Role of ambient air on photoluminescence and electrical conductivity of assembly of ZnO Nanoparticles

Effect of ambient gases on photoluminescence (PL) and electrical conductivity of films prepared using ZnO nanoparticles (NPs) have been investigated. It is observed that NPs of size below 20 nm kept inside a chamber exhibit complete reduction in their visible PL when oxygen partial pressure of the surrounding gases is decreased by evacuation. However the visible PL from ZnO NPs is insensitive to other major gases present in the ambient air. The rate of change of PL intensity with pressure is inversely proportional to the ambient air pressure and increases when particle size decreases due to the enhanced surface to volume ratio. On the other hand an assembly of ZnO NPs behaves as a complete insulator in the presence of dry air and its major components like N2, O2 and CO2. Electrical conduction having resistivity ~102 - 103 {\Omega}m is observed in the presence of humid air. The depletion layer formed at the NP surface after acquiring donor electrons of ZnO by the adsorbed oxygen, has been found to control the visible PL and increases the contact potential barrier between the NPs which in turn enhances the resistance of the film.Comment: arXiv admin note: significant text overlap with arXiv:1008.249

Towards Full Aircraft Airframe Noise Prediction: Detached Eddy Simulations

Results from a computational study on the aeroacoustic characteristics of an 18%-scale, semi-span Gulf-stream aircraft model are presented in this paper. NASA's FUN3D unstructured compressible Navier-Stokes solver was used to perform steady and unsteady simulations of the flow field associated with this high-fidelity aircraft model. Solutions were obtained for free-air at a Mach number of 0.2 with the flap deflected at 39 deg, with the main gear off and on (the two baseline configurations). Initially, the study focused on accurately predicting the prominent noise sources at both flap tips for the baseline configuration with deployed flap only. Building upon the experience gained from this initial effort, subsequent work involved the full landing configuration with both flap and main landing gear deployed. For the unsteady computations, we capitalized on the Detached Eddy Simulation capability of FUN3D to capture the complex time-dependent flow features associated with the flap and main gear. To resolve the noise sources over a broad frequency range, the tailored grid was very dense near the flap inboard and outboard tips and the region surrounding the gear. Extensive comparison of the computed steady and unsteady surface pressures with wind tunnel measurements showed good agreement for the global aerodynamic characteristics and the local flow field at the flap inboard tip. However, the computed pressure coefficients indicated that a zone of separated flow that forms in the vicinity of the outboard tip is larger in extent along the flap span and chord than measurements suggest. Computed farfield acoustic characteristics from a FW-H integral approach that used the simulated pressures on the model solid surface were in excellent agreement with corresponding measurements

The Incremental Cooperative Design of Preventive Healthcare Networks

This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person

Fluid flow in the osteocyte mechanical environment : a fluid-structure interaction approach

Osteocytes are believed to be the primary sensor of mechanical stimuli in bone, which orchestrate osteoblasts and osteoclasts to adapt bone structure and composition to meet physiological loading demands. Experimental studies to quantify the mechanical environment surrounding bone cells are challenging, and as such, computational and theoretical approaches have modelled either the solid or fluid environment of osteocytes to predict how these cells are stimulated in vivo. Osteocytes are an elastic cellular structure that deforms in response to the external fluid flow imposed by mechanical loading. This represents a most challenging multi-physics problem in which fluid and solid domains interact, and as such, no previous study has accounted for this complex behaviour. The objective of this study is to employ fluid–structure interaction (FSI) modelling to investigate the complex mechanical environment of osteocytes in vivo. Fluorescent staining of osteocytes was performed in order to visualise their native environment and develop geometrically accurate models of the osteocyte in vivo. By simulating loading levels representative of vigorous physiological activity (3,000με compression and 300 Pa pressure gradient), we predict average interstitial fluid velocities (∼60.5μ m/s ) and average maximum shear stresses (∼11 Pa ) surrounding osteocytes in vivo. Interestingly, these values occur in the canaliculi around the osteocyte cell processes and are within the range of stimuli known to stimulate osteogenic responses by osteoblastic cells in vitro. Significantly our results suggest that the greatest mechanical stimulation of the osteocyte occurs in the cell processes, which, cell culture studies have indicated, is the most mechanosensitive area of the cell. These are the first computational FSI models to simulate the complex multi-physics mechanical environment of osteocyte in vivo and provide a deeper understanding of bone mechanobiology

Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid‐dynamics model of the pulmonary circulation

The past few decades have witnessed an explosive synergy between physics and the life sciences. In particular, physical modelling in medicine and physiology is a topical research area. The present work focuses on parameter inference and uncertainty quantification in a 1D fluid‐dynamics model for quantitative physiology: the pulmonary blood circulation. The practical challenge is the estimation of the patient‐specific biophysical model parameters, which cannot be measured directly. In principle this can be achieved based on a comparison between measured and predicted data. However, predicting data requires solving a system of partial differential equations (PDEs), which usually have no closed‐form solution, and repeated numerical integrations as part of an adaptive estimation procedure are computationally expensive. In the present article, we demonstrate how fast parameter estimation combined with sound uncertainty quantification can be achieved by a combination of statistical emulation and Markov chain Monte Carlo (MCMC) sampling. We compare a range of state‐of‐the‐art MCMC algorithms and emulation strategies, and assess their performance in terms of their accuracy and computational efficiency. The long‐term goal is to develop a method for reliable disease prognostication in real time, and our work is an important step towards an automatic clinical decision support system