Jawaharlal Nehru University

About: Jawaharlal Nehru University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Politics. The organization has 6082 authors who have published 13455 publications receiving 245407 citations. The organization is also known as: JNU.

Exponentially Enhanced Quantum Metrology

Abstract: We show that when a suitable entanglement-generating unitary operator depending on a parameter is applied on $N$ qubits in parallel, a precision of the order of ${2}^{\ensuremath{-}N}$ in estimating the parameter may be achieved. This exponentially improves the precision achievable in classical and in quantum nonentangling strategies.

A combination of artificial neural network and random walk models for financial time series forecasting

Abstract: Properly comprehending and modeling the dynamics of financial data has indispensable practical importance. The prime goal of a financial time series model is to provide reliable future forecasts which are crucial for investment planning, fiscal risk hedging, governmental policy making, etc. These time series often exhibit notoriously haphazard movements which make the task of modeling and forecasting extremely difficult. As per the research evidence, the random walk (RW) is so far the best linear model for forecasting financial data. Artificial neural network (ANN) is another promising alternative with the unique capability of nonlinear self-adaptive modeling. Numerous comparisons of the performances of RW and ANN models have also been carried out in the literature with mixed conclusions. In this paper, we propose a combination methodology which attempts to benefit from the strengths of both RW and ANN models. In our proposed approach, the linear part of a financial dataset is processed through the RW model, and the remaining nonlinear residuals are processed using an ensemble of feedforward ANN (FANN) and Elman ANN (EANN) models. The forecasting ability of the proposed scheme is examined on four real-world financial time series in terms of three popular error statistics. The obtained results clearly demonstrate that our combination method achieves reasonably better forecasting accuracies than each of RW, FANN and EANN models in isolation for all four financial time series.

Regulation of metabolic pathways in liver and kidney during experimental diabetes: Effects of antidiabetic compounds.

Abstract: Diabetes has been classified as a disease of glucose overproduction by tissues, mainly liver and glucose underutilization by insulin requiring tissues like liver, adipose and muscle due to lack of insulin. There is, however, glucose over utilization in tissues not dependent on insulin for glucose transport like kidney, nerve and brain. There are serious complications due to this excess glucose in these tissues and their reversal is important for a good metabolic control and normalisation of other parameters. Insulin, trace metals and some plant extracts have been used to see the reversal effects of the complications of diabetes in liver and kidney in experimental diabetes. Almost complete reversal of the metabolic changes has been achieved in the activities of key enzymes of metabolic pathways in liver and kidney and an effective glucose control has been achieved suggesting a combination of therapies in the treatment of metabolic disturbance of the diabetic state.