Modelling Cued-Target Recall using Quantum Inspired Models of Target Activation

This article presents and evaluates Quantum Inspired models of Target Activation using Cued-Target Recall Memory Modelling over multiple sources of Free Association data. Two components were evaluated: Whether Quantum Inspired models of Target Activation would provide a better framework than their classical psychological counterparts and how robust these models are across the different sources of Free Association data. In previous work, a formal model of cued-target recall did not exist and as such Target Activation was unable to be assessed directly. Further to that, the data source used was suspected of suffering from temporal and geographical bias. As a consequence, Target Activation was measured against cued-target recall data as an approximation of performance. Since then, a formal model of cued-target recall (PIER3) has been developed [10] with alternative sources of data also becoming available. This allowed us to directly model target activation in cued-target recall with human cued-target recall pairs and use multiply sources of Free Association Data. Featural Characteristics known to be important to Target Activation were measured for each of the data sources to identify any major differences that may explain variations in performance for each of the models. Each of the activation models were used in the PIER3 memory model for each of the data sources and was benchmarked against cued-target recall pairs provided by the University of South Florida (USF). Two methods where used to evaluate performance. The first involved measuring the divergence between the sets of results using the Kullback Leibler (KL) divergence with the second utilizing a previous statistical analysis of the errors [9]. Of the three sources of data, two were sourced from human subjects being the USF Free Association Norms and the University of Leuven (UL) Free Association Networks. The third was sourced from a new method put forward by Galea and Bruza, 2015 in which pseudo Free Association Networks (Corpus Based Association Networks - CANs) are built using co-occurrence statistics on large text corpus. It was found that the Quantum Inspired Models of Target Activation not only outperformed the classical psychological model but was more robust across a variety of data sources.

Modelling and Forecasting Property Rents and Returns (summary section only)

Recently, focus of real estate investment has expanded from the building-specific level to the aggregate portfolio level. The portfolio perspective requires investment analysis for real estate which is comparable with that of other asset classes, such as stocks and bonds. Thus, despite its distinctive features, such as heterogeneity, high unit value, illiquidity and the use of valuations to measure performance, real estate should not be considered in isolation. This means that techniques which are widely used for other assets classes can also be applied to real estate. An important part of investment strategies which support decisions on multi-asset portfolios is identifying the fundamentals of movements in property rents and returns, and predicting them on the basis of these fundamentals. The main objective of this thesis is to find the key drivers and the best methods for modelling and forecasting property rents and returns in markets which have experienced structural changes. The Finnish property market, which is a small European market with structural changes and limited property data, is used as a case study. The findings in the thesis show that is it possible to use modern econometric tools for modelling and forecasting property markets. The thesis consists of an introduction part and four essays. Essays 1 and 3 model Helsinki office rents and returns, and assess the suitability of alternative techniques for forecasting these series. Simple time series techniques are able to account for structural changes in the way markets operate, and thus provide the best forecasting tool. Theory-based econometric models, in particular error correction models, which are constrained by long-run information, are better for explaining past movements in rents and returns than for predicting their future movements. Essay 2 proceeds by examining the key drivers of rent movements for several property types in a number of Finnish property markets. The essay shows that commercial rents in local markets can be modelled using national macroeconomic variables and a panel approach. Finally, Essay 4 investigates whether forecasting models can be improved by accounting for asymmetric responses of office returns to the business cycle. The essay finds that the forecast performance of time series models can be improved by introducing asymmetries, and the improvement is sufficient to justify the extra computational time and effort associated with the application of these techniques.

Nanoscale studies of materials using x-ray synchrotron radiation and mesoscopic modelling

X-ray synchrotron radiation was used to study the nanostructure of cellulose in Norway spruce stem wood and powders of cobalt nanoparticles in cellulose support. Furthermore, the growth of metallic clusters was modelled and simulated in the mesoscopic size scale. Norway spruce was characterized with x-ray microanalysis at beamline ID18F of the European Synchrotron Radiation Facility in Grenoble. The average dimensions and the orientation of cellulose crystallites was determined using x-ray microdiffraction. In addition, the nutrient element content was determined using x-ray fluorescence spectroscopy. Diffraction patterns and fluorescence spectra were simultaneously acquired. Cobalt nanoparticles in cellulose support were characterized with x-ray absorption spectroscopy at beamline X1 of the Deutsches Elektronen-Synchrotron in Hamburg, complemented by home lab experiments including x-ray diffraction, electron microscopy and measurement of magnetic properties with a vibrating sample magnetometer. Extended x-ray absorption fine structure spectroscopy (EXAFS) and x-ray diffraction were used to solve the atomic arrangement of the cobalt nanoparticles. Scanning- and transmission electron microscopy were used to image the surfaces of the cellulose fibrils, where the growth of nanoparticles takes place. The EXAFS experiment was complemented by computational coordination number calculations on ideal spherical nanocrystals. The growth process of metallic nanoclusters on cellulose matrix is assumed to be rather complicated, affected not only by the properties of the clusters themselves, but essentially depending on the cluster-fiber interfaces as well as the morphology of the fiber surfaces. The final favored average size for nanoclusters, if such exists, is most probably a consequence of these two competing tendencies towards size selection, one governed by pore sizes, the other by the cluster properties. In this thesis, a mesoscopic model for the growth of metallic nanoclusters on porous cellulose fiber (or inorganic) surfaces is developed. The first step in modelling was to evaluate the special case of how the growth proceeds on flat or wedged surfaces.

Relaxation system based sub-grid scale modelling for large eddy simulation of Burgers' equation

An implicit sub-grid scale model for large eddy simulation is presented by utilising the concept of a relaxation system for one dimensional Burgers' equation in a novel way. The Burgers' equation is solved for three different unsteady flow situations by varying the ratio of relaxation parameter (epsilon) to time step. The coarse mesh results obtained with a relaxation scheme are compared with the filtered DNS solution of the same problem on a fine mesh using a fourth-order CWENO discretisation in space and third-order TVD Runge-Kutta discretisation in time. The numerical solutions obtained through the relaxation system have the same order of accuracy in space and time and they closely match with the filtered DNS solutions.

Joining of dissimilar metals: issues and modelling techniques

Joining of dissimilar metals involves a number of scientific issues, the modelling of which offers unique challenges. This review discusses the complexities in different joining processes and dissimilar combinations, and the corresponding computational techniques that have the potential to address the same. Future directions in modelling at both macroscopic and microscopic scales are also suggested.

Modelling plane mixing layers using vortex points and sheets

We present here a critical assessment of two vortex approaches (both two-dimensional) to the modelling of turbulent mixing layers. In the first approach the flow is represented by point vortices, and in the second it is simulated as the evolution of a continuous vortex sheet composed of short linear elements or ''panels''. The comparison is based on fresh simulations using approximately the same number of elements in either model, paying due attention in both to the boundary conditions far downstream as well as those on the splitter plate from which the mixing layer issues. The comparisons show that, while both models satisfy the well-known invariants of vortex dynamics approximately to the same accuracy, the vortex panel model, although ultimately not convergent, leads to smoother roll-up and values of stresses and moments that are in closer agreement with the experiment, and has a higher computational efficiency for a given degree of convergence on moments. The point vortex model, while faster for a given number of elements, produces an unsatisfactory roll-up which (for the number of elements used) is rendered worse by the incorporation of the Van der Vooren correction for sheet curvature.

Effect of capillarity and heterogeneity in the numerical modelling of multiphase flow of fluids in unsaturated porous medium

The multiphase flow of fluids in the unsaturated porous medium is considered as a three phase flow of water, NAPL, and air simultaneously in the porous medium. The adaptive solution fully implicit modified sequential method is used for the numerical modelling. The effect of capillarity and heterogeneity effect at the interface between the media is studied and it is observed that the interface criteria has to be taken into account for the correct prediction of NAPL migration especially in heterogeneous media. The modified Newton Raphson method is used for the linearization and Hestines and Steifel Conjugate Gradient method is used as the solver.

Average Modelling of a Voltage Source Inverter with Dead-Time in a Synchronous Reference Frame

Voltage source inverters are an integral part of renewable power sources and smart grid systems. Computationally efficient and fairly accurate models for the voltage source inverter are required to carry out extensive simulation studies on complex power networks. Accuracy requires that the effect of dead-time be incorporated in the inverter model. The dead-time is essentially a short delay introduced between the gating pulses to the complementary switches in an inverter leg for the safety of power devices. As the modern voltage source inverters switch at fairly high frequencies, the dead-time significantly influences the output fundamental voltage. Dead-time also causes low-frequency harmonic distortion and is hence important from a power quality perspective. This paper studies the dead-time effect in a synchronous dq reference frame, since dynamic studies and controller design are typically carried out in this frame of reference. For the sake of computational efficiency, average models are derived, incorporating the dead-time effect, in both RYB and dq reference frames. The average models are shown to consume less computation time than their corresponding switching models, the accuracies of the models being comparable. The proposed average synchronous reference frame model, including effect of dead-time, is validated through experimental results.

An open source massively parallel solver for Richards equation: Mechanistic modelling of water fluxes at the watershed scale

In this paper we present a massively parallel open source solver for Richards equation, named the RichardsFOAM solver. This solver has been developed in the framework of the open source generalist computational fluid dynamics tool box OpenFOAM (R) and is capable to deal with large scale problems in both space and time. The source code for RichardsFOAM may be downloaded from the CPC program library website. It exhibits good parallel performances (up to similar to 90% parallel efficiency with 1024 processors both in strong and weak scaling), and the conditions required for obtaining such performances are analysed and discussed. These performances enable the mechanistic modelling of water fluxes at the scale of experimental watersheds (up to few square kilometres of surface area), and on time scales of decades to a century. Such a solver can be useful in various applications, such as environmental engineering for long term transport of pollutants in soils, water engineering for assessing the impact of land settlement on water resources, or in the study of weathering processes on the watersheds. (C) 2014 Elsevier B.V. All rights reserved.

SInCRe-structural interactome computational resource for Mycobacterium tuberculosis

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein-protein and protein-small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein-protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host-pathogen protein-protein interactions. Together they provide prerequisites for identification of off-target binding.