African swine fever (ASF) was officially reported in Vietnam in February 2019 and spread across the whole country, affecting all 63 provinces and cities.
Material and Methods
In this study, ASF virus (ASFV) VN/Pig/HaNam/2019 (VN/Pig/HN/19) strain was isolated in primary porcine alveolar macrophage (PAM) cells from a sample originating from an outbreak farm in Vietnam’s Red River Delta region. The isolate was characterised using the haemadsorption (HAD) test, real-time PCR, and sequencing. The activity of antimicrobial feed products was evaluated via a contaminated ASFV feed assay.
Phylogenetic analysis of the viral p72 and EP402R genes placed VN/Pig/HN/19 in genotype II and serogroup 8 and related it closely to Eastern European and Chinese strains. Infectious titres of the virus propagated in primary PAMs were 106 HAD50/ml. Our study reports the activity against ASFV VN/Pig/HN/19 strain of antimicrobial Sal CURB RM E Liquid, F2 Dry and K2 Liquid. Our feed assay findings suggest that the antimicrobial RM E Liquid has a strong effect against ASFV replication. These results suggest that among the Sal CURB products, the antimicrobial RM E Liquid may have the most potential as a mitigant feed additive for ASFV infection. Therefore, further studies on the use of antimicrobial Sal CURB RM E Liquid in vivo are required.
Our study demonstrates the threat of ASFV and emphasises the need to control and eradicate it in Vietnam by multiple measures.
The problem of extracting knowledge from decision tables in terms of functional dependencies is one of the important problems in knowledge discovery and data mining. Based on some results in relational database, in this paper we propose two algorithms. The first one is to find all reducts of a consistent decision table. The second is to infer functional dependencies from a consistent decision table. The second algorithm is based on the result of the first. We show that the time complexity of the two algorithms proposed is exponential in the number of attributes in the worst case.
The problem of finding reducts plays an important role in processing information on decision tables. The objective of the attribute reduction problem is to reject a redundant attribute in order to find a core attribute for data processing. The attribute reduction in decision tables is the process of finding a minimal subset of conditional attributes which preserve the classification ability of decision tables. In this paper we present the time complexity of the problem of finding all reducts of a consistent decision table. We prove that this time complexity is exponential with respect to the number of attributes of the decision tables. Our proof is performed in two steps. The first step is to show that there exists an exponential algorithm which finds all reducts. The other step is to prove that the time complexity of the problem of finding all reducts of a decision table is not less than exponential.
Feature selection is a vital problem which needs to be effectively solved in knowledge discovery in databases and pattern recognition due to two basic reasons: minimizing costs and accurately classifying data. Feature selection using rough set theory is also called attribute reduction. It has attracted a lot of attention from researchers and numerous potential results have been gained. However, most of them are applied on static data and attribute reduction in dynamic databases is still in its early stages. This paper focuses on developing incremental methods and algorithms to derive reducts, employing a distance measure when decision systems vary in condition attribute set. We also conduct experiments on UCI data sets and the experimental results show that the proposed algorithms are better in terms of time consumption and reducts’ cardinality in comparison with non-incremental heuristic algorithm and the incremental approach using information entropy proposed by authors in .
The paper presents the results of computational evaluation of the hull-propeller interaction coefficients, also referred to propulsive coefficients, based on the unsteady RANS flow model. To obtain the propulsive coefficients, the ship resistance, the open-water characteristics of the propeller, and the flow past the hull with working propeller were computed. For numerical evaluation of propeller open-water characteristics, the rotating reference frame approach was used, while for self-propulsion simulation, the rigid body motion method was applied. The rotating propeller was modelled with the sliding mesh technique. The dynamic sinkage and trim of the vessel were considered. The free surface effects were included by employing the volume of fluid method (VOF) for multi-phase flows. The self-propulsion point was obtained by performing two runs at constant speed with different revolutions. The well-known Japan Bulk Carrier (JBC) test cases were used to verify and validate the accuracy of the case studies. The solver used in the study was the commercial package Star-CCM+ from SIEMENS.
In rough set theory, the number of all reducts for a given decision table can be exponential with respect to the number of attributes. This paper investigates the problem of determining the set of all reductive attributes which are present in at least one reduct of an incomplete decision table. We theoretically prove that this problem can be solved in polynomial time. This result shows that the problem of determining the union of all reducts can be solved in polynomial time, and the problem of determining the set of all redundant attributes which are not present in any reducts can also be solved in polynomial time.
According to traditional rough set theory approach, attribute reduction methods are performed on the decision tables with the discretized value domain, which are decision tables obtained by discretized data methods. In recent years, researches have proposed methods based on fuzzy rough set approach to solve the problem of attribute reduction in decision tables with numerical value domain. In this paper, we proposeafuzzy distance between two partitions and an attribute reduction method in numerical decision tables based on proposed fuzzy distance. Experiments on data sets show that the classification accuracy of proposed method is more efficient than the ones based fuzzy entropy.
Mining High Utility Sequential Patterns (HUSP) is an emerging topic in data mining which attracts many researchers. The HUSP mining algorithms can extract sequential patterns having high utility (importance) in a quantitative sequence database. In real world applications, the time intervals between elements are also very important. However, recent HUSP mining algorithms cannot extract sequential patterns with time intervals between elements. Thus, in this paper, we propose an algorithm for mining high utility sequential patterns with the time interval problem. We consider not only sequential patterns’ utilities, but also their time intervals. The sequence weight utility value is used to ensure the important downward closure property. Besides that, we use four time constraints for dealing with time interval in the sequence to extract more meaningful patterns. Experimental results show that our proposed method is efficient and effective in mining high utility sequential pattern with time intervals.