The sexually-transmitted Western Australia wild-plant virus yellow tailflower mild mottle virus: Does it pose a threat to global food security?

Yellow tailflower mild mottle virus is a species in the internationally-distributed genus Tobamovirus, other species of which are some of the most damaging plant viruses known. Yellow tailflower mild mottle virus (YTMMV) is the first tobamovirus described only from Australia and only from native plants. Because of the bad reputation of related tobamoviruses such as tobacco mosaic virus and cucumber green mottle mosaic virus as destroyers of valuable crops, we studied YTMMV to understand aspects of its biology and to assess its potential to spillover from the indigenous flora and threaten crops on national and international stages. Unlike many damaging plant viruses, tobamoviruses are not transmitted host-to-host by vectors such as aphids. Thus, understanding how YTMMV is transmitted between host plants is key to understanding aspects of its epidemiology. A further aim of our work was to assess the damage we might expect to see in some susceptible crops should YTMMV spillover

APPLYING ANALYTIC HIERARCHY PROCESS (AHP) TO SELECT CLIMATE CHANGE ADAPTATION METHODS IN AGRICULTURAL SECTOR: A LITERATURE REVIEW

Abstract: According to Conference of the Parties 22 (COP22) statement, climate change adaptation is the concern of not only an individual but also the whole society. Since the climate change issue is a multidimensional problem, decision-making in climate change adaptation is a complex process. In this paper, we analyze the advantages and disadvantages of three main group of decision-support tools, namely Expert preference, Monetary valuation, and Multi-criteria analysis (MCA). The paper recommends MCA in general and AHP in particular as effective tools to compensate for the disadvantages of other techniques as well as to overcome the challenges and requirements from the climate change adaptation decision-making process.Keywords: climate change, AHP, MCA, monetary valuation, expert preferenc

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/

COMMON DIFFICULTIES IN SPEAKING OF ENGLISH-MAJORED FRESHMEN AT TAY DO UNIVERSITY, VIETNAM

It is said that English speaking plays a crucial role in this modern society. Nowadays, more and more non-native English people have studied English speaking for a better future in working. However, this skill is a big challenge for those who want to master it, particularly for English-majored students. For this reason, the research is conducted to figure out some difficulties that first-year students have faced in English speaking. Accordingly, the participants of this research are English-majored freshmen coming from course 15 at Tay Do University. The questionnaire and the interview are the two main research instruments that are used to collect information. The findings could point out some difficulties in speaking experienced by English-majored freshmen at Tay Do University.  Article visualizations

FIELD EVALUATION OF AGRONOMIC PARAMETERS OF PROMISED-INTRODUCED TOMATO CULTIVARS (Solanum Lycopersicon Mill) IN WINTER-SPRING SEASON 2016–2017 IN THUA THIEN HUE, VIETNAM

Abstract: The main objective of this study is to evaluate the growth ability and yield of promised-introduced tomato cultivars during winter-spring season 2016–2017 in Thua Thien Hue province. A total of eight cultivar treatments were used, namely GC171, GC173, CLN2001A, CLN5915, CLN1621L, Hawai7996, Cherry, and ThuanDien. Three promising cultivars (CLN2001A, CLN5915, and CLN1621L) were selected from two previous experiments. The field experiment was laid out in a randomized complete block design with three replications. Ten plants per replication were examined. The results show that CLN2001A, GC171, CLN1621L, CLN5915, and Hawai7996 have an early harvest period, ranging from 106 to 109 days, and their morphological and vegetative characteristics of are suitable under Thua Thien Hue conditions. Cultivars CLN5915, CLN1621L, and CLN2001A have a high actual yield with 15.7, 12.1, and 7.8 ton/ha, respectively. The Brix degree of high fruit quality ranges from 4.1 to 4.6 Bx. Therefore, these introduced cultivars can be considered as promising for tomato breeding and cultivation under the local conditions.Keywords: tomato, agronomic characteristics, yield, Thua Thien Hu

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

Análisis comparativo de K-NN, Naïve-Bayes y regresión logística para la detección de fraude con tarjetas de crédito

Introduction: This paper highlights the outcome of the comparative study of “Various Machine learning algorithms namely K-NN, Naive Bayes, and Logistic Regression for Credit Card Fraud Detection” carried out based on a dataset taken from UCI.com in 2022-23 at Manav Rachna International Institute of Research and Studies. Problem: Credit card fraud is still rife today and the modes are increasingly varied. Quite often we hear of fraud cases that cause irreplaceable injury to banks and financial institutions which cannot be compensated in terms of costs. To avoid scams with various modes of credit cards, we must be able to identify and find out the modes often used by fraudsters. This scheme liberates such financial institutions and banks with complete and appropriate information using Machine Learning Techniques, not only about the modes that scammers or fraudsters often use but also ways to protect against such frauds. Objective: The present paper discusses the various machine learning models based on classification and regression, namely K-Nearest Neighbors, Naïve Bayes, and Logistic Regression, which are successfully able to achieve the classification accuracy of 80% using Logistic Regression with a Precision of 78%, Recall of 100%, and F1-Score of 88% for fraudulent credit card transactions. Methodology: The comparative analysis demonstrates that for Precision, Recall, and Accuracy parameters, the K-Nearest Neighbor is a better approach for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes. Results: The accuracy is marginal high in Logistic Regression but the False Positive parameters are not able to identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K-Nearest Neighbor deems fit for such cases. Conclusion: This scheme depicts the automated fraud classification systems using machine learning techniques, namely K-Nearest Neighbor, Logistic Regression, and Naive Bayes, to produce a model that can distinguish valid and invalid credit card transactions. Originality: Through this research, the most relevant features are used to go through the visualization of accuracy with the confusion matrix, and accuracy calculations are obtained from the dataset used.Limitations: Deep learning techniques could have been used to fetch even better results.Introducción: este artículo muestra el resultado de un estudio comparativo de “varios algoritmos de machine learning, a saber, K-NN, Naïve-Bayes y regresión logística para la detección de fraudes con tarjetas de crédito”, realizado con base en un conjunto de datos tomado de UCI.com en 2022-23 en el Instituto Internacional de Investigaciones y Estudios Manav Rachna. Problema: el fraude con tarjetas de crédito está muy extendido hoy en día y las modalidades son cada vez más variadas. A menudo, se oye hablar de casos de fraude que causan daños irreparables a bancos e instituciones financieras, que no pueden ser compensados en términos de costos. Para evitar estafas con diversos modos de tarjetas de crédito, se debe poder identificar y descubrir los modos que suelen utilizar los estafadores. Este esquema proporciona a dichas instituciones financieras y bancos información completa y adecuada utilizando técnicas de machine learning, no solo sobre los modos que suelen utilizar los estafadores o defraudadores, sino también sobre las formas de protegerse contra dichos fraudes. Objetivo: el presente artículo analiza los diversos modelos de machine learning basados en clasificación y regresión, a saber, K-Nearest Neighbors (K-NN), Naïve Bayes y regresión logística, que pueden lograr con éxito una precisión de clasificación del 80% utilizando regresión logística con una precisión de 78%, Retiro del 100% y F1 Score del 88% para transacciones fraudulentas con tarjeta de crédito. Método: el análisis comparativo muestra que, para los parámetros de precisión, recuperación y exactitud, el K-NN es un mejor enfoque para detectar transacciones fraudulentas que la regresión logística y el Naïve Bayes.Resultados: la precisión es marginalmente alta en la regresión logística, pero los parámetros de falso positivo no pueden identificar los datos desequilibrados; por lo tanto, disfrazan los resultados y la precisión de la regresión logística y el K-NN se considera adecuado para tales casos. Conclusión: este esquema describe los sistemas automatizados de clasificación de fraude que utilizan técnicas de machine learning, a saber, K-NN, Regresión logística y Naïve Bayes, para producir un modelo que pueda distinguir transacciones con tarjetas de crédito válidas e inválidas. Originalidad: a través de esta investigación, se utilizan las características más relevantes para visualizar la precisión con la matriz de confusión y se obtienen cálculos de precisión a partir del conjunto de datos utilizado.Limitaciones: se podrían haber utilizado técnicas de Deep learning para obtener mejores resultados

INFLUENCE OF GROWING MEDIA AND VARIETIES ON GROWTH AND DEVELOPMENT OF MOKARA IN TAM KY, QUANG NAM PROVINCE

Abstract: Mokara Orchid is a trigeneric hybrid between the Ascocentrum, Vanda and Arachnis Orchids and was created in Singapore in 1969. Mokara is now popularly grown in Vietnam; however, research on variety or growing medium for Mokara orchid in Quang Nam has not been reported yet. The objective of this study is to identify adapted Moraka varieties and suitable growing media for the cultivation of Mokara orchids under the local conditions. The experiment was conducted from June 2016 to December 2017 at Truong Xuan Ward, Tam Ky City, Quang Nam province using a subplot design, where the growing medium is the main plot and Mokara orchid varietyis the split-plot. The experiment includes three Mokara varieties belonging to genus Mokara with 3 colors: yellow, lime, and pink spotand four growing media with different ratios of peanut shell, charcoal, and coir. The results show that the growing media significantly affect the plant height and flower yield, but they do not affect the leaf number, leaf length, leaf width, root number, and flower quality of the Mokara varieties. The varieties affect the growth and development, flower quality, and yield of Mokara. Using the same media, the pink spot Mokara variety gains the best growth and development, and the yellow Mokara variety provides the highest yield. These Mokara varieties gain the best flower quality. The growing medium with 50% coir and 50% peanut shell can be used to plant yellow Mokara (or pink spot Mokara) under the local conditions.Keywords: Mokara orchid varieties, growing medium, coir, peanut shell, yellow Mokara, pink spot Mokar

Applying analytic hierarchy process to adaptation to saltwater intrusion in Vietnam

Given the multidimensional nature of climate change issues, decision-making in climate change adaptation is a complex process, and suitable decision support methods are needed. The aim of this paper was to rank saltwater intrusion adaptation options for farmers in two provinces in the central coastal region of Vietnam using the analytical hierarchy process method. Data for the analysis were obtained through a literature review, field observations, and face-to-face interviews and focus group discussions with key informants. We combined two ways of weighting to arrive at final scores for each of the identified adaptation options: prioritizing criteria and subcriteria by pairwise comparison and rating the different alternatives with respect to the lowest level subcriteria. In doing so, we also investigated differences in the priority sets and final rankings of the analytical hierarchy process applications in both provinces. In our study, we worked with group consensus scores on both the criteria weights and the ratings for the different adaptation options for each of the criteria. Our results revealed that “sustainability and equity” was the most important criteria, while coherence ranked lowest. The final ranking of adaptation options differed between both provinces due to differences in the geographical and socioeconomic characteristics of the study areas. The consistency ratios for all pairwise matrices were less than 0.1, indicating that judgments from the focus group discussions with respect to the different criteria were highly consistent. A sensitivity analysis of our results confirmed the robustness of the rankings in our research