The Current Adoption of Dry-Direct Seeding Rice (DDSR) in Thailand and Lessons Learned for Mekong River Delta of Vietnam

The paper documents the joint study trip, organized by CCAFS Southeast Asia for Vietnamese rice researchers, extension workers, as well as local decision makers, to visit Thailand in April 2018. The goal of the study trip was to observe and learn the experience of Thai farmers on the large-scale adoption process of dry-direct seeding rice (DDSR), a viable alternative to address regional scarcity of fresh water in irrigation caused by the drought and salinity intrusion in the Mekong River Delta

An Unsupervised Algorithm for Host Identification in Flaviviruses

Early characterization of emerging viruses is essential to control their spread, such as the Zika Virus outbreak in 2014. Among other non-viral factors, host information is essential for the surveillance and control of virus spread. Flaviviruses (genus Flavivirus), akin to other viruses, are modulated by high mutation rates and selective forces to adapt their codon usage to that of their hosts. However, a major challenge is the identification of potential hosts for novel viruses. Usually, potential hosts of emerging zoonotic viruses are identified after several confirmed cases. This is inefficient for deterring future outbreaks. In this paper, we introduce an algorithm to identify the host range of a virus from its raw genome sequences. The proposed strategy relies on comparing codon usage frequencies across viruses and hosts, by means of a normalized Codon Adaptation Index (CAI). We have tested our algorithm on 94 flaviviruses and 16 potential hosts. This novel method is able to distinguish between arthropod and vertebrate hosts for several flaviviruses with high values of accuracy (virus group 91.9% and host type 86.1%) and specificity (virus group 94.9% and host type 79.6%), in comparison to empirical observations. Overall, this algorithm may be useful as a complementary tool to current phylogenetic methods in monitoring current and future viral outbreaks by understanding host–virus relationships

An Unsupervised Algorithm for Host Identification in Flaviviruses

Early characterization of emerging viruses is essential to control their spread, such as the Zika Virus outbreak in 2014. Among other non-viral factors, host information is essential for the surveillance and control of virus spread. Flaviviruses (genus Flavivirus), akin to other viruses, are modulated by high mutation rates and selective forces to adapt their codon usage to that of their hosts. However, a major challenge is the identification of potential hosts for novel viruses. Usually, potential hosts of emerging zoonotic viruses are identified after several confirmed cases. This is inefficient for deterring future outbreaks. In this paper, we introduce an algorithm to identify the host range of a virus from its raw genome sequences. The proposed strategy relies on comparing codon usage frequencies across viruses and hosts, by means of a normalized Codon Adaptation Index (CAI). We have tested our algorithm on 94 flaviviruses and 16 potential hosts. This novel method is able to distinguish between arthropod and vertebrate hosts for several flaviviruses with high values of accuracy (virus group 91.9% and host type 86.1%) and specificity (virus group 94.9% and host type 79.6%), in comparison to empirical observations. Overall, this algorithm may be useful as a complementary tool to current phylogenetic methods in monitoring current and future viral outbreaks by understanding host–virus relationships

Natural frequencies and critical loads of functionally graded single span beams resting on winkler’s elastic foundation

Natural frequencies and critical loads of functionally graded single span beams resting on Winkler’s elastic foundation with general boundary conditions are presented in this paper. The analytical model of the beam is described by using the first order shear deformation theory, however, the transverse shear stress is derived from expression of the normal stress and equilibrium equation and thus, its shear correction factor is then obtained analytically. The effective material properties of the beam are assumed to follow simple power law form. The governing equation of motion of the beam is derived based on Lagrange’s equations with specific boundary conditions satisfied with the Lagrange’s multipliers. Comparisons between the results of present study with available results in the literature show a good agreement. In addition, parametric analysis is carried out, including material distribution, boundary conditions and axial load as well as foundation factor and slenderness ratio

Torque ripple minimization in non-sinusoidal synchronous reluctance motors based on artificial neural networks

This paper proposes a new method based on Artificial Neural Networks for reducing the torque ripple in a non-sinusoidal Synchronous Reluctance Motor. The Lagrange optimization method is used to solve the problem of calculating optimal currents in the d-q frame. A neural control scheme is then proposed as an adaptive solution to derive the optimal stator currents giving a constant electromagnetic torque and minimizing the ohmic losses. Thanks to the online learning capacity of neural networks, the optimal currents can be obtained online in real time. With this neural control, each machine’s parameters estimation errors and current controller errors can be compensated. Simulation and experimental results are presented which confirm the validity of the proposed method.Bourse de l'Ambassade de France au Vietna

An Unsupervised Algorithm for Host Identification in Flaviviruses

Early characterization of emerging viruses is essential to control their spread, such as the Zika Virus outbreak in 2014. Among other non-viral factors, host information is essential for the surveillance and control of virus spread. Flaviviruses (genus Flavivirus), akin to other viruses, are modulated by high mutation rates and selective forces to adapt their codon usage to that of their hosts. However, a major challenge is the identification of potential hosts for novel viruses. Usually, potential hosts of emerging zoonotic viruses are identified after several confirmed cases. This is inefficient for deterring future outbreaks. In this paper, we introduce an algorithm to identify the host range of a virus from its raw genome sequences. The proposed strategy relies on comparing codon usage frequencies across viruses and hosts, by means of a normalized Codon Adaptation Index (CAI). We have tested our algorithm on 94 flaviviruses and 16 potential hosts. This novel method is able to distinguish between arthropod and vertebrate hosts for several flaviviruses with high values of accuracy (virus group 91.9% and host type 86.1%) and specificity (virus group 94.9% and host type 79.6%), in comparison to empirical observations. Overall, this algorithm may be useful as a complementary tool to current phylogenetic methods in monitoring current and future viral outbreaks by understanding host-virus relationships

STUDY ON TREATMENT OF THE LEACHATE FROM LANDFILL SITE AT NAMSON, SOCSON, HANOI

Joint Research on Environmental Science and Technology for the Eart

A novel negevirus isolated from Aedes vexans mosquitoes in Finland

Negeviruses are insect-specific enveloped RNA viruses that have been detected in mosquitoes and sandflies from various geographical locations. Here, we describe a new negevirus from Northern Europe, isolated from pool ofAedes vexansmosquitoes collected in Finland, designated as Mekrijarvi negevirus (MEJNV). MEJNV had a typical negevirus genome organization, is 9,740 nucleotides in length, and has a GC content of 47.53%. The MEJNV genome contains three ORFs, each containing the following identified conserved domains: ORF1 (7,068 nt) encodes a viral methyltransferase, an FtsJ-like methyltransferase, a viral RNA helicase, and an RNA-dependent RNA polymerase, ORF2 (1,242 nt) encodes a putative virion glycoprotein, and ORF3 (660 nt) encodes a putative virion membrane protein. A distinctive feature relative to other currently known negeviruses is a 7-nucleotide-long overlap between ORF1 and ORF2. MEJNV shares the highest sequence identity with Ying Kou virus from China, with 67.71% nucleotide and 75.19% and 59.00% amino acid sequence identity in ORF 1 and ORF 2, respectively. ORF3 had the highest amino acid sequence similarity to Daeseongdong virus 1 and negevirus Nona 1, both with 77.61% identity, and to Ying Kou virus, with 71.22% identity. MEJNV is currently the northernmost negevirus described. Our report supports the view that negeviruses are a globally distributed, diverse group of viruses that can be found from mosquitoes in a wide range of terrestrial biomes from tropical to boreal forests.Peer reviewe

Optimal Efficiency Control of Synchronous Reluctance Motors-based ANN Considering Cross Magnetic Saturation and Iron Loss

This paper presents a new idea by using the Artificial Neural Networks (ANNs) for estimating the parameters of the machine which achieving the maximum efficiency of the Synchronous Reluctance Motor (SynRM). This model take into consideration the magnetic saturation, cross-coupling and iron loss. With Finite Element Analysis (FEA), the characteristics of the SynRM including inductances and iron loss resistance are determined. Because of the non-linear characteristics, an ANN trained off-line, is then proposed to obtain the d-q inductances and iron loss resistance from Id,Iq currents and the speed. After learning process, an analytical expression of the optimal currents is given thanks to Lagrange optimization. Therefore, the optimal currents will be obtained online in real time. This method can be achieved with maximum efficiency and high-precision torque control. Simulation and experimental results are presented to confirm the validity of the proposed method

Characterisation of the RNA Virome of Nine Ochlerotatus Species in Finland

RNA viromes of nine commonly encountered Ochlerotatus mosquito species collected around Finland in 2015 and 2017 were studied using next-generation sequencing. Mosquito homogenates were sequenced from 91 pools comprising 16–60 morphologically identified adult females of Oc. cantans, Oc. caspius, Oc. communis, Oc. diantaeus, Oc. excrucians, Oc. hexodontus, Oc. intrudens, Oc. pullatus and Oc. punctor/punctodes. In total 514 viral Reverse dependent RNA polymerase (RdRp) sequences of 159 virus species were recovered, belonging to 25 families or equivalent rank, as follows: Aliusviridae, Aspiviridae, Botybirnavirus, Chrysoviridae, Chuviridae, Endornaviridae, Flaviviridae, Iflaviridae, Negevirus, Partitiviridae, Permutotetraviridae, Phasmaviridae, Phenuiviridae, Picornaviridae, Qinviridae, Quenyavirus, Rhabdoviridae, Sedoreoviridae, Solemoviridae, Spinareoviridae, Togaviridae, Totiviridae, Virgaviridae, Xinmoviridae and Yueviridae. Of these, 147 are tentatively novel viruses. One sequence of Sindbis virus, which causes Pogosta disease in humans, was detected from Oc. communis from Pohjois-Karjala. This study greatly increases the number of mosquito-associated viruses known from Finland and presents the northern-most mosquito-associated viruses in Europe to date