A new kinetic model for bulk polymerization of vinyl chloride based on two-phase hypothesis

A kinetic model has been developed for the bulk polymerization of vinyl chloride using Talamini's hypothesis of two-phase polymerization and a new concept of kinetic solubility which assumes that rapidly growing polymer chains have considerably greater solubility than the thermodynamic solubility of preformed polymer molecules of the same size and so can remain in solution even under thermodynamically unfavourable conditions. It is further assumed that this kinetic solubility is a function of chain length. The model yields a rate expression consistent with the experimental data for vinyl chloride bulk polymerization and moreover is able to explain several characteristic kinetic features of this system. Application of the model rate expression to the available rate data has yielded 2.36 × 108l mol−1 sec−1 for the termination rate constant in the polymer-rich phase; as expected, this value is smaller than that reported for homogenous polymerization by a factor of 10–30.

Analysis-By-Synthesis Based Switched Transform Domain Split Vq Using Gaussian Mixture Model

Using analysis-by-synthesis (AbS) approach, we develop a soft decision based switched vector quantization (VQ) method for high quality and low complexity coding of wideband speech line spectral frequency (LSF) parameters. For each switching region, a low complexity transform domain split VQ (TrSVQ) is designed. The overall rate-distortion (R/D) performance optimality of new switched quantizer is addressed in the Gaussian mixture model (GMM) based parametric framework. In the AbS approach, the reduction of quantization complexity is achieved through the use of nearest neighbor (NN) TrSVQs and splitting the transform domain vector into higher number of subvectors. Compared to the current LSF quantization methods, the new method is shown to provide competitive or better trade-off between R/D performance and complexity.

Topological Quantum Computation - An Analysis of an Anyon Model Based on Quantum Double Symmetries

There exists various suggestions for building a functional and a fault-tolerant large-scale quantum computer. Topological quantum computation is a more exotic suggestion, which makes use of the properties of quasiparticles manifest only in certain two-dimensional systems. These so called anyons exhibit topological degrees of freedom, which, in principle, can be used to execute quantum computation with intrinsic fault-tolerance. This feature is the main incentive to study topological quantum computation. The objective of this thesis is to provide an accessible introduction to the theory. In this thesis one has considered the theory of anyons arising in two-dimensional quantum mechanical systems, which are described by gauge theories based on so called quantum double symmetries. The quasiparticles are shown to exhibit interactions and carry quantum numbers, which are both of topological nature. Particularly, it is found that the addition of the quantum numbers is not unique, but that the fusion of the quasiparticles is described by a non-trivial fusion algebra. It is discussed how this property can be used to encode quantum information in a manner which is intrinsically protected from decoherence and how one could, in principle, perform quantum computation by braiding the quasiparticles. As an example of the presented general discussion, the particle spectrum and the fusion algebra of an anyon model based on the gauge group S_3 are explicitly derived. The fusion algebra is found to branch into multiple proper subalgebras and the simplest one of them is chosen as a model for an illustrative demonstration. The different steps of a topological quantum computation are outlined and the computational power of the model is assessed. It turns out that the chosen model is not universal for quantum computation. However, because the objective was a demonstration of the theory with explicit calculations, none of the other more complicated fusion subalgebras were considered. Studying their applicability for quantum computation could be a topic of further research.

An insight into the adoption of accountable-eHealth systems: n empirical research model based on the Australian context

This paper provides a first look at the acceptance of Accountable-eHealth (AeH) systems–a new genre of eHealth systems designed to manage information privacy concerns that hinder the proliferation of eHealth. The underlying concept of AeH systems is appropriate use of information through after-the-fact accountability for intentional misuse of information by healthcare professionals. An online questionnaire survey was utilised for data collection from three educational institutions in Queensland, Australia. A total of 23 hypotheses relating to 9 constructs were tested using a structural equation modelling technique. The moderation effects on the hypotheses were also tested based on six moderation factors to understand their role on the designed research model. A total of 334 valid responses were received. The cohort consisted of medical, nursing and other health related students studying at various levels in both undergraduate and postgraduate courses. Hypothesis testing provided sufficient data to accept 7 hypotheses. The empirical research model developed was capable of predicting 47.3% of healthcare professionals’ perceived intention to use AeH systems. All six moderation factors showed significant influence on the research model. A validation of this model with a wider survey cohort is recommended as a future study.

A model for mammalian male determination based on a passive Y chromosome

A model is suggested for mammalian male determination based on interactions postulated to occur among an autosomal repressor gene, an X-linked male-determining gene termed Tdx, and multiple copies of certain DNA sequences on the Y chromosome that do not code for any protein. The repressor, synthesised in limited amounts, has higher affinity for the Y-linked sequences than for Tdx and its affinity for Tdx is greater than that of RNA polymerase. In XY cells the Y effectively binds all available repressor, permitting transcription of Tdx to occur. In XX cells, since competition from the Y-linked high-affinity sequences is absent, the repressor binds to Tdx and prevents transcription. As a result of this competition between Tdx and the Y-linked high-affinity sites for limiting concentrations of the autosomal repressor, the product of the Tdx gene (TDX) is synthesized in the male but not in the female. It is suggested that in determination of the male sex, the role of the Y chromosome is to serve as a sink for the Tdx repressor. The proposed interactions provide a plausible explanation for the genetic properties of several anomalies of sexual development in mouse, man, and other mammals. The model suggests that the postulated multiple, highaffinity sequences on the Y chromosome of the mouse are included among the DNA sequences referred to as the Sxr-Bkm sequences.

Reputation model based on rating data and application in recommender systems

This thesis introduced two novel reputation models to generate accurate item reputation scores using ratings data and the statistics of the dataset. It also presented an innovative method that incorporates reputation awareness in recommender systems by employing voting system methods to produce more accurate top-N item recommendations. Additionally, this thesis introduced a personalisation method for generating reputation scores based on users' interests, where a single item can have different reputation scores for different users. The personalised reputation scores are then used in the proposed reputation-aware recommender systems to enhance the recommendation quality.

Pharmacokinetic interactions of pioglitazone

Pioglitazone is a thiazolidinedione compound used in the treatment of type 2 diabetes. It has been reported to be metabolised by multiple cytochrome P450 (CYP) enzymes, including CYP2C8, CYP2C9 and CYP3A4 in vitro. The aims of this work were to identify the CYP enzymes mainly responsible for the elimination of pioglitazone in order to evaluate its potential for in vivo drug interactions, and to investigate the effects of CYP2C8- and CYP3A4-inhibiting drugs (gemfibrozil, montelukast, zafirlukast and itraconazole) on the pharmacokinetics of pioglitazone in healthy volunteers. In addition, the effect of induction of CYP enzymes on the pharmacokinetics of pioglitazone in healthy volunteers was investigated, with rifampicin as a model inducer. Finally, the effect of pioglitazone on CYP2C8 and CYP3A enzyme activity was examined in healthy volunteers using repaglinide as a model substrate. Study I was conducted in vitro using pooled human liver microsomes (HLM) and human recombinant CYP isoforms. Studies II to V were randomised, placebo-controlled cross-over studies with 2-4 phases each. A total of 10-12 healthy volunteers participated in each study. Pretreatment with clinically relevant doses with the inhibitor or inducer was followed by a single dose of pioglitazone or repaglinide, whereafter blood and urine samples were collected for the determination of drug concentrations. In vitro, the elimination of pioglitazone (1 µM) by HLM was markedly inhibited, in particular by CYP2C8 inhibitors, but also by CYP3A4 inhibitors. Of the recombinant CYP isoforms, CYP2C8 metabolised pioglitazone markedly, and CYP3A4 also had a significant effect. All of the tested CYP2C8 inhibitors (montelukast, zafirlukast, trimethoprim and gemfibrozil) concentration-dependently inhibited pioglitazone metabolism in HLM. In humans, gemfibrozil raised the area under the plasma concentration-time curve (AUC) of pioglitazone 3.2-fold (P < 0.001) and prolonged its elimination half-life (t½) from 8.3 to 22.7 hours (P < 0.001), but had no significant effect on its peak concentration (Cmax) compared with placebo. Gemfibrozil also increased the excretion of pioglitazone into urine and reduced the ratios of the active metabolites M-IV and M-III to pioglitazone in plasma and urine. Itraconazole had no significant effect on the pharmacokinetics of pioglitazone and did not alter the effect of gemfibrozil on pioglitazone pharmacokinetics. Rifampicin decreased the AUC of pioglitazone by 54% (P < 0.001) and shortened its dominant t½ from 4.9 to 2.3 hours (P < 0.001). No significant effect on Cmax was observed. Rifampicin also decreased the AUC of the metabolites M-IV and M-III, shortened their t½ and increased the ratios of the metabolite to pioglitazone in plasma and urine. Montelukast and zafirlukast did not affect the pharmacokinetics of pioglitazone. The pharmacokinetics of repaglinide remained unaffected by pioglitazone. These studies demonstrate the principal role of CYP2C8 in the metabolism of pioglitazone in humans. Gemfibrozil, an inhibitor of CYP2C8, increases and rifampicin, an inducer of CYP2C8 and other CYP enzymes, decreases the plasma concentrations of pioglitazone, which can necessitate blood glucose monitoring and adjustment of pioglitazone dosage. Montelukast and zafirlukast had no effects on the pharmacokinetics of pioglitazone, indicating that their inhibitory effect on CYP2C8 is negligible in vivo. Pioglitazone did not increase the plasma concentrations of repaglinide, indicating that its inhibitory effect on CYP2C8 and CYP3A4 is very weak in vivo.

Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Is there a role for microsampling in antibiotic pharmacokinetic studies?

- Introduction Clinical pharmacokinetic studies of antibiotics can establish evidence-based dosing regimens that improve the likelihood of eradicating the pathogen at the site of infection, reduce the potential for selection of resistant pathogens, and minimize harm to the patient. Innovations in small volume sampling (< 50 μL) or ‘microsampling’ may result in less-invasive sample collection, self-sampling and dried storage. Microsampling may open up opportunities in patient groups where sampling is challenging. - Areas Covered The challenges for implementation of microsampling to assure suitability of the results, include: acceptable study design, regulatory agency acceptance, and meeting bioanalytical validation requirements. This manuscript covers various microsampling methods, including dried blood/plasma spots, volumetric absorptive microsampling, capillary microsampling, plasma preparation technologies and solid-phase microextraction. - Expert Opinion The available analytical technology is being underutilized due to a lack of bridging studies and validated bioanalytical methods. These deficiencies represent major impediments to the application of microsampling to antibiotic pharmacokinetic studies. A conceptual framework for the assessment of the suitability of microsampling in clinical pharmacokinetic studies of antibiotics is provided. This model establishes a ‘contingency approach’ with consideration of the antibiotic and the type and location of the patient, as well as the more prescriptive bioanalytical validation protocols.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

A generic individual-based spatially explicit model as a novel tool for investigating insect-plant interactions: A case study of the behavioural ecology of frugivorous Tephritidae

Computational modelling of mechanisms underlying processes in the real world can be of great value in understanding complex biological behaviours. Uptake in general biology and ecology has been rapid. However, it often requires specific data sets that are overly costly in time and resources to collect. The aim of the current study was to test whether a generic behavioural ecology model constructed using published data could give realistic outputs for individual species. An individual-based model was developed using the Pattern-Oriented Modelling (POM) strategy and protocol, based on behavioural rules associated with insect movement choices. Frugivorous Tephritidae (fruit flies) were chosen because of economic significance in global agriculture and the multiple published data sets available for a range of species. The Queensland fruit fly (Qfly), Bactrocera tryoni, was identified as a suitable individual species for testing. Plant canopies with modified architecture were used to run predictive simulations. A field study was then conducted to validate our model predictions on how plant architecture affects fruit flies’ behaviours. Characteristics of plant architecture such as different shapes, e.g., closed-canopy and vase-shaped, affected fly movement patterns and time spent on host fruit. The number of visits to host fruit also differed between the edge and centre in closed-canopy plants. Compared to plant architecture, host fruit has less contribution to effects on flies’ movement patterns. The results from this model, combined with our field study and published empirical data suggest that placing fly traps in the upper canopy at the edge should work best. Such a modelling approach allows rapid testing of ideas about organismal interactions with environmental substrates in silico rather than in vivo, to generate new perspectives. Using published data provides a saving in time and resources. Adjustments for specific questions can be achieved by refinement of parameters based on targeted experiments.

A model based factorization approach for dense 3D recovery from monocular video

Feature track matrix factorization based methods have been attractive solutions to the Structure-front-motion (Sfnl) problem. Group motion of the feature points is analyzed to get the 3D information. It is well known that the factorization formulations give rise to rank deficient system of equations. Even when enough constraints exist, the extracted models are sparse due the unavailability of pixel level tracks. Pixel level tracking of 3D surfaces is a difficult problem, particularly when the surface has very little texture as in a human face. Only sparsely located feature points can be tracked and tracking error arc inevitable along rotating lose texture surfaces. However, the 3D models of an object class lie in a subspace of the set of all possible 3D models. We propose a novel solution to the Structure-from-motion problem which utilizes the high-resolution 3D obtained from range scanner to compute a basis for this desired subspace. Adding subspace constraints during factorization also facilitates removal of tracking noise which causes distortions outside the subspace. We demonstrate the effectiveness of our formulation by extracting dense 3D structure of a human face and comparing it with a well known Structure-front-motion algorithm due to Brand.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.