Characterization and iodide adsorption behaviour of HDPY+ modified bentonite

Owing to its favourable physical, chemical and rheological properties, densely compacted bentonite or bentonite-sand mix is considered as a suitable buffer material in deep geological repositories to store high level nuclear waste. Iodine-129 is one of the significant nuclides in the high level waste owing to its long half life and poor sorption onto most geologic media. Bentonite by virtue of negatively charged surface has negligible affinity to retain iodide ions. As organo-bentonites are known to retain iodide ions, the present study characterizes hexadecylpyridinium chloride (HDPyCl.H2O) treated bentonite from Barmer India (referred as HDPy+B) for physico-chemical properties, engineering properties and the iodide adsorption behavior of the organo clay. Batch experiments revealed that HDPy+ ions are largely retained (94 % retention) via cation exchange; the ion-exchange process neutralizes the negative surface charge and bridges clay particles leading to reduction in Atterberg limits, clay content and sediment volume. The organo clay retains iodide by Coulombic attraction (at primary sites) and anion exchange (at secondary sites). The free-energy change (Delta G (o) = -25.5 kJ/mol) value indicated that iodide retention by organo clay is favored physical adsorption process. Iodide adsorption capacity of organo clay decreased significantly (85-100 %) on dilution with 50-80 % bentonite. On the other hand, dilution of bentonite with 50 % organo clay caused 58 % reduction in swell potential and 21 % reduction in swell pressure.

Active Sequential Hypothesis Testing with Application to a Visual Search Problem

We consider a visual search problem studied by Sripati and Olson where the objective is to identify an oddball image embedded among multiple distractor images as quickly as possible. We model this visual search task as an active sequential hypothesis testing problem (ASHT problem). Chernoff in 1959 proposed a policy in which the expected delay to decision is asymptotically optimal. The asymptotics is under vanishing error probabilities. We first prove a stronger property on the moments of the delay until a decision, under the same asymptotics. Applying the result to the visual search problem, we then propose a ``neuronal metric'' on the measured neuronal responses that captures the discriminability between images. From empirical study we obtain a remarkable correlation (r = 0.90) between the proposed neuronal metric and speed of discrimination between the images. Although this correlation is lower than with the L-1 metric used by Sripati and Olson, this metric has the advantage of being firmly grounded in formal decision theory.

Solid State and Solution Mediated Multistep Sequential Transformations in Metal-Organic Coordination Networks

Sequential transformation in a family of metal-organic framework compounds has been investigated employing both a solid-state as well as a solution mediated route. The compounds, cobalt oxy-bis(benzoate) and manganese oxybis(benzoate) having a two-dimensional structure, were reacted with bipyridine forming cobalt oxy-bis(benzoate)-4,4'-bipyridine and manganese oxy-bis(benzoate)-4,4'-bipyridine, respectively. The bipyridine containing compounds appear to form sequentially through stable intermediates. For the cobalt system, the transformation from a two-dimensional compound, Co(H2O)(2)(OBA)] (OBA = 4,4'-oxy-bis(benzoate)), I, to two different three-dimensional compounds, Co(bpy)(OBA)]center dot bpy, II, (bpy = 4,4'-bipyridine) and Co(bpy)(0.5)(OBA)], III, and reversibility between II and III have been investigated. In the manganese system, transformation from a two-dimensional compound, Mn(H2O)(2)(OBA)], Ia, to two different three-dimensional compounds, Mn (bpy)(OBA)]center dot bpy, Ha and Ha to Mn(bpy)(0.5)(OBA)], Ilia, has been investigated. It has also been possible to identify intermediate products during these transformation reactions. The possible pathways for the formation of the compounds were postulated.

Performance of a class of multi-robot deploy and search strategies based on centroidal voronoi configurations

This article considers a class of deploy and search strategies for multi-robot systems and evaluates their performance. The application framework used is deployment of a system of autonomous mobile robots equipped with required sensors in a search space to gather information. The lack of information about the search space is modelled as an uncertainty density distribution. The agents are deployed to maximise single-step search effectiveness. The centroidal Voronoi configuration, which achieves a locally optimal deployment, forms the basis for sequential deploy and search (SDS) and combined deploy and search (CDS) strategies. Completeness results are provided for both search strategies. The deployment strategy is analysed in the presence of constraints on robot speed and limit on sensor range for the convergence of trajectories with corresponding control laws responsible for the motion of robots. SDS and CDS strategies are compared with standard greedy and random search strategies on the basis of time taken to achieve reduction in the uncertainty density below a desired level. The simulation experiments reveal several important issues related to the dependence of the relative performances of the search strategies on parameters such as the number of robots, speed of robots and their sensor range limits.

Nodal length fluctuations for arithmetic random waves

Using the spectral multiplicities of the standard torus, we endow the Laplace eigenspaces with Gaussian probability measures. This induces a notion of random Gaussian Laplace eigenfunctions on the torus (''arithmetic random waves''). We study the distribution of the nodal length of random eigenfunctions for large eigenvalues, and our primary result is that the asymptotics for the variance is nonuniversal. Our result is intimately related to the arithmetic of lattice points lying on a circle with radius corresponding to the energy.

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

Novel algorithms for distributed sequential hypothesis testing

This paper considers sequential hypothesis testing in a decentralized framework. We start with two simple decentralized sequential hypothesis testing algorithms. One of which is later proved to be asymptotically Bayes optimal. We also consider composite versions of decentralized sequential hypothesis testing. A novel nonparametric version for decentralized sequential hypothesis testing using universal source coding theory is developed. Finally we design a simple decentralized multihypothesis sequential detection algorithm.

Cooperative distributed sequential spectrum sensing

We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.

A novel algorithm for cooperative distributed sequential spectrum sensing in Cognitive Radio

This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.

Cooperative distributed sequential spectrum sensing

We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.

Random forest algorithm with derived geographical layers for improved classification of remote sensing data

Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. The LC dynamics are being captured using multi-resolution remote sensing (RS) data with appropriate classification strategies. RS data with important environmental layers (either remotely acquired or derived from ground measurements) would however be more effective in addressing LC dynamics and associated changes. These ancillary layers provide additional information for delineating LC classes' decision boundaries compared to the conventional classification techniques. This communication ascertains the possibility of improved classification accuracy of RS data with ancillary and derived geographical layers such as vegetation index, temperature, digital elevation model (DEM), aspect, slope and texture. This has been implemented in three terrains of varying topography. The study would help in the selection of appropriate ancillary data depending on the terrain for better classified information.

Realistic minimum desorption temperatures and compressor sizing for activated carbon + HFC 134a adsorption coolers

A low thermal diffusivity of adsorption beds induces a large thermal gradient across cylindrical adsorbers used in adsorption cooling cycles. This reduces the concentration difference across which a thermal compressor operates. Slow adsorption kinetics in conjunction with the void volume effect further diminishes throughputs from those adsorption thermal compressors. The problem can be partially alleviated by increasing the desorption temperatures. The theme of this paper is the determination the minimum desorption temperature required for a given set of evaporating/condensing temperatures for an activated carbon + HFC 134a adsorption cooler. The calculation scheme is validated from experimental data. Results from a parametric analysis covering a range of evaporating/condensing/desorption temperatures are presented. It is found that the overall uptake efficiency and Carnot COP characterize these bounds. A design methodology for adsorber sizing is evolved. (c) 2012 Elsevier Ltd. All rights reserved.

Synthesis and phase evolution in Nb/Si multilayers obtained by sequential laser ablation

The paper reports the synthesis of Nb/Si multilayers (48/27 nm) deposited on Si single crystal substrate by sequential laser ablation of elemental Nb and Si. Significant amount of Nb is found in the amorphous Si layer (similar to 25-35 at.% Nb). The Nb layer is found to be polycrystalline. The phase evolution of the multilayer has been studied by annealing at 600 degrees C for various times and carrying out cross sectional electron microscopic studies. We report the formation of amorphous silicide layer at the Nb/Si interface followed by the formation of the NbSi2 phase in the Si layer. Further annealing leads to the nucleation of hexagonal Nb5Si3 grains in amorphous silicide layers at Nb/NbSi2 interfaces. These results are different from those reported for sputter deposited multilayer. (C) 2013 Elsevier B. V. All rights reserved.

Sequential alternating proximal method for scalable sparse structural SVMs

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.

Proactive control of sequential saccades in the human supplementary eye field

Our ability to regulate behavior based on past experience has thus far been examined using single movements. However, natural behavior typically involves a sequence of movements. Here, we examined the effect of previous trial type on the concurrent planning of sequential saccades using a unique paradigm. The task consisted of two trial types: no-shift trials, which implicitly encouraged the concurrent preparation of the second saccade in a subsequent trial; and target-shift trials, which implicitly discouraged the same in the next trial. Using the intersaccadic interval as an index of concurrent planning, we found evidence for context-based preparation of sequential saccades. We also used functional MRI-guided, single-pulse, transcranial magnetic stimulation on human subjects to test the role of the supplementary eye field (SEF) in the proactive control of sequential eye movements. Results showed that (i) stimulating the SEF in the previous trial disrupted the previous trial type-based preparation of the second saccade in the nonstimulated current trial, (ii) stimulating the SEF in the current trial rectified the disruptive effect caused by stimulation in the previous trial, and (iii) stimulating the SEF facilitated the preparation of second saccades based on previous trial type even when the previous trial was not stimulated. Taken together, we show how the human SEF is causally involved in proactive preparation of sequential saccades.