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Viscosity estimation of binary mixtures of ionic liquids through a multi-layer perceptron model
Pablo Díaz-Rodríguez,John C. Cancilla,Gemma Matute,José S. Torrecilla 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.21 No.1
Ionic liquids (ILs) are chemical compounds which are being more and more employed due to theiradvantageous properties which are useful in many chemical and industrial fields. The study of thephysicochemical properties of ILs and their mixtures is essential in order to improve their efficacy. Oneway to study these properties is by designing powerful mathematical estimative models. To do so, anartificial neural network multilayer perceptron (MLP) model has been proposed and developed toestimate the viscosity of four IL binary mixtures (1-hexyl-3-methylimidazolium tetrafluoroborate([C6MIM,BF4]) + 1-ethyl-3-methylimidazolium tetrafluoroborate ([C2MIM,BF4]), [C6MIM,BF4] + 1-butyl-3-methylimidazolium tetrafluoroborate ([C4MIM,BF4]), [C4MIM,BF4] + 1-butyl-3-methylimidazoliummethylsulfate ([C4MIM,MeSO4]), and [C4MIM,BF4] + 1-butyl-3-methylimidazolium hexafluorophosphate([C4MIM,PF6])) inside the temperature range 298.15–308.15 K. The statistical results offered by thedesigned MLP model confirmed that it can be used to accurately estimate the viscosity of the tested ILmixtures (mean prediction errors around 1.5% for a k-fold cross-validation and about 2.3% for three blindtests). Additionally, it is possible to assess the purity level of the studied ILs or binary mixtures throughthe estimated viscosity values.
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Alberto Prieto,Ricardo Fernández-Antonio,Gonzalo López-Lorenzo,José Manuel Díaz-Cao,Cynthia López-Novo,Susana Remesar,Rosario Panadero,Pablo Díaz,Patrocinio Morrondo,Pablo Díez-Baños,Gonzalo Fernández 대한수의학회 2020 Journal of Veterinary Science Vol.21 No.4
Background: Aleutian mink disease virus (AMDV) causes major economic losses in fur-bearing animal production. The control of most AMDV outbreaks is complex due to the difficulties of establishing the source of infection based only on the available on-farm epidemiological data. In this sense, phylogenetic analysis of the strains present in a farm may help elucidate the origin of the infection and improve the control and biosecurity measures. Objectives: This study had the following aims: characterize the AMDV strains from most outbreaks produced at Spanish farms between 2012–2019 at the molecular level, and assess the utility of the combined use of molecular and epidemiological data to track the possible routes of infection. Methods: Thirty-seven strains from 17 farms were partially sequenced for the NS1 and VP2 genes and analyzed phylogenetically with other strains described worldwide. Results: Spanish AMDV strains are clustered in four major clades that generally show a good geographical correlation, confirming that most had been established in Spain a long time ago. The combined study of phylogenetic results and epidemiological information of each farm suggests that most of the AMDV outbreaks since 2012 had been produced by within-farm reservoirs, while a few of them may have been due to the introduction of the virus through international trade. Conclusions: The combination of phylogenetic inference, together with epidemiological data, helps assess the possible origin of AMDV infections in mink farms and improving the control and prevention of this disease.
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Numerical investigation of truck aerodynamics on several classes of infrastructures
Alejandro Alonso-Estébanez,Juan J. del Coz Díaz,Felipe P. Álvarez Rabanal,Pablo Pascual-Muñoz,Paulino J. García Nieto 한국풍공학회 2018 Wind and Structures, An International Journal (WAS Vol.26 No.1
This paper describes the effect of different testing parameters (configuration of infrastructure and truck position on road) on truck aerodynamic coefficients under cross wind conditions, by means of a numerical approach known as Large Eddy Simulation (LES). In order to estimate the air flow behaviour around both the infrastructure and the truck, the filtered continuity and momentum equations along with the Smagorinsky–Lilly model were solved. A solution for these non-linear equations was approached through the finite volume method (FVM) and using temporal and spatial discretization schemes. As for the results, the aerodynamic coefficients acting on the truck model exhibited nearly constant values regardless of the Reynolds number. The flat ground is the infrastructure where the rollover coefficient acting on the truck model showed lowest values under cross wind conditions (yaw angle of 90), while the worst infrastructure studied for vehicle stability was an embankment with downward-slope on the leeward side. The position of the truck on the road and the value of embankment slope angle that minimizes the rollover coefficient were determined by successfully applying the Response Surface Methodology.
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Moisé,s Roberto Vallejo Pé,rez,Hugo Ricardo Navarro Contreras,Jesú,s A. Sosa Herrera,José,Pablo Lara Á,vila,Hugo Magdaleno Ramí,rez Tobí,as,Fernando Dí,az-Barriga Mart& 한국식물병리학회 2018 Plant Pathology Journal Vol.34 No.5
Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in México. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy (532 nm λ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants × 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and 1510 cm-1 were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively).
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