Bismuth tungsten-oxide bronzes - a study of intergrowth phases and related aspects

Reaction of bismuth metal with WO$_3$ in the absence of oxygen yields interesting bronze-like phases. From analytical electron microscopy and X-ray photoelectron spectroscopy, the product phases are found to have the general composition Bi$_x$ WO$_3$ with bismuth in the 3+ state. Structural investigations made with high resolution electron micrscopy and cognate techniques reveal that when x < 0.02, a perovskite bronze is formed. When x $\geqslant$ 0.02, however, intergrowth tungsten bronzes (i.t.b.) containing varying widths of the WO$_3$ slab are formed, the lattice periodicity being in the range 2.3-5.1 nm in a direction perpendicular to the WO$_3$ slabs. Image-matching studies indicate that the bismuth atoms are in the tunnels of the hexagonal tungsten bronze (h.t.b.) strips and the h.t.b. strips always remain one-tunnel wide. Annealed samples show a satellite structure around the superlattice spots in the electron diffraction patterns, possibly owing to ordering of the bismuth atoms in the tunnels. The i.t.b. phases show recurrent intergrowths extending up to 100 nm in several crystals. The periodicity varies considerably within the same crystal wherever there is disordered intergrowth, but unit cell dimensions can be assigned from X-ray and electron diffraction patterns. The maximum value of x in the i.t.b. phases is ca. 0.07 and there is no evidence for the i.t.b. phase progressively giving way to the h.t.b. phase with increase in x. Hexagonal tungsten bronzes that contain bismuth with x up to 0.02 can be formed by starting from hexagonal WO$_3$, but the h.t.b. phase seems to be metastable. Optical, magnetic and electron transport properties of the i.t.b. phases have been measured and it appears that the electrons become itinerant when x > 0.05.

Dimorphism and structural modulation in quinoxaline

The Z' = 1 and Z' = 5 structures of quinoxaline are compared. The nature of the intermolecular interactions in the Z' = 5 structure is studied by means of variable-temperature single-crystal X-ray diffraction. The C-H center dot center dot center dot N and pi ... pi it interactions in these structures are of a stabilizing nature. The high Z' structure has the better interactions, whereas the low Z' structure has the better stability. This trade-off is a recurrent theme in molecular crystals and is a manifestation of the distinction between thermodynamically and kinetically favoured crystal forms.

State Dependent Attempt Rate modeling of single cell IEEE 802.11 WLANs with homogeneous nodes and Poisson packet arrivals

We study a State Dependent Attempt Rate (SDAR) approximation to model M queues (one queue per node) served by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol as standardized in the IEEE 802.11 Distributed Coordination Function (DCF). The approximation is that, when n of the M queues are non-empty, the (transmission) attempt probability of each of the n non-empty nodes is given by the long-term (transmission) attempt probability of n saturated nodes. With the arrival of packets into the M queues according to independent Poisson processes, the SDAR approximation reduces a single cell with non-saturated nodes to a Markovian coupled queueing system. We provide a sufficient condition under which the joint queue length Markov chain is positive recurrent. For the symmetric case of equal arrival rates and finite and equal buffers, we develop an iterative method which leads to accurate predictions for important performance measures such as collision probability, throughput and mean packet delay. We replace the MAC layer with the SDAR model of contention by modifying the NS-2 source code pertaining to the MAC layer, keeping all other layers unchanged. By this model-based simulation technique at the MAC layer, we achieve speed-ups (w.r.t. MAC layer operations) up to 5.4. Through extensive model-based simulations and numerical results, we show that the SDAR model is an accurate model for the DCF MAC protocol in single cells. (C) 2012 Elsevier B.V. All rights reserved.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define `synconset wave' as a cascade of first spikes within a synchronisation event. Synconset waves would occur in `synconset chains', which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.

The knight of sugar industry: T. S. Venkatraman (1884-1963)

Tiruvadi Sambasiva Venkatraman (TSV) was a plant breeder. In response to a call from Pundit Madan Mohan Malaviya, he made it his mission to develop high-yielding varieties of sugarcane for manufacturing sugar and making it available as a sweetening agent and an energy source for the malnourished children of India. Using Saccharum officinarum, then under cultivation in India, as the female parent, he artificially fertilized it with pollen from S. barberi, which grew wild in Coimbatore. After 4-5 recurrent backcrossings of S. officinarum Chi wild Sorghum spontaneum with S. officinarum as the female parent, TSV selected the `rare' interspecies hybrid cane varieties that resembled sugarcane and had approximately 2.5 cm thick juicy stems containing 16-18% sucrose - nearly 35 times more than what occurred in parent stocks. The hybrid canes matured quickly, were resistant to waterlogging, drought, and to the red-rot disease caused by Glomerella tucumanensis (Sordariomycetes: Glomerellaceae), and to the sereh-virus disease. Most importantly, they were amenable for propagation using stem cuttings. In recognition of the development of high-yielding sugarcane varieties, TSV was conferred the titles Rao Bahadur, Rao Sahib, and Sir by the British Government, and Padma Bhushan by the Republic of India. In the next few decades, consequent to TSV's work, India turned into the second largest sugar producer in the world, after Brazil. The hybrid sugarcane varieties developed are the foundational stocks for new sugarcane x bamboo hybrids, and for possible resistance to Puccinia megalocephala (Pucciniomycetes: Pucciniaceae) and Ustilago scitaminea (Ustilaginomycetes: Ustilaginaceae) using molecular techniques.

Time-Domain Approach to Estimate Series Capacitance of an Isolated Phase Winding of a Transformer

Information available in frequency response data is equivalently available in the time domain as a response due to an impulse excitation. The idea to pursue this equivalence to estimate series capacitance is linked to the well-known fact that under impulse excitation, the line/neutral current in a transformer has three distinct components, of which, the initial capacitive component is the first to manifest, followed by the oscillatory and inductive components. Of these, the capacitive component is temporally well separated from the rest-a crucial feature permitting its direct access and analysis. Further, the winding initially behaves as a pure capacitive network, so the initial component must obviously originate from only the (series and shunt) capacitances. With this logic, it should therefore be possible to estimate series capacitance, just by measuring the initial capacitive component of line current and the total shunt capacitance. The principle of the method and details of its implementation on two actual isolated transformerwindings (uniformly wound) are presented. For implementation, a low-voltage recurrent surge generator, a current probe, and a digital oscilloscope are all that is needed. The method is simple and requires no programming and needs least user intervention, thus paving the way for its widespread use.