Development of a quasi-chemical viscosity model for fully liquid slags in the Al2O3-CaO-'FeO'-MgO-SiO2 system. Part 3. Summary of the model predictions for the Al2O3-CaO-MgO-SiO2 system and its sub-systems

A structurally-based quasi-chemical viscosity model for fully liquid slags in the Al2O3 CaO-'FeO'-MgOSiO2 system has been developed. The focus of the work described in the present paper is the analysis of the experimental data and viscosity models in the quaternary system Al2O3 CaO-MgO-SiO2 and its subsystems. A review of the experimental data, viscometry methods used and viscosity models available in the Al2O3 CaO-MgO-SiO2 and its sub-systems is reported. The quasi-chemical viscosity model is shown to provide good agreement between experimental data and predictions over the whole compositional range.

Testing predictions of macroscopic binary diffusion coefficients using lattice models with site heterogeneity

Quantitatively predicting mass transport rates for chemical mixtures in porous materials is important in applications of materials such as adsorbents, membranes, and catalysts. Because directly assessing mixture transport experimentally is challenging, theoretical models that can predict mixture diffusion coefficients using Only single-component information would have many uses. One such model was proposed by Skoulidas, Sholl, and Krishna (Langmuir, 2003, 19, 7977), and applications of this model to a variety of chemical mixtures in nanoporous materials have yielded promising results. In this paper, the accuracy of this model for predicting mixture diffusion coefficients in materials that exhibit a heterogeneous distribution of local binding energies is examined. To examine this issue, single-component and binary mixture diffusion coefficients are computed using kinetic Monte Carlo for a two-dimensional lattice model over a wide range of lattice occupancies and compositions. The approach suggested by Skoulidas, Sholl, and Krishna is found to be accurate in situations where the spatial distribution of binding site energies is relatively homogeneous, but is considerably less accurate for strongly heterogeneous energy distributions.

Combining spatially distributed predictions from neural networks

In this report we discuss the problem of combining spatially-distributed predictions from neural networks. An example of this problem is the prediction of a wind vector-field from remote-sensing data by combining bottom-up predictions (wind vector predictions on a pixel-by-pixel basis) with prior knowledge about wind-field configurations. This task can be achieved using the scaled-likelihood method, which has been used by Morgan and Bourlard (1995) and Smyth (1994), in the context of Hidden Markov modelling

Differences between theoretical predictions and operational performance in a stock control application

Over the past decade, several experienced Operational Researchers have advanced the view that the theoretical aspects of model building have raced ahead of the ability of people to use them. Consequently, the impact of Operational Research on commercial organisations and the public sector is limited, and many systems fail to achieve their anticipated benefits in full. The primary objective of this study is to examine a complex interactive Stock Control system, and identify the reasons for the differences between the theoretical expectations and the operational performance. The methodology used is to hypothesise all the possible factors which could cause a divergence between theory and practice, and to evaluate numerically the effect each of these factors has on two main control indices - Service Level and Average Stock Value. Both analytical and empirical methods are used, and simulation is employed extensively. The factors are divided into two main categories for analysis - theoretical imperfections in the model, and the usage of the system by Buyers. No evidence could be found in the literature of any previous attempts to place the differences between theory and practice in a system in quantitative perspective nor, more specifically, to study the effects of Buyer/computer interaction in a Stock Control system. The study reveals that, in general, the human factors influencing performance are of a much higher order of magnitude than the theoretical factors, thus providing objective evidence to support the original premise. The most important finding is that, by judicious intervention into an automatic stock control algorithm, it is possible for Buyers to produce results which not only attain but surpass the algorithmic predictions. However, the complexity and behavioural recalcitrance of these systems are such that an innately numerate, enquiring type of Buyer needs to be inducted to realise the performance potential of the overall man/computer system.

A comparison of sales response predictions from demand models applied to store-level versus panel data

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

Tablet App halometer for the assessment of dysphotopsia

Purpose To assess the validity and repeatability of the Aston Halometer. Setting University clinic, United Kingdom. Design Prospective, repeated-measures experimental study. Methods The halometer comprises a bright light-emitting-diode (LED) glare source in the center of an iPad4. Letters subtending 0.21° (∼0.3 logMAR) were moved centrifugally from the LED in 0.05 degree steps in 8 orientations separated by 45 degrees for each of 4 contrast levels (1000, 500, 100, and 25 Weber contrast units [Cw]) in random order. Bangerter occlusion foils were inserted in front of the right eye to simulate monocular glare conditions in 20 subjects (mean age 27.7 ± 3.1 years). Subjects were positioned 2 meters from the screen in a dark room with the iPad controlled from an iPhone via Bluetooth operated by the researcher. The C-Quant straylight meter was also used with each of the foils to measure the level of straylight over the retina. Halometry and straylight repeatability was assessed at a second visit. Results Halo size increased with the different occlusion foils and target contrasts (F = 29.564, P <.001) as expected and in a pattern similar to straylight measures (F = 80.655, P <0.001). Lower contrast letters showed better sensitivity but larger glare-obscured areas, resulting in ceiling effects caused by the screen's field-of-view, with 500 Cw being the best compromise. Intraobserver and interobserver repeatability of the Aston Halometer was good (500Cw: 0.84 to 0.93 and 0.53 to 0.73) and similar to the straylight meter. Conclusion The halometer provides a sensitive, repeatable way of quantifying a patient-recognized form of disability glare in multiple orientations to add objectivity to subjectively reported discomfort glare.

On the hydrophobicity of peptides:comparing empirical predictions of peptide log P values

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient logP, is useful for the development of predictive Quantitative Structure-Activity Relationships (QSARs). We have investigated the accuracy of available programs for the prediction of logP values for peptides with known experimental values obtained from the literature. Eight prediction programs were tested, of which seven programs were fragment-based methods: XLogP, LogKow, PLogP, ACDLogP, AlogP, Interactive Analysis's LogP and MlogP; and one program used a whole molecule approach: QikProp. The predictive accuracy of the programs was assessed using r(2) values, with ALogP being the most effective (r( 2) = 0.822) and MLogP the least (r(2) = 0.090). We also examined three distinct types of peptide structure: blocked, unblocked, and cyclic. For each study (all peptides, blocked, unblocked and cyclic peptides) the performance of programs rated from best to worse is as follows: all peptides - ALogP, QikProp, PLogP, XLogP, IALogP, LogKow, ACDLogP, and MlogP; blocked peptides - PLogP, XLogP, ACDLogP, IALogP, LogKow, QikProp, ALogP, and MLogP; unblocked peptides - QikProp, IALogP, ALogP, ACDLogP, MLogP, XLogP, LogKow and PLogP; cyclic peptides - LogKow, ALogP, XLogP, MLogP, QikProp, ACDLogP, IALogP. In summary, all programs gave better predictions for blocked peptides, while, in general, logP values for cyclic peptides were under-predicted and those of unblocked peptides were over-predicted.

Hydrogen production by steam reforming of DME in a large scale CFB reactor. Part I:computational model and predictions

This study presents a computational fluid dynamic (CFD) study of Dimethyl Ether steam reforming (DME-SR) in a large scale Circulating Fluidized Bed (CFB) reactor. The CFD model is based on Eulerian-Eulerian dispersed flow and solved using commercial software (ANSYS FLUENT). The DME-SR reactions scheme and kinetics in the presence of a bifunctional catalyst of CuO/ZnO/Al2O3+ZSM-5 were incorporated in the model using in-house developed user-defined function. The model was validated by comparing the predictions with experimental data from the literature. The results revealed for the first time detailed CFB reactor hydrodynamics, gas residence time, temperature distribution and product gas composition at a selected operating condition of 300 °C and steam to DME mass ratio of 3 (molar ratio of 7.62). The spatial variation in the gas species concentrations suggests the existence of three distinct reaction zones but limited temperature variations. The DME conversion and hydrogen yield were found to be 87% and 59% respectively, resulting in a product gas consisting of 72 mol% hydrogen. In part II of this study, the model presented here will be used to optimize the reactor design and study the effect of operating conditions on the reactor performance and products.

Mobile app reading speed test

Aim: To validate the accuracy and repeatability of a mobile app reading speed test compared with the traditional paper version. Method: Twenty-one subjects wearing their full refractive correction glasses read 14 sentences of decreasing print size between 1.0 and -0.1 logMAR, each consisting of 14 words (Radner reading speed test) at 40 cm with a paper-based chart and twice on iPad charts. Time duration was recorded with a stop watch for the paper chart and on the App itself for the mobile chart allowing critical print size (CPS) and optimal reading speed (ORS) to be derived objectively. Results: The ORS was higher for the mobile app charts (194±29 wpm; 195±25 wpm) compared with the paper chart (166±20 wpm; F=57.000, p<0.001). The CPS was lower for the mobile app charts (0.17±0.20 logMAR; 0.18±0.17 logMAR) compared with the paper chart (0.25±0.17 logMAR; F=5.406, p=0.009). The mobile app test had a mean difference repeatability of 0.30±22.5 wpm, r=0.917 for ORS, and a CPS of 0.0±0.2 logMAR, r=0.769. Conclusions: Repeatability of the app reading speed test is as good (ORS) or better (CPS) than previous studies on the paper test. While the results are not interchangeable with paper-based charts, mobile app tablet-based tests of reading speed are reliable and rapid to perform, with the potential to capture functional visual ability in research studies and clinical practice.

Mobile app Aston contrast sensitivity test

BACKGROUND: Contrast detection is an important aspect of the assessment of visual function; however, clinical tests evaluate limited spatial frequencies and contrasts. This study validates the accuracy and inter-test repeatability of a swept-frequency near and distance mobile app Aston contrast sensitivity test, which overcomes this limitation compared to traditional charts. METHOD: Twenty subjects wearing their full refractive correction underwent contrast sensitivity testing on the new near application (near app), distance app, CSV-1000 and Pelli-Robson charts with full correction and with vision degraded by 0.8 and 0.2 Bangerter degradation foils. In addition repeated measures using the 0.8 occluding foil were taken. RESULTS: The mobile apps (near more than distance, p = 0.005) recorded a higher contrast sensitivity than printed tests (p < 0.001); however, all charts showed a reduction in measured contrast sensitivity with degradation (p < 0.001) and a similar decrease with increasing spatial frequency (interaction > 0.05). Although the coefficient of repeatability was lowest for the Pelli-Robson charts (0.14 log units), the mobile app charts measured more spatial frequencies, took less time and were more repeatable (near: 0.26 to 0.37 log units; distance: 0.34 to 0.39 log units) than the CSV-1000 (0.30 to 0.93 log units). The duration to complete the CSV-1000 was 124 ± 37 seconds, Pelli-Robson 78 ± 27 seconds, near app 53 ± 15 seconds and distance app 107 ± 36 seconds. CONCLUSIONS: While there were differences between charts in contrast levels measured, the new Aston near and distance apps are valid, repeatable and time-efficient method of assessing contrast sensitivity at multiple spatial frequencies.

APP controls the formation of PI(3,5)P2 vesicles through its binding of the PIKfyve complex

Phosphoinositides are signalling lipids that are crucial for major signalling events as well as established regulators of membrane trafficking. Control of endosomal sorting and endosomal homeostasis requires phosphatidylinositol-3-phosphate (PI(3)P) and phosphatidylinositol-3,5-bisphosphate (PI(3,5)P2), the latter a lipid of low abundance but significant physiological relevance. PI(3,5)P2 is formed by phosphorylation of PI(3)P by the PIKfyve complex which is crucial for maintaining endosomal homeostasis. Interestingly, loss of PIKfyve function results in dramatic neurodegeneration. Despite the significance of PIKfyve, its regulation is still poorly understood. Here we show that the Amyloid Precursor Protein (APP), a central molecule in Alzheimer’s disease, associates with the PIKfyve complex (consisting of Vac14, PIKfyve and Fig4) and that the APP intracellular domain directly binds purified Vac14. We also show that the closely related APP paralogues, APLP1 and 2 associate with the PIKfyve complex. Whether APP family proteins can additionally form direct protein–protein interaction with PIKfyve or Fig4 remains to be explored. We show that APP binding to the PIKfyve complex drives formation of PI(3,5)P2 positive vesicles and that APP gene family members are required for supporting PIKfyve function. Interestingly, the PIKfyve complex is required for APP trafficking, suggesting a feedback loop in which APP, by binding to and stimulating PI(3,5)P2 vesicle formation may control its own trafficking. These data suggest that altered APP processing, as observed in Alzheimer’s disease, may disrupt PI(3,5)P2 metabolism, endosomal sorting and homeostasis with important implications for our understanding of the mechanism of neurodegeneration in Alzheimer’s disease.