte, Natal, RN, 59072-970, Brasil.
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Evaluation of Agricultural Machinery Using Multi-Criteria Analysis Methods. SUSTAINABILITY 2022. [DOI: 10.3390/su14148675] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
To achieve the highest possible agricultural production, it is necessary to procure the appropriate agricultural machinery. A tractor is the most useful machine in agriculture that performs various functions. Therefore, the selection of a tractor is one of the key decisions in the agriculture-production process. This study aims to evaluate heavy tractors for agricultural production in Bosnia and Herzegovina. Since this is a selection between different tractors, which are evaluated using several criteria, the methods of multi-criteria analysis (MCDA) were used in this study. Five different methods were used to determine the weight of the criteria, of which a modified standard-deviation method is a new method used in practice, while the tractor ranking was performed using the CRADIS (compromise ranking of alternatives from distance to ideal solution) method. The results showed that the best-ranked tractor is A4, while the most deviations from the ranking occur when the entropy method is used. The contribution of this study is in the systematization of the methods for the objective determination of the criteria weights and the development of new methods to facilitate decision-making in agriculture and other industries.
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YANG S, ZHANG H, FAN W. Characteristic wavelengths selection of rice spectrum based on adaptive sliding window permutation entropy. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.38922] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Sen YANG
- Northeast Forestry University, China
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Esmaiel H, Xie D, Qasem ZAH, Sun H, Qi J, Wang J. Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise. SENSORS (BASEL, SWITZERLAND) 2021; 22:112. [PMID: 35009653 PMCID: PMC8747422 DOI: 10.3390/s22010112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.
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Affiliation(s)
- Hamada Esmaiel
- Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 316005, China; (H.E.); (Z.A.H.Q.)
- Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
| | - Dongri Xie
- China Electronics Technology Avionics Co., Ltd., Chengdu 610100, China;
| | - Zeyad A. H. Qasem
- Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 316005, China; (H.E.); (Z.A.H.Q.)
| | - Haixin Sun
- Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 316005, China; (H.E.); (Z.A.H.Q.)
| | - Jie Qi
- School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China;
| | - Junfeng Wang
- Department of Information and Communication Engineering, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin 300383, China;
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Hongwei Z, Haiyan W, Haiyang Y, Haitao D, Xiaohong S. Phase trajectory entropy: A promising tool for passive diver detection. JASA EXPRESS LETTERS 2021; 1:076003. [PMID: 36154639 DOI: 10.1121/10.0005598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Passive diver detection is really significant as it constitutes a potential real-time monitoring of serious underwater threats. Up to now, there is still a lack of an efficient approach to characterize the complexity and fickleness with non-parametric and non-information priors. To achieve an improvement, a phase trajectory entropy method is proposed that should be promising. A coarser-grained distribution is created during entropy counting. The value of phase trajectory entropy is demonstrated by simulation and applied to real recorded data. The results show that phase trajectory entropy method considerably outperforms narrowband energy detection and the bubble entropy method.
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Affiliation(s)
- Zhang Hongwei
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an, Shaanxi, 710072, China
| | - Wang Haiyan
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China , , , ,
| | - Yao Haiyang
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an, Shaanxi, 710072, China
| | - Dong Haitao
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an, Shaanxi, 710072, China
| | - Shen Xiaohong
- Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an, Shaanxi, 710072, China
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Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise. ENTROPY 2021; 23:e23050503. [PMID: 33922283 PMCID: PMC8145884 DOI: 10.3390/e23050503] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/14/2021] [Accepted: 04/20/2021] [Indexed: 12/03/2022]
Abstract
The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.
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Rosso OA, Montani F. Information Theoretic Measures and Their Applications. ENTROPY 2020; 22:e22121382. [PMID: 33297309 PMCID: PMC7762303 DOI: 10.3390/e22121382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 11/29/2020] [Accepted: 12/01/2020] [Indexed: 11/17/2022]
Affiliation(s)
- Osvaldo A. Rosso
- Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970, Brazil
- Correspondence: (O.A.R.); (F.M.)
| | - Fernando Montani
- Instituto de Física La Plata, CONICET-Universidad Nacional de la Plata, La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Argentina
- Correspondence: (O.A.R.); (F.M.)
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Xie D, Esmaiel H, Sun H, Qi J, Qasem ZAH. Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E468. [PMID: 33286242 PMCID: PMC7516952 DOI: 10.3390/e22040468] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/15/2020] [Accepted: 04/16/2020] [Indexed: 11/17/2022]
Abstract
Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.
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Affiliation(s)
- Dongri Xie
- School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China;
| | - Hamada Esmaiel
- Department of Information and Communication, School of Informatics, Xiamen University, Xiamen 361005, China;
- Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
| | - Haixin Sun
- School of Informatics, Xiamen University, Xiamen 316005, China; (H.S.); (Z.A.H.Q.)
| | - Jie Qi
- School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China;
| | - Zeyad A. H. Qasem
- School of Informatics, Xiamen University, Xiamen 316005, China; (H.S.); (Z.A.H.Q.)
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Parametric Jensen-Shannon Statistical Complexity and Its Applications on Full-Scale Compartment Fire Data. Symmetry (Basel) 2019. [DOI: 10.3390/sym12010022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The order/disorder characteristics of a compartment fire are researched based on experimental data. From our analysis performed by new, pioneering methods, we claim that the parametric Jensen-Shannon complexity can be successfully used to detect unusual data, and that one can use it also as a means to perform relevant analysis of fire experiments. Thoroughly comparing the performance of different algorithms (known as permutation entropy and two-length permutation entropy) to extract the probability distribution is an essential step. We discuss some of the theoretical assumptions behind each step and stress that the role of the parameter is to fine-tune the results of the Jensen-Shannon statistical complexity. Note that the Jensen-Shannon statistical complexity is symmetric, while its parametric version displays a symmetric duality due to the a priori probabilities used.
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