Oxygen potentials, Gibbs' energies and phase relations in the Cu-Cr-O system

Thermodynamic properties of ternary compounds, cuprous and cupric chromites (CuCrO2, CuCr204), and oxygen potentials corresponding to three three-phase regions in the Cu-Cr-O system have been measured in the temperature range 900 to 1350 K using a solid state galvanic cell incorporating calcia-stabilized zirconia. Cuprous chromite was found to be nearly stoichiometric. The compositions of non-stoichiometric cupric chromite saturated with CuO and Cr203 have been determined using electron microprobe and energy dispersive X-ray analysis. The results of this study resolve discrepancies in Gibbs" energies of cuprous and cupric chromites reported in the literature. A ternary phase diagram for the Cu-Cr-O system at 1150 K and phase relations in air for the Cu20-CuO-Cr203 system as a function of temperature have been derived based on the new thermodynamic data. The phase diagram given in the literature is found to be inaccurate

Phase relations and activities in the Co-Ni-O system at 1373 K

The tie-lines delineating equilibria between CoO-NiO and Co-Ni solid solutions in the ternary Co-Ni-O system at 1373 K have been determined by electron microprobe andedax point count analysis of the oxide phase equilibrated with the alloy. The oxygen potentials corresponding to the tie-line compositions have been measured using a solid oxide galvanic cell with calcia-stabilized zirconia electrolyte and Ni+NiO reference electrode. Activities in the metallic and oxide solid solution have been derived using a new Gibbs-Duhem integration technique. Both phases exhibit small positive deviations from ideality; the values ofGE/X1X2 are 2640 J mol−1 for the metallic phase and 2870 J mol−1 for the oxide solid solution

Thermodynamics and Kinetics of the Carbonyl process for the Refining of Nickel

The discovery by Langer and Mond in 1889 of the reaction of carbon monoxide at atmospheric pressure with active nickel at 315-353K to form gaseous Ni(C0)4 and its ready reversibility at higher temperatures paved the way for the development of the carbonyl refining process for nickel. Subsequently, to improve the kinetics, a high pressure process was developed for the carbonyl refining of Ni at somewhat higher temperatures. The carbonyl process makes it possible to produce nickel of very high purity and extr-emely fine size. This paper describes the thermodynamics and kinetics of the formation of nickel carbonyl from pure nickel and its alloys, its vapour phase transport and its decomposition.. The thermodynamic analysis includes const-ruction of Ellingham diagrams, pressure-temperature rela-tionships, partial pressuretemperature relationships and productivity function-temperature-pressure relationships for the various carbonyls. The kinetics of Ni(CO)4 forma-tion and decomposition has been analyzed based on the information available in the literature

Assessment of Marine Weather forecasts over the Indian Sector of Southern Ocean

The Southern Ocean (SO) is one of the important regions where significant processes and feedbacks of the Earth\u27s climate take place. Expeditions to the SO provide useful data for improving global weather/climate simulations and understanding many processes. Some of the uncertainties in these weather/climate models arise during the first few days of simulation/forecast and do not grow much further. NCMRWF issued real-time five day weather forecasts of mean sea level pressure, surface winds, winds at 500 hPa & 850 hPa and rainfall, daily to NCAOR to provide guidance for their expedition to Indian sector of SO during the austral summer of 2014–2015. Evaluation of the skill of these forecasts indicates possible error growth in the atmospheric model at shorter time scales. The error growth is assessed using the model analysis/reanalysis, satellite data and observations made during the expedition. The observed variability of sub-seasonal rainfall associated with mid-latitude systems is seen to exhibit eastward propagations and are well reproduced in the model forecasts. All cyclonic disturbances including the sub-polar lows and tropical cyclones that occurred during this period were well captured in the model forecasts. Overall, this model performs reasonably well over the Indian sector of the SO in medium range time scale

A multichannel pulse amplitude analyser

This article does not have an abstract

Effect of Trends on Detrended Fluctuation Analysis

Detrended fluctuation analysis (DFA) is a scaling analysis method used to estimate long-range power-law correlation exponents in noisy signals. Many noisy signals in real systems display trends, so that the scaling results obtained from the DFA method become difficult to analyze. We systematically study the effects of three types of trends -- linear, periodic, and power-law trends, and offer examples where these trends are likely to occur in real data. We compare the difference between the scaling results for artificially generated correlated noise and correlated noise with a trend, and study how trends lead to the appearance of crossovers in the scaling behavior. We find that crossovers result from the competition between the scaling of the noise and the ``apparent'' scaling of the trend. We study how the characteristics of these crossovers depend on (i) the slope of the linear trend; (ii) the amplitude and period of the periodic trend; (iii) the amplitude and power of the power-law trend and (iv) the length as well as the correlation properties of the noise. Surprisingly, we find that the crossovers in the scaling of noisy signals with trends also follow scaling laws -- i.e. long-range power-law dependence of the position of the crossover on the parameters of the trends. We show that the DFA result of noise with a trend can be exactly determined by the superposition of the separate results of the DFA on the noise and on the trend, assuming that the noise and the trend are not correlated. If this superposition rule is not followed, this is an indication that the noise and the superimposed trend are not independent, so that removing the trend could lead to changes in the correlation properties of the noise.Comment: 20 pages, 16 figure

Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient

Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusions: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.Comment: Keywords:EEG, epilepsy, Correlation dimension, Surrogate analysis, Hurst exponent. 9 page

A stochastic model for heart rate fluctuations

Normal human heart rate shows complex fluctuations in time, which is natural, since heart rate is controlled by a large number of different feedback control loops. These unpredictable fluctuations have been shown to display fractal dynamics, long-term correlations, and 1/f noise. These characterizations are statistical and they have been widely studied and used, but much less is known about the detailed time evolution (dynamics) of the heart rate control mechanism. Here we show that a simple one-dimensional Langevin-type stochastic difference equation can accurately model the heart rate fluctuations in a time scale from minutes to hours. The model consists of a deterministic nonlinear part and a stochastic part typical to Gaussian noise, and both parts can be directly determined from the measured heart rate data. Studies of 27 healthy subjects reveal that in most cases the deterministic part has a form typically seen in bistable systems: there are two stable fixed points and one unstable one.Comment: 8 pages in PDF, Revtex style. Added more dat

Learning Multimodal Temporal Representation for Dubbing Detection in Broadcast Media

Person discovery in the absence of prior identity knowledge requires accurate association of visual and auditory cues. In broadcast data, multimodal analysis faces additional challenges due to narrated voices over muted scenes or dubbing in different languages. To address these challenges, we define and analyze the problem of dubbing detection in broadcast data, which has not been explored before. We propose a method to represent the temporal relationship between the auditory and visual streams. This method consists of canonical correlation analysis to learn a joint multimodal space, and long short term memory (LSTM) networks to model cross-modality temporal dependencies. Our contributions also include the introduction of a newly acquired dataset of face-speech segments from TV data, which we have made publicly available. The proposed method achieves promising performance on this real world dataset as compared to several baselines