Phase relations in the system Cu-Eu-O and thermodynamic properties of CuEu2O4 and CuEuO2

Phase relations in the system Cu-Eu-O have been determined by equilibrating samples of different average composition at 1200 K and by phase analysis after quenching using optical microscopy (OM), x-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive x-ray (EDX). The equilibration experiments were conducted in evacuated ampoules and under flowing inert gas and pure oxygen. The Cu-Eu alloys were found to be in equilibrium with EuO. The higher oxides of europium, Eu3O4 and Eu2O3, coexist with metallic copper. Two ternary oxides CuEu2O4 and CuEuO2 were found to be stable. The ternary oxide CuEuO2, with copper in the monovalent state, can coexist with Cu, Cu2O, Eu2O3 and CuEu2O4 in different phase fields. The compound CuEu2O4 can be in equilibrium with Cu2O, CuO, CuEuO2, Eu2O3, and O2 gas under different conditions at 1200 K. Thermodynamic properties of the ternary oxides were determined using three solid-state cells based on yttria-stabilized zirconia as the electrolyte in the temperature range from 875 to 1250 K. The cells essentially measure the oxygen chemical potential in the three-phase fields: Cu+Eu2O3+CuEuO2, Cu2O+CuEuO2+CuEu2O4, and Eu2O3+CuEuO2+CuEu2O4. The thermodynamic properties of the ternary oxides can be represented by the equations: $\begin{gathered} {\raise0.5ex\hbox{$Couldn't find \end for begin{gathered} Thermogravimetric analysis (TGA) studies in Ar+O2 mixtures confirmed the results from emf measurements. An oxygen potential diagram for the system Cu-Eu-O at 1200 K was evaluated from the results of this study and information available in the literature on the binary phases.

On the Implementation of a Streaming Video over Peer to Peer network using Middleware Components

Video streaming applications have hitherto been supported by single server systems. A major drawback of such a solution is that it increases the server load. The server restricts the number of clients that can be simultaneously supported due to limitation in bandwidth. The constraints of a single server system can be overcome in video streaming if we exploit the endless resources available in a distributed and networked system. We explore a P2P system for streaming video applications. In this paper we build a P2P streaming video (SVP2P) service in which multiple peers co-operate to serve video segments for new requests, thereby reducing server load and bandwidth used. Our simulation shows the playback latency using SVP2P is roughly 1/4th of the latency incurred when the server directly streams the video. Bandwidth consumed for control messages (overhead) is as low as 1.5% of the total data transfered. The most important observation is that the capacity of the SVP2P grows dynamically.

Discovering injective episodes with general partial orders

Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.

A survey of temporal data mining

Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining.We mainly concentrate on algorithms for pattern discovery in sequential data streams.We also describe some recent results regarding statistical analysis of pattern discovery methods.

Controls on intense silicate weathering in a tropical river, southwestern India

The Silicate Weathering Rate (SWR) and associated Carbon dioxide Consumption Rate (CCR) in tropical silicate terrain is assessed through a study of the major ion chemistry in a small west flowing river of Peninsular India, the Nethravati River. The specific features of the river basin are high mean annual rainfall and temperature, high runoff and a Precambrian basement composed of granitic-gneiss, charnockite and minor metasediments. The water samples (n = 56) were collected from three locations along the Nethravati River and from two of its tributaries over a period of twelve months. Chemical Weathering Rate (CWR) for the entire watershed is calculated by applying rainwater correction using river chloride as a tracer. Chemical Weathering Rate in the Nethravati watershed is estimated to 44 t.km(-2).y(-1) encompassing a SWR of 42 t.km(-2).y(-1) and a maximum carbonate contribution of 2 t.km(-2).y(-1). This SWR is among the highest reported for granito-gneissic terrains. The assessed CCR is 2.9 . 10(5) mol.km(-2).y(-1). The weathering index (Re). calculated from molecular ratios of dissolved cations and silica in the river, suggests an intense silicate weathering leading to kaolinite-gibbsite precipitation in the weathering covers. The intense SWR and CCR could be due to the combination of high runoff and temperature along with the thickness and nature of the weathering cover. The comparison of silicate weathering fluxes with other watersheds reveals that under similar morpho-climatic settings basalt weathering would be 2.5 times higher than the granite-gneissic rocks. (C) 2012 Elsevier B.V. All rights reserved.

Transition due to base roughness in a dense granular flow down an inclined plane

Particle simulations based on the discrete element method are used to examine the effect of base roughness on the granular flow down an inclined plane. The base is composed of a random configuration of fixed particles, and the base roughness is decreased by decreasing the ratio of diameters of the base and moving particles. A discontinuous transition from a disordered to an ordered flow state is observed when the ratio of diameters of base and moving particles is decreased below a critical value. The ordered flowing state consists of hexagonally close packed layers of particles sliding over each other. The ordered state is denser (higher volume fraction) and has a lower coordination number than the disordered state, and there are discontinuous changes in both the volume fraction and the coordination number at transition. The Bagnold law, which states that the stress is proportional to the square of the strain rate, is valid in both states. However, the Bagnold coefficients in the ordered flowing state are lower, by more than two orders of magnitude, in comparison to those of the disordered state. The critical ratio of base and moving particle diameters is independent of the angle of inclination, and varies very little when the height of the flowing layer is doubled from about 35 to about 70 particle diameters. While flow in the disordered state ceases when the angle of inclination decreases below 20 degrees, there is flow in the ordered state at lower angles of inclination upto 14 degrees. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4710543]

Synthesis of a mixed-metal spinel, NiMn2O4, through site-selective substitution in MOF, (NiMn2){C6H3(COO)(3)}(2)]: synthesis, structure and magnetic studies

A mixed-metal metal-organic framework (MOF) compound NiMn2{C6H3(COO)(3)}(2)], I, is prepared hydrothermally by replacing one of the octahedral Mn2+ ions in Mn-3{C6H3(COO)(3)}(2)] by Ni2+ ions. Magnetic studies on I suggest antiferromagnetic interactions with weak canted antiferromagnetism below 8 K. On heating in flowing air I transforms to NiMn2O4 spinel at low temperature (T < 400 degrees C). The thermal decomposition of I at different temperatures results in NiMn2O4 with particle sizes in the nano regime. The nanoparticle nature of NiMn2O4 was confirmed using PXRD and TEM studies. Magnetic studies on the nanoparticles of NiMn2O4 indicate ferrimagnetism. The transition temperature of NiMn2O4 nanoparticles exhibits a direct correlation with the particle size. This study highlights the usefulness of MOF compound as a single-source precursor for the preparation of important ceramic oxides with better control on the stoichiometry and particle size.

Incentive Compatible Influence Maximization in Social Networks with Application to Viral Marketing

Information diffusion and influence maximization are important and extensively studied problems in social networks. Various models and algorithms have been proposed in the literature in the context of the influence maximization problem. A crucial assumption in all these studies is that the influence probabilities are known to the social planner. This assumption is unrealistic since the influence probabilities are usually private information of the individual agents and strategic agents may not reveal them truthfully. Moreover, the influence probabilities could vary significantly with the type of the information flowing in the network and the time at which the information is propagating in the network. In this paper, we use a mechanism design approach to elicit influence probabilities truthfully from the agents. Our main contribution is to design a scoring rule based mechanism in the context of the influencer-influencee model. In particular, we show the incentive compatibility of the mechanisms and propose a reverse weighted scoring rule based mechanism as an appropriate mechanism to use.

Yielding and large deviations in micellar gels: a model

We present a simple model that can be used to account for the rheological behaviour observed in recent experiments on micellar gels. The model combines attachment detachment kinetics with stretching due to shear, and shows well-defined jammed and flowing states. The large-deviation function (LDF) for the coarse-grained velocity becomes increasingly non-quadratic as the applied force F is increased, in a range near the yield threshold. The power fluctuations are found to obey a steady-state fluctuation relation (FR) at small F. However, the FR is violated when F is near the transition from the flowing to the jammed state although the LDF still exists; the antisymmetric part of the LDF is found to be nonlinear in its argument. Our approach suggests that large fluctuations and motion in a direction opposite to an imposed force are likely to occur in a wider class of systems near yielding.

Gibbs energy of formation of Ca7V4O17

Thermodynamic properties of Ca7V4O17 are measured for the first time using a solid-state electrochemical cell incorporating single crystal of CaF2 as the electrolyte over the temperature range from (900 to 1175) K. An equimolar mixture of CaO and CaF2 is used as the reference electrode and a mixture of Ca3V2O8, Ca7V4O17 and CaF2 as the measuring electrode. Both the electrodes are placed under flowing oxygen gas at ambient pressure. The standard Gibbs energy change for the reaction: 2Ca(3)V(2)O(8) + CaO -> Ca7V4O17; which is related to the chemical potential of CaO in the two-phase region (Ca3V2O8 + Ca7V4O17) of the pseudo-binary system CaO + V2O5, is obtained from the electromotive force of the cell as: Delta(r)G(o) +/- 127/(J . mol(-1)) = Delta mu(CaO) = -11453 + 8.273(T/K). The derived standard enthalpy of formation of Ca7V4O17 from elements in their normal standard states is ( 8208.97 +/- 8) kJ . mol (1) and its standard entropy is (560.05 +/- 7.5) J . K (1) . mol (1), both at T = 298.15 K. The results indicate that Ca7V4O17 decomposes into Ca3V2O8 and CaO at T = (1384 +/- 3) K.

The effect of base roughness on the development of a dense granular flow down an inclined plane

The development of the flow of a granular material down an inclined plane starting from rest is studied as a function of the base roughness. In the simulations, the particles are rough frictional spheres interacting via the Hertz contact law. The rough base is made of a random configuration of fixed spheres with diameter different from the flowing particles, and the base roughness is decreased by decreasing the diameter of the base particles. The transition from an ordered to a disordered flowing state at a critical value of the base particle diameter, first reported by Kumaran and Maheshwari Phys. Fluids 24, 053302 (2012)] for particles with the linear contact model, is observed for the Hertzian contact model as well. The flow development for the ordered and disordered flows is very different. During the development of the disordered flow for the rougher base, there is shearing throughout the height. During the development of the ordered flow for the smoother base, there is a shear layer at the bottom and a plug region with no internal shearing above. In the shear layer, the particles are layered and hexagonally ordered in the plane parallel to the base, and the velocity profile is well approximated by Bagnold law. The flow develops in two phases. In the first phase, the thickness of the shear layer and the maximum velocity increase linearly in time till the shear front reaches the top. In the second phase, after the shear layer encompasses the entire flow, there is a much slower increase in the maximum velocity until the steady state is reached. (C) 2013 AIP Publishing LLC.

A general geomorphological recession flow model for river basins

Recession flows in a basin are controlled by the temporal evolution of its active drainage network (ADN). The geomorphological recession flow model (GRFM) assumes that both the rate of flow generation per unit ADN length (q) and the speed at which ADN heads move downstream (c) remain constant during a recession event. Thereby, it connects the power law exponent of -dQ/dt versus Q (discharge at the outlet at time t) curve, , with the structure of the drainage network, a fixed entity. In this study, we first reformulate the GRFM for Horton-Strahler networks and show that the geomorphic ((g)) is equal to D/(D-1), where D is the fractal dimension of the drainage network. We then propose a more general recession flow model by expressing both q and c as functions of Horton-Strahler stream order. We show that it is possible to have = (g) for a recession event even when q and c do not remain constant. The modified GRFM suggests that is controlled by the spatial distribution of subsurface storage within the basin. By analyzing streamflow data from 39 U.S. Geological Survey basins, we show that is having a power law relationship with recession curve peak, which indicates that the spatial distribution of subsurface storage varies across recession events. Key Points The GRFM is reformulated for Horton-Strahler networks. The GRFM is modified by allowing its parameters to vary along streams. Sub-surface storage distribution controls recession flow characteristics.

Effect of side confining walls on the growth rate of compressible mixing layers

Model free simulations are performed to study the effect of the presence of side wall in compressible mixing of two parallel dissimilar gaseous streams with significant temperature difference. The turbulence statistics shows the three dimensional nature of the flow with and without the presence of side walls. The presence of side wall neither makes the flow field two dimensional, nor suppresses three dimensional disturbances. However, the comparison of shear layer growth rate and wall pressures reveal a better match with the two dimensional simulation results. This better match is explained on the basis of formation of oblique structures due to the presence of side walls which also suppress the distribution of momentum in third direction making the pressures to be higher as compared with the case without side walls. (C) 2013 Elsevier Ltd. All rights reserved.

Pattern-growth based frequent serial episode discovery

Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarm management in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web click streams and so on. In this paper, we address the discovery of serial episodes. In the episodes context, there have been multiple ways to quantify the frequency of an episode. Most of the current algorithms for episode discovery under various frequencies are apriori-based level-wise methods. These methods essentially perform a breadth-first search of the pattern space. However currently there are no depth-first based methods of pattern discovery in the frequent episode framework under many of the frequency definitions. In this paper, we try to bridge this gap. We provide new depth-first based algorithms for serial episode discovery under non-overlapped and total frequencies. Under non-overlapped frequency, we present algorithms that can take care of span constraint and gap constraint on episode occurrences. Under total frequency we present an algorithm that can handle span constraint. We provide proofs of correctness for the proposed algorithms. We demonstrate the effectiveness of the proposed algorithms by extensive simulations. We also give detailed run-time comparisons with the existing apriori-based methods and illustrate scenarios under which the proposed pattern-growth algorithms perform better than their apriori counterparts. (C) 2013 Elsevier B.V. All rights reserved.

A prototype of a first-responder indoor localization system

In this paper we present an approach to build a prototype. model of a first-responder localization system intended for disaster relief operations. This system is useful to monitor and track the positions of the first-responders in an indoor environment, where GPS is not available. Each member of the first responder team is equipped with two zero-velocity-update-aided inertial navigation systems, one on each foot, a camera mounted on a helmet, and a processing platform strapped around the waist of the first responder, which fuses the data from the different sensors. The fusion algorithm runs real-time on the processing platform. The video is also processed using the DSP core of the computing machine. The processed data consisting of position, velocity, heading information along with video streams is transmitted to the command and control system via a local infrastructure WiFi network. A centralized cooperative localization algorithm, utilizing the information from Ultra Wideband based inter-agent ranging devices combined with the position estimates and uncertainties of each first responder, has also been implemented.