Silica-free sealing glass for sodium-beta alumina battery

A CaO-Bi₂O₃-Al₂O₃-B₂O₃ glass system was studied as a sealant for sodium-sulfur battery. The thermal properties such as thermal expansion coefficient, glass transition, and softening temperature were determined by dilatometry and differential scanning calorimetry. Selected glasses, based on the thermal properties, were bonded with α-alumina substrate followed by aging in air at 400°C for 100 hours and in sodium vapor at 350°C for 100 hours. The interfacial compatibility and resistance to sodium vapor corrosion of the bonded and aged samples were evaluated by structural and microstructural analysis using X-ray diffractometer (XRD) and scanning electron microscope (SEM) attached with energy dispersive spectroscope (EDS). Helium leakage test was performed at room temperature to examine the sealing ability of the select glass. It is found that Bi₂O₃ increases the thermal expansion coefficient, decreases the glass transition and softening temperature, shows excellent interfacial compatibility and thermal cycling resistance, improves sealing ability, and degrades sodium corrosion resistance.     

Effectiveness of sodium silicate as gangue depressants in iron ore slimes flotation

The recovery of iron from the screw classifier overflow slimes by direct flotation was studied.The relative effectiveness of sodium silicates with different silica-to-soda mole ratios as depressants for silica and silicate bearing minerals was investigated.Silica-to-soda mole ratio and silicate dosage were found to have significant effect on the separation efficiency.The results show that an increase of Fe content in the concentrate is observed with concomitant reduction in SiO2 and Al2O3 levels when a particular type of sodium silicate at a proper dosage is used.The concentrate of 58.89wt％ Fe,4.68wt％ SiO2,and 5.28wt％ Al2O3 with the weight recovery of 38.74％ and the metal recovery of 41.13％ can be obtained from the iron ore slimes with 54.44wt％ Fe,6.72wt％ SiO2,and 6.80wt％ Al2O3,when Na2SiO3 with a silica-to-soda mole ratio of 2.19 is used as a depressant at a feed rate of 0.2 kg/t.     

Simulation of nanoparticle synthesis in an aerosol flame reactor using a coupled flame dynamics–monodisperse population balance model

Flame aerosol synthesis is one of the commonly employed techniques for producing ultra fine particles of commodity chemicals such as titanium dioxide, silicon dioxide and carbon black. Large volumes of these materials are produced in industrial flame reactors. Particle size distribution of product powder is the most important variable and it depends strongly on flame dynamics inside the reactor, which in turn is a function of input process variables such as reactant flow rate and concentration, flow rates of air, fuel and the carrier gas and the burner geometry. A coupled flame dynamics–monodisperse population balance model for nanoparticle synthesis in an aerosol flame reactor is presented here. The flame dynamics was simulated using the commercial computational fluid dynamics software CFX and the particle population dynamics was represented using a monodisperse population balance model for continuous processes that predicts the evolution of particle number concentration, particle volume and surface area. The model was tested with published experimental data for synthesis of silica nanoparticles using different burner configurations and with different reactor operating conditions. The model predictions for radial flame temperature profiles and for the effects of process variables like precursor concentration and oxygen flow rate on particle specific surface area and mean diameter are in close agreement with published experimental data.     

Non-linear properties of conditional returns under scale mixtures

This article provides a framework for analyzing multifactor financial returns that violate the Gaussian distributional assumption. Analytical expressions are provided for the non-linear regression equation and its prediction error (heteroscedasticity) by modeling the returns of financial assets as scale mixtures of the multivariate normal distribution. The expressions involve conditional moments of the mixing variable. These conditional moments are explicitly derived when the mixing variable belongs to the generalized inverse Gaussian family, of which gamma, inverse gamma and the inverse Gaussian distributions are distinguished members. The derived expressions are non-linear in the parameters and involve the modified Bessel function of the third kind. The effects of the non-linear model, in terms of both the regression equation and heteroscedasticity against the corresponding values for the standard linear regression model, are captured through simulations for the gamma, inverse gamma and inverse Gaussian distributions. The proposed scale mixture models extend the well-known arbitrage pricing theory (APT) in financial modeling to non-Gaussian cases. The methodology is applied to analyze the intra-day log returns quarterly data for DELL and COKE regressed against S&P 500 for the years 1998-2000.     

Using text mining for personalization and recommendation for an enriched hybrid learning experience

Students are nowadays given many options to consume educational content in digital formats as alternatives to printed material. Previous research suggests that while digital content has advantages, printed media still provides other benefits that cannot be matched by digital. Therefore, technology should leverage the benefits of both. In this paper, we present the Meaningful Education and Training Information System, a multifaceted hybrid textbook learning platform. The goal of the system is to provide an easy digital‐to‐print‐to‐digital content creation and reading service. The Meaningful Education and Training Information System incorporates technologies for layout, personalization, cocreation, and assessments. These facilitate common teacher/student tasks and help provide a richer, more effective learning experience. Our system has been demonstrated in multiple international education events, partner engagements, and pilots with local universities and high schools.     

Pyramid coding based rate control for constant bit rate video streaming

In this paper, a novel pyramid coding based rate control scheme is proposed for video streaming applications constrained by a constant channel bandwidth. To achieve the target bit rate with the best quality, the initial quantization parameter (QP) is determined by the average spatio-temporal complexity of the sequence, its resolution and the target bit rate. Simple linear estimation models are then used to predict the number of bits that would be necessary to encode a frame for a given complexity and QP. The experimental results demonstrate that the proposed rate control scheme significantly outperforms the existing rate control scheme in the Joint Model (JM) reference software in terms of Peak Signal to Noise Ratio (PSNR) and consistent perceptual visual quality while achieving the target bit rate. Finally, the proposed scheme is validated through experimental evaluation over a miniature test-bed.

     

Language independent unsupervised learning of short message service dialect

Noise in textual data such as those introduced by multilinguality, misspellings, abbreviations, deletions, phonetic spellings, non-standard transliteration, etc. pose considerable problems for text-mining. Such corruptions are very common in instant messenger and short message service data and they adversely affect off-the-shelf text mining methods. Most techniques address this problem by supervised methods by making use of hand labeled corrections. But they require human generated labels and corrections that are very expensive and time consuming to obtain because of multilinguality and complexity of the corruptions. While we do not champion unsupervised methods over supervised when quality of results is the singular concern, we demonstrate that unsupervised methods can provide cost effective results without the need for expensive human intervention that is necessary to generate a parallel labeled corpora. A generative model based unsupervised technique is presented that maps non-standard words to their corresponding conventional frequent form. A hidden Markov model (HMM) over a “subsequencized” representation of words is used, where a word is represented as a bag of weighted subsequences. The approximate maximum likelihood inference algorithm used is such that the training phase involves clustering over vectors and not the customary and expensive dynamic programming (Baum–Welch algorithm) over sequences that is necessary for HMMs. A principled transformation of maximum likelihood based “central clustering” cost function of Baum–Welch into a “pairwise similarity” based clustering is proposed. This transformation makes it possible to apply “subsequence kernel” based methods that model delete and insert corruptions well. The novelty of this approach lies in that the expensive (Baum–Welch) iterations required for HMM, can be avoided through an approximation of the loglikelihood function and by establishing a connection between the loglikelihood and a pairwise distance. Anecdotal evidence of efficacy is provided on public and proprietary data.