High performance concentrators and superconcentrators usingmultiplexing schemes

Concentrators are used to interface and combine together low speed communication channels onto higher speed transmission links to alleviate transmission costs. They are also used to construct more powerful switching fabrics such as permutation and broadcast networks. Using an adaptive binary sorting network model, this paper constructs new concentrators and superconcentrators. Unlike the previously reported concentrators and superconcentrators, these new constructions are fast, and ran easily be implemented using simple switching devices. More specifically, for n inputs, they can be constructed with O(n lg lg n) constant fanin bit-level multiplexers and demultiplexers, and can be routed in O(lg² n) bit-level time     

Assessment of Copper Bioavailability in Spinach (Spinacia oleracea L) Leaves by Chemical Fractionation

An analytical fractionation scheme has been developed based on water, acetone, chloroform, diethyl ether, ethanol, n-hexane, or methanol extractions to identify free and/or copper bound complexes in spinach samples, sample extracts being analyzed by inductively coupled plasma-mass spectrometry. The total copper contents were determined after digestion of the samples in a microwave-assisted digestion system. Method validation parameters were defined in terms of the detection limits, accuracy, and precision. The limits of detection and quantification for copper were 0.07 and 0.23 mg kg^?1, respectively. The precision in terms of the repeatability and reproducibility, calculated from the relative standard deviations (%RSD), were 3.0 and 4.8 %, respectively. Solvent systems, which simulated gastric and intestinal or gastrointestinal digestion together with n-octanol extraction and activated carbon adsorption, were evaluated with respect to both sample-solvent compatibility and bioavailability.     

Psychotic symptoms in post-traumatic stress disorder

The mouse has become the leading animal model for studying biological processes in mammals. Creation of additional genomic and genetic resources will make the mouse an even more useful model for the research community. On the basis of recommendations from the scientific community, the National Institutes of Health (NIH) plans to support grants to generate a 'working draft' sequence of the mouse genome by 2003, systematic mutagenesis and phenotyping centres, repositories for mouse strain maintenance, distribution and cryopreservation and training fellowships in mouse pathobiology.     

Au/Porous silicon‐based sodium borohydride fuel cells

The Au/Porous silicon structure (Au/PS) was developed as hydrogen fuel cell. The use of a porous silicon filled with hydrochloric acid as a proton‐conducting membrane and thin gold film as a catalyst in Au/PS/Si fuel cell is demonstrated. The devices were fabricated by first creating 10–20 µm thick porous silicon layer by anodization etching in a standard silicon wafer and then depositing the gold catalyst film onto the porous silicon. Using sodium borohydride (NaBH₄) solution as the fuel, generation of the open‐circuit voltage of 0.55 V and the fuel cell peak power density of 13 mW cm⁻² at room temperature was achieved. Moreover production of hydrogen by evolution (out‐diffusion) of hydrogen from solid sodium borohydride during thermal annealing at 30–120°C was investigated. Data on the effective diffusion coefficient of the hydrogen in NaBH₄ were determined from intensity changes of infrared vibration peaks of B–H bond (2280 and 3280 cm⁻¹), as a result of thermal annealing of NaBH₄ samples. The relatively high values of the diffusion coefficient of hydrogen, increasing from 1×10⁻⁶ cm² s⁻¹ to 2×10⁻⁴ cm² s⁻¹ suggest that a thermo‐stimulated evolution process can be used for producing hydrogen from NaBH₄. Copyright © 2010 John Wiley & Sons, Ltd.     

Neural network model for resilient modulus of emulsified asphalt mixtures

This paper explores the potential use of neural networks (NNs) in the field of emulsified asphalt mixtures. A neural network model is developed for predicting, with sufficient approximation, relationship between the factors affecting resilient modulus (inputs: curing time, cement addition level, and residual asphalt content) and the resilient modulus (output) of emulsified asphalt mixture. A backpropagation neural network of three layers is employed. First resilient modulus data are obtained by conducting laboratory resilient modulus tests on emulsified asphalt samples, and then the results are used to train the neural network. The effectiveness of different neural network configurations is investigated. Effect of parameters such as curing time, cement addition level and residual asphalt content that influence the resilient modulus is also explored. Results indicate that NN predicts the resilient modulus with high accuracy. It is also demonstrated that NN is an excellent method that can reduce the time consumed and can be used as an important tool in evaluating the factors affecting resilient modulus of emulsified asphalt mixture at the design stage.     

On self-routing in Clos connection networks

A self-routing connection network is a switching device where the routing of each switch can be determined in terms of the destination addresses of its inputs alone, i.e. independent of the routing information regarding the other switches in the network. One family of connection networks that were considered in the literature for self-routing are Clos networks. Earlier studies indicate that some Clos networks can be self-routed for certain permutations. In this paper, it is proved that the only category of Clos networks that can be self-routed for all permutations are those with at most two switches in their outer stages     

Determination of selenium in garlic (Allium sativum) and onion (Allium cepa) by electro thermal atomic absorption spectrometry

A separation/enrichment procedure has been developed for the determination of selenium in garlic and onion samples by electrothermal atomic absorption spectrometry (ET-AAS) as a slurry sampling after preconcentration with 3,3-diaminobenzidine (DAB) reagent on the activated carbon. The influences of pH, time, amount of carbon and complexing reagent were outlined. The effect of acids used in the digestion of samples was also studied and compared. Selenium level was found to be 0.024 μg g⁻¹ for onion (n = 5; LOD – 0.5 μg L⁻¹; LOQ – 1.7 μg L⁻¹) and 0.015 μg g⁻¹ for garlic (n = 5; LOD – 1.3 μg L⁻¹; LOQ – 3.3 μg L⁻¹). Three different samples of garlic were analyzed by k₀-instrumental neutron activation analysis (k₀-INAA) at the Jozef Stefan Institute (JSI). The data obtained by k₀-INAA show that the content of selenium overlapped the results obtained by ET-AAS.     

A fast parallel algorithm for routing unicast assignments in Benesnetworks

This paper presents a new parallel algorithm for routing unicast (one-to-one) assignments in Benes networks. Parallel routing algorithms for such networks were reported earlier, but these algorithms were designed primarily to route permutation assignments. The routing algorithm presented in this paper removes this restriction without an increase in the order of routing cost or routing time. We realize this new routing algorithm on two different topologies. The algorithm routes a unicast assignment involving O(k) pairs of inputs and outputs in O(lg ² k+lg n) time on a completely connected network of n processors and in O(lg⁴ k+lg² k lg n) time on an extended shuffle-exchange network of n processors. Using O(n lg n) professors, the same algorithm can be pipelined to route α unicast assignments each involving O(k) pairs of inputs and outputs, in O(lg² k+lg n+(α-1) lg k) time on a completely connected network and in O(lg⁴ k+lg² k lg n+(α-1)(lg ³ k+lg k lg n)) time on the extended shuffle-exchange network. These yield an average routing time of O(lg k) in the first case, and O(lg³ k+1g k lg n) in the second case, for all α⩾lg n. These complexities indicate that the algorithm given in this paper is as fast as Nassimi and Sahni's algorithm for unicast assignments, and with pipelining, it is faster than the same algorithm at least by a factor of O(lg n) on both topologies. Furthermore, for sparse assignments, i.e., when k=O(1), it is the first algorithm which has an average routing time of O(1g n) on a topology with O(n) links     

A fault-tolerant permutation network modulo arithmetic processor

Conventional fault-tolerant modulo arithmetic processors rely on the properties of a residue number system with L redundant moduli to detect up to L/2 errors. In this paper, we propose a new scheme that combines r-out-of-s residue codes with Berger codes to concurrently detect any number of module errors without any redundant moduli. In addition, this scheme can tolerate L faults if L redundant moduli are used, and has the property of graceful degradation when the number of faulty moduli exceeds L. Finally, it is shown that the added cost for fault tolerance is much less than those were reported earlier in the literature     

Neural Models for the Resonant Frequency of Electrically Thin and Thick Circular Microstrip Antennas and the Characteristic Parameters of Asymmetric Coplanar Waveguides Backed with a Conductor

Neural models for computing the resonant frequency of electrically thin and thick circular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Five learning algorithms, delta-bar-delta, extended delta-bar-delta, quick-propagation, directed random search and genetic algorithms, are used to train the multilayered perceptrons. The radial basis function network is trained according to its learning strategy. The resonant frequency results of neural models are in very good agreement with the experimental results available in the literature. In this paper, the characteristic impedance and the effective permittivity of the asymmetric coplanar waveguide backed with a conductor are also computed by using only one neural model trained by the backpropagation with momentum and the extended delta-bar-delta algorithms. When the performances of neural models are compared with each other, the best results for test are obtained from the multilayered perceptrons trained by the extended delta-bar-delta algorithm.