The effect of intervertebral staple insertion on bovine spine segment stiffness

Introduction There is growing interest in the biomechanics of ‘fusionless’ implant constructs used for deformity correction in the thoracic spine. Intervertebral stapling is a leading method of fusionless corrective surgery. Although used for a number of years, there is limited evidence as to the effect these staples have on the stiffness of the functional spinal unit. Materials and Methods Thoracic spines from 6-8 week old calves were dissected and divided into motion segments including levels T4-T11 (n=14). Each segment was potted in polymethylemethacrylate. An Instron Biaxial materials testing machine with a custom made jig was used for testing. The segments were tested in flexion/extension, lateral bending and axial rotation at 37⁰C and 100% humidity, using moment control to a maximum 1.75 Nm with a loading rate of 0.3 Nm per second. This torque was found sufficient to achieve physiologically representative ranges of movement. The segments were initially tested uninstrumented with data collected from the tenth load cycle. Next a left anterolateral Shape Memory Alloy (SMA) staple was inserted (Medtronic Sofamor Danek, USA). Biomechanical testing was repeated as before with data collected from the tenth load cycle. Results In flexion/extension there was an insignificant drop in stiffness of 3% (p=0.478). In lateral bending there was a significant drop in stiffness of 21% (p<0.001). This was mainly in lateral bending away from the staple, where the stiffness reduced by 30% (p<0.001). This was in contrast to lateral bending towards the staple where it dropped by 12% which was still statistically significant (p=0.036). In axial rotation there was an overall near significant drop in stiffness of 11% (p=0.076). However, this was more towards the side of the staple measuring a decrease of 14% as opposed to 8% away from the staple. In both cases it was a statistically insignificant drop (p=0.134 and p=0.352 respectively). Conclusion Insertion of intervertebral SMA staples results in a significant reduction in motion segment stiffness in lateral bending especially in the direction away from the staple. The staple had less effect on axial rotation stiffness and minimal effect on flexion/extension stiffness.

Keyword field-free conjunctive keyword searches on encrypted data and extension for dynamic groups

We consider the following problem: a user stores encrypted documents on an untrusted server, and wishes to retrieve all documents containing some keywords without any loss of data confidentiality. Conjunctive keyword searches on encrypted data have been studied by numerous researchers over the past few years, and all existing schemes use keyword fields as compulsory information. This however is impractical for many applications. In this paper, we propose a scheme of keyword field-free conjunctive keyword searches on encrypted data, which affirmatively answers an open problem asked by Golle et al. at ACNS 2004. Furthermore, the proposed scheme is extended to the dynamic group setting. Security analysis of our constructions is given in the paper.

A three-phase to single-phase matrix converter based bi-directional IPT system for charging electric vehicles

This paper presents a novel three-phase to single-phase matrix converter (TSMC) based bi-directional inductive power transfer (IPT) system for vehicle-to-grid (V2G) applications. In contrast to existing techniques, the proposed technique which employs a TSMC to drive an 8th order high frequency resonant network, requires only a single-stage power conversion process to facilitate bi-directional power transfer between electric vehicles (EVs) and a three-phase utility power supply. A mathematical model is presented to demonstrate that both magnitude and direction of power flow can be controlled by regulating either relative phase angles or magnitudes of voltages generated by converters. The viability of the proposed mathematical model is verified using simulated results of a 10 kW bi-directional IPT system and the results suggest that the proposed system is efficient, reliable and is suitable for high power applications which require contactless power transfer.

A model for estimating grid side harmonics of matrix converter based bi-directional IPT systems

Matrix converter (MC) based bi-directional inductive power transfer (BD-IPT) systems are gaining popularity as an efficient and reliable technique with single stage grid integration as opposed to two stage grid integration of conventional grid connected BD-IPT systems. However MCs are invariably rich in harmonics and thus affect both power quality and power factor on the grid side. This paper proposes a mathematical model through which the grid side harmonics of MC based BD-IPT systems can accurately be estimated. The validity of the proposed mathematical model is verified using simulated results of a 3 kW BD-IPT system and results suggest that the MC based BD-IPT systems have a better power factor with higher power quality over conventional grid connected rectifier based systems.

Lower partial moment and information entropy-based dynamic electricity purchasing strategy

In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.

Application of a hybrid Entropy–McKinsey Matrix method in evaluating sustainable urbanization : a China case study

Although urbanization can promote social and economic development, it can also cause various problems. As the key decision makers of urbanization, local governments should be able to evaluate urbanization performance, summarize experiences, and find problems caused by urbanization. This paper introduces a hybrid Entropy–McKinsey Matrix method for evaluating sustainable urbanization. The McKinsey Matrix is commonly referred to as the GE Matrix. The values of a development index (DI) and coordination index (CI) are calculated by employing the Entropy method and are used as a basis for constructing a GE Matrix. The matrix can assist in assessing sustainable urbanization performance by locating the urbanization state point. A case study of the city of Jinan in China demonstrates the process of using the evaluation method. The case study reveals that the method is an effective tool in helping policy makers understand the performance of urban sustainability and therefore formulate suitable strategies for guiding urbanization toward better sustainability.

Detection of dynamic primary user with cooperative spectrum sensing

In this paper we demonstrate that existing cooperative spectrum sensing formulated for static primary users cannot accurately detect dynamic primary users regardless of the information fusion method. Performance error occurs as the sensing parameters calculated by the conventional detector result in sensing performance that violates the sensing requirements. Furthermore, the error is accumulated and compounded by the number of cooperating nodes. To address this limitation, we design and implement the duty cycle detection model for the context of cooperative spectrum sensing to accurately calculate the sensing parameters that satisfy the sensing requirements. We show that longer sensing duration is required to compensate for dynamic primary user traffic.

A matrix converter based Inductive Power Transfer system

Typical Inductive Power Transfer (IPT) systems employ two power conversion stages to generate a high frequency current from low frequency utility supply. This paper proposes a matrix converter based IPT system that facilitates the generation of high frequency current through a single power conversion stage. The proposed matrix converter topology transforms a 3-phase low frequency voltage system to a high frequency single phase voltage which in turn powers a series compensated IPT system. A comprehensive mathematical model is developed to investigate the behavior of the proposed IPT topology. Theoretical results are presented in comparison to simulations, which are performed in Matlab/ Simulink, to demonstrate the applicability of the proposed concept and the validity of the developed model.

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.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

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.

Differential subcellular and extracellular localisations of proteins required for insulin-like growth factor- and extracellular matrix-induced signalling events in breast cancer progression

Background: Cancer metastasis is the main contributor to breast cancer fatalities as women with the metastatic disease have poorer survival outcomes than women with localised breast cancers. There is an urgent need to develop appropriate prognostic methods to stratify patients based on the propensities of their cancers to metastasise. The insulin-like growth factor (IGF)-I:IGF binding protein (IGFBP):vitronectin complexes have been shown to stimulate changes in gene expression favouring increased breast cancer cell survival and a migratory phenotype. We therefore investigated the prognostic potential of these IGF- and extracellular matrix (ECM) interaction-induced proteins in the early identification of breast cancers with a propensity to metastasise using patient-derived tissue microarrays. Methods: Semiquantitative immunohistochemistry analyses were performed to compare the extracellular and subcellular distribution of IGF- and ECM-induced signalling proteins among matched normal, primary cancer and metastatic cancer formalin-fixed paraffin-embedded breast tissue samples. Results: The IGF- and ECM-induced signalling proteins were differentially expressed between subcellular and extracellular localisations. Vitronectin and IGFBP-5 immunoreactivity was lower while β1 integrin immunoreactivity was higher in the stroma surrounding metastatic cancer tissues, as compared to normal breast and primary cancer stromal tissues. Similarly, immunoreactive stratifin was found to be increased in the stroma of primary as well as metastatic breast tissues. Immunoreactive fibronectin and β1 integrin was found to be highly expressed at the leading edge of tumours. Based on the immunoreactivity it was apparent that the cell signalling proteins AKT1 and ERK1/2 shuffled from the nucleus to the cytoplasm with tumour progression. Conclusion: This is the first in-depth, compartmentalised analysis of the distribution of IGF- and ECM-induced signalling proteins in metastatic breast cancers. This study has provided insights into the changing pattern of cellular localisation and expression of IGF- and ECM-induced signalling proteins in different stages of breast cancer. The differential distribution of these biomarkers could provide important prognostic and predictive indicators that may assist the clinical management of breast disease, namely in the early identification of cancers with a propensity to metastasise, and/or recur following adjuvant therapy.

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.

Investigating the effect of internal fixation stiffness on metaphyseal fracture healing in a mouse model

This project examined the differences in healing of metaphyseal bone, when the implants of variable stiffness are used for fracture fixation. This knowledge is important in development of novel orthopaedic implants, used in orthopaedic surgery to stabilise the fractures. Dr Koval used a mouse model to create a fracture, and then assessed its healing with a combination of mechanical testing, microcomputed tomography and histomorphometric examination.

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.