Quantum Coherence, Coherent Information and Information Gain in Quantum Measurement

A measurement is deemed successful, if one can maximize the information gain by the measurement apparatus. Here, we ask if quantum coherence of the system imposes a limitation on the information gain during quantum measurement. First, we argue that the information gain in a quantum measurement is nothing but the coherent information or the distinct quantum information that one can send from the system to apparatus. We prove that the maximum information gain from a pure state, using a mixed apparatus is upper bounded by the initial coherence of the system. Further, we illustrate the measurement scenario in the presence of environment. We argue that the information gain is upper bounded by the entropy exchange between the system and the apparatus. Also, to maximize the information gain, both the initial coherence of the apparatus, and the final entanglement between the system and apparatus should be maximum. Moreover, we find that for a fixed amount of coherence in the final apparatus state the more robust apparatus is, the more will be the information gain.Comment: 6 Pages, Comments are welcom

Calibration for measurements of droplet size distributions of ground based clouds - a laboratory investigation

Water droplets of varying sizes, released through an atomizer, were collected on glass slides coated with uniform layers of magnesium oxide or carbon soot and silicone oil. Assuming that the droplets retain their original shapes in the oil film, calibrations were obtained for their spreading on oxide and soot layers of known thickness. The calibrations have been further applied to evaluate droplet size distributions of ground-based clouds

Weathered basalt application for management of Vertisols: A traditional knowledge of groundnut (Arachis hypogaea) growers of Gujarat, India

793-799Gujarat tops with 27.87% of total groundnut production. The basaltic shrink-swell soils are generally evaluated as unsuitable for groundnut production in Saurtashtra region of Gujarat. They have untapped source of traditional knowledge for managing heavy shrink-swell soils of basaltic terrain. Groundnut growers of the region are applying weathered basalt (WB, Vēraḍēḍa bēsālṭanuṁ in Gujarati) in pure form which is naturally available or sometimes treated by mixing the farmyard manures (FYM) or groundnut husk, and/or fortified with nitrogenous and phosphatic fertilizers. A study was planned to find out the reason for higher production of groundnut with the application of WB before sowing the seed. For this study the farmers were divided in to 05 groups on the basis of forms and combinations of WB application in groundnut fields. The participatory approaches and personal interviews were combined to collect the data from 25 farmers of each group. After interviewing the farmers, we came to know that this practice is being followed since 40 years. The study revealed that the practice significantly reduces the contracting and expanding phenomenon in black Vertisols and improve physico-chemical properties of soils like hydraulic characteristics (infiltration, permeability, percolation and drainage), aeration, bulk density, porosity, thermal conductivity and also improve availability of secondary (Ca, Mg & S) and micronutrients (Fe, Mn, Zn & Cu). The present study forms the basis for upgrading the traditional management packages for sustainable groundnut production in black soil region of India

Markers of small cell lung cancer

Lung cancer is the number one cause of cancer death; however, no specific serum biomarker is available till date for detection of early lung cancer. Despite good initial response to chemotherapy, small-cell lung cancer (SCLC) has a poor prognosis. Therefore, it is important to identify molecular markers that might influence survival and may serve as potential therapeutic targets. The review aims to summarize the current knowledge of serum biomarkers in SCLC to improve diagnostic efficiency in the detection of tumor progression in lung cancer. The current knowledge on the known serum cytokines and tumor biomarkers of SCLC is emphasized. Recent findings in the search for novel diagnostic and therapeutic molecular markers using the emerging genomic technology for detecting lung cancer are also described. It is believed that implementing these new research techniques will facilitate and improve early detection, prognostication and better treatment of SCLC

Subsea cable tracking in an uncertain environment using particle filters

Localization of subsea cables is a demanding and challenging task. Among the few methods reported in the literature, magnetic field detection is the most promising one, as the cable does not require to be seen visually. Magnetic noise and a quick attenuation of the magnetic field propagating in sea water often make available methods unreliable. The authors propose a novel method of using particle filters for estimating the position of a subsea cable in a highly uncertain environment. The method was tested on data collected from a buried cable in the Baltic Sea, Denmark and shown to have a close approximation to the true location of the subsea cable. The method can be used to localize a subsea cable in an offshore noisy and uncertain environment and provides an inexpensive alternative to the use of a diver or a remotely operated platform

Application of artificial neural networks to weighted interval Kalman filtering

The interval Kalman filter is a variant of the traditional Kalman filter for systems with bounded parametric uncertainty. For such systems, modelled in terms of intervals, the interval Kalman filter provides estimates of the system state also in the form of intervals, guaranteed to contain the Kalman filter estimates of all point-valued systems contained in the interval model. However, for practical purposes, a single, point-valued estimate of the system state is often required. This point value can be seen as a weighted average of the interval bounds provided by the interval Kalman filter. This article proposes a methodology based on the application of artificial neural networks by which an adequate weight can be computed at each time step, whereby the weighted average of the interval bounds approximates the optimal estimate or estimate which would be obtained using a Kalman filter if no parametric uncertainty was present in the system model, even when this is not the case. The practical applicability and robustness of the method are demonstrated through its application to the navigation of an uninhabited surface vehicle. © IMechE 2014