Antimicrobial activities and diversity of sponge derived microbes

In this study, marine sponges collected in Irish waters were analysed for their associated microbiota. Of the approximately 240 bacterial isolates obtained from two sponges several showed antimicrobial activity; among them members of genera which have rarely been shown to produce antimicrobial compounds. Differences observed from the sponge-derived groups of isolates in terms of bioactivity suggests that S. carnosus isolates may be a better source of antibacterial compounds, while Leucosolenia sp. isolates appear to be a better source of antifungal compounds. More than 60% of fungal isolates obtained from 12 sponge samples proved to be bioactive. One of the isolates, which was closely related to Fusarium oxysporum and showed activity against bacteria and fungi, was investigated for its secondary metabolite genes. At least 5 different NRPS genes, with a sequence similarity as low as 50 % to known genes, were identified highlighting the likelihood that this isolate may be capable of producing novel secondary metabolites. A Micromonospora sp. was isolated from a Haliclona simulans sample collected in Irish waters. The isolate inhibited the growth of Gram positive bacterial test strains in three different antimicrobial assays. Employing preparative layer chromatography the compound responsible for the bioactivity could be isolated. According to LC-MS andNMR data the bioactive compound could indeed be novel. Finally, two deep water sponges were shown to host a remarkably different bacterial and archaeal diversity by application of 454 Pyrosequencing. The L. diversichela –proteobacterial community was dominated by a single ƴ-proteobacterial bacterium whereas the S. normani sample hosted a largely sponge specific microbial community, even more diverse than has been previously reported for shallow water sponges. Organisms potentially involved in nitrification, sulphate reduction and secondary metabolite production were found to be spatially distributed in the sponge. Furthermore, a deep sea specific population was implied.

Chemical vapour deposition of complex ferroic oxide thin films

Herein is presented a novel chemical vapour deposition (CVD) route for the fabrication of oxide ferroelectrics. A versatile layer-by-layer growth mode was developed to prepare naturally super-latticed bismuth based materials belonging to the Aurivillius phase family, with which good control over composition and crystal structure was achieved. In chapter 3, the effect of epitaxial strain on one of the very simple oxide materials TiO2 was studied. It has been found that the ultra-thin TiO2 films demonstrate ferroelectric behaviour when grown on NdGaO3 substrates. TiO2 exists in various crystal phases, but none of them show ferroelectric behaviour. The epitaxial strain due to the substrate, changes the crystal structure from tetragonal to orthorhombic which in turn leads to ferroelectric behaviour. In chapter 4, a unique growth method for multiferroic BiFeO3 (BFO) thin films is shown, where a phase pure BFO thin films can be prepared even in the presence of excess bismuth precursor during the growth process. This type of growth is usually called adsorption controlled growth and can be used for growing various bismuth containing compounds, where the volatility of bismuth can create various types of defects. Chapter 5 describes the growth of Bi4Ti3O12 thin films in a layer-by-layer growth mode. In this section, the effect of Bi and Ti precursor flows on the growth of thin films is discussed and it is shown that how change in precursor flows leads to out-ofphase boundary defects during the layer-by-layer growth mode. In chapter 6, the growth of a compound Bi5Ti3FeO15, which is a 1:1 mixture of BiFeO3 and Bi4Ti3O12, is presented. The growth mechanism of Bi5Ti3FeO15 thin films is presented, where the Fe precursor flow was controlled from zero to the insertion of one full BiFeO3 perovskite unit cell into the Bi4Ti3O12 structure in addition, the effect of iron precursor flow on crystalline properties is demonstrated. The methods presented in this thesis can be adopted to grow ferroelectric and multiferroic films for industrial applications.

Analyses on Gold and US Dollar in Vietnam's Transitional Economy

This paper looks into economic insights offerred by considerations of two important financial markets in Vietnam, gold and USD. In general, the paper focuses on time series properties, mainly returns at different frequencies, and test the weak-form efficient market hypothesis. All the test rejects the efficiency of both gold and foreign exchange markets. All time series exhibit strong serial correlations. ARMA-GARCH specifications appear to have performed well with different time series. In all cases the changing volatility phenomenon is strongly supported through empirical data. An additional test is performed on the daily USD return to try to capture the impacts of Asian financial crisis and daily price limits applicable. No substantial impacts of the Asian crisis and the central bank-devised limits are found to influence the risk level of daily USD return.

Mergers and Acquisitions in Vietnam’s Emerging Market Economy, 1990-2009

This paper is the first major and thorough study on the M&A activities in Vietnam’s emerging market economy, covering almost entirely the M&A history after the launch of Doi Moi. The surge in these activities since mid-2000s by no means incidentally coincides with the jump in FDI and FPI inflows into the nation. M&A industry in Vietnam has its socio-cultural traits that could help explain economic happenings, with anomalies and transitional characteristics, far better than even the most complete set of empirical data. Proceeds from sales of existing assets and firms have mainly flowed into the highly speculative industries of securities, banking, non-bank financials, portfolio investments and real estates. The impacts of M&A on Vietnam’s long-term prosperity are, thus, highly questionable. An observable high degree of volatility in the M&A processes would likely blow outthe high ex ante expectations by many speculators, when ex post realizations finally arrive. The effect of the past M&A evolution in Vietnam has been indecisively positive or negative, with significant presence of rent-seeking and likelihood of causing destructive entrepreneurship. From a socio-economic and cultural view, the degree of positive impacts it may result in for domestic entrepreneurship will perhaps be the single most important indicator.

Nonparametric estimation of structural models for high-frequency currency market data

Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities. This paper investigates whether an equilibrium monetary model can account for nonlinearities in weekly data. The model incorporates time-nonseparable preferences and a transaction cost technology. Simulated sample paths are generated using Marcet's parameterized expectations procedure. The paper also develops a new method for estimation of structural economic models. The method forces the model to match (under a GMM criterion) the score function of a nonparametric estimate of the conditional density of observed data. The estimation uses weekly U.S.-German currency market data, 1975-90. © 1995.

A multivariate generalized ARCH approach to modeling risk premia in forward foreign exchange rate markets

Assuming that daily spot exchange rates follow a martingale process, we derive the implied time series process for the vector of 30-day forward rate forecast errors from using weekly data. The conditional second moment matrix of this vector is modelled as a multivariate generalized ARCH process. The estimated model is used to test the hypothesis that the risk premium is a linear function of the conditional variances and covariances as suggested by the standard asset pricing theory literature. Little supportt is found for this theory; instead lagged changes in the forward rate appear to be correlated with the 'risk premium.'. © 1990.

Assessing individual risk attitudes using field data from lottery games

We use information from the television game show with the highest guaranteed average payoff in the United States, Hoosier Millionaire, to analyze risktaking in a high-stakes experiment. We characterize gambling decisions under alternative assumptions about contestant behavior and preferences, and derive testable restrictions on individual risk attitudes based on this characterization. We then use an extensive sample of gambling decisions to estimate distributions of risk-aversion parameters consistent with the theoretical restrictions and revealed preferences. We find that although most contestants display risk-averse preferences, the extent of the risk aversion implied by our estimates varies substantially with the stakes involved in the different decisions.

Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.

A soft robust model for optimization under ambiguity

In this paper, we propose a framework for robust optimization that relaxes the standard notion of robustness by allowing the decision maker to vary the protection level in a smooth way across the uncertainty set. We apply our approach to the problem of maximizing the expected value of a payoff function when the underlying distribution is ambiguous and therefore robustness is relevant. Our primary objective is to develop this framework and relate it to the standard notion of robustness, which deals with only a single guarantee across one uncertainty set. First, we show that our approach connects closely to the theory of convex risk measures. We show that the complexity of this approach is equivalent to that of solving a small number of standard robust problems. We then investigate the conservatism benefits and downside probability guarantees implied by this approach and compare to the standard robust approach. Finally, we illustrate theme thodology on an asset allocation example consisting of historical market data over a 25-year investment horizon and find in every case we explore that relaxing standard robustness with soft robustness yields a seemingly favorable risk-return trade-off: each case results in a higher out-of-sample expected return for a relatively minor degradation of out-of-sample downside performance. © 2010 INFORMS.

Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online. © 2013 Copyright Taylor and Francis Group, LLC.

Prices versus quantities versus bankable quantities

Quantity-based regulation with banking allows regulated firms to shift obligations across time in response to periods of unexpectedly high or low marginal costs. Despite its wide prevalence in existing and proposed emission trading programs, banking has received limited attention in past welfare analyses of policy choice under uncertainty. We address this gap with a model of banking behavior that captures two key constraints: uncertainty about the future from the firm's perspective and a limit on negative bank values (e.g. borrowing). We show conditions where banking provisions reduce price volatility and lower expected costs compared to quantity policies without banking. For plausible parameter values related to U.S. climate change policy, we find that bankable quantities produce behavior quite similar to price policies for about two decades and, during this period, improve welfare by about a $1 billion per year over fixed quantities. © 2012 Elsevier B.V.

Temporal representation of state transitions

This paper describes a knowledge-based temporal representation of state transitions for industrial real-time systems. To allow expression of uncertainty, we shall define fluents as disjuncts of positive/negative time-varying properties. A state of the world is represented as a collection of fluents, which is usually incomplete in the sense that neither the positive form nor the negative form of some properties can be implied from it. The world under consideration is assumed to persist in a given state until an action(s) takes place to effect a transition of it into another state, where actions may either be instantaneous or durative. High-level causal laws are characterized in terms of relationships between actions and the involved world states. An effect completion axiom is imposed on each causal law to guarantee that all the fluents that can be affected by the performance of the corresponding action are governed. This completion requirement is practical for most industrial real-time applications and in fact provides a simple and effective treatment to the so-called frame problem.

A policy-definition language and prototype implementation library for policy-based autonomic systems

This paper presents work towards generic policy toolkit support for autonomic computing systems in which the policies themselves can be adapted dynamically and automatically. The work is motivated by three needs: the need for longer-term policy-based adaptation where the policy itself is dynamically adapted to continually maintain or improve its effectiveness despite changing environmental conditions; the need to enable non autonomics-expert practitioners to embed self-managing behaviours with low cost and risk; and the need for adaptive policy mechanisms that are easy to deploy into legacy code. A policy definition language is presented; designed to permit powerful expression of self-managing behaviours. The language is very flexible through the use of simple yet expressive syntax and semantics, and facilitates a very diverse policy behaviour space through both hierarchical and recursive uses of language elements. A prototype library implementation of the policy support mechanisms is described. The library reads and writes policies in well-formed XML script. The implementation extends the state of the art in policy-based autonomics through innovations which include support for multiple policy versions of a given policy type, multiple configuration templates, and meta-policies to dynamically select between policy instances and templates. Most significantly, the scheme supports hot-swapping between policy instances. To illustrate the feasibility and generalised applicability of these tools, two dissimilar example deployment scenarios are examined. The first is taken from an exploratory implementation of self-managing parallel processing, and is used to demonstrate the simple and efficient use of the tools. The second example demonstrates more-advanced functionality, in the context of an envisioned multi-policy stock trading scheme which is sensitive to environmental volatility

Generic support for policy-based self-adaptive systems

This paper presents a policy definition language which forms part of a generic policy toolkit for autonomic computing systems in which the policies themselves can be modified dynamically and automatically. Targeted enhancements to the current state of practice include: policy self-adaptation where the policy itself is dynamically modified to match environmental conditions; improved support for non autonomics-expert developers; and facilitating easy deployment of adaptive policies into legacy code. The policy definition language permits powerful expression of self-managing behaviours and facilitates a diverse policy behaviour space. Features include support for multiple versions of a given policy type, multiple configuration templates, and meta policies to dynamically select between policy instances. An example deployment scenario illustrates advanced functionality in the context of a multi policy stock trading system which is sensitive to environmental volatility.

Numerical solutions of certain nonlinear models in European options on a distributed computing environment

Financial modelling in the area of option pricing involves the understanding of the correlations between asset and movements of buy/sell in order to reduce risk in investment. Such activities depend on financial analysis tools being available to the trader with which he can make rapid and systematic evaluation of buy/sell contracts. In turn, analysis tools rely on fast numerical algorithms for the solution of financial mathematical models. There are many different financial activities apart from shares buy/sell activities. The main aim of this chapter is to discuss a distributed algorithm for the numerical solution of a European option. Both linear and non-linear cases are considered. The algorithm is based on the concept of the Laplace transform and its numerical inverse. The scalability of the algorithm is examined. Numerical tests are used to demonstrate the effectiveness of the algorithm for financial analysis. Time dependent functions for volatility and interest rates are also discussed. Applications of the algorithm to non-linear Black-Scholes equation where the volatility and the interest rate are functions of the option value are included. Some qualitative results of the convergence behaviour of the algorithm is examined. This chapter also examines the various computational issues of the Laplace transformation method in terms of distributed computing. The idea of using a two-level temporal mesh in order to achieve distributed computation along the temporal axis is introduced. Finally, the chapter ends with some conclusions.