A new cyrtodactylus (Squamata: Gekkonidae) from khanh hoa province, southern Vietnam

We describe a new species of the genus Cyrtodactylus from southern Vietnam, based on morphological and molecular datasets. Cyrtodactylus cucdongensis sp. nov. is described on the basis of seven specimens collected from Cuc Dong Cape, Khanh Hoa Province. The new species can be distinguished from the remaining bent-toed geckos by a combination of the following characters: maximum SVL 65.9 mm; 16-19 dorsal tubercle rows; 41-44 ventral scales at midbody; 5 or 6 precloacal pores in males, 4-6 pitted precloacal scales in females; no femoral pores; 6-13 enlarged precloacal scales; 5- 9 enlarged femoral scales; no transversally enlarged subcaudals; dorsal pattern consisting of irregular dark bands. This is the 33rd species of Cyrtodactylus known from Vietnam

Title: 

A new cyrtodactylus (Squamata: Gekkonidae) from khanh hoa province, southern Vietnam
Authors: SCHNEIDER, NICOLE
PHUNG, TRUNG MY
LE, MINH DUC
NGUYEN, TRUONG QUANG
ZIEGLER, ZIEGLER
Keywords: Cyrtodactylus cucdongensis sp
Gekkonidae; Molecular phylog-eny
Morphology
New species
Nov
Taxonomy
Issue Date: 2014
Publisher: Zootaxa
Citation: Scopus
Abstract: We describe a new species of the genus Cyrtodactylus from southern Vietnam, based on morphological and molecular datasets. Cyrtodactylus cucdongensis sp. nov. is described on the basis of seven specimens collected from Cuc Dong Cape, Khanh Hoa Province. The new species can be distinguished from the remaining bent-toed geckos by a combination of the following characters: maximum SVL 65.9 mm; 16-19 dorsal tubercle rows; 41-44 ventral scales at midbody; 5 or 6 precloacal pores in males, 4-6 pitted precloacal scales in females; no femoral pores; 6-13 enlarged precloacal scales; 5- 9 enlarged femoral scales; no transversally enlarged subcaudals; dorsal pattern consisting of irregular dark bands. This is the 33rd species of Cyrtodactylus known from Vietnam
Description: Zootaxa, Volume 3785, Issue 4, 7 April 2014, Pages 518-532
Zootaxa
URI: http://repository.vnu.edu.vn/handle/VNU_123/31583
ISSN: 11755326
Appears in Collections:Bài báo của ĐHQGHN trong Scopus

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Measurement of the isospin asymmetry in B → K (*)μ +μ - decays

The isospin asymmetries of B → K (*)μ +μ - decays and the partial branching fractions of B 0 → K 0μ +μ - and B +→ K *+μ +μ - are measured as a function of the di-muon mass squared q 2 using an integrated luminosity of 1.0fb -1 collected with the LHCb detector. The B → Kμ +μ - isospin asymmetry integrated over q 2 is negative, deviating from zero with over 4 σ significance. The B → K *μ +μ - decay measurements are consistent with the Standard Model prediction of negligible isospin asymmetry. The observation of the decay B 0 → K s 0μ +μ - is reported with 5.7 σ significance. Assuming that the branching fraction of B 0 → K 0μ +μ - is twice that of B 0 → K s 0μ +μ -, the branching fractions of B 0 → K 0μ +μ - and B → K *+μ +μ - are found to be (0.31 +0.07 -0.06) × 10 -6 and (1.16 ±0.19) × 10 -6, respectively. Title:  Measurement of the isospin asymmetry in B → K (*)μ +μ - decays Authors:  Aaij, Roel Abellán Beteta, C. Adametz, Aleksandra Keywords:  Hadron ...
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Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

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This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R2), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha−1 (average = 55.8 Mg ha−1); below-ground biomass ranged between 4.06 and 436.47 Mg ha−1 (average = 81.47 M...
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