Electrothermal study of carbon loaded ethylene-vinylacetate copolymer

Temperature dependence of resistance of carbon black loaded ethylene-vinylacetate (EVA) copolymer was studied from ?100 to 250°C in a modified differential scanning calorimeter (DSC) apparatus. Both room temperature resistivity and positive temperature coefficient of resistance (PTC) effects can be correlated with carbon black parameters (structure, surface area, porosity). Room temperature resistivity and the degree of increase of resistivity, (ratio of maximum to minimum) progressively decrease with carbon black structure if occlusion of polymer by carbon black is considered in calculating the volume fraction. An integrated surface areastructure-porosity equivalence of the carbon black is derived by introducing factors proportional to the volume and porosity of the black used. Any black parameter that increases conductivity (loading, surface area, structure, porosity) decreases the PTC effect. A cable compounder can therefore minimize the undesirable impact of PTC by suitable choice of black-parameters. The necessary considerations for other uses, where PTC effect is used for microswitches, heaters, etc., will be just the opposite. The modified DSC method provides a quick scanning tool for determining the suitability of semicrystalline polymer recipes, either for cable or for any other material using the PTC effect.     

Isolation kernel: the X factor in efficient and effective large scale online kernel learning

Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.

     

Development of a high pressure automated lag time apparatus for experimental studies and statistical analyses of nucleation and growth of gas hydrates

Nucleation in a supercooled or a supersaturated medium is a stochastic event, and hence statistical analyses are required for the understanding and prediction of such events. The development of reliable statistical methods for quantifying nucleation probability is highly desirable for applications where control of nucleation is required. The nucleation of gas hydrates in supercooled conditions is one such application. We describe the design and development of a high pressure automated lag time apparatus (HP-ALTA) for the statistical study of gas hydrate nucleation and growth at elevated gas pressures. The apparatus allows a small volume (≈150 μl) of water to be cooled at a controlled rate in a pressurized gas atmosphere, and the temperature of gas hydrate nucleation, T(f), to be detected. The instrument then raises the sample temperature under controlled conditions to facilitate dissociation of the gas hydrate before repeating the cooling-nucleation cycle again. This process of forming and dissociating gas hydrates can be automatically repeated for a statistically significant (>100) number of nucleation events. The HP-ALTA can be operated in two modes, one for the detection of hydrate in the bulk of the sample, under a stirring action, and the other for the detection of the formation of hydrate films across the water-gas interface of a quiescent sample. The technique can be applied to the study of several parameters, such as gas pressure, cooling rate and gas composition, on the gas hydrate nucleation probability distribution for supercooled water samples.     

Stiction issues and actuation of RF LIGA-MEMS variable capacitors

High aspect ratio variable capacitors have been fabricated using deep X-ray lithography and electroplating. Stiction phenomena applicable to high aspect ratio devices are presented, including the conditions for stiction to occur and the critical dimensions of structures. Actuation tests at 3 GHz are also presented and show a maximum capacitance of 0.86 pF with no actuation voltage and a minimum capacitance of 0.70 pF with an actuation voltage of 20 V just before pull-in, which gives a tuning range of 1.23:1. Corresponding Q-factor values are 49.3 and 70.8 respectively. After pull-in, the measured capacitance is 0.61 pF, corresponding to a tuning range of 1.41:1, with a maximum Q-factor of 102.9.     

Modeling the microstructural changes during hot tandem rolling of AA5XXX aluminum alloys: Part I. Microstructural evolution

A comprehensive mathematical model of the hot tandem rolling process for aluminum alloys has been developed. Reflecting the complex thermomechanical and microstructural changes effected in the alloys during rolling, the model incorporated heat flow, plastic deformation, kinetics of static recrystallization, final recrystallized grain size, and texture evolution. The results of this microstructural engineering study, combining computer modeling, laboratory tests, and industrial measurements, are presented in three parts. In this Part I, laboratory measurements of static recrystallization kinetics and final recrystallized grain size are described for AA5182 and AA5052 aluminum alloys and expressed quantitatively by semiempirical equations. In Part II, laboratory measurements of the texture evolution during static recrystallization are described for each of the alloys and expressed mathematically using a modified form of the Avrami equation. Finally, Part III of this article describes the development of an overall mathematical model for an industrial aluminum hot tandem rolling process which incorporates the microstructure and texture equations developed and the model validation using industrial data. The laboratory measurements for the microstructural evolution were carried out using industrially rolled material and a state-of-the-art plane strain compression tester at Alcan International. Each sample was given a single deformation and heat treated in a salt bath at 400 °C for various lengths of time to effect different levels of recrystallization in the samples. The range of hot-working conditions used for the laboratory study was chosen to represent conditions typically seen in industrial aluminum hot tandem rolling processes, i.e., deformation temperatures of 350 °C to 500 °C, strain rates of 0.5 to 100 seconds and total strains of 0.5 to 2.0. The semiempirical equations developed indicated that both the recrystallization kinetics and the final recrystallized grain size were dependent on the deformation history of the material i.e., total strain and Zener-Hollomon parameter (Z), where and time at the recrystallization temperature.     

Congenital fibrosis of the extraocular muscles type 2, an inherited exotropic strabismus fixus, maps to distal 11q13

The extraocular fibrosis syndromes are congenital ocular-motility disorders that arise from dysfunction of the oculomotor, trochlear, and abducens nerves and/or the muscles that they innervate. Each is marked by a specific form of restrictive paralytic ophthalmoplegia with or without ptosis. Individuals with the classic form of congenital fibrosis of the extraocular muscles (CFEOM1) are born with bilateral ptosis and a restrictive infraductive external ophthalmoplegia. We previously demonstrated that CFEOM1 is caused by an autosomal dominant locus on chromosome 12 and results from a developmental absence of the superior division of the oculomotor nerve. We now have mapped a variant of CFEOM, exotropic strabismus fixus ("CFEOM2"). Affected individuals are born with bilateral ptosis and restrictive ophthalmoplegia with the globes "frozen" in extreme abduction. This autosomal recessive disorder is present in members of three consanguineous Saudi Arabian families. Genetic analysis of 70 individuals (20 affected individuals) reveals linkage to markers on chromosome 11q13, with a combined LOD score of 12.3 at the single nonrecombinant marker, D11S1314. The 2.5-cM CFEOM2 critical region is flanked by D11S4196/D11S4162 and D11S4184/1369. Two of the three families share a common disease-associated haplotype, suggesting a founder effect for CFEOM2. We hypothesize that CFEOM2 results from an analogous developmental defect to CFEOM1, one that affects both the superior and inferior divisions of the oculomotor nerve and their corresponding alpha motoneurons and extraocular muscles.     

Crystal structure at 2.4 angstroms resolution of the complex of transducin betagamma and its regulator, phosducin

The crystal structure of transducin's betagamma subunits complexed with phosducin, which regulates Gtbetagamma activity, has been solved to 2.4 angstroms resolution. Phosducin has two domains that wrap around Gtbetagamma to form an extensive interface. The N-terminal domain binds loops on the "top" Gtbeta surface, overlapping the Gtalpha binding surface, explaining how phosducin blocks Gtbetagamma's interaction with Gtalpha. The C-terminal domain shows structural homology to thioredoxin and binds the outer strands of Gtbeta's seventh and first blades in a manner likely to disrupt Gtbetagamma's normal orientation relative to the membrane and receptor. Phosducin's Ser-73, which when phosphorylated inhibits phosducin's function, points away from Gtbetagamma, toward a large flexible loop. Thus phosphorylation is not likely to affect the interface directly, but rather indirectly through an induced conformational change.     

Interleukin-6-type cytokines in vivo: regulated bioavailability

Investigators have traditionally thought of the class of inflammation- and injury-associated cytokines in large part as "free" entities in the peripheral circulation. In the case of interleukin-6 (IL-6), the cytokine can be found in blood in complexes of molecular mass 400-500, 150-200, and 25-35 kDa in association with binding proteins that can include soluble IL-6 receptor (sIL-6R), anti-IL-6, and anti-sIL-6R IgG, and others. Sustained high levels of different particular IL-6 complexes are observed in the human circulation in cancer patients subjected to particular active anticancer immunotherapy regimens. In the "chaperoned" state, circulating IL-6 complexes display differential immunoreactivity in different ELISAs and possess differential biological activity as assayed ex vivo. The discovery of "chaperoned" circulating IL-6 in humans points to a new level of modulation of cytokine function, that of regulated bioavailability of IL-6 in vivo.     

Feature-subspace aggregating: ensembles for stable and unstable learners

This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds local models instead of global models. Feating is a generic ensemble approach that can enhance the predictive performance of both stable and unstable learners. In contrast, most existing ensemble approaches can improve the predictive performance of unstable learners only. Our analysis shows that the new approach reduces the execution time to generate a model in an ensemble through an increased level of localisation in Feating. Our empirical evaluation shows that Feating performs significantly better than Boosting, Random Subspace and Bagging in terms of predictive accuracy, when a stable learner SVM is used as the base learner. The speed up achieved by Feating makes feasible SVM ensembles that would otherwise be infeasible for large data sets. When SVM is the preferred base learner, we show that Feating SVM performs better than Boosting decision trees and Random Forests. We further demonstrate that Feating also substantially reduces the error of another stable learner, k-nearest neighbour, and an unstable learner, decision tree.