Effects of a strong polyelectrolyte on the rheological properties of concentrated cementitious suspensions

Strong polyelectrolytes, referred to as superplasticizers, are known to improve the initial fluidity of concentrated cement suspensions. To quantify how the polyelectrolytes affect the fluidity, we have studied the effect of a strong anionic polyelectrolyte, melamine formaldehyde sulfonate (MFS), on the zeta potential of cement particles and on the steady-shear and low-amplitude rheological properties of cement suspensions. Adsorption of low concentrations of MFS onto the cement particles leads to an inversion in the sign of the surface potential, causing the electrostatically flocculated particles to become electrostatically dispersed and giving rise to a corresponding decrease in the steady-shear viscosity and storage modulus. At an intermediate MFS concentration, the steady-shear viscosity and the storage modulus each display a minimum. This concentration corresponds to that at which the zeta potential becomes constant. Larger concentrations of MFS result in an increase in the viscosity and storage modulus, which is attributed to depletion flocculation. These results thus relate the interaction between particles to the suspension fluidity through the analysis on the surface potential of particles and microstructure of suspension.     

Model reduction based on limited‐time interval impulse response Gramians

In this paper, three new Gramians are introduced namely ‐ limited‐time interval impulse response Gramians (LTIRG), generalized limited‐time Gramians (GLTG) and generalized limited‐time impulse response Gramians (GLTIRG). GLTG and GLTIRG are applicable to both unstable systems and also to systems which have eigenvalues of opposite polarities and equal magnitude. The concept of these Gramians is utilized to develop model reduction algorithms for linear time‐invariant continuous‐time single‐input single‐output (SISO) systems. In the cases of GLTIRG and GLTG based model reduction, the standard time‐limited Gramians are generalized to be applied to unstable systems by transforming the original system into a new system which requires the solution of two Riccati equations. Two numerical examples are included to illustrate the proposed methods. The results are also compared with standard techniques.     

Optimization of the in Situ Transesterification of Grain Sorghum (Milo) DDGS to Fatty Acid Methyl Esters and Fatty Acid Ethyl Esters

Distillers grains and solubles generated from the ethanol fermentation of grains contain acylglycerols (AG) that can be successfully converted to fatty acid methyl esters (FAME) and fatty acid ethyl esters (FAEE), commonly known as biodiesel. However, when grain sorghum (milo) DDGS were used as a feedstock for in situ transesterification (IST) under the previously established optimal conditions for other AG-bearing substrates, the yield plateaued at only 32.2% (corrected in this study to 24.2%). Several IST studies have reported significantly higher conversions of AG-bearing substrates to FAME. Therefore, the goal of this IST study was to improve the conversion of the AG in milo DDGS to FAME and FAEE by varying the temperature of reaction, the concentrations of the base (sodium methylate, NaOMe), volume of methanol and ethanol, and the amount of moisture in DDGS. Methyl tert-butyl ester was also evaluated as a co-solvent intended to improve miscibility and reaction rate. Among these variables, the most effective change was an increase in temperature from 40 to 65 °C. The most successful reaction used a AG:NaOMe:MeOH molar ratio of 1.0:2.6:168.9. Those reaction conditions used 4.8 mmol NaOMe dissolved in 12.6 mL MeOH and resulted in a 79.8% conversion of AG to FAME.     

Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper)

Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method.     

Effect of photoaligning azo dye structure on liquid crystal anchoring energy

A method of controlling anchoring energy of surface interaction of liquid crystal photoaligning substances is considered to enhance the liquid crystal display performances. An important parameter of the dye's molecular structure that determines the ratio of polar and azimuthal anchoring energy is the ability to form dimers. The values of dimerization thermodynamic potentials have been found. The probability of the formation of dye molecules dimers is evaluated. The bonds conjugation transfer via intermolecular hydrogen bond is revealed, and anisotropy of polarizability of the hydrogen bond is evaluated. The effect of dimerization on polar and azimuthal anchoring energy of liquid crystal — azo dye system — is shown.     

Extracting Microfacet‐based BRDF Parameters from Arbitrary Materials with Power Iterations

We introduce a novel fitting procedure that takes as input an arbitrary material, possibly anisotropic, and automatically converts it to a microfacet BRDF. Our algorithm is based on the property that the distribution of microfacets may be retrieved by solving an eigenvector problem that is built solely from backscattering samples. We show that the eigenvector associated to the largest eigenvalue is always the only solution to this problem, and compute it using the power iteration method. This approach is straightforward to implement, much faster to compute, and considerably more robust than solutions based on nonlinear optimizations. In addition, we provide simple conversion procedures of our fits into both Beckmann and GGX roughness parameters, and discuss the advantages of microfacet slope space to make our fits editable. We apply our method to measured materials from two large databases that include anisotropic materials, and demonstrate the benefits of spatially varying roughness on texture mapped geometric models.     

A benchmark of kriging-based infill criteria for noisy optimization

Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.     

On the Need for Mixed Media in Distributed Requirements Negotiations

Achieving agreement with respect to software requirements is a collaborative process that traditionally relies on same-time, same-place interactions. As the trend toward geographically distributed software development continues, colocated meetings are becoming increasingly problematic. Our research investigates the impact of computer-mediated communication on the performance of distributed client/developer teams involved in the collaborative development of a requirements specification. Drawing on media-selection theories, we posit that a combination of lean and rich media is needed for an effective process of requirements negotiations when stakeholders are geographically dispersed. In this paper, we present an empirical study that investigates the performance of six educational global project teams involved in a negotiation process using both asynchronous text-based and synchronous videoconferencing-based communication modes. The findings indicate that requirement negotiations were more effective when the groups conducted asynchronous structured discussions of requirement issues prior to the synchronous negotiation meeting. Asynchronous discussions were useful in resolving issues related to uncertainty in requirements, thus allowing synchronous negotiations to focus more on removing ambiguities in the requirements.