Linear estimation of flux sensitivity to uncertainty in porous media

We derive an integral expression for the flux of a single-phase fluid through a porous medium with prescribed boundary conditions. Taking variations with respect to the parameters of a given permeability model yields an integral expression for the sensitivity of the flux. We then extend the method to consider linear changes in permeability. This yields a linearised flux expression which is independent of changes in the pressure field that result from the changes in the permeability. For demonstration purposes, we first consider an idealised layered porous medium with a point source and point sink. We show how the effects of changes in permeability are affected by the position of the source and sink relative to the layered structure as well as the layer height and orientation of the layered structure. The results demonstrate that, even in a simple porous system, flux estimates are sensitive to the way in which the permeability is represented. We derive relationships between the statistical moments of the flux and of the permeability parameters which are modelled as random variables. This allows us to estimate the number of permeability parameters that should be varied in a fully nonlinear calculation to determine the variance of the flux. We demonstrate application of the methods to permeability fields generated through fast Fourier transform and kriging methods. We show that the linear estimates for the variability in flux show good agreement with fully nonlinear calculations for sufficiently small standard deviations in the underlying permeability.The support of an Industrial CASE studentship from EPSRC and BP for A. J. Evans entitled "Optimal Reservoir Modelling" is gratefully acknowledged.This is the accepted manuscript. The final version is available from CUP at http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9603056&fileId=S0022112015001020

Sets of Priors Reflecting Prior-Data Conflict and Agreement

In Bayesian statistics, the choice of prior distribution is often debatable, especially if prior knowledge is limited or data are scarce. In imprecise probability, sets of priors are used to accurately model and reflect prior knowledge. This has the advantage that prior-data conflict sensitivity can be modelled: Ranges of posterior inferences should be larger when prior and data are in conflict. We propose a new method for generating prior sets which, in addition to prior-data conflict sensitivity, allows to reflect strong prior-data agreement by decreased posterior imprecision.Comment: 12 pages, 6 figures, In: Paulo Joao Carvalho et al. (eds.), IPMU 2016: Proceedings of the 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Eindhoven, The Netherland

Runtime Distributions and Criteria for Restarts

Randomized algorithms sometimes employ a restart strategy. After a certain number of steps, the current computation is aborted and restarted with a new, independent random seed. In some cases, this results in an improved overall expected runtime. This work introduces properties of the underlying runtime distribution which determine whether restarts are advantageous. The most commonly used probability distributions admit the use of a scale and a location parameter. Location parameters shift the density function to the right, while scale parameters affect the spread of the distribution. It is shown that for all distributions scale parameters do not influence the usefulness of restarts and that location parameters only have a limited influence. This result simplifies the analysis of the usefulness of restarts. The most important runtime probability distributions are the log-normal, the Weibull, and the Pareto distribution. In this work, these distributions are analyzed for the usefulness of restarts. Secondly, a condition for the optimal restart time (if it exists) is provided. The log-normal, the Weibull, and the generalized Pareto distribution are analyzed in this respect. Moreover, it is shown that the optimal restart time is also not influenced by scale parameters and that the influence of location parameters is only linear

Reversible metallisation of soft UV patterned substrates

Soft UV (365 nm) patterning of ortho-nitrobenzyl functionalized thiol-on-gold self-assembled monolayers (SAMs) using acid catalysis, produces surfaces which can be used for the selective electro-deposition of copper. Exploiting the difference in the reduction peak potential between the photolysed and the masked regions of the SAM allows copper to be deposited selectively on those areas that have been exposed to the light. The copper can be removed by raising the electrode potential. The process is fully reversible so that depositing a pattern of copper, and removing it again is something that can be repeated many times. The copper deposited on the photolysed regions, like copper deposited on bare gold, forms a film of copper oxide, and so it is presumably formed on top of the SAM. Preliminary results for two-photon photocleavage show that it is also possible to implement patterning with sub-wavelength features

Ab initio structure prediction methods for battery materials a review of recent computational efforts to predict the atomic level structure and bonding in materials for rechargeable batteries

Portable electronic devices, electric vehicles and stationary energy storage applications, which encourage carbon-neutral energy alternatives, are driving demand for batteries that have concurrently higher energy densities, faster charging rates, safer operation and lower prices. These demands can no longer be met by incrementally improving existing technologies but require the discovery of new materials with exceptional properties. Experimental materials discovery is both expensive and time consuming: before the efficacy of a new battery material can be assessed, its synthesis and stability must be well-understood. Computational materials modelling can expedite this process by predicting novel materials, both in stand-alone theoretical calculations and in tandem with experiments. In this review, we describe a materials discovery framework based on density functional theory (DFT) to predict the properties of electrode and solid-electrolyte materials and validate these predictions experimentally. First, we discuss crystal structure prediction using the Ab initio random structure searching (AIRSS) method. Next, we describe how DFT results allow us to predict which phases form during electrode cycling, as well as the electrode voltage profile and maximum theoretical capacity. We go on to explain how DFT can be used to simulate experimentally measurable properties such as nuclear magnetic resonance (NMR) spectra and ionic conductivities. We illustrate the described workflow with multiple experimentally validated examples: materials for lithium-ion and sodium-ion anodes and lithium-ion solid electrolytes. These examples highlight the power of combining computation with experiment to advance battery materials research.(1) Gates Cambridge Trust, University of Cambridge, UK (2) EPSRC Centre for Doctoral Training in Computational Methods for Materials Science, UK, Grant No. EP/L015552/1. (3) Winton Programme for the Physics of Sustainability, University of Cambridge, UK (4) Sims Fund, University of Cambridge, UK (5) EPSRC Grant No. EP/P003532/1 (6) EPSRC Collaborative Computational Projects on the Electronic Structure of Condensed Matter (CCP9), Grant No. EP/M022595/1, and NMR crystallography, Grant No. EP/M022501/1 (7) Computing resources on the Tier 1 resource ARCHER were provided through the UKCP EPSRC High-End computational consortium (EP/P022561/1) and on the Tier 2 resources HPC Midlands+ (EP/P020232/1) and CSD3 (EP/P020259/1)

FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.Comment: FICC201

Targeting Btk/Etk of prostate cancer cells by a novel dual inhibitor.

Btk and Etk/BMX are Tec-family non-receptor tyrosine kinases. Btk has previously been reported to be expressed primarily in B cells and has an important role in immune responses and B-cell malignancies. Etk has been shown previously to provide a strong survival and metastasis signal in human prostate cancer cells, and to confer androgen independence and drug resistance. While the role of Etk in prostate carcinogenesis is well established, the functions of Btk in prostate cancer have never been investigated, likely due to the perception that Btk is a hematopoietic, but not epithelial, kinase. Herein, we found that Btk is overexpressed in prostate cancer tissues and prostate cancer cells. The level of Btk in prostate cancer tissues correlates with cancer grades. Knockdown of Btk expression selectively inhibits the growth of prostate cancer cells, but not that of the normal prostate epithelial cells, which express very little Btk. Dual inhibition of Btk and Etk has an additive inhibitory effect on prostate cancer cell growth. To explore Btk and Etk as targets for prostate cancer, we developed a small molecule dual inhibitor of Btk and Etk, CTN06. Treatment of PC3 and other prostate cancer cells, but not immortalized prostate epithelial cells with CTN06 resulted in effective cell killing, accompanied by the attenuation of Btk/Etk signals. The killing effect of CTN06 is more potent than that of commonly used inhibitors against Src, Raf/VEGFR and EGFR. CTN06 induces apoptosis as well as autophagy in human prostate cancer cells, and is a chemo-sensitizer for docetaxel (DTX), a standard of care for metastatic prostate cancer patients. CTN06 also impeded the migration of human prostate cancer cells based on a 'wound healing' assay. The anti-cancer effect of CTN06 was further validated in vivo in a PC3 xenograft mouse model