Redistribution of charged aluminum nanoparticles on oil droplets in water in response to applied electrical field

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s11051-016-3390-5.Janus droplets with two opposite faces of different physical or chemical properties have great potentials in many fields. This paper reports a new method for making Janus droplets by covering one side of the droplet with charged nanoparticles in an externally applied DC electric field. In this paper, aluminum oxide nanoparticles on micro-sized and macro-sized oil droplets were studied. In order to control the surface area covered by the nanoparticles on the oil droplets, the effects of the concentration of nanoparticle suspension, the droplet size as well as the strength of electric field on the final accumulation area of the nanoparticles are studied

Vortices around Janus droplets under externally applied electrical field

The final publication is available at Springer via http://dx.doi.org/10.1007/s10404-016-1741-2.In this study, the Janus droplet is an oil droplet covered with aluminum oxide nanoparticles on one side of the droplet surface under applied DC electrical field. The vortices around Janus droplets fixed on a horizontal surface were studied in this paper. A numerical model was set up to simulate the vortices around the Janus droplet in electric field. The simulation results illustrate that the electric field determines the strength of the vortices around a fixed Janus droplet, and the surface coverage of the positively charged nanoparticles on a Janus droplet affects the size and location of the vortices. The numerically predicted results were further validated experimentally by visualizing the vortices around Janus droplets in an externally applied DC electric field. Furthermore, as the Janus droplets are generated in electric field, the surface coverage by the nanoparticles depends on the strength of the electric field; therefore, the effect of the electric field on the nanoparticle covered surface area of a Janus droplet and the vortices was analyzed

Redistribution of mobile surface charges of an oil droplet in water in applied electric field

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.cis.2016.08.006. © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Most researches on oil droplets immersed in aqueous solutions assume that the surface charges of oil droplets are, similar to that of solid particles, immobile and distributed uniformly under external electric field. However, the surface charges at the liquid–liquid interface are mobile and will redistribute under external electric field. This paper studies the redistribution of surface charges on an oil droplet under the influence of the external electrical field. Analytical expressions of the local zeta potential on the surface of an oil droplet after the charge redistribution in a uniform electrical field were derived. The effects of the initial zeta potential, droplet radius and strength of applied electric field on the surface charge redistribution were studied. In analogy to the mobile surface charges, the redistribution of Al2O3-passivated aluminum nanoparticles on the oil droplet surface was observed under applied electrical field. Experimental results showed that these nanoparticles moved and accumulated towards one side of the oil droplet under electric field. The redistribution of the nanoparticles is in qualitative agreement with the redistribution model of the mobile surface charges developed in this work

Integrated analysis of epidemiological and phylogenetic data to elucidate viral transmission dynamics

While infectious disease outbreaks are often summarised by population averages such as the reproductive number, variation between individuals in terms of onwards transmissions modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. This heterogeneity among individuals can be quantified by the dispersion parameter k of the offspring distribution, a distribution that defines the number of secondary infections per infected individual. I have developed an inference framework to estimate k and other epidemiological parameters by fitting stochastic transmission models to both incidence time series and the pathogen phylogeny. Applying the framework to simulated data, I found that more accurate, less biased and more precise estimates of the reproductive number and k were obtained by combining epidemiologic and phylogenetic analyses. Accurately estimating k was necessary for unbiased estimates of the reproductive number, but it did not affect the accurate estimation of epidemic start date and the probability of sampling an infection. I further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. In addition to methodological contributions, I found that the inclusion of sequences in statistical inference for polio improved the precision of parameter estimates. Based on sequences collected from patients during a poliovirus outbreak, the estimated values of k were high regardless of the data used. On the other hand, the k estimates were low when a transmission model was fit to environmental sequences collected in Pakistan, which is still endemic for wild poliovirus. Furthermore, analysis of environmental sequences was informative of seasonality parameters whereas inference from incidence time series alone was not. This type of analysis using environmental sequences would be useful as polio eradication draws to a close as the number of symptomatic cases approaches zero.Open Acces

Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Consistency, multiplicative and ordinal, of fuzzy preference relations (FPRs) is investigated. The geometric consistency index (GCI) approximated thresholds are extended to measure the degree of consistency for an FPR. For inconsistent FPRs, two algorithms are devised (1) to find the multiplicative inconsistent elements, and (2) to detect the ordinal inconsistent elements. An integrated algorithm is proposed to improve simultaneously the ordinal and multiplicative consistencies. Some examples, comparative analysis, and simulation experiments are provided to demonstrate the effectiveness of the proposed methods

Graph Neural Network Backend for Speaker Recognition

Currently, most speaker recognition backends, such as cosine, linear discriminant analysis (LDA), or probabilistic linear discriminant analysis (PLDA), make decisions by calculating similarity or distance between enrollment and test embeddings which are already extracted from neural networks. However, for each embedding, the local structure of itself and its neighbor embeddings in the low-dimensional space is different, which may be helpful for the recognition but is often ignored. In order to take advantage of it, we propose a graph neural network (GNN) backend to mine latent relationships among embeddings for classification. We assume all the embeddings as nodes on a graph, and their edges are computed based on some similarity function, such as cosine, LDA+cosine, or LDA+PLDA. We study different graph settings and explore variants of GNN to find a better message passing and aggregation way to accomplish the recognition task. Experimental results on NIST SRE14 i-vector challenging, VoxCeleb1-O, VoxCeleb1-E, and VoxCeleb1-H datasets demonstrate that our proposed GNN backends significantly outperform current mainstream methods