India in Space: Factors Shaping the Indian Trajectory

By launching its space probe to the Moon, Chandrayaan-1, on 22 October 2008, India joined the United States (U.S.), Japan, Europe, Russia, and China in this accomplishment. The principal goal of the probe was to conduct mapping of the lunar surface, and among the scientific payloads it carried two were from the United States and three from the European Space Agency (ESA). This was a unique mission as it was an attempt to map highresolution, 3-D topography of entire Moon, get mineral composition of surface, and investigate the availability of water and Helium-3. Chandrayaan-1 operated until August 2009, coming to an abrupt end after 312 days, as opposed to the intended two years. Despite the setback, Chandrayaan-1 did achieve 95 percent of its planned objectives, and made the significant discovery of water ice molecules on the lunar surface

Enhancement of reactivity and increased usage of low lime class -F-fly ash-possible avenues

The low lime class-F fly ash available in the country shows high degree of variability in the quality, higher content of crystallites , lower glassy phase which accounts for lower of usage in cement and concrete . The time reactivity test used for assessing the pozzolanicity of fly ash did not always correlate with its observed reactivity in Blended cements . An alternative rapid alkali reactivity rest developed at the authors ' laboratory is illustrated in the paper. The paper also discusses the possibility of increasing the reactivity of fly ash and effect of the reactive fly ash on characteristics of PPC and concrete. The paper further discusses other avenues of fly ash utilisation, which could be categorised as low, medium and high value applications. One of such applications developed at the authors ' laboratory that merits special interest, is the Hydrogel process of clinkerisation , which has a potential for utilisation of 20-30% fly ash as a raw material in cement manufacture

Towards the prediction of supersonic jet noise predictions using a unified asymptotic approximation for the adjoint vector Green's function

In this paper we continue efforts aimed at modeling jet noise using self-consistent analytical approaches within the generalized acoustic analogy (GAA) formulation. The GAA equations show that the far-field pressure fluctuation is given by a convolution product between a propagator tensor that depends on the (true) non-parallel jet mean flow and a generalized fluctuating stress tensor that is a stationary random function of time and includes the usual fluctuating Reynolds’ stress tensor as well as enthalpy fluctuation components. Here, we focus on approximating the propagator tensor by determining an appropriate asymptotic solution to the adjoint vector Green’s function that it depends on by using an asymptotic approach at all frequencies of interest for jet noise prediction. The Green’s function is then rationally approximated by a composite formula in which the GSA (Goldstein-Sescu-Afsar, J. Fluid Mech., vol. 695, pp. 199-234, 2012) non-parallel flow Green’s function asymptotic solution is used at low frequencies and the O(1) frequency parallel flow Green’s function is used for all frequencies thereafter. The former solution uses the fact that non-parallelism will have a leading order effect on the Green’s function everywhere in the jet under a distinguished scaling in which the jet spread rate is of the same order as the Strouhal number for a slowly-diverging mean flow expansion. Since this solution, however, is expected to apply up to the peak frequency, the latter O(1) frequency Green’s function in a parallel flow must be used at frequencies thereafter. We investigate the predictive capability of the composite Green’s function for the prediction of supersonic axi-symmetric round jets at fixed jet Mach number of 1.5 and two different temperature ratios (isothermal & heated) using Large-eddy simulation data. Our results show that, in the first instance, excellent jet noise predictions are obtained using the non-parallel flow asymptotic approach, remarkably, up to a Strouhal number of 0.5. This is true for both heated and un-heated jets. Furthermore, we develop the analytical approach required to extend this solution by appropriate asymptotic approximation to O(1) frequencies

Molecular tailoring of thermoreversible copolymer gels: some new mechanistic insights

We earlier reported the role of hydrophobic and hydrogen bonding interactions on the transition temperatures of thermoreversible copolymer gels. We show here that the chemical structure of the hydrophobe and its concentration determine the transition temperatures [lower critical solution temperature (LCST)] and the heat of transition of new hydrophobically modified poly(N-isopropyl acrylamide) [PNIPAm] copolymer gels. The gels, prepared by copolymerizing NIPAm monomer with hydrophobic comonomers containing increasing lengths of alkyl side groups and a terminal carboxyl acid group, showed lower LCST and lower heat of transition when compared to pure PNIPAm gel. The experimental results were also compared with theoretical calculations based on a lattice-fluid-hydrogen-bond [LFHB] model. We show experimentally and theoretically that a linear correlation exists between the transition temperature and length of the hydrophobic alkyl side group. Also, in apparent contradiction to previous work, we found a reduction in the heat of transition with increasing hydrophobicity. We propose that the presence of the terminal carboxyl acid group on the hydrophobic side chain of the comonomer prevents the association of water molecules around the hydrophobe, thereby causing a reduction in the heat of transition. The LFHB model supports this argument

Cross-View Action Recognition from Temporal Self-Similarities

This paper concerns recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such measures across views. Building upon this key observation we develop an action descriptor that captures the structure of temporal similarities and dissimilarities within an action sequence. Despite this descriptor not being strictly view-invariant, we provide intuition and experimental validation demonstrating the high stability of self-similarities under view changes. Self-similarity descriptors are also shown stable under action variations within a class as well as discriminative for action recognition. Interestingly, self-similarities computed from different image features possess similar properties and can be used in a complementary fashion. Our method is simple and requires neither structure recovery nor multi-view correspondence estimation. Instead, it relies on weak geometric cues captured by self-similarities and combines them with machine learning for efficient cross-view action recognition. The method is validated on three public datasets, it has similar or superior performance compared to related methods and it performs well even in extreme conditions such as when recognizing actions from top views while using side views for training only