Electrospinning predictions using artificial neural networks

Electrospinning is a relatively simple method of producing nanofibres. Currently there is no method to predict the characteristics of electrospun fibres produced from a wide range of polymer/solvent combinations and concentrations without first measuring a number of solution properties. This paper shows how artificial neural networks can be trained to make electrospinning predictions using only commonly available prior knowledge of the polymer and solvent. Firstly, a probabilistic neural network was trained to predict the classification of three possibilities: no fibres (electrospraying); beaded fibres; and smooth fibres with > 80% correct predictions. Secondly, a generalised neural network was trained to predict fibre diameter with an average absolute percentage error of 22.3% for the validation data. These predictive tools can be used to reduce the parameter space before scoping exercises

Understanding India’s Green Revolution: A Case Study for Contemporary Agrarian Reform

Faced with large-scale food insecurity in the mid-twentieth century, India adopted innovative agricultural technologies as part of the Green Revolution. While these technologies expanded agricultural productivity, this paper argues that the program was a disruptive force to Indian social, economic, and political systems, specifically in the rural setting. An analysis of outcomes of the Green Revolution reveals that inadequate attention was given to India’s unique colonial history as well as to the regional differences, both ecological and socioeconomic, found within the country. Advocating for a holistic approach to global development, this paper offers a framework of policy recommendations aimed at minimizing the disruptive potential for contemporary agrarian reform. The potential for disconnect between economics and social systems is a major theme throughout

The effects of corpus size and homogeneity on language model quality

Generic speech recognition systems typically use language models that are trained to cope with a broad variety of input. However, many recognition applications are more constrained, often to a specific topic or domain. In cases such as these, a knowledge of the particular topic can be used to advantage. This report describes the development of a number of techniques for augmenting domain-specific language models with data from a more general source. Two investigations are discussed. The first concerns the problem of acquiring a suitable sample of the domain-specific language data from which to train the models. The issue here is essentially one of quality, since it is shown that not all domain-specific corpora are equal. Moreover, they can display significantly different characteristics that affect the quality of any language models built therefrom. These characteristics are defined using a number of statistical measures, and their significance for language modelling is discussed. The second investigation concerns the empirical development and evaluation of a set of language models for the task of email speech-u>-text dictation. The issue here is essentially one of quantity, since it is shown that effective language models can be built from very modestly sized corpora, providing the training data matches the target appfication. Evaluations show that a language model trained on only 2 million words can perform better than one trained on a corpus of over 100 times that size

Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels

We present an efficient matrix-free point spread function (PSF) method for approximating operators that have locally supported non-negative integral kernels. The method computes impulse responses of the operator at scattered points, and interpolates these impulse responses to approximate integral kernel entries. Impulse responses are computed by applying the operator to Dirac comb batches of point sources, which are chosen by solving an ellipsoid packing problem. Evaluation of kernel entries allows us to construct a hierarchical matrix (H-matrix) approximation of the operator. Further matrix computations are performed with H-matrix methods. We use the method to build preconditioners for the Hessian operator in two inverse problems governed by partial differential equations (PDEs): inversion for the basal friction coefficient in an ice sheet flow problem and for the initial condition in an advective-diffusive transport problem. While for many ill-posed inverse problems the Hessian of the data misfit term exhibits a low rank structure, and hence a low rank approximation is suitable, for many problems of practical interest the numerical rank of the Hessian is still large. But Hessian impulse responses typically become more local as the numerical rank increases, which benefits the PSF method. Numerical results reveal that the PSF preconditioner clusters the spectrum of the preconditioned Hessian near one, yielding roughly 5x-10x reductions in the required number of PDE solves, as compared to regularization preconditioning and no preconditioning. We also present a numerical study for the influence of various parameters (that control the shape of the impulse responses) on the effectiveness of the advection-diffusion Hessian approximation. The results show that the PSF-based preconditioners are able to form good approximations of high-rank Hessians using a small number of operator applications

Effect of Adding Nanofibers into Sunflower (Helianthus annuus) Oil on Oil Viscosity

The significant effect of the addition of very small proportions of nanofibres and nano-particulates on the physical and mechanical properties of solid materials has been long observed and described. The effect of addition of electrospun fibres on the physical properties of liquid has not been so widely examined. In this paper, the effect of the addition of polyvinyl alcohol (PVOH), and zein nanofibers in sunflower (Helianthus annuus) oil on its viscosity was observed and quantified. For a given amount of material, a trend of increasing effectiveness was found as fibre diameter reduced. The addition of 0.01% (by mass) of fibre increased the kinematic viscosity of oil samples by 15%. The presence of fibre in oil was confirmed by light microscopy whilst the size of the fibres was measured by the analysis of scanning electron microscope images (SEM). This phenomenon of a low concentration of nanofibers significantly increasing viscosity may find practical applications as a foodstuff thickener

The history of the science and technology of electrospinning from 1600 to 1995

A significant challenge inThis paper outlines the story of the inventions and discoveries that directly relate to the genesis and development of electrostatic production and drawing of fibres: electrospinning. Current interest in the process is due to the ease with which nano-scale fibers can be produced in the laboratory. In 1600, the first record of the electrostatic attraction of a liquid was observed by William Gilbert. Christian Friedrich Schönbein produced highly nitrated cellulose in 1846. In 1887 Charles Vernon Boys described the process in a paper on nano-fiber manufacture. John Francis Cooley filed the first electrospinning patent in 1900. In 1914 John Zeleny published work on the behaviour of fluid droplets at the end of metal capillaries. His effort began the attempt to mathematically model the behavior of fluids under electrostatic forces. Between 1931 and 1944 Anton Formhals took out at least 22 patents on electrospinning. In 1938, N.D. Rozenblum and I.V. Petryanov-Sokolov generated electrospun fibers, which they developed into filter materials. Between 1964 and 1969 Sir Geoffrey Ingram Taylor produced the beginnings of a theoretical underpinning of electrospinning by mathematically modelling the shape of the (Taylor) cone formed by the fluid droplet under the effect of an electric field. In the early 1990s several research groups (notably that of Reneker who popularised the name electrospinning) demonstrated electrospun nano-fibers. Since 1995, the number of publications about electrospinning has been increasing exponentially every year

Complementary characterization data in support of uniaxially aligned electrospun nanocomposite based on a model PVOH-epoxy system

This paper presents complementary data corresponding to char- acterization tests done for our research article entitled “Uniaxially aligned electrospun fibers for advanced nanocomposites based on a model PVOH-epoxysystem”. Poly(vinyl alcohol) and epoxy resin were selected as a model system and the effect of electrospun fiber loading on polymer properties was examined in conjunction with two manufacturing methods. A novel electrospinning technology for production of uniaxially aligned nanofiber arrays was used. A conventional wet lay-up fabrication method is compared against a novel, hybrid electro- spinning–electrospraying approach.The structure and thermo- mechanical properties of resulting composite materials were examined using scanning electron microscopy, dynamic mechanical thermal analysis, and Fourier transform infra-red spectroscopy