Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing

Nasiri, Mahdi; Shanbeh, Mohsen; Tavanai, Hossein

doi:10.1109/cimca.2005.1631314

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…AhGPAT9 affects oil content in peanut, but it is not the sole gene determining this trait. Similar results were found in a polymorphism analysis of the TaDREB1 gene in wheat germplasm for the dissection of the drought resistance trait 56 .…”

Section: Discussionsupporting

confidence: 83%

Characterization of glycerol-3-phosphate acyltransferase 9 (AhGPAT9) genes, their allelic polymorphism and association with oil content in peanut (Arachis hypogaea L.)

Zhang

Luo

et al. 2020

Sci Rep

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GPAT, the rate-limiting enzyme in triacylglycerol (TAG) synthesis, plays an important role in seed oil accumulation. In this study, two AhGPAT9 genes were individually cloned from the A-and B-genomes of peanut, which shared a similarity of 95.65%, with 165 site differences. The overexpression of AhGPAT9 or the knock-down of its gene expression increased or decreased the seed oil content, respectively. Allelic polymorphism analysis was conducted in 171 peanut germplasm, and 118 polymorphic sites in AhGPAT9A formed 64 haplotypes (a1 to a64), while 94 polymorphic sites in AhGPAT9B formed 75 haplotypes (b1 to b75). The haplotype analysis showed that a5, b57, b30 and b35 were elite haplotypes related to high oil content, whereas a7, a14, a48, b51 and b54 were low oil content types. Additionally, haplotype combinations a62/b10, a38/b31 and a43/b36 were associated with high oil content, but a9/b42 was a low oil content haplotype combination. The results will provide valuable clues for breeding new lines with higher seed oil content using hybrid polymerization of highoil alleles of AhGPAT9A and AhGPAT9B genes. Plant lipids, including glycerolipids, membrane lipids, signaling molecules, photosynthetic pigments, plant hormones and plant surface protective substances, play important roles in plant growth, development and stress responses. Glycerolipids, including phospholipids, glycolipids, triacylglycerol, and extracellular lipids such as cutin and suberin, are the main components of plant lipids, and are formed by the acylation of glycerol at the sn-1, sn-2, or sn-3 sites using glycerol as the molecular framework 1. Triacylglycerol (TAG) is the main form of plant storage oil, and accumulates in the flower petals, pollen grains, developing seeds and fruits of many plant species 2,3 , providing energy and carbon sources for seed germination and biological metabolism 4,5. In plant cells, TAG synthesis occurs in three ways. The first is the assembly of free fatty acids and glycerol into TAG at the ER via the Kennedy pathway 1 , the second is the production of TAG and lysophosphatidylcholine (LPC) by transferring an acyl group from phosphatidylcholine (PC) to diacylglycerol (DAG) 6 , and the last is the reverse lipidotransferase (TA) transfer of one of the acyl groups from one diacylglycerol molecule to another diacylglcerol to form TAG and monoacylglycerol (MAG) 7. In the classical Kennedy pathway, there are three major acyltransferases: GPAT (EC.2.3.1.15), 2-lysophosphatidic acid acyltransferase (LPAAT, EC.2.3.1.51) and diacylglycerol acyltransferase (DGAT, EC.2.3.1.20) 8-13. The enzyme activity of GPAT in plants was first observed in the mesocarp of avocado 14. To date, three types of GPATs have been identified, which are located in the mitochondria, chloroplasts and endoplasmic reticulum (ER) 15-17. In Arabidopsis thaliana, 10 genes have been shown to encode GPAT proteins, designated GPAT1 to

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Section: Discussionsupporting

confidence: 83%

Characterization of glycerol-3-phosphate acyltransferase 9 (AhGPAT9) genes, their allelic polymorphism and association with oil content in peanut (Arachis hypogaea L.)

Zhang

Luo

et al. 2020

Sci Rep

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“…ANN has been employed extensively in various textile disciplines ranging from yarn manufacturing, fabric formation and fabric properties [16][17][18][19][20][21][22][23][24][25]. There are many researches using this algorithm such as the research of Beltran et.al (2005) on the pilling tendency of wool knits [18].…”

Section: A Brief Overview Of Artificial Neural Networkmentioning

confidence: 99%

“…There are many researches using this algorithm such as the research of Beltran et.al (2005) on the pilling tendency of wool knits [18]. The performance of ANN model was compared with statistical regression and fuzzy regression to develop the predictive models for polyester dyeing [19]. The ANN model has also been used to predict cotton yarn hairiness [20].…”

Section: A Brief Overview Of Artificial Neural Networkmentioning

confidence: 99%

An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming

Moghassem

Fallahpour

Shanbeh

2012

Journal of Engineered Fibers and Fabrics

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Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, Gene Expression Programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R2-Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R2-value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm.

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“…DNA samples from soybean with opposite phenotypes were subjected to BSA to detect DNA markers that exhibited differences between the two different samples to identify the QTL (Mansur et al, 1993). Genome-wide insertion/deletion (InDel) markers have been used for fine mapping of important economical traits in rice (Feng et al, 2005; Li et al, 2017), wheat (Chen et al, 2005; Shang, 2009), and tomato (Su et al, 2019). In this study, a mixed major gene plus polygene inheritance model was used to analyze FW resistance in cucumber, and the major effect QTL of FW was investigated by BSA.…”

Section: Introductionmentioning

confidence: 99%

Inheritance and Quantitative Trait Locus Mapping of Fusarium Wilt Resistance in Cucumber

Dong

et al. 2019

Front. Plant Sci.

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Fusarium wilt (FW) is a very serious soil-borne disease worldwide, which usually results in huge yield losses in cucumber production. However, the inheritance and molecular mechanism of the response to FW are still unknown in cucumber (Cucumis sativus L.). In this study, two inbred cucumber lines Superina (P1) and Rijiecheng (P2) were used as the sensitive and resistant lines, respectively. A mixed major gene plus polygene inheritance model was used to analyze the resistance to FW in different generations of cucumber, namely, P1, P2, F1 (P1×P2), B1, and B2, obtained by backcrossing F1 plants with Superina (B1) or Rijiecheng (B2), and F2, obtained by self-crossing the F1 plants. After screening 18 genetic models, we chose the E-1 model, which included two pairs of additive-dominance-epistatic major genes and additive-dominance polygenes, as the optimal model for resistance to FW on the basis of fitness tests. The major effect quantitative trait locus (QTL) fw2.1 was detected in a 1.91-Mb-long region of chromosome 2 by bulked-segregant analysis. We used five insertion/deletion markers to fine-map the fw2.1 to a 0.60 Mb interval from 1,248,093 to 1,817,308 bp on chromosome 2 that contained 80 candidate genes. We also used the transcriptome data of Rijiecheng inoculated with Fusarium oxysporum f. sp. cucumerinum (Foc) to screen the candidate genes. Twelve differentially expressed genes were detected in fw2.1, and five of them were significantly induced by FW. The expression levels of the five genes were higher in FW-resistant Rijiecheng inoculated with Foc than in the control inoculated with water. Our results will contribute to a better understanding of the genetic basis of FW resistance in cucumber, which may help in breeding FW-resistant cucumber lines in the future.

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Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing

Cited by 9 publications

References 14 publications

Characterization of glycerol-3-phosphate acyltransferase 9 (AhGPAT9) genes, their allelic polymorphism and association with oil content in peanut (Arachis hypogaea L.)

Characterization of glycerol-3-phosphate acyltransferase 9 (AhGPAT9) genes, their allelic polymorphism and association with oil content in peanut (Arachis hypogaea L.)

An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming

Inheritance and Quantitative Trait Locus Mapping of Fusarium Wilt Resistance in Cucumber

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