Entropy scaling based viscosity predictions for hydrocarbon mixtures and diesel fuels up to extreme conditions

An entropy scaling based technique using the Perturbed-Chain Statistical Associating Fluid Theory is described for predicting the viscosity of hydrocarbon mixtures and diesel fuels up to high temperatures and high pressures. The compounds found in diesel fuels or hydrocarbon mixtures are represented as a single pseudo-component. The model is not fit to viscosity data but is predictive up to high temperatures and pressures with input of only two calculated or measured mixture properties: the number averaged molecular weight and hydrogen to carbon ratio. Viscosity is predicted less accurately when the mixture contains high concentrations of iso-alkanes and cyclohexanes. However, it is shown that predictions for these mixtures are improved by fitting a third parameter to a single viscosity data point at a chosen reference state. For hydrocarbon mixtures, viscosity is predicted with average mean absolute percent deviations (MAPDs) of 12.2% using the two-parameter model and 7.3% using the three-parameter model from 293 to 353 K and up to 1000 bar. For two different diesel fuels, viscosity is predicted with an average MAPD of 21.4% using the two-parameter model and 9.4% using the three-parameter model from 323 to 423 K and up to 3500 bar

High-Temperature, High-Pressure Viscosities and Densities of n-Hexadecane, 2,2,4,4,6,8,8-Heptamethylnonane, and Squalane Measured Using a Universal Calibration for a Rolling-Ball Viscometer/Densimeter

The development of reference correlations for viscous fluids is predicated on the availability of accurate viscosity data, especially at high pressure, high temperature (HPHT) conditions. The rolling ball viscometer (RBV) is a facile technique for obtaining such HPHT viscosity data. A new, universal RBV calibration methodology is described and applied over a broad T-p region and for a wide range of viscosities. The new calibration equation is used to obtain viscosities for n-hexadecane (HXD), 2,2,4,4,6,8,8-heptamethylnonane (HMN), and 2,6,10,15,19,23-hexamethyltetracosane (squalane) from 298 – 530 K and pressures to 250 MPa. The available literature data base for HMN is expanded to 520 K and 175 MPa and for squalane to 525 K and 250 MPa. The combined expanded uncertainties are 0.6% and 2.5% for the densities and viscosities, respectively, each with a coverage factor, k = 2. The reliability of the viscosity data is validated by comparison of HXD and squalane viscosities to accepted reference correlations and HMN viscosities to available literature data. The necessity of this new calibration approach is confirmed by the large deviations observed between HXD, HMN, and squalane viscosities determined using the new, universal RBV calibration equation and viscosities determined using a quadratic polynomial calibration equation. HXD, HMN, and squalane densities are predicted with the Perturbed Chain Statistical Associating Fluid Theory using pure component parameters calculated with a previously reported group contribution (GC) method. HXD, HMN, and squalane viscosities are compared to Free Volume Theory (FVT) predictions using FVT parameters calculated from a literature correlation for nalkanes. Although the FVT predictions for HXD, a normal alkane, result in an average absolute percent deviation (∆AAD) of 3.8%, predictions for HMN and squalane, two branched alkanes, are four to 13 times larger. The fit of the FVT model for the branched alkanes is dramatically improved if the FVT parameters are allowed to vary with temperature

Purely predictive method for density, compressibility, and expansivity for hydrocarbon mixtures and diesel and jet fuels up to high temperatures and pressures

This study presents a pseudo-component method using the Perturbed-Chain Statistical Associating Fluid Theory to predict density, isothermal compressibility, and the volumetric thermal expansion coefficient (expansivity) of hydrocarbon mixtures and diesel and jet fuels. The model is not fit to experimental density data but is predictive to high temperatures and pressures using only two calculated or measured mixture properties as inputs: the number averaged molecular weight and hydrogen to carbon ratio. Mixtures are treated as a single pseudo-component; therefore binary interaction parameters are not needed. Density is predicted up to 470 K and 3,500 bar for hydrocarbon mixtures and fuels with 1% average mean absolute percent deviation (MAPD). Isothermal compressibility is predicted with 4% average MAPD for hydrocarbon mixtures and 9% for fuels. The volumetric thermal expansion coefficient is predicted with 7% average MAPD for hydrocarbon mixtures and 13% for fuels

Thin film dielectric microstrip kinetic inductance detectors

Microwave Kinetic Inductance Detectors, or MKIDs, are a type of low temperature detector that exhibit intrinsic frequency domain multiplexing at microwave frequencies. We present the first theory and measurements on a MKID based on a microstrip transmission line resonator. A complete characterization of the dielectric loss and noise properties of these resonators is performed, and agrees well with the derived theory. A competitive noise equivalent power of 5$\times10^{-17}$ W Hz$^{-1/2}$ at 1 Hz has been demonstrated. The resonators exhibit the highest quality factors known in a microstrip resonator with a deposited thin film dielectric.Comment: 10 pages, 4 figures, APL accepte

Reduction of Ion Heating During Magnetic Reconnection by Large-Scale Effective Potentials

The physical processes that control the partition of released magnetic energy between electrons and ions during reconnection is explored through particle-in-cell simulations and analytical techniques. We demonstrate that the development of a large-scale parallel electric field and its associated potential controls the relative heating of electrons and ions. The potential develops to restrain heated exhaust electrons and enhances their heating by confining electrons in the region where magnetic energy is released. Simultaneously the potential slows ions entering the exhaust below the Alfv\'enic speed expected from the traditional counterstreaming picture of ion heating. Unexpectedly, the magnitude of the potential and therefore the relative partition of energy between electrons and ions is not a constant but rather depends on the upstream parameters and specifically the upstream electron normalized temperature (electron beta). These findings suggest that the fraction of magnetic energy converted into the total thermal energy may be independent of upstream parameters

Vapor-liquid equilibria and mixture densities for 2,2,4,4,6,8,8-heptamethylnonane + N2 and n-hexadecane + N2 binary mixtures up to 535 K and 135 MPa

In this work, we report high-pressure, high-temperature (HPHT) mixture density and T-p isopleth (bubble (BP) and dew (DP) point) data for hexadecane (HXD) + N2 and heptamethylnonane (HMN) + N2 mixtures from ~323 to 523 K and pressures to ~100 MPa. Isothermal, mixture density data for both mixtures are measured in the single–phase region from the BP pressure to ~135 MPa and with ~ 14 to 90 mol% N2. A HPHT variable-volume, windowed view cell is used for both density and phase behavior measurements using the synthetic method. Mixture densities are correlated with the modified Tait equation and isothermal BP/DP data are correlated with an Antoine-type equation to allow for reliable interpolation of the data sets. Mixture densities and BP/DP pressures are modeled with the PC-SAFT equation coupled with pure component parameters calculated with two different group contribution methods. Although fairly reasonable predictions of liquid mixture densities are obtained when the binary interaction parameter, kij, is set to zero for both HXD + N2 and HMN + N2 mixtures, a value of kij equal to at least 0.119 is needed for both systems to obtain reasonable predictions of isothermal p-x behavior

‘On the high street’ tuition for primary-aged children in London: Critiquing discourses of accessibility, attainment and assistance

Private tuition, often referred to as ‘shadow education’, is commercially provided, supplementary education which has been variously constructed to support children in their academic abilities. As growing numbers of children are engaging with tuition, it is receiving greater scrutiny and scholarly attention. This paper explores the growth and role of commercial tuition centres for primary-aged children. Such centres, which operate ‘on the high street’, are not a new phenomenon, but their expansion and assertive commercialisation is notable. With attention to managers’ and tutors’ perspectives, we interrogate the positioning of these services and critically analyse the discursive construction of three ‘As’ of their offer: accessibility of service, promise of enhanced attainment, and assistance with learning. In so doing, tuition centres lead the (re)positioning of private tuition as highly visible private businesses, located within and amongst other commercial enterprises, with an emerging focus on younger children, and are worthy of further research

Comparison of System Call Representations for Intrusion Detection

Over the years, artificial neural networks have been applied successfully in many areas including IT security. Yet, neural networks can only process continuous input data. This is particularly challenging for security-related non-continuous data like system calls. This work focuses on four different options to preprocess sequences of system calls so that they can be processed by neural networks. These input options are based on one-hot encoding and learning word2vec or GloVe representations of system calls. As an additional option, we analyze if the mapping of system calls to their respective kernel modules is an adequate generalization step for (a) replacing system calls or (b) enhancing system call data with additional information regarding their context. However, when performing such preprocessing steps it is important to ensure that no relevant information is lost during the process. The overall objective of system call based intrusion detection is to categorize sequences of system calls as benign or malicious behavior. Therefore, this scenario is used to evaluate the different input options as a classification task. The results show, that each of the four different methods is a valid option when preprocessing input data, but the use of kernel modules only is not recommended because too much information is being lost during the mapping process.Comment: 12 pages, 1 figure, submitted to CISIS 201

Normative Alethic Pluralism

Some philosophers have argued that truth is a norm of judgement and have provided a variety of formulations of this general thesis. In this paper, I shall side with these philosophers and assume that truth is a norm of judgement. What I am primarily interested in here are two core questions concerning the judgement-truth norm: (i) what are the normative relationships between truth and judgement? And (ii) do these relationships vary or are they constant? I argue for a pluralist picture—what I call Normative Alethic Pluralism (NAP)—according to which (i) there is more than one correct judgement-truth norm and (ii) the normative relationships between truth and judgement vary in relation to the subject matter of the judgement. By means of a comparative analysis of disagreement in three areas of the evaluative domain—refined aesthetics, basic taste and morality—I show that there is an important variability in the normative significance of disagreement—I call this the variability conjecture. By presenting a variation of Lynch’s scope problem for alethic monism, I argue that a monistic approach to the normative function of truth is unable to vindicate the conjecture. I then argue that normative alethic pluralism provides us with a promising model to account for it

Combination treatment with ionising radiation and gefitinib ('Iressa', ZD1839), an epidermal growth factor receptor (EGFR) inhibitor, significantly inhibits bladder cancer cell growth in vitro and in vivo

Purpose: External beam radiotherapy (EBRT) is the principal bladder-preserving monotherapy for muscle-invasive bladder cancer. Seventy percent of muscle-invasive bladder cancers express epidermal growth factor receptor (EGFR), which is associated with poor prognosis. Ionising radiation (IR) stimulates EGFR causing activation of cytoprotective signalling cascades and thus may be an underlying cause of radioresistance in bladder tumours. Materials and methods: We assessed the ability of IR to activate EGFR in bladder cancer cells and the effect of the anti-EGFR therapy, gefitinib on potential radiation-induced activation. Subsequently we assessed the effect of IR on signalling pathways downstream of EGFR. Finally we assessed the activity of gefitinib as a monotherapy, and in combination with IR, using clonogenic assay in vitro, and a murine model in vivo. Results: IR activated EGFR and gefitinib partially inhibited this activation. Radiation-induced activation of EGFR activated the MAPK and Akt pathways. Gefitinib partially inhibited activation of the MAPK pathway but not the Akt pathway. Treatment with combined gefitinib and IR significantly inhibited bladder cancer cell colony formation more than treatment with gefitinib alone (p = 0.001-0.03). J82 xenograft tumours treated with combined gefitinib and IR showed significantly greater growth inhibition than tumours treated with IR alone (p = 0.04). Conclusions: Combining gefitinib and IR results in significantly greater inhibition of invasive bladder cancer cell colony formation in vitro and significantly greater tumour growth inhibition in vivo. Given the high frequency of EGFR expression by bladder tumours and the low toxicity of gefitinib there is justification to translate this work into a clinical trial.Peer-reviewedPublisher Version1721