A scientist’s walk through Hanoi Old Quarter Houses

Hanoi – the capital city of Vietnam and the land of thousand years of civilization – invokes among both locals and tourists the image of the ‘Sword Lake’ with its ancient ‘Turtle Tower’ and the charming Old Quarter with its preserved old houses lying along small commercial alleys. The houses in the Old Quarter were constructed over a century ago which feature tube houses with inclined tile roofs and a blend of French architecture create the infusions of history and memory. Researchers of various fields have attempted to capture and explain the essence of these townhouses in their works, either in the collectibles of many authors, the quintessential drawings of talented painters, or in publications on the history of the Old Quarter. Among these, the recent work by Vuong et al. (2019) adds a unique view of the architectural features of Hanoi’s ancient townhouses as these features are viewed as dependent and independent variables. The study titled ‘Cultural evolution in Vietnam’s early 20th century: A Bayesian network analysis of Hanoi Franco-Chinese house designs’ aims to find traces of cultural evolution in the early 20th century in Vietnam and highlight the most notable elements that affect the Vietnamese people’s perception of cultural evolutions

Book Review: The Vietnamese Social Sciences at a Fork in the Road

For individual researchers and academics, research outputs are a crucial asset that enables public gaining of recognition and acknowledgments of their expertise. For institutions, industry sectors, and countries, scientific publications serve a more critical role, being a measure and an index that indicates scientific development and competitiveness. As a country that is transitioning to modernization and industrialization, Vietnam recognizes science and education as the driving force for its socio-economic development. The government has made sizeable efforts to enhance the research capacity for research institutes and higher education establishments across the country. This involves introducing policies that prioritize research activities and elevating research funding and monitoring schemes to some certain levels

On the epidemiology and evolution of white spot syndrome virus of shrimp

WSSV causes a devastating disease in shrimp aquaculture that has spread worldwide and probably increased in virulence over time. Understanding WSSV epidemiology and evolution is therefore important for developing novel intervention and management strategies. Both of these goals require finding suitable molecular markers to identify and discriminate WSSV strains, and hereby help infer their origin and track their spread. Five major variable WSSV genomic loci were evaluated as markers for virus identification and virus spread on different spatiotemporal scales. In this thesis the genetic variation between WSSV isolates from the key shrimp production regions in Vietnam was analyzed. A statistically supported model of spread suggests that multiple introductions of WSSV occurred in central Vietnam, and that the virus radiated out over time to the south and the north. Spurious variation was generated during molecular cloning of WSSV VNTR sequences, while no variation occurred in multiple replicates of PCR amplification of VNTRs. Moreover, VNTR sequences were stable over two passages of infection in vivo, indicating that in vivo cloning can be applied to study heterogeneity within WSSV isolates originating from a single shrimp. Genetic deletion of variable region variants appear to be more stable in extensive farms compare to intensive farms over time, indicating that farm practices affect the evolutionary dynamics of WSSV. Genetic variation between Asian WSSV isolates provides support for evolution of genome size according to a geometric model of adaptation, where incrementally smaller genomic deletions are substituted over time. The relationship between the molecular data and the time of first disease occurrence implies that shrimp transportation played an important role in the quick, long range spread of WSSV. Overall, the thesis results show that WSSV variable loci can be effectively employed as molecular markers to study WSSV spread and evolution on different spatiotemporal scales. However, the markers have different properties and the choice of a suitable marker for a pertinent question is critical. <br/

Evaluation of white spot syndrome virus variable DNA loci as molecular markers of virus spread at intermediate spatiotemporal scales

Variable genomic loci have been employed in a number of molecular epidemiology studies of white spot syndrome virus (WSSV), but it is unknown which loci are suitable molecular markers for determining WSSV spread on different spatiotemporal scales. Although previous work suggests that multiple introductions of WSSV occurred in central Vietnam, it is largely uncertain how WSSV was introduced and subsequently spread. Here, we evaluate five variable WSSV DNA loci as markers of virus spread on an intermediate (i.e. regional) scale, and develop a detailed and statistically supported model for the spread of WSSV. The genotypes of 17 WSSV isolates from along the coast of Vietnam – nine of which were newly characterized in this study – were analysed to obtain sufficient samples on an intermediate scale and to allow statistical analysis. Only the ORF23/24 variable region is an appropriate marker on this scale, as geographically proximate isolates show similar deletion sizes. The ORF14/15 variable region and variable-number tandem repeat (VNTR) loci are not useful as markers on this scale. ORF14/15 may be suitable for studying larger spatiotemporal scales, whereas VNTR loci are probably suitable for smaller scales. For ORF23/24, there is a clear pattern in the spatial distribution of WSSV: the smallest genomic deletions are found in central Vietnam, and larger deletions are found in the south and the north. WSSV genomic deletions tend to increase over time with virus spread in cultured shrimp, and our data are therefore congruent with the hypothesis that WSSV was introduced in central Vietnam and then radiated ou

GIS-Based Mapping of Seismic Parameters for the Pyrenees

In the present paper, three of the main seismic parameters, maximum magnitude -Mmax, b-value, and annual rate -AR, have been studied for the Pyrenees range in southwest Europe by a Geographic Information System (GIS). The main aim of this work is to calculate, represent continuously, and analyze some of the most crucial seismic indicators for this belt. To this end, an updated and homogenized Poissonian earthquake catalog has been generated, where the National Geographic Institute of Spain earthquake catalog has been considered as a starting point. Herein, the details about the catalog compilation, the magnitude homogenization, the declustering of the catalog, and the analysis of the completeness, are exposed. When the catalog has been produced, a GIS tool has been used to drive the parameters’ calculations and representations properly. Different grids (0.5 × 0.5° and 1 × 1°) have been created to depict a continuous map of these parameters. The b-value and AR have been obtained that take into account different pairs of magnitude–year of completeness. Mmax has been discretely obtained (by cells). The analysis of the results shows that the Central Pyrenees (mainly from Arudy to Bagnères de Bigorre) present the most pronounced seismicity in the range

The linear invariants (dn ) and (Ω) for spaces of germs of holomorphic functions on compact subsets of Cn

For a compact subset K of Cn, we give necessary and sufficient conditions for [H(K)]0 to have the property (DN), and similarly for the property (Ω). We also show that H(D) is isomorphic to H(∆ n), where ∆n is the unit polydisc in Cn and D is any bounded Reinhardt domain in Cn. This last result requires a generalization of the classical Hartogs phenomenon

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Preparation of landslide susceptibility map is the first step for landslide hazard mitigation and risk assessment. The main aim of this study is to explore potential applications of two new models such as two-class Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China). The ADT has not been used in landslide susceptibility modeling and this paper attempts a novel application of this technique. For the purpose of comparison, a conventional method of Support Vector Machines (SVM) which has been widely used in the literature was included and their results were assessed. At first, a landslide inventory map with 187 landslide locations for the study area was constructed from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide models and for the model validation. Then a spatial database with a total of fourteen landslide conditioning factors was prepared, including slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI),sediment transport index (STI), plan curvature, land use, normalized difference vegetation index (NDVI), lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Using the KLR, the SVM, and the ADT, three landslide susceptibility models were constructed using the training dataset. The three resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and five statistical evaluation measures. In addition, pairwise comparisons of the area under the ROC curve were carried out to assess if there are significant differences on the overall performance of the three models. The goodness-of- fits are 92.5%(the KLR model), 88.8% (the SVM model), and 95.7% (the ADT model). The prediction capabilities are 81.1%,84.2%, and 93.3% for the KLR, the SVM, and the ADT models, respectively. The result shows that the ADT model yielded better overall performance and accurate results than the KLR and SVM models. The KLR model considered slightly better than SVM model in terms of the positive prediction values. The ADT and KLR are the two promising data mining techniques which might be considered to use in landslide susceptibility mapping. The results from this study may be useful for land use planning and decision making in landslide prone areas

Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline

The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m

Assessment and Simulation of Impacts of Climate Change on Erosion and Water Flow by Using the Soil and Water Assessment Tool and GIS: Case Study in Upper Cau River basin in Vietnam

The Upper Cau river basin which plays an important role in socio-economic developments the North of Vietnam is sensitive to changes of climate influencing flows, erosion, and water resources. The main objective of this study is to assess and simulate impacts of climate change on erosion and water flow in the basin. Using a GIS database and Soil and Water Assessment Tool (SWAT) model, the water flow and soil loss were assessed with data in period 1980-1999 called the based period, then simulated until 2100 considering the medium emission scenario (B2). The simulation result showed that the total annual runoff and soil loss tends to increase compared to the base period. For flow, the change rate of the simulation period is higher than the base period; the water flow rate will increase by 0.22% (2020-2039) and up to 1.37% (2080-2100). 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