Concentration dependence of viscometric properties of model short chain polymer solutions

We study the concentration dependence of the conformational and viscometric behaviour of short-chain polymer solutions in shear flow by conducting a series of non-equilibrium molecular dynamics simulations, covering the entire concentration range. Our model explicitly incorporates all of the important generic features of real polymer solutions—excluded volume, hydrodynamic interactions and finite chain extensibility. Hydrodynamic interactions are included exactly by treating the solvent explicitly as an atomic fluid. The polymer molecules studied consist of 20-site bead-rod model molecules, which correspond approximately to 12 Kuhn steps in the melt. For polyethylene, this represents a molar mass of 1800 g mol⁻¹. In some respects, our results are consistent with experimental and theoretical results obtained for long-chain polymer solutions. We calculate the Flory-Fox constant and find a value that agrees reasonably well with results for long chain polymer solutions. Due to the short chain length of the molecules investigated, no semidilute region exists for these solutions. However, the radius of gyration and viscosity still exhibit strong concentration dependence, which is well described by power series, rather than power law expressions, in contrast to the behaviour usually observed in long-chain polymer solutions.     

Saccadic suppression: A review and an analysis.

Reviews the history of saccadic suppression and describes the suggested causes of the phenomenon. Primary emphasis is placed on "ordinary" saccadic suppression (i.e., decreased sensitivity for stimulation received from the outside environment either during or within a few hundred milliseconds of the time of occurrence of a saccade). However, the suppression of afterimages and entopic images is also considered. Some suggestions are made about the role of suppression in maintaining a stable visual world (constancy of visual direction) when voluntary saccades occur. (98 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A review of machine learning applications in IoT-integrated modern power systems

This paper aims to provide a systematic and comprehensive survey of state-of-the-art machine learning techniques and their potential applications in IoT-integrated power systems.     

Simultaneous multi-objective optimization of a new promoted ethylene dimerization catalyst using grey relational analysis and entropy measurement

A hybrid approach between the Taguchi method and grey relational analysis (GRA) with entropy measurement was applied to determine a single optimum setting for reaction factors of the proposed ethylene dimerization catalyst having overall selectivity to 1-butene (S_1-btn (%)) and turnover frequency (TOF (h^-1)) as multiple quality characteristics. Titanium tetrabutoxide (Ti(OC₄H₉)₄) catalyst precursor in combination with triethyl aluminum (TEA) activator, 1,4-dioxane as a suitable modifier, and ethylene dichloride (EDC) as a novel promoter were used in the catalysis. Control factors of temperature, pressure, Al/Ti, 1,4-dioxane/Ti, and EDC/Ti mol ratios were investigated on three levels and their main effects were discussed. The effect of the binary interaction between temperature, pressure, and Al/Ti mol ratio was also examined. Weight of the responses was determined using entropy. Analysis of variance (ANOVA) for data obtained from GRA indicated that EDC/Ti mol ratio with 27.64% contribution had the most profound effect on the multiple quality characteristics. Development of the weighted Grey-Taguchi method used the Taguchi method as its basic structure, adopted GRA to deal with multiple responses, and entropy to enhance the reasonability of the comprehensive index produced by GRA to make the results more objective and accurate. Overall, these combined mathematical techniques improved catalytic performance for 1-butene production.     

Farm production diversity and dietary quality: linkages and measurement issues

Recent research has analyzed whether higher levels of farm production diversity contribute to improved diets in smallholder farm households. We add to this literature by using and comparing different indicators, thus helping to better understand some of the underlying linkages. The analysis builds on data from Indonesia, Kenya, and Uganda. On the consumption side, we used 7-day food recall data to calculate various dietary indicators, such as dietary diversity scores, consumed quantities of fruits and vegetables, calories and micronutrients, and measures of nutritional adequacy. On the production side, we used a simple farm species count in addition to looking at the number of different food groups produced. Regression models showed that production diversity measured through simple species count is positively associated with most dietary indicators. However, when measuring production diversity in terms of the number of food groups produced, the association turns insignificant in many cases. Further analysis revealed that diverse subsistence production often contributes less to dietary diversity than cash income generated through market sales. If farm diversification responds to market incentives and builds on comparative advantage, it can contribute to improved income and nutrition. Yet, increasing the number of food groups produced on the farm independent of market incentives may foster subsistence, reduce income, and thus rather worsen dietary quality. The results suggest that improving the functioning of agricultural markets and smallholder market access are key strategies to enhance nutrition.