A modular holistic approach to prosody modelling for Standard Yorùbá speech synthesis

This paper presents a novel prosody model in the context of computer text-to-speech synthesis applications for tone languages. We have demonstrated its applicability using the Standard Yorùbá (SY) language. Our approach is motivated by the theory that abstract and realised forms of various prosody dimensions should be modelled within a modular and unified framework [Coleman, J.S., 1994. Polysyllabic words in the YorkTalk synthesis system. In: Keating, P.A. (Ed.), Phonological Structure and Forms: Papers in Laboratory Phonology III, Cambridge University Press, Cambridge, pp. 293–324]. We have implemented this framework using the Relational Tree (R-Tree) technique. R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. The underlying assumption of this research is that it is possible to develop a practical prosody model by using appropriate computational tools and techniques which combine acoustic data with an encoding of the phonological and phonetic knowledge provided by experts. To implement the intonation dimension, fuzzy logic based rules were developed using speech data from native speakers of Yorùbá. The Fuzzy Decision Tree (FDT) and the Classification and Regression Tree (CART) techniques were tested in modelling the duration dimension. For practical reasons, we have selected the FDT for implementing the duration dimension of our prosody model. To establish the effectiveness of our prosody model, we have also developed a Stem-ML prosody model for SY. We have performed both quantitative and qualitative evaluations on our implemented prosody models. The results suggest that, although the R-Tree model does not predict the numerical speech prosody data as accurately as the Stem-ML model, it produces synthetic speech prosody with better intelligibility and naturalness. The R-Tree model is particularly suitable for speech prosody modelling for languages with limited language resources and expertise, e.g. African languages. Furthermore, the R-Tree model is easy to implement, interpret and analyse.

Sensory thresholds obtained from MEG data:cortical psychometric functions

Sensory sensitivity is typically measured using behavioural techniques (psychophysics), which rely on observers responding to very large numbers of stimulus presentations. Psychophysics can be problematic when working with special populations, such as children or clinical patients, because they may lack the compliance or cognitive skills to perform the behavioural tasks. We used an auditory gap-detection paradigm to develop an accurate measure of sensory threshold derived from passively-recorded MEG data. Auditory evoked responses were elicited by silent gaps of varying durations in an on-going noise stimulus. Source modelling was used to spatially filter the MEG data and sigmoidal ‘cortical psychometric functions’ relating response amplitude to gap duration were obtained for each individual participant. Fitting the functions with a curve and estimating the gap duration at which the evoked response exceeded one standard deviation of the prestimulus brain activity provided an excellent prediction of psychophysical threshold. Thus we have demonstrated that accurate sensory thresholds can be reliably extracted from MEG data recorded while participants listen passively to a stimulus. Because we required no behavioural task, the method is suitable for studies of populations where variations in cognitive skills or vigilance make traditional psychophysics unsuitable.

Similarity between class A and class B G-protein-coupled receptors exemplified through calcitonin gene-related peptide receptor modelling and mutagenesis studies

Modelling class B G-protein-coupled receptors (GPCRs) using class A GPCR structural templates is difficult due to lack of homology. The plant GPCR, GCR1, has homology to both class A and class B GPCRs. We have used this to generate a class A-class B alignment, and by incorporating maximum lagged correlation of entropy and hydrophobicity into a consensus score, we have been able to align receptor transmembrane regions. We have applied this analysis to generate active and inactive homology models of the class B calcitonin gene-related peptide (CGRP) receptor, and have supported it with site-directed mutagenesis data using 122 CGRP receptor residues and 144 published mutagenesis results on other class B GPCRs. The variation of sequence variability with structure, the analysis of polarity violations, the alignment of group-conserved residues and the mutagenesis results at 27 key positions were particularly informative in distinguishing between the proposed and plausible alternative alignments. Furthermore, we have been able to associate the key molecular features of the class B GPCR signalling machinery with their class A counterparts for the first time. These include the [K/R]KLH motif in intracellular loop 1, [I/L]xxxL and KxxK at the intracellular end of TM5 and TM6, the NPXXY/VAVLY motif on TM7 and small group-conserved residues in TM1, TM2, TM3 and TM7. The equivalent of the class A DRY motif is proposed to involve Arg(2.39), His(2.43) and Glu(3.46), which makes a polar lock with T(6.37). These alignments and models provide useful tools for understanding class B GPCR function.

Computationsl modelling of dimethyl ether separation and steam reforming in fluidized bed reactors

This study presents a computational fluid dynamic (CFD) study of Dimethyl Ether (DME) gas adsorptive separation and steam reforming (DME-SR) in a large scale Circulating Fluidized Bed (CFB) reactor. The CFD model is based on Eulerian-Eulerian dispersed flow and solved using commercial software (ANSYS FLUENT). Hydrogen is currently receiving increasing interest as an alternative source of clean energy and has high potential applications, including the transportation sector and power generation. Computational fluid dynamic (CFD) modelling has attracted considerable recognition in the engineering sector consequently leading to using it as a tool for process design and optimisation in many industrial processes. In most cases, these processes are difficult or expensive to conduct in lab scale experiments. The CFD provides a cost effective methodology to gain detailed information up to the microscopic level. The main objectives in this project are to: (i) develop a predictive model using ANSYS FLUENT (CFD) commercial code to simulate the flow hydrodynamics, mass transfer, reactions and heat transfer in a large scale dual fluidized bed system for combined gas separation and steam reforming processes (ii) implement a suitable adsorption models in the CFD code, through a user defined function, to predict selective separation of a gas from a mixture (iii) develop a model for dimethyl ether steam reforming (DME-SR) to predict hydrogen production (iv) carry out detailed parametric analysis in order to establish ideal operating conditions for future industrial application. The project has originated from a real industrial case problem in collaboration with the industrial partner Dow Corning (UK) and jointly funded by the Engineering and Physical Research Council (UK) and Dow Corning. The research examined gas separation by adsorption in a bubbling bed, as part of a dual fluidized bed system. The adsorption process was simulated based on the kinetics derived from the experimental data produced as part of a separate PhD project completed under the same fund. The kinetic model was incorporated in FLUENT CFD tool as a pseudo-first order rate equation; some of the parameters for the pseudo-first order kinetics were obtained using MATLAB. The modelling of the DME adsorption in the designed bubbling bed was performed for the first time in this project and highlights the novelty in the investigations. The simulation results were analysed to provide understanding of the flow hydrodynamic, reactor design and optimum operating condition for efficient separation. Bubbling bed validation by estimation of bed expansion and the solid and gas distribution from simulation agreed well with trends seen in the literatures. Parametric analysis on the adsorption process demonstrated that increasing fluidizing velocity reduced adsorption of DME. This is as a result of reduction in the gas residence time which appears to have much effect compared to the solid residence time. The removal efficiency of DME from the bed was found to be more than 88%. Simulation of the DME-SR in FLUENT CFD was conducted using selected kinetics from literature and implemented in the model using an in-house developed user defined function. The validation of the kinetics was achieved by simulating a case to replicate an experimental study of a laboratory scale bubbling bed by Vicente et al [1]. Good agreement was achieved for the validation of the models, which was then applied in the DME-SR in the large scale riser section of the dual fluidized bed system. This is the first study to use the selected DME-SR kinetics in a circulating fluidized bed (CFB) system and for the geometry size proposed for the project. As a result, the simulation produced the first detailed data on the spatial variation and final gas product in such an industrial scale fluidized bed system. The simulation results provided insight in the flow hydrodynamic, reactor design and optimum operating condition. The solid and gas distribution in the CFB was observed to show good agreement with literatures. The parametric analysis showed that the increase in temperature and steam to DME molar ratio increased the production of hydrogen due to the increased DME conversions, whereas the increase in the space velocity has been found to have an adverse effect. Increasing temperature between 200 oC to 350 oC increased DME conversion from 47% to 99% while hydrogen yield increased substantially from 11% to 100%. The CO2 selectivity decreased from 100% to 91% due to the water gas shift reaction favouring CO at higher temperatures. The higher conversions observed as the temperature increased was reflected on the quantity of unreacted DME and methanol concentrations in the product gas, where both decreased to very low values of 0.27 mol% and 0.46 mol% respectively at 350 °C. Increasing the steam to DME molar ratio from 4 to 7.68 increased the DME conversion from 69% to 87%, while the hydrogen yield increased from 40% to 59%. The CO2 selectivity decreased from 100% to 97%. The decrease in the space velocity from 37104 ml/g/h to 15394 ml/g/h increased the DME conversion from 87% to 100% while increasing the hydrogen yield from 59% to 87%. The parametric analysis suggests an operating condition for maximum hydrogen yield is in the region of 300 oC temperatures and Steam/DME molar ratio of 5. The analysis of the industrial sponsor’s case for the given flow and composition of the gas to be treated suggests that 88% of DME can be adsorbed from the bubbling and consequently producing 224.4t/y of hydrogen in the riser section of the dual fluidized bed system. The process also produces 1458.4t/y of CO2 and 127.9t/y of CO as part of the product gas. The developed models and parametric analysis carried out in this study provided essential guideline for future design of DME-SR at industrial level and in particular this work has been of tremendous importance for the industrial collaborator in order to draw conclusions and plan for future potential implementation of the process at an industrial scale.

Assessment of physical and hydrological properties of biological soil crusts using x-ray microtomography and modelling

Biological soil crusts (BSCs) are formed by aggregates of soil particles and communities of microbial organisms and are common in all drylands. The role of BSCs on infiltration remains uncertain due to the lack of data on their role in affecting soil physical properties such as porosity and structure. Quantitative assessment of these properties is primarily hindered by the fragile nature of the crusts. Here we show how the use of a combination of non-destructive imaging X-ray microtomography (XMT) and Lattice Boltzmann method (LBM) enables quantification of key soil physical parameters and the modeling of water flow through BSCs samples from Kalahari Sands, Botswana. We quantify porosity and flow changes as a result of mechanical disturbance of such a fragile cyanobacteria-dominated crust. Results show significant variations in porosity between different types of crusts and how they affect the flow and that disturbance of a cyanobacteria-dominated crust results in the breakdown of larger pore spaces and reduces flow rates through the surface layer. We conclude that the XMT–LBM approach is well suited for study of fragile surface crust samples where physical and hydraulic properties cannot be easily quantified using conventional methods.

The definition, theoretical modelling and empirical research of front-line service employee corporate identity within a UK national grocery retailer:determining the FLE corporate brand identification construct

Over the past forty years the corporate identity literature has developed to a point of maturity where it currently contains many definitions and models of the corporate identity construct at the organisational level. The literature has evolved by developing models of corporate identity or in considering corporate identity in relation to new and developing themes, e.g. corporate social responsibility. It has evolved into a multidisciplinary domain recently incorporating constructs from other literature to further its development. However, the literature has a number of limitations. It remains that an overarching and universally accepted definition of corporate identity is elusive, potentially leaving the construct with a lack of clear definition. Only a few corporate identity definitions and models, at the corporate level, have been empirically tested. The corporate identity construct is overwhelmingly defined and theoretically constructed at the corporate level, leaving the literature without a detailed understanding of its influence at an individual stakeholder level. Front-line service employees (FLEs), form a component in a number of corporate identity models developed at the organisational level. FLEs deliver the services of an organisation to its customers, as well as represent the organisation by communicating and transporting its core defining characteristics to customers through continual customer contact and interaction. This person-to-person contact between an FLE and the customer is termed a service encounter, where service encounters influence a customer’s perception of both the service delivered and the associated level of service quality. Therefore this study for the first time defines, theoretically models and empirically tests corporate identity at the individual FLE level, termed FLE corporate identity. The study uses the services marketing literature to characterise an FLE’s operating environment, arriving at five potential dimensions to the FLE corporate identity construct. These are scrutinised against existing corporate identity definitions and models to arrive at a definition for the construct. In reviewing the corporate identity, services marketing, branding and organisational psychology literature, a theoretical model is developed for FLE corporate identity, which is empirically and quantitatively tested, with FLEs in seven stores of a major national retailer. Following rigorous construct reliability and validity testing, the 601 usable responses are used to estimate a confirmatory factor analysis and structural equation model for the study. The results for the individual hypotheses and the structural model are very encouraging, as they fit the data well and support a definition of FLE corporate identity. This study makes contributions to the branding, services marketing and organisational psychology literature, but its principal contribution is to extend the corporate identity literature into a new area of discourse and research, that of FLE corporate identity

The role of ECL2 in CGRP receptor activation:a combined modelling and experimental approach

The calcitonin gene-related peptide (CGRP) receptor is a complex of a cal-citonin receptor-like receptor (CLR), which is a family B G-protein-coupled receptor (GPCR) and receptor activity modifying protein 1. The role of the second extracellular loop (ECL2) of CLR in binding CGRP and coupling to Gs was investigated using a combination of mutagenesis and modelling. An alanine scan of residues 271-294 of CLR showed that the ability of CGRP to produce cAMP was impaired by point mutations at 13 residues; most of these also impaired the response to adrenomedullin (AM). These data were used to select probable ECL2-modelled conformations that are involved in agonist binding, allowing the identification of the likely contacts between the peptide and receptor. The implications of the most likely structures for receptor activation are discussed. © 2013 The Authors.

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

An overall profit Malmquist productivity index with fuzzy and interval data

Although crisp data are fundamentally indispensable for determining the profit Malmquist productivity index (MPI), the observed values in real-world problems are often imprecise or vague. These imprecise or vague data can be suitably characterized with fuzzy and interval methods. In this paper, we reformulate the conventional profit MPI problem as an imprecise data envelopment analysis (DEA) problem, and propose two novel methods for measuring the overall profit MPI when the inputs, outputs, and price vectors are fuzzy or vary in intervals. We develop a fuzzy version of the conventional MPI model by using a ranking method, and solve the model with a commercial off-the-shelf DEA software package. In addition, we define an interval for the overall profit MPI of each decision-making unit (DMU) and divide the DMUs into six groups according to the intervals obtained for their overall profit efficiency and MPIs. We also present two numerical examples to demonstrate the applicability of the two proposed models and exhibit the efficacy of the procedures and algorithms. © 2011 Elsevier Ltd.

Interval data without sign restrictions in DEA

Conventional DEA models assume deterministic, precise and non-negative data for input and output observations. However, real applications may be characterized by observations that are given in form of intervals and include negative numbers. For instance, the consumption of electricity in decentralized energy resources may be either negative or positive, depending on the heat consumption. Likewise, the heat losses in distribution networks may be within a certain range, depending on e.g. external temperature and real-time outtake. Complementing earlier work separately addressing the two problems; interval data and negative data; we propose a comprehensive evaluation process for measuring the relative efficiencies of a set of DMUs in DEA. In our general formulation, the intervals may contain upper or lower bounds with different signs. The proposed method determines upper and lower bounds for the technical efficiency through the limits of the intervals after decomposition. Based on the interval scores, DMUs are then classified into three classes, namely, the strictly efficient, weakly efficient and inefficient. An intuitive ranking approach is presented for the respective classes. The approach is demonstrated through an application to the evaluation of bank branches. © 2013.

Modelling and simulation of biomass gasification in a circulating fluidized bed reactor

Computational Fluid Dynamics (CFD) has found great acceptance among the engineering community as a tool for research and design of processes that are practically difficult or expensive to study experimentally. One of these processes is the biomass gasification in a Circulating Fluidized Bed (CFB). Biomass gasification is the thermo-chemical conversion of biomass at a high temperature and a controlled oxygen amount into fuel gas, also sometime referred to as syngas. Circulating fluidized bed is a type of reactor in which it is possible to maintain a stable and continuous circulation of solids in a gas-solid system. The main objectives of this thesis are four folds: (i) Develop a three-dimensional predictive model of biomass gasification in a CFB riser using advanced Computational Fluid Dynamic (CFD) (ii) Experimentally validate the developed hydrodynamic model using conventional and advanced measuring techniques (iii) Study the complex hydrodynamics, heat transfer and reaction kinetics through modelling and simulation (iv) Study the CFB gasifier performance through parametric analysis and identify the optimum operating condition to maximize the product gas quality. Two different and complimentary experimental techniques were used to validate the hydrodynamic model, namely pressure measurement and particle tracking. The pressure measurement is a very common and widely used technique in fluidized bed studies, while, particle tracking using PEPT, which was originally developed for medical imaging, is a relatively new technique in the engineering field. It is relatively expensive and only available at few research centres around the world. This study started with a simple poly-dispersed single solid phase then moved to binary solid phases. The single solid phase was used for primary validations and eliminating unnecessary options and steps in building the hydrodynamic model. Then the outcomes from the primary validations were applied to the secondary validations of the binary mixture to avoid time consuming computations. Studies on binary solid mixture hydrodynamics is rarely reported in the literature. In this study the binary solid mixture was modelled and validated using experimental data from the both techniques mentioned above. Good agreement was achieved with the both techniques. According to the general gasification steps the developed model has been separated into three main gasification stages; drying, devolatilization and tar cracking, and partial combustion and gasification. The drying was modelled as a mass transfer from the solid phase to the gas phase. The devolatilization and tar cracking model consist of two steps; the devolatilization of the biomass which is used as a single reaction to generate the biomass gases from the volatile materials and tar cracking. The latter is also modelled as one reaction to generate gases with fixed mass fractions. The first reaction was classified as a heterogeneous reaction while the second reaction was classified as homogenous reaction. The partial combustion and gasification model consisted of carbon combustion reactions and carbon and gas phase reactions. The partial combustion considered was for C, CO, H2 and CH4. The carbon gasification reactions used in this study is the Boudouard reaction with CO2, the reaction with H2O and Methanation (Methane forming reaction) reaction to generate methane. The other gas phase reactions considered in this study are the water gas shift reaction, which is modelled as a reversible reaction and the methane steam reforming reaction. The developed gasification model was validated using different experimental data from the literature and for a wide range of operating conditions. Good agreement was observed, thus confirming the capability of the model in predicting biomass gasification in a CFB to a great accuracy. The developed model has been successfully used to carry out sensitivity and parametric analysis. The sensitivity analysis included: study of the effect of inclusion of various combustion reaction; and the effect of radiation in the gasification reaction. The developed model was also used to carry out parametric analysis by changing the following gasifier operating conditions: fuel/air ratio; biomass flow rates; sand (heat carrier) temperatures; sand flow rates; sand and biomass particle sizes; gasifying agent (pure air or pure steam); pyrolysis models used; steam/biomass ratio. Finally, based on these parametric and sensitivity analysis a final model was recommended for the simulation of biomass gasification in a CFB riser.

Modelling the human accommodation system using finite element analysis

The human accommodation system has been extensively examined for over a century, with a particular focus on trying to understand the mechanisms that lead to the loss of accommodative ability with age (Presbyopia). The accommodative process, along with the potential causes of presbyopia, are disputed; hindering efforts to develop methods of restoring accommodation in the presbyopic eye. One method that can be used to provide insight into this complex area is Finite Element Analysis (FEA). The effectiveness of FEA in modelling the accommodative process has been illustrated by a number of accommodative FEA models developed to date. However, there have been limitations to these previous models; principally due to the variation in data on the geometry of the accommodative components, combined with sparse measurements of their material properties. Despite advances in available data, continued oversimplification has occurred in the modelling of the crystalline lens structure and the zonular fibres that surround the lens. A new accommodation model was proposed by the author that aims to eliminate these limitations. A novel representation of the zonular structure was developed, combined with updated lens and capsule modelling methods. The model has been designed to be adaptable so that a range of different age accommodation systems can be modelled, allowing the age related changes that occur to be simulated. The new modelling methods were validated by comparing the changes induced within the model to available in vivo data, leading to the definition of three different age models. These were used in an extended sensitivity study on age related changes, where individual parameters were altered to investigate their effect on the accommodative process. The material properties were found to have the largest impact on the decline in accommodative ability, in particular compared to changes in ciliary body movement or zonular structure. Novel data on the importance of the capsule stiffness and thickness was also established. The new model detailed within this thesis provides further insight into the accommodation mechanism, as well as a foundation for future, more detailed investigations into accommodation, presbyopia and accommodative restoration techniques.

A simple Bayesian modelling approach to event extraction from Twitter

With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.© 2014 Association for Computational Linguistics.

Quasi-lossless data transmission with ultra-long Raman fibre laser based amplification

The project consists of an experimental and numerical modelling study of the applications of ultra-long Raman fibre laser (URFL) based amplification techniques for high-speed multi-wavelength optical communications systems. The research is focused in telecommunications C-band 40 Gb/s transmission data rates with direct and coherent detection. The optical transmission performance of URFL based systems in terms of optical noise, gain bandwidth and gain flatness for different system configurations is evaluated. Systems with different overall span lengths, transmission fibre types and data modulation formats are investigated. Performance is compared with conventional Erbium doped fibre amplifier based system to evaluate system configurations where URFL based amplification provide performance or commercial advantages.

Chemometric modelling to relate antioxidants, neutral lipid fatty acids and flavour components in chicken breast

Relationships among quality factors in retailed free-range, corn-fed, organic, and conventional chicken breasts (9) were modeled using chemometric approaches. Use of principal component analysis (PCA) to neutral lipid composition data explained the majority (93%) of variability (variance) in fatty acid contents in 2 significant multivariate factors. PCA explained 88 and 75% variance in 3 factors for, respectively, flame ionization detection (FID) and nitrogen phosphorus (NPD) components in chromatographic flavor data from cooked chicken after simultaneous distillation extraction. Relationships to tissue antioxidant contents were modeled. Partial least square regression (PLS2), interrelating total data matrices, provided no useful models. By using single antioxidants as Y variables in PLS (1), good models (r2 values > 0.9) were obtained for alpha-tocopherol, glutathione, catalase, glutathione peroxidase, and reductase and FID flavor components and among the variables total mono and polyunsaturated fatty acids and subsets of FID, and saturated fatty acid and NPD components. Alpha-tocopherol had a modest (r2 = 0.63) relationship with neutral lipid n-3 fatty acid content. Such factors thus relate to flavor development and quality in chicken breast meat.