Odd mean labeling for two star graph

Sudhakar, S.; Francis, Silviya; Balaji, V.

doi:10.21042/amns.2017.1.00016

Cited by 15 publications

(14 citation statements)

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“…Triethylamine (3.97%) is widely used in manufacturing medicines, pesticides, polymerization inhibitors, high-energy fuels, vulcanizing agents of rubber, and so forth [39]. It should be noted that the content of Lupeol (5.20%) in the phenyl alcohol extract of quercetin leaves reaches 5.20%, which has significant effects on anti-tumor, anti-inflammatory, and antioxidant effects [40][41][42].…”

Section: The Gc-ms Analysismentioning

confidence: 99%

Extracts of Quercus dentata leaf

Peng

Sun

Wang³

et al. 2020

Therm sci

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Along with the development of our economic society and the growing demand for wood, the value of the use of Quercus dentata Thunb has also increased year by year. However, the traditional trees used are resources that are cut down, and development primarily uses only the branches, while ignoring the value in using the rest of the trees. Thiscauses a waste of resources. In response, this study uses benzene, an alcohol found in Quercus dentata Thunb leaf extract. Fourier transform infrared spectroscopy (FT-IR) and gas chromatography-mass spectrometry (GC-MS) were used for catalytic cracking products, and the efficient extraction of pyrolysis products were studied. Analysis shows that Quercus dentata Thunb leaf extract species contain a variety of biological active ingredients; and the ingredients in pharmaceutical and chemical production have very high value. For example, the Quercus dentata Thunb leaf of benzyl alcohol extract, Lupeol, has significant anti-cancer, anti-oxidation, and anti-inflammatory effects. The analysis of the chemical composition of the Quercus dentata Thunb leaf provides a new theoretical basis.

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Section: The Gc-ms Analysismentioning

confidence: 99%

Extracts of Quercus dentata leaf

Peng

Sun

Wang³

et al. 2020

Therm sci

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“…Then, π = {R i : i = 1, 2..., m + 4} is a partition of V(C m n ) and c : V(C m n ) −→ {1, 2, ..., m + t} defined by c(x i ) = j for any x i ∈ R j is a locating coloring of C m n . (2) For n = q(m + 1) + i, where 3 ≤ i < t let R 1 , R 2 , ..., R m+4 similar to the case n = q(m + 1) + 2. Set R m+j = {x (m+1)q+(j−2) }, 5 ≤ j ≤ i + 2.…”

Section: Proofmentioning

confidence: 99%

“…(1) If x n ∈ R m+1 , then c π (x n ) = c π (x m+1 ). (2) If x n ∈ R l , l ≥ m + 2 and x n−1 ∈ R k , where k = m or m + 1. Then, (T\{x 2m+1 }) ∩ R i = φ for all i ≥ m + 3 and i = l. However, k = m, which gives N(x k ) = ({x n , x 1 , x 2 , ..., x m+2 }\{x m }) ∪ (T\{x 2m+1 }) and k = m + 1, which gives N(x k ) = ({x 1 , x 2 , ..., x m+2 }\{x m+1 }) ∪ T, while N(x n−1 ) = {x 1 , x 2 , ..., x m−1 } ∪ (S 2 \{x n−1 }).…”

Section: (6)mentioning

confidence: 99%

“…For any vertex v of G, the color code of v with respect to π, c π (v), is defined as the ordered k-tuple (d(v, R 1 ), d(v, R 2 ), ..., d(v, R k )), where d(v, R i ) is the minimum distance from v to each other vertex u ∈ R i for 1 ≤ i ≤ k. If distinct vertices of G have distinct color codes, then we call c a locating coloring of G. The locating chromatic number of G, χ L (G), is the minimum number of colors needed in a locating coloring of G. The locating-chromatic number of a graph is a combined concept between the coloring and partition dimension of a graph. There are many applications of graph coloring and labeling in various fields, for instance, this notion relates to different applications in computer science and communication network and it plays an important role in solving scheduling problems, storage problem of chemical substances and placement problem of particular different objects-see, for example, [1,2]. The concept of locating chromatic number of a graph was introduced and studied by Chartrand et al [3] in 2002.…”

Section: Introductionmentioning

confidence: 99%

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Locating Chromatic Number of Powers of Paths and Cycles

2019

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Let c be a proper k-coloring of a graph G. Let π = { R 1 , R 2 , … , R k } be the partition of V ( G ) induced by c, where R i is the partition class receiving color i. The color code c π ( v ) of a vertex v of G is the ordered k-tuple ( d ( v , R 1 ) , d ( v , R 2 ) , … , d ( v , R k ) ) , where d ( v , R i ) is the minimum distance from v to each other vertex u ∈ R i for 1 ≤ i ≤ k . If all vertices of G have distinct color codes, then c is called a locating k-coloring of G. The locating-chromatic number of G, denoted by χ L ( G ) , is the smallest k such that G admits a locating coloring with k colors. In this paper, we give a characterization of the locating chromatic number of powers of paths. In addition, we find sharp upper and lower bounds for the locating chromatic number of powers of cycles.

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“…The results showed that, as the CARS-PLS model was based on the optimal key variables, it only used 15.6% of variables in the original information. In this way, the number of variables was reduced [13]. Moreover, the CARS-PLS model could more accurately predict the SSC content in Yali pear than the full-variable PLS model.…”

Section: Introductionmentioning

confidence: 99%

CARS Algorithm-Based Detection of Wheat Moisture Content before Harvest

Wang

Chong

et al. 2020

Symmetry

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To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R2 = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R2v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350–2500 nm, bands of 728–907 nm, 1407–1809 nm, and 1940–2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R2c = 0.995, RMSEP = 2.676, R2p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest.

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Odd mean labeling for two star graph

Cited by 15 publications

References 1 publication

Extracts of Quercus dentata leaf

Extracts of Quercus dentata leaf

Locating Chromatic Number of Powers of Paths and Cycles

CARS Algorithm-Based Detection of Wheat Moisture Content before Harvest

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