Imperilled waterscapes

This paper examines the historical waterscapes of Bengaluru, now imperilled by development. Earlier a garden city, the agrarian landscape of Bengaluru was formerly supplied with water from an interconnected lake system. This system has since been fragmented due to urbanization and changes in land cover, impacting local institutions and livelihoods dependent on the lakes. In this paper, we use the case of the city’s largest lake, Bellandur, to demonstrate the transformation of the waterscape from an open semi-arid landscape pre-dating the city into an agrarian water-dependent landscape characterized by flows of water in pre-colonial and colonial Bengaluru, and finally into a concretized landscape and the individualization of lakes in the “modern” city. Claims to and associations with the lake ecosystem have altered through changing hydrological, institutional, and social relations, leading to shifts in imaginations of the lake as well

Initial wave packets and the various power-law decreases of scattered wave packets at long times

The long time behavior of scattered wave packets $\psi (x,t)$ from a finite-range potential is investigated, by assuming $\psi (x,t)$ to be initially located outside the potential. It is then shown that $\psi (x,t)$ can asymptotically decrease in the various power laws at long time, according to its initial characteristics at small momentum. As an application, we consider the square-barrier potential system and demonstrate that $\psi (x,t)$ exhibits the asymptotic behavior $t^{-3/2}$, while another behavior like $t^{-5/2}$ can also appear for another $\psi (x,t)$.Comment: 5 pages, 1 figur

A note on perfect scalar fields

We derive a condition on the Lagrangian density describing a generic, single, non-canonical scalar field, by demanding that the intrinsic, non-adiabatic pressure perturbation associated with the scalar field vanishes identically. Based on the analogy with perfect fluids, we refer to such fields as perfect scalar fields. It is common knowledge that models that depend only on the kinetic energy of the scalar field (often referred to as pure kinetic models) possess no non-adiabatic pressure perturbation. While we are able to construct models that seemingly depend on the scalar field and also do not contain any non-adiabatic pressure perturbation, we find that all such models that we construct allow a redefinition of the field under which they reduce to pure kinetic models. We show that, if a perfect scalar field drives inflation, then, in such situations, the first slow roll parameter will always be a monotonically decreasing function of time. We point out that this behavior implies that these scalar fields can not lead to features in the inflationary, scalar perturbation spectrum.Comment: v1: 11 pages; v2: 11 pages, minor changes, journal versio

Thinking beyond fairy lights and fountains: lessons from the waterscape of Bengaluru

In March 2013, I was part of a group of researchers participating in a discussion with a nodal agency responsible for maintaining one of periurban Bengaluru's information technology (IT) hubs. The objective was to seek academic collaboration to understand the ecology of two lakes earmarked for rejuvenation and evolve a plan to clear „weeds‟. We quickly found out „weeds‟ included forms of vegetation (like reeds) that were not pleasing to the human eye although they were important resources feeding informal economies integral to this landscape. The discussion included propositions to make these lakes „attractive‟– musical fountains, lights, jogging tracks … – in essence, everything that would fuel aspirations of the urban middle and upper classes but would exclude people whose lives and livelihoods were directly supported by those water bodies – farmers, fishermen, commercial launderers, urban foragers, and livestock owners. For them, the lake was not an embodiment of beauty and pleasure, but something sustaining their ways of life

Sonoluminescence as Quantum Vaccum Radiation

We argue that the available experimental data is not compatible with models of sonoluminescence which invoke dynamical properties of the interface without regard to the compositional properties of the trapped gas inside the bubble.Comment: 2 pages,Revtex,No figures,Submitted to PRL(comments

Free initial wave packets and the long-time behavior of the survival and nonescape probabilities

The behavior of both the survival S(t) and nonescape P(t) probabilities at long times for the one-dimensional free particle system is shown to be closely connected to that of the initial wave packet at small momentum. We prove that both S(t) and P(t) asymptotically exhibit the same power-law decrease at long times, when the initial wave packet in momentum representation behaves as O(1) or O(k) at small momentum. On the other hand, if the integer m becomes greater than 1, S(t) and P(t) decrease in different power-laws at long times.Comment: 4 pages, 3 figures, Title and organization changed, however the results not changed, To appear in Phys. Rev.

Scalar Field Dark Energy Perturbations and their Scale Dependence

We estimate the amplitude of perturbation in dark energy at different length scales for a quintessence model with an exponential potential. It is shown that on length scales much smaller than hubble radius, perturbation in dark energy is negligible in comparison to that in in dark matter. However, on scales comparable to the hubble radius ($\lambda_{p}>1000\mathrm{Mpc}$) the perturbation in dark energy in general cannot be neglected. As compared to the $\Lambda$CDM model, large scale matter power spectrum is suppressed in a generic quintessence dark energy model. We show that on scales $\lambda_{p} < 1000\mathrm{Mpc}$, this suppression is primarily due to different background evolution compared to $\Lambda$CDM model. However, on much larger scales perturbation in dark energy can effect matter power spectrum significantly. Hence this analysis can act as a discriminator between $\Lambda$CDM model and other generic dark energy models with $w_{de} \neq -1$.Comment: 12 pages, 13 figures, added new section, accepted for publication in Phys. Rev.

Noise-robust method for image segmentation

Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial linage context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods

Development of health parameter model for risk prediction of CVD using SVM

Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD). The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model