A novel matrix converter based resonant dual active bridge for V2G applications

Dual-active bridges (DABs) can be used to deliver isolated and bidirectional power to electric vehicles (EVs) or to the grid in vehicle-to-grid (V2G) applications. However, such a system essentially requires a two-stage power conversion process, which significantly increases the power losses. Furthermore, the poor power factor associated with DAB converters further reduces the efficiency of such systems. This paper proposes a novel matrix converter based resonant DAB converter that requires only a single-stage power conversion process to facilitate isolated bi-directional power transfer between EVs and the grid. The proposed converter comprises a matrix converter based front end linked with an EV side full-bridge converter through a high frequency isolation transformer and a tuned LCL network. A mathematical model, which predicts the behavior of the proposed system, is presented to show that both the magnitude and direction of the power flow can be controlled through either relative phase angle or magnitude modulation of voltages produced by converters. Viability of the proposed concept is verified through simulations. The proposed matrix converter based DAB, with a single power conversion stage, is low in cost, and suites charging and discharging in single or multiple EVs or V2G applications.

Optimizing I/O cost and managing memory for composition vector method based on correlation matrix calculation in bioinformatics

The generation of a correlation matrix for set of genomic sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. Each sequence may be millions of bases long and there may be thousands of such sequences which we wish to compare, so not all sequences may fit into main memory at the same time. Each sequence needs to be compared with every other sequence, so we will generally need to page some sequences in and out more than once. In order to minimize execution time we need to minimize this I/O. This paper develops an approach for faster and scalable computing of large-size correlation matrices through the maximal exploitation of available memory and reducing the number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different bioinformatics problems with different correlation matrix sizes. The significant performance improvement of the approach over previous work is demonstrated through benchmark examples.

A modular matrix converter for transformer-less PMSG wind generation systems

An offshore wind turbine usually has the grid step-up transformer integrated in the nacelle. This increases mechanical loading of the tower. In that context, a transformer-less, high voltage, highly-reliable and compact converter system for nacelle installation would be an attractive solution for large offshore wind turbines. This paper, therefore, presents a transformer-less grid integration topology for PMSG based large wind turbine generator systems using modular matrix converters. Each matrix converter module is fed from three generator coils of the PMSG which are phase shifted by 120°. Outputs of matrix converter modules are connected in series to increase the output voltage and thus eliminate the need of a coupling step-up transformer. Moreover, dc-link capacitors found in conventional back-to-back converter topologies are eliminated in the proposed system. Proper multilevel output voltage generation and power sharing between converter modules are achieved through an advanced switching strategy. Simulation results are presented to validate the proposed modular matrix converter system, modulation method and control techniques.

Interactive content analysis : evaluating interactive variants of non-negative Matrix Factorisation and Latent Dirichlet Allocation as qualitative content analysis aids

This thesis addressed issues that have prevented qualitative researchers from using thematic discovery algorithms. The central hypothesis evaluated whether allowing qualitative researchers to interact with thematic discovery algorithms and incorporate domain knowledge improved their ability to address research questions and trust the derived themes. Non-negative Matrix Factorisation and Latent Dirichlet Allocation find latent themes within document collections but these algorithms are rarely used, because qualitative researchers do not trust and cannot interact with the themes that are automatically generated. The research determined the types of interactivity that qualitative researchers require and then evaluated interactive algorithms that matched these requirements. Theoretical contributions included the articulation of design guidelines for interactive thematic discovery algorithms, the development of an Evaluation Model and a Conceptual Framework for Interactive Content Analysis.

A comparative study of the proliferation and osteogenic differentiation of human periodontal ligament cells cultured on β-TCP ceramics and demineralized bone matrix with or without osteogenic inducers in vitro

The repair of bone defects that result from periodontal diseases remains a clinical challenge for periodontal therapy. β-tricalcium phosphate (β-TCP) ceramics are biodegradable inorganic bone substitutes with inorganic components that are similar to those of bone. Demineralized bone matrix (DBM) is an acid-extracted organic matrix derived from bone sources that consists of the collagen and matrix proteins of bone. A few studies have documented the effects of DBM on the proliferation and osteogenic differentiation of human periodontal ligament cells (hPDLCs). The aim of the present study was to investigate the effects of inorganic and organic elements of bone on the proliferation and osteogenic differentiation of hPDLCs using three-dimensional porous β-TCP ceramics and DBM with or without osteogenic inducers. Primary hPDLCs were isolated from human periodontal ligaments. The proliferation of the hPDLCs on the scaffolds in the growth culture medium was examined using a Cell‑Counting kit‑8 (CCK-8) and scanning electron microscopy (SEM). Alkaline phosphatase (ALP) activity and the osteogenic differentiation of the hPDLCs cultured on the β-TCP ceramics and DBM were examined in both the growth culture medium and osteogenic culture medium. Specific osteogenic differentiation markers were examined using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). SEM images revealed that the cells on the β-TCP were spindle-shaped and much more spread out compared with the cells on the DBM surfaces. There were no significant differences observed in cell proliferation between the β-TCP ceramics and the DBM scaffolds. Compared with the cells that were cultured on β-TCP ceramics, the ALP activity, as well as the Runx2 and osteocalcin (OCN) mRNA levels in the hPDLCs cultured on DBM were significantly enhanced both in the growth culture medium and the osteogenic culture medium. The organic elements of bone may exhibit greater osteogenic differentiation effects on hPDLCs than the inorganic elements.

Triphasic pattern in the ex vivo response of human proliferative phase endometrium to oestrogens

The aim of this study was to evaluate the ex vivo oestrogen responsiveness of human proliferative phase endometrium using short-term explant cultures. The effects of oestrogen (17beta-E2) on proliferation and the expression of oestrogen-responsive genes known to be involved in regulating endometrial function were evaluated. Three distinct response patterns could be distinguished: (1) the menstrual (M) phase pattern (cycle days 2-5), which is characterised by a complete lack in the proliferative response to 17beta-E2, while an increased expression of AR (2.6-fold, P<0.01), PR (2.7-fold, P<0.01) and COX-2 (3.5-fold, P<0.01) at the mRNA level was observed and a similar upregulation was also found for AR, PR and COX-2 at the protein level; (2) the early proliferative (EP) phase pattern (cycle days 6-10) with 17beta-E2 enhanced proliferation in the stroma (1.7-fold, P<0.05), whereas the expression of AR, PR and COX-2 were not affected at the mRNA and protein levels and ER-a mRNA and protein levels were significantly reduced by 17beta-E2; (3) the late proliferative (LP) phase pattern (cycle days 11-14), which is characterised by a moderate stimulation of proliferation (1.4-fold, P<0.05) and PR mRNA expression (1.7-fold, P<0.01) by 17beta-E2. In conclusion, three distinct response patterns to 17beta-E2 could be identified with respect to proliferation and the expression of known oestrogen-responsive genes in human proliferative phase endometrium explant cultures.

Ruptures in the heterosexual matrix through teenage flows and multiplicities

Feminist research on girlhood has drawn extensively on Butler's conceptual work in order to theorise the normative forces of heterosexuality in the everyday construction of gender. This chapter explores girlhood by drawing on memories and artwork generated in a collective biography workshop held in Australian on the topic of girlhood and sexuality. We are interested in thinking through Butler's notion of the heterosexual matrix. Following Renold and Ringrose (2008) we do so with the help of Deleuze and Guattari, who invite us to think about difference as differenciation or continuous becoming, where difference is an evolutionary multiplicity.

Profiling of immunoglobulins in resting and mechanically stimulated saliva

Background: Dysregulation of salivary immunoglobulins has been implicated in illnesses ranging from periodontal disease to HIV aids and malignant cancers. Despite these advances there is a lack of agreement among studies with regard to the salivary immunoglobulin levels in healthy controls. Methodology: Resting and mechanically stimulated saliva samples and matching serum samples were collected from healthy individuals (n = 33; 40-55 years of age; gender: 23 female, 10 male). A matrix-matched AlphaLISA((R)) assay was developed to determine the concentrations of IgG1 and IgG4 in serum and saliva samples. Conclusion: Clear relationships were observed in the flow rate and concentration of each immunoglobulin in the two types of saliva. This study affirms the need to establish and standardize collection methods before salivary IgGs are used for diagnostic purposes.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Applying the Haddon Matrix in the context of work-related road safety

The Haddon Matrix was developed in the 1960s road safety arena, and has since been used in many public health settings. The literature and two specific case studies are reviewed to describe the background to the Haddon Matrix, identify how it has been critiqued and developed over time and practical applications in the work-related road safety context. Haddon’s original focus on the road, vehicle and driver has been extended and applied to include organisational safety culture, journey management and wider issues in society that affect occupational drivers and the communities in which they work. The paper shows that the Haddon Matrix has been applied in many projects and contexts. Practical work-related road safety applications include providing a comprehensive systems-based safety management framework to inform strategy. It has also been used to structure the review or gap analysis of current programs and processes, identify and develop prevention measures and as a tool for effective post-event investigations.

A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

To enhance the efficiency of regression parameter estimation by modeling the correlation structure of correlated binary error terms in quantile regression with repeated measurements, we propose a Gaussian pseudolikelihood approach for estimating correlation parameters and selecting the most appropriate working correlation matrix simultaneously. The induced smoothing method is applied to estimate the covariance of the regression parameter estimates, which can bypass density estimation of the errors. Extensive numerical studies indicate that the proposed method performs well in selecting an accurate correlation structure and improving regression parameter estimation efficiency. The proposed method is further illustrated by analyzing a dental dataset.