The scratch hardness and friction of a soft rigid-plastic solid

The paper describes an experimental and analytical study of the normal and scratch hardnesses of a model soft rigid-plastic solid. The material known as ‘Plasticine’, a mixture of dry particles and a mineral oil, has been deformed with a range of rigid conical indentors with included angles of between 30° and 170°. The sliding velocity dependence of the computed scratch hardness and friction has been examined in the velocity range 0.19 mm/s to 7.3 m/s. Data are also described for the time dependence of the normal hardness and also the estimated rate dependence of the intrinsic flow stress. The latter values were estimated from data obtained during the upsetting of right cylinders. Three major conclusions are drawn from these data and the associated analysis. (1) A first-order account of the scratching force may be provided by adopting a model which sums the computed plastic deformation and interfacial sliding contributions to the total sliding work. This is tantamount to the adoption of the two-term non-interacting model of friction. (2) For this system during sliding, at high sliding velocities at least, the interface shear stress which defines the boundary condition is not directly related to the bulk shear stress. The interface rheological characteristics indicate an appreciable dependence on the imposed strain or strain rate. In particular, the relative contributions of the slip and stick boundary conditions appear to be a function of the imposed sliding velocity. (3) The computed normal and scratch hardness values are not simply interrelated primarily because of the evolving boundary conditions which appear to exist in the scratching experiments.

Hot-working characteristics of Zircaloy-2 in the temperature range of 650–950°C

The hot-working characteristics of Zircaloy-2 have been studied in the temperature range of 650 to 950°C and in the strain-rate range of 10−3 to 102 s−1 using power dissipation maps which describe the variation of the efficiency of power dissipation, η = 2m /(m + 1) where m is the strain-rate sensitivity of flow stress. The individual domains exhibited by the map have been interpreted and validated by detailed metallographic investigations. Dynamic recrystallization occurs in the temperature range of 730 to 830°C and in the strain-rate range of 10−2 to 2 s−1. The peak efficiency occurs at 800°C and 0.1 s−1 which may be considered as the optimum hot-working parameters in the α-phase field of Zircaloy-2. Superplastic behaviour, characterized by a high efficiency of power dissipation is observed at temperatures greater than 860°C and at strain rates lower than 10−2 s−1. When deformed at 650°C and 10−3 s−1, the primary restoration mechanism is dynamic recovery, while at rates higher than 2s−1, the material exhibits microstructural instabilities in the form of localized shear bands.

A Study of Long-range Correlations in Schizophrenia EEG using Detrended Fluctuation Analysis

In this paper, we have studied electroencephalogram (EEG) activity of schizophrenia patients, in resting eyes closed condition, with detrended fluctuation analysis (DFA). The DFA gives information about scaling and long-range correlations in time series. We computed DFA exponents from 30 scalp locations of 18 male neuroleptic-naIve, recent-onset schizophrenia (NRS) subjects and 15 healthy male control subjects. Our results have shown two scaling regions in all the scalp locations in all the subjects, with different slopes, corresponding to two scaling exponents. No significant differences between the groups were found with first scaling exponent (short-range). However, the second scaling exponent (long-range) were significantly lower in control subjects at all scalp locations (p<0.05, Kruskal-Wallis test). These findings suggest that the long-range scaling behavior of EEG is sensitive to schizophrenia, and this may provide an additional insight into the brain dysfunction in schizophrenia.

A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.

Spin-Selective Correlation Experiment for Measurement of Long-Range J Couplings and for Assignment of (R/S) Enantiomers from the Residual Dipolar Couplings and DFT

We report the C-HETSERF experiment for determination of long- and short-range homo- and heteronuclear scalar couplings ((n)J(HH) and (n)J(XH), n >= 1) of organic molecules with a low sensitivity dilute heteronucleus in natural abundance. The method finds significant advantage in measurement of relative signs of long-range heteronuclear total couplings in chiral organic liquid crystal. The advantage of the method is demonstrated for the measurement of residual dipolar couplings (RDCs) in enantiomers oriented in the chiral liquid crystal with a focus to unambiguously assign R/S designation in a 2D spectrum. The alignment tensor calculated from the experimental RDCs and with the computed structures of enantiomers obtained by DFT calculations provides the size of the back-calculated RDCs. Smaller root-mean-square deviations (rmsd) between experimental and calculated RDCs indicate better agreement with the input structure and its correct designation of the stereogenic center.