Cosmological consequences in the framework of generalized Rastall theory of gravity

The paper deals with generalized Rastall theory of gravity and its cosmological consequences in the background of homogeneous and isotropic flat FLRW model with perfect fluid as the matter context. The model shows a non singular era (emergent scenario) at the early phase of expansion for a particular choice of the Rastall parameter. Also the model finds to be equivalent to the particle creation mechanism in Einstein gravity in the framework of non-equilibrium thermodynamics. Universal thermodynamics is briefly presented and it is found that the entropy function in Rastall theory is the usual Bekenstein entropy and there is no correction to it. Finally, a complete cosmic history starting from inflation to late time acceleration is presented for suitable choices of the Rastall parameter

Power-law corrections to black-hole entropy via entanglement

We consider the entanglement between quantum field degrees of freedom inside and outside the horizon as a plausible source of black-hole entropy. We examine possible deviations of black hole entropy from area proportionality. We show that while the area law holds when the field is in its ground state, a correction term proportional to a fractional power of area results when the field is in a superposition of ground and excited states. We compare our results with the other approaches in the literature.Comment: 10 pages, 5 figures, to appear in the Proceedings of "BH2, Dynamics and Thermodynamics of Blackholes and Naked Singularities", May 10-12 2007, Milano, Italy; conference website: http://www.mate.polimi.it/bh2

Pulsed Photonic Curing of Conformal Printed Electronics

As next-generation electronic products emerge, there is a need to create more electronic functionality in compact spaces. One of the techniques to achieve this is by integrating electronic circuitry on mechanical stress bearing parts of electro-mechanical products. Direct-write printing processes like inkjet printing and aerosol jet printing can be used to print conductive inks on conformal surfaces of mechanical components. Advanced curing/sintering processes such as pulsed photonic curing can be used to cure/sinter printed inks to produce conductive traces. However, the use of photonic curing on conformal surfaces introduces two sources of variability into the process, which are the distance and slope between the flash lamps and the conformal substrate. This research studies the effects that distance and slope between the flash lamps and substrate have on the characteristics of the photonically cured material. Screen printed samples of copper nanoparticle ink on paper substrates were photonically cured at various distances and slope settings in a Novacentrix Pusleforge 3300 machine. Analysis of the experimental data reveals that there is significant decrease in the conductivity of the cured copper ink with increase in both the distance and slope between the flash lamps and the substrate. The lowering of conductivity of the coupons with increase in distance was correlated to the reduction in the intensity of pulsed light with distance from the source. Similarly, the lowering of conductivity of the coupons with increase in slope was correlated to the reduction in the intensity of pulsed light with increase in angle between the incident light and the surface normal. A spectrophotometer was used to correlate the lowering of the conductivity of the printed coupon to the reduction in the amount of light absorbed by the coupon surface with increase in the slope from the flash lamps. This research highlights that distance and slope variations are important considerations to achieve uniform electrical properties in conformal printed electronics undergoing photonic curing

Blind Change Point Detection And Regime Segmentation Using Gaussian Process Regression

Time-series analysis is used heavily in modeling and forecasting weather, economics, medical data as well as in various other fields. Change point detection (CPD) means finding abrupt changes in the time-series when the statistical property of a certain part of it starts to differ. CPD has attracted a lot of attention in the artificial intelligence, machine learning and data mining communities. In this thesis, a novel CPD algorithm is introduced for segmenting multivariate time-series data. The proposed algorithm is a general pipeline to process any high dimensional multivariate time-series data using nonlinear non-parametric dynamic system. It consists of manifold learning technique for dimensionality reduction, Gaussian process regression to model the non-linear dynamics of the data and predict the next possible time-step, as well as outlier detection based on Mahalanobis distance to determine the change points. The performance of the new CPD algorithm is assessed on synthetic as well as real-world data for validation. The pipeline is used on federal reserve economic data (FRED) to detect recession. Finally, functional magnetic resonance imaging (fMRI) data of larval zebrafish is used to segment regions of homogeneous brain activity