Frequency-adjustable positive sequence detector for power conditioning applications

This paper proposes a novel and simple positive sequence detector (PSD), which is inherently self-adjustable to fundamental frequency deviations by means of a software-based PLL (Phase Locked Loop). Since the proposed positive sequence detector is not based on Fortescue's classical decomposition and no special input filtering is needed, its dynamic response may be as fast as one fundamental cycle. The digital PLL ensures that the positive sequence components can be calculated even under distorted waveform conditions and fundamental frequency deviations. For the purpose of validating the proposed models, the positive sequence detector has been implemented in a PC-based Power Quality Monitor and experimental results illustrate its good performance. The PSD algorithm has also been evaluated in the control loop of a Series Active Filter and simulation results demonstrate its effectiveness in a closed-loop system. Moreover, considering single-phase applications, this paper also proposes a general single-phase PLL and a Fundamental Wave Detector (FWD) immune to frequency variations and waveform distortions. © 2005 IEEE.

Lanczos subspace filter diagonalization: Homogeneous recursive filtering and a low-storage method for the calculation of matrix elements

We develop a new iterative filter diagonalization (FD) scheme based on Lanczos subspaces and demonstrate its application to the calculation of bound-state and resonance eigenvalues. The new scheme combines the Lanczos three-term vector recursion for the generation of a tridiagonal representation of the Hamiltonian with a three-term scalar recursion to generate filtered states within the Lanczos representation. Eigenstates in the energy windows of interest can then be obtained by solving a small generalized eigenvalue problem in the subspace spanned by the filtered states. The scalar filtering recursion is based on the homogeneous eigenvalue equation of the tridiagonal representation of the Hamiltonian, and is simpler and more efficient than our previous quasi-minimum-residual filter diagonalization (QMRFD) scheme (H. G. Yu and S. C. Smith, Chem. Phys. Lett., 1998, 283, 69), which was based on solving for the action of the Green operator via an inhomogeneous equation. A low-storage method for the construction of Hamiltonian and overlap matrix elements in the filtered-basis representation is devised, in which contributions to the matrix elements are computed simultaneously as the recursion proceeds, allowing coefficients of the filtered states to be discarded once their contribution has been evaluated. Application to the HO2 system shows that the new scheme is highly efficient and can generate eigenvalues with the same numerical accuracy as the basic Lanczos algorithm.

The role of extracellular matrix macromolecules in cancer and diabetic macroangiopathy – with special reference to decorin and hyaluronan

The central role of extracellular matrix (ECM) macromolecules in diseases such as cancer and atherosclerotic vascular diseases including diabetic macroangiopathy is indisputable. Decorin and hyaluronan (HA) represent vital ECM macromolecules in the microenvironment of cells and are centrally involved in human cancer and cardiovascular biology. In cancer, decorin is considered to play a tumor suppressive role. However, there is some discrepancy whether malignant cells express it. Regarding HA, its contribution to the development of atherosclerotic vascular diseases has been well established. Nevertheless, the precise role of HA in arterial narrowing associated with diabetes is not known. The present study focused on two vital ECM macromolecules, namely decorin and HA. First, decorin expression was studied in human tumorigenesis. Furthermore, the effect of adenovirus-mediated decorin transduction on selected cancer cell lines was investigated. The results invariably showed that cancer cells completely lacked decorin expression. The study also demonstrated that transducing cancer cells with decorin adenoviral vector markedly inhibited their malignant behavior. In line with this, a strong induction of decorin expression in normal human embryonic stem cells (hESCs), but not in abnormal hESCs was observed during their differentiation. Secondly, the significance of HA in the development of diabetic macroangiopathy in response to hyperglycemia was evaluated. Results showed that the synthesis of HA by vascular smooth muscle cells was significantly increased in response to high glucose concentration. This increase was associated with the diminished ability of the cells to contract collagen-rich matrix suggesting that HA participates in the disturbed vascular remodeling of diabetic patients. The results of this study support endeavours to develop novel ECM macromolecule -based therapies targeting cancer and cardiovascular diseases.

A Matrix PRNG with S-Box Output Filtering

We describe a modification to a previously published pseudorandom number generator improving security while maintaining high performance. The proposed generator is based on the powers of a word-packed block upper triangular matrix and it is designed to be fast and easy to implement in software since it mainly involves bitwise operations between machine registers and, in our tests, it presents excellent security and statistical characteristics. The modifications include a new, key-derived s-box based nonlinear output filter and improved seeding and extraction mechanisms. This output filter can also be applied to other generators.

On the Filtering Problem for Continuous-Time Markov Jump Linear Systems with no Observation of the Markov Chain

We consider in this paper the optimal stationary dynamic linear filtering problem for continuous-time linear systems subject to Markovian jumps in the parameters (LSMJP) and additive noise (Wiener process). It is assumed that only an output of the system is available and therefore the values of the jump parameter are not accessible. It is a well known fact that in this setting the optimal nonlinear filter is infinite dimensional, which makes the linear filtering a natural numerically, treatable choice. The goal is to design a dynamic linear filter such that the closed loop system is mean square stable and minimizes the stationary expected value of the mean square estimation error. It is shown that an explicit analytical solution to this optimal filtering problem is obtained from the stationary solution associated to a certain Riccati equation. It is also shown that the problem can be formulated using a linear matrix inequalities (LMI) approach, which can be extended to consider convex polytopic uncertainties on the parameters of the possible modes of operation of the system and on the transition rate matrix of the Markov process. As far as the authors are aware of this is the first time that this stationary filtering problem (exact and robust versions) for LSMJP with no knowledge of the Markov jump parameters is considered in the literature. Finally, we illustrate the results with an example.

Removing ambiguities in the neutrino mass matrix

We suggest that the weak-basis independent condition det(M-nu) = 0 for the effective neutrino mass matrix can be used in order to remove the ambiguities in the reconstruction of the neutrino mass matrix from input data available from present and future feasible experiments. In this framework, we study the full reconstruction of M-nu with special emphasis on the correlation between the Majorana CP-violating phase and the various mixing angles. The impact of the recent KamLAND results on the effective neutrino mass parameter is also briefly discussed. (C) 2003 Elsevier Science B.V. All rights reserved.

Improving Transition Outcomes: Resource Mapping Workshop: Service Matrix

Resource Mapping tool from the Improving Transition Outcomes Resource Mapping Workshops

Building a Social Accounting Matrix within the ESA95 Framework: Obtaining a Dataset for Applied General Equilibrium Modelling

This research provides a description of the process followed in order to assemble a "Social Accounting Matrix" for Spain corresponding to the year 2000 (SAMSP00). As argued in the paper, this process attempts to reconcile ESA95 conventions with requirements of applied general equilibrium modelling. Particularly, problems related to the level of aggregation of net taxation data, and to the valuation system used for expressing the monetary value of input-output transactions have deserved special attention. Since the adoption of ESA95 conventions, input-output transactions have been preferably valued at basic prices, which impose additional difficulties on modellers interested in computing applied general equilibrium models. This paper addresses these difficulties by developing a procedure that allows SAM-builders to change the valuation system of input-output transactions conveniently. In addition, this procedure produces new data related to net taxation information.

Building a Social Accounting Matrix within the ESA95 Framework: Obtaining a Dataset for Applied General Equilibrium Modelling

This research provides a description of the process followed in order to assemble a "Social Accounting Matrix" for Spain corresponding to the year 2000 (SAMSP00). As argued in the paper, this process attempts to reconcile ESA95 conventions with requirements of applied general equilibrium modelling. Particularly, problems related to the level of aggregation of net taxation data, and to the valuation system used for expressing the monetary value of input-output transactions have deserved special attention. Since the adoption of ESA95 conventions, input-output transactions have been preferably valued at basic prices, which impose additional difficulties on modellers interested in computing applied general equilibrium models. This paper addresses these difficulties by developing a procedure that allows SAM-builders to change the valuation system of input-output transactions conveniently. In addition, this procedure produces new data related to net taxation information.

Thermoplastic bioactive composite – with special reference to dissolution behaviour and tissue response

Bioactive glasses are surface-active ceramic materials which support and accelerate bone growth in the body. During the healing of a bone fracture or a large bone defect, fixation is often needed. The aim of this thesis was to determine the dissolution behaviour and biocompatibility of a composite consisting of poly(ε-caprolactone-co-DL-lactide) and bioactive glass (S53P4). In addition the applicability as an injectable material straight to a bone defect was assessed. In in vitro tests the dissolution behaviour of plain copolymer and composites containing bioactive glass granules was evaluated, as well as surface reactivity and the material’s capability to form apatite in simulated body fluid (SBF). The human fibroblast proliferation was tested on materials in cell culture. In in vivo experiments, toxicological tests, material degradation and tissue reactions were tested both in subcutaneous space and in experimental bone defects. The composites containing bioactive glass formed a unified layer of apatite on their surface in SBF. The size and amount of glass granules affected the degradation of polymer matrix, as well the material’s surface reactivity. In cell culture on the test materials the human gingival fibroblasts proliferated and matured faster compared with control materials. In in vitro tests a connective tissue capsule was formed around the specimens, and became thinner in the course of time. Foreign body cell reactions in toxicological tests were mild. In experimental bone defects the specimens with a high concentration of small bioactive glass granules (<45 μm) formed a dense apatite surface layer that restricted the bone ingrowth to material. The range of large glass granules (90-315 μm) with high concentrations formed the best bonding with bone, but slow degradation on the copolymer restricted the bone growth only in the superficial layers. In these studies, the handling properties of the material proved to be good and tissue reactions were mild. The reactivity of bioactive glass was retained inside the copolymer matrix, thus enabling bone conductivity with composites. However, the copolymer was noticed to degradate too slowly compared with the bone healing. Therefore, the porosity of the material should be increased in order to improve tissue healing.

Phase voltage control and filtering in a converter-fed single-phase customer-end system of the LVDC distribution network

In recent years, the network vulnerability to natural hazards has been noticed. Moreover, operating on the limits of the network transmission capabilities have resulted in major outages during the past decade. One of the reasons for operating on these limits is that the network has become outdated. Therefore, new technical solutions are studied that could provide more reliable and more energy efficient power distributionand also a better profitability for the network owner. It is the development and price of power electronics that have made the DC distribution an attractive alternative again. In this doctoral thesis, one type of a low-voltage DC distribution system is investigated. Morespecifically, it is studied which current technological solutions, used at the customer-end, could provide better power quality for the customer when compared with the current system. To study the effect of a DC network on the customer-end power quality, a bipolar DC network model is derived. The model can also be used to identify the supply parameters when the V/kW ratio is approximately known. Although the model provides knowledge of the average behavior, it is shown that the instantaneous DC voltage ripple should be limited. The guidelines to choose an appropriate capacitance value for the capacitor located at the input DC terminals of the customer-end are given. Also the structure of the customer-end is considered. A comparison between the most common solutions is made based on their cost, energy efficiency, and reliability. In the comparison, special attention is paid to the passive filtering solutions since the filter is considered a crucial element when the lifetime expenses are determined. It is found out that the filter topology most commonly used today, namely the LC filter, does not provide economical advantage over the hybrid filter structure. Finally, some of the typical control system solutions are introduced and their shortcomings are presented. As a solution to the customer-end voltage regulation problem, an observer-based control scheme is proposed. It is shown how different control system structures affect the performance. The performance meeting the requirements is achieved by using only one output measurement, when operating in a rigid network. Similar performance can be achieved in a weak grid by DC voltage measurement. An additional improvement can be achieved when an adaptive gain scheduling-based control is introduced. As a conclusion, the final power quality is determined by a sum of various factors, and the thesis provides the guidelines for designing the system that improves the power quality experienced by the customer.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Prognostic Factors In Breast Cancer. With Special Reference to Cyclins A, B1, D1 and E, MMP-1 and Decorin

Breast cancer is a highly heterogenous malignancy, which despite of the similar histological type shows different clinical behaviour and response to therapy. Prognostic factors are used to estimate the risk for recurrence and the likelihood of treatment effectiveness. Because breast cancer is one of the most common causes of cancer death in women worldwide, identification of new prognostic markers are needed to develop more specific and targeted therapies. Cancer is caused by uncontrolled cell proliferation. The cell cycle is controlled by specific proteins, which are known as cyclins. They function at important checkpoints by activating cyclin-dependent kinase enzymes. Overexpression of different cyclins has been linked to several cancer types and altered expression of cyclins A, B1, D1 and E has been associated with poor survival. Little is known about the combined expression of cyclins in relation to the tumour grade, breast cancer subtype and other known prognostic factors. In this study cyclins A, B1 and E were shown to correlate with histological grade, Ki-67 and HER2 expression. Overexpression of cyclin D1 correlated with receptor status and non-basal breast cancer suggesting that cyclin D1 might be a marker of good prognosis. Proteolysis in the surrounding tumour stroma is increased during cancer development. Matrix metalloproteinases (MMPs) are proteolytic enzymes that are capable of degrading extracellular matrix proteins. Increased expression and activation of several MMPs have been found in many cancers and MMPs appear to be important regulators of invasion and metastasis. In this study MMP-1 expression was analysed in breast cancer epithelial cells and in cancer associated stromal cells. MMP-1 expression by breast cancer epithelial cells was found to carry an independent prognostic value as did Ki-67 and bcl-2. The results suggest that in addition to stromal cells MMP-1 expression in tumour cells control breast cancer progression. Decorin is a small proteoglycan and an important component of the extracellular matrix. Decorin has been shown to inhibit growth of tumour cells and reduced decorin expression is associated with a poor prognosis in several cancer types. There has been some suspicion wheather different cancer cells express decorin. In this study decorin expression was shown to localize only in the cells of the original stroma, while breast cancer epithelial cells were negative for decorin expression. However, transduction of decorin in decorin-negative human breast cancer cells markedly modulated the growth pattern of these cells. This study provides evidence that targeted decorin transduction to breast cancer cells could be used as a novel adjuvant therapy in breast malignancies.

Experimental Studies on Non-Metallic Composite Bone Implants With a Special Reference to Staphylococcal Biofilm Infection

Non-metallic implants made of bioresorbable or biostable synthetic polymers are attractive options in many surgical procedures, ranging from bioresorbable suture anchors of arthroscopic surgery to reconstructive skull implants made of biostable fiber-reinforced composites. Among other benefits, non-metallic implants produce less interference in imaging. Bioresorbable polymer implants may be true multifunctional, serving as osteoconductive scaffolds and as matrices for simultaneous delivery of bone enhancement agents. As a major advantage for loading conditions, mechanical properties of biostable fiber-reinforced composites can be matched with those of the bone. Unsolved problems of these biomaterials are related to the risk of staphylococcal biofilm infections and to the low osteoconductivity of contemporary bioresorbable composite implants. This thesis was focused on the research and development of a multifunctional implant model with enhanced osteoconductivity and low susceptibility to infection. In addition, the experimental models for assessment, diagnostics and prophylaxis of biomaterial-related infections were established. The first experiment (Study I) established an in vitro method for simultaneous evaluation of calcium phosphate and biofilm formation on bisphenol-Aglycidyldimethacrylate and triethylenglycoldimethacrylate (BisGMA-TEGDMA) thermosets with different content of bioactive glass 45S5. The second experiment (Study II) showed no significant difference in osteointegration of nanostructured and microsized polylactide-co-glycolide/β-tricalcium phosphate (PLGA /β-TCP) composites in a minipig model. The third experiment (Study III) demonstrated that positron emission tomography (PET) imaging with the novel 68Ga labelled 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) CD33 related sialic-acid immunoglobulin like lectins (Siglec-9) tracer was able to detect inflammatory response to S. epidermidis and S. aureus peri-implant infections in an intraosseous polytetrafluoroethylene catheter model. In the fourth experiment (Study IV), BisGMATEGDMA thermosets coated with lactose-modified chitosan (Chitlac) and silver nanoparticles exhibited antibacterial activity against S. aureus and P. aeruginosa strains in an in vitro biofilm model and showed in vivo biocompatibility in a minipig model. In the last experiment (Study V), a selective androgen modulator (SARM) released from a poly(lactide)-co-ε-caprolactone (PLCL) polymer matrix failed to produce a dose-dependent enhancement of peri-implant osteogenesis in a bone marrow ablation model.