Characterization of poly(vinyl alcohol)/sodium bromide polymer electrolytes for electrochemical cell applications

Sodium‐ion conducting polymer electrolytes based on poly(vinyl alcohol) complexed with sodium bromide were prepared with a solution‐casting technique. The structure of these films was determined with X‐ray diffraction, and the complexation of the salt with the polymer was confirmed with Fourier transform infrared spectroscopy studies. Electrical conductivity was measured with an alternating‐current impedance analyzer in the frequency range of 100 Hz to 1 MHz and in the temperature range of 303–373 K. It was observed that the magnitude of conductivity increased with the increase in the salt concentration as well as the temperature. The nature of the charge transport in these polymer electrolyte films was determined with both Wagner's polarization technique and the Watanabe technique. The dominant conducting species were found to be ions, particularly anions. Optical absorption studies were performed in the wavelength range of 200–600 nm, and the absorption edge, direct band gap, and indirect band gap values were evaluated. Electrochemical cells were fabricated, and their discharge characteristics were studied. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

CONTEXTUALIZING LEARNING OBJECTS USING ONTOLOGIES

Educational research over the past three years has intensified such that the context of learning resources needs to be properly modeled. Many researchers have described and even mandated the use of ontologies in the research being conducted, yet the process of actually connecting one or more ontologies to a learning object has not been extensively discussed. This paper describes a practical model for associating multiple ontologies with learning objects while making full use of the IEEE LOM specification. The model categorizes these ontologies according to five major categories of context based on the most popular fields of study actively being pursued by the educational research community: Thematic context, Pedagogical context, Learner context, Organizational context, and Historical/Statistical context.     

Mutual information based registration of multimodal stereo videos for person tracking

Research presented in this paper deals with the systematic examination, development, and evaluation of a novel multimodal registration approach that can perform accurately and robustly for relatively close range surveillance applications. An analysis of multimodal image registration gives insight into the limitations of assumptions made in current approaches and motivates the methodology of the developed algorithm. Using calibrated stereo imagery, we employ maximization of mutual information in sliding correspondence windows that inform a disparity voting algorithm to demonstrate successful registration of objects in color and thermal imagery. Extensive evaluation of scenes with multiple objects at different depths and levels of occlusion shows high rates of successful registration. Ground truth experiments demonstrate the utility of the disparity voting techniques for multimodal registration by yielding qualitative and quantitative results that outperform approaches that do not consider occlusions. A basic framework for multimodal stereo tracking is investigated and promising experimental studies show the viability of using registration disparity estimates as a tracking feature.     

Component fatty acids of Cladosporium herbarum fat

Cladosporium herbarum grown as a surface culture in a chemically defined medium of sucrose and inorganic salts gave a high yield of fat (29.2%). The component acids of this fat have been found to be palmitic (34.2%); oleic (15.9%); linoleic (34.2%) and linolenic (15.7%). The combined proportion of linoleic and linolenic acids being about 50 per cent, the fat will be of great value for the manufacture of oil-modified resins as the films produced by such resins do not become yellow with age.     

Treatment with the oral growth hormone secretagogue MK-677 increases markers of bone formation and bone resorption in obese young males

The effect of 2 months of treatment with the oral growth hormone (GH) secretagogue MK-677 on markers of bone metabolism was determined in healthy obese male subjects. This was a randomized, double-blind, parallel, placebo-controlled study. Twenty-four healthy obese males, 19-49 years of age, with body mass index > 30 kg/m2 were treated with MK-677 (25 mg/day; n = 12) or placebo (n = 12) for 8 weeks. MK-677 increased markers of bone formation; a 23% increase in the carboxy-terminal propeptide of type I procollagen levels and a 28% increase in procollagen III peptide levels were seen with as little as 2 weeks of MK-677 treatment (p < 0.01 and p = 0.001 vs. placebo, respectively) while a 15% increase in serum levels of osteocalcin was not detected until 8 weeks of treatment (p < 0.01 vs. placebo). Markers of bone resorption were induced within 2 weeks of treatment with MK-677; serum levels of the carboxy-terminal cross-linked telopeptide of type I collagen were increased 26% at 8 weeks (p = 0.001 vs. placebo), and urine hydroxyproline/creatinine and calcium/creatinine ratios at 8 weeks were increased by 23% (p < 0.05 vs. placebo) and 46% (p < 0.05 vs placebo), respectively, MK-677 increased serum insulin-like growth factor binding protein-5 (IGFBP-5) by 43-44% after 2-8 weeks of treatment (p < 0.01 vs. placebo). Serum IGFBP-4 was increased by 25% after 2 weeks of treatment (p < 0.001 vs. placebo) but no significant change from baseline was observed after 8 weeks of treatment. Plasma interleukin-6 was not significantly changed by active treatment. In conclusion, short-term treatment of healthy obese male volunteers with the GH secretagogue MK-677 increases markers of both bone resorption and formation. Large increases in serum levels of IGF-1 and IGFBP-5 and a transient increase in serum IGFBP-4 were found. Future long-term studies are needed to investigate if prolonged treatment with MK-677 increases bone mass.     

Noise analysis of double gate composite InAs based HEMTs for high frequency applications

Microsystem Technologies - The quantitative analysis of microwave noise available in the double gate (DG) high electron transistors of mobility (HEMT) is reported in this paper. For this analysis,...     

Rheology of Zirconia Suspensions in a Nonpolar Organic Medium

Three dispersants (stearic acid, oleic acid, and poly(12-hydroxystearic acid)) are compared for their ability to produce low-viscosity suspensions of zirconia in kerosene. Rheological measurements and sediment packing density measurements show that poly(12-hydroxystearic acid) is a better dispersant than stearic acid or oleic acid; this is explained in terms of the longer tail of the poly(12-hydroxystearic acid) surfactant molecule. The amount of dispersant can be optimized to reduce viscosity and yield point of the suspension, and to eliminate thixotropic hysteresis. The use of a dispersion medium of lower viscosity than the dispersant makes it easy to detect when complete monolayer coverage has been achieved. The loss of pseudoplasticity, brought about by a higher degree of deflocculation, can be recovered by increasing the volume fraction of solids of a suspension and this is beneficial in the plastic forming of ceramics. Rheological measurements showed that these suspensions reach a "critical state" above a critical shear stress (τ_c). This critical state is described by several parameters, i.e., the Bingham yield stress (τ_b), the plastic viscosity (ν_Pl), and the critical shear rate (γ_c), which are dependent on the volume fraction of solids.     

Active learning for on-road vehicle detection: a comparative study

In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have been performed to comparatively assess costs and merits. In this study, we provide a cost-sensitive analysis of three popular active learning methods for on-road vehicle detection. The generality of active learning findings is demonstrated via learning experiments performed with detectors based on histogram of oriented gradient features and SVM classification (HOG–SVM), and Haar-like features and Adaboost classification (Haar–Adaboost). Experimental evaluation has been performed on static images and real-world on-road vehicle datasets. Learning approaches are assessed in terms of the time spent annotating, data required, recall, and precision.