A new iterative diakoptics-based multilevel moments method forplanar circuits

This paper combines a multilevel moments method (MMM) scheme with a modified diakoptics (MD) technique and a block Gauss-Seidel (BGS) iterative technique to reduce the solution time of large planar microwave structures. The proposed MMM scheme has two levels. On the lower level, the planar circuit is divided into several subcircuits using two types of artificial ports. At the higher level, general basis functions defined over the complete circuit are generated in an iterative way. The validity and the efficiency of the new technique are validated by several examples, including a large low-pass filter     

Bubble size effect on low liquid input drift-flux parameters

We investigated the effect of bubble size on the drift-flux parameters at low liquid flow conditions by measuring the radial profiles of void fraction and phase velocities in a vertical bubbly pipe flow of diameter and height . To study the effect of the bubble size we used two different types of bubble inlets. We measured the local bubble fraction and velocity U_g by using single and four-point-optical fibre probes, and we used Laser Doppler Anemometry to determine the liquid velocity U_l. The distribution parameter C₀ and the weighted mean drift velocity |U_drift| were directly computed from the local measurements at a height on our experimental set-up. Both parameters were influenced by the bubble size. Provided no liquid flow reversal occurred at the near wall region, the distribution parameter reached a below unity minimum plateau value of C₀=0.95 for wall peaking void fraction profiles. At low liquid input conditions both the liquid input and bubble size had an influence on the distribution parameter. Extreme values such as C₀>2 were measured. From these measurements we developed models for the drift-flux parameters to take into account the effect of bubble size and input-flow conditions for our intermediate pipe diameter value. These models were tested and validated with separately collected experimental data.     

Background and foreground interaction: Influence of complementary colors on the search task

Labels are indispensable visual (communication) elements that completely deliver the geospatial message of maps. The objective of this study is to investigate the impact of complementary colors between the map's background and text on the readability of cartographic texts and thus on the efficiency of the map user's search task. This is compared with the use of the “traditional” black labels on the corresponding colored backgrounds. Furthermore, a number of user characteristics, such as gender and expertise, are taken into account as well. The users' eye movements were registered to study their attentive behavior during the visual search task. In addition to the complement of the color's hue, the analyses were based on the difference in luminance, which could also affect the labels' readability. The difference between the black and colored label design was significantly different versus the eye‐tracking metrics. A correlation was found between the color difference and reaction time measurement and between the luminance difference and fixation duration. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 437–445, 2015     

Oxidation of polycyclic aromatic hydrocarbons with hydrogen peroxide catalyzed by Iron(III)porphyrins

A variety of iron-metallated functional porphyrins have been synthesized and their catalytic activity to oxidize polycyclic aromatic hydrocarbons, (PAHs) using hydrogen peroxide as oxidant was investigated. It was found that iron-metallated porphyrins bearing strong electronegative groups at β-pyrrolic positions show an exceptionally high catalytic efficiency.     

SOFT COMPUTING TECHNIQUES FOR PREDICTION OF BOILING HEAT TRANSFER COEFFICIENT OF LIQUIDS ON COPPER-COATED TUBES

In this article pool boiling heat transfer coefficient (HTC) of liquids (isopropanol, methanol, and distilled water) on copper-coated heating tubes over a wide range of pressure conditions is computed experimentally. The objective is to find the applicability of soft computing techniques, swarm-intelligence based neural network, and adaptive fuzzy models in the prediction of boiling HTC. The results are compared with those computed experimentally. The performance of models for prediction of HTC is analyzed in terms of root mean square of prediction error. The minimum/maximum value obtained by zero-order fuzzy model with six membership function is 0.0023/3.4383 among all the liquids considered. The model is found to predict HTC with a maximum error of ±0.5% for boiling of liquids over all the coated tubes with pressure varying from atmospheric to subatmospheric levels. The study shows an excellent agreement between the experimental and predicted data.     

A hybrid evolutionary approach to the registration area planning problem

The last two decades have witnessed a phenomenal growth in the number of cellular wireless network users which in turn stressed the need to utilize the limited network bandwidth in an efficient manner. The network bandwidth is consumed not only by user traffic, but also by control traffic needed for ensuring the mobility of users. As we don’t have any control over the volume of user traffic, all attempts to efficiently use bandwidth are based on frequency reuse and minimizing the control traffic. The registration area planning (RAP) problem seeks a partition of the cells of the network into contiguous areas called registration areas so that the bandwidth consumed by control signals is minimized. RAP problem in an \(\mathcal {NP}\) -Hard problem. In this paper, we present a steady-state grouping genetic algorithm with local search to solve this problem. We have compared our approach with the state-of-the-art approaches reported in the literature. Computational results show the effectiveness of our approach.     

Nonlinear predictive control for a NNARX hydro plant model

A neural network (NN)-based nonlinear predictive control (NPC) is described for control of turbine power with variation in gate position. The studied plant includes the tunnel, surge tank and penstock effect dynamics. Multilayer perceptron neural network is chosen to represent a neural network nonlinear autoregressive with exogenous signal model of hydro power plant. With the said NN model configuration, quasi-Newton and Levenberg–Marquardt iterative optimization algorithms are applied in order to determine optimal predictive control parameters. The controlled response is simulated on different amplitude step function and trapezoidal shape reference signal. The study also discusses comparison with an approximate predictive control approach, being linearized around operating points. It is shown that NPC strategy gives impressive results in comparison to the approximated one.     

Debittering of spent brewer's yeast for food purposes

The analysis of spent brewer's yeast slurry exhibited a five-day biochemical oxygen demand (B.O.D.) of 158 400 mg/l at 20% yeast concentration. A treatment process for debittering was tried. The steps include removal of extraneous materials, reduction of the viscosity of slurry, debittering the yeast cells and centrifuging, washing and drying the debittered yeast-cell biomass in a drum dryer. Debittering was carried out successfully by adjusting the pH and temperature of the slurry. pH 10 at 50 °C of the slurry resulted in the complete debittering of the yeast-cells in a single treatment without affecting the chemical and essential amino acid compositions.