Biological management of the invasive Vachellia nilotica ssp. indica (Benth.) Kyal. & Boatwr. (Fabales: Mimosoideae) in tropical Australia: stress-inducing potential of Anomalococcus indicus Ramakrishna (Insecta: Hemiptera: Coccoidea: Lecanodiaspididae), an agent of promise

Vachellia nilotica ssp. indica (hereafter, V. n. indica) is an important tree weed in Australia. Its dense populations induce undesirable changes in the vast areas of northern Australia. Because chemical and mechanical management options appear unviable for various reasons, biological management of this tree is considered a better option. Among the many trialled arthropods in Australian context, Anomalococcus indicus, a lecanodiaspid native to India, has been identified as a potent-candidate, since in India, its native terrain, it is the most widespread and occurs throughout the year. Severe infestations of A. indicus cause defoliation, wilting and death of branches, and occasionally the tree. Populations of A. indicus have been brought into Australia and are being tested for its host specificity under quarantine conditions. This article reports the physiological damage and stress it inflicts in the shoots of V. n. indica. Younger-nymphal instars of A. indicus feed on cortical-parenchyma cells of young stems, whereas the older instars and adults feed from the phloem of old stems. Two conspicuous responses of V. n. indica arising in response to the feeding action of A. indicus are changes in the cell-wall dynamics and irregular cell divisions. The feeding action of A. indicus elicits a sequence of reactions in the stem tissues of V. n. indica such as differentiation of thick-walled elements in the outer cortical parenchyma, differential thickening of cells with supernumerary layers of either suberin or lignin, proliferations of parenchyma and phloem, wall thickening and obliteration of inner lumen of phloem cells, and the sieve plates plugged with callosic deposits. The responses are the culminations of interaction between the virulence factor (one or more of the salivary proteins?) from A. indicus and the resistance factor in V. n. indica. We have analysed structural changes in the context of their functions, by comparing the feeding action of A. indicus with that of other hemipteroids. From the level of stress it induces, this study confirms that A. indicus has the potential to be an effective biological management of V. n. indica in Australia. © 2014 © 2014 Taylor & Francis and Aboricultural Association.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

Proton NMR spectra including 13C- and 77Se-satellites in organoselenium compounds oriented in the nematic phase : Part 1. The spectrum of 2,1,3-benzoselenadiazole

The vibrationally corrected structure of 2,1,3-benzoselenadiazole is derived from the proton NMR spectrum including 13C-H and 77Se-H satellites, in a nematic solvent. The results indicate considerable bond-fixation in the 6-membered ring. References

The leaf-feeding geometrid Isturgia disputaria (Guenee)-A potential biological control agent for prickly acacia, Vachellia nilotica subsp. indica (Benth.) Kyal. & Boatwr. (Mimosaceae) in Australia

Prickly acacia (Vachellia nilotica subsp. indica), a native multipurpose tree in India, is a weed of National significance, and a target for biological control in Australia. Based on plant genetic and climatic similarities, native range surveys for identifying potential biological control agents for prickly acacia were conducted in India during 2008-2011. In the survey leaf-feeding geometrid, Isturgia disputaria Guenee (syn. Tephrina pulinda), widespread in Tamil Nadu and Karnataka States, was prioritized as a potential biological control agent based on field host range, damage potential and no choice test on non target plant species. Though the field host range study exhibited that V. nilotica ssp. indica and V. nilotica ssp. tomentosa were the primary hosts for successful development of the insect, I. disputaria, replicated no - choice larval feeding and development tests conducted on cut foliage and live plants of nine non-target acacia test plant species in India revealed the larval feeding and development on three of the nine non-target acacia species, V. tortilis, V. planiferons and V. leucophloea in addition to the V. nilotica ssp. indica and V. nilotica ssp. tomentosa. However, the proportion of larvae developing into adults was higher on V. nilotica subsp. indica and V. nilotica subsp. tomentosa, with 90% and 80% of the larvae completing development, respectively. In contrast, the larval mortality was higher on V. tortilis (70%), V. leucophloea (90%) and V. planiferons (70%). The no-choice test results support the earlier host specificity test results of I. disputaria from Pakistan, Kenya and under quarantine in Australia. Contrasting results between field host range and host use pattern under no-choice conditions are discussed.

Mathematical Aspects of Financial Markets with Frictions

Frictions are factors that hinder trading of securities in financial markets. Typical frictions include limited market depth, transaction costs, lack of infinite divisibility of securities, and taxes. Conventional models used in mathematical finance often gloss over these issues, which affect almost all financial markets, by arguing that the impact of frictions is negligible and, consequently, the frictionless models are valid approximations. This dissertation consists of three research papers, which are related to the study of the validity of such approximations in two distinct modeling problems. Models of price dynamics that are based on diffusion processes, i.e., continuous strong Markov processes, are widely used in the frictionless scenario. The first paper establishes that diffusion models can indeed be understood as approximations of price dynamics in markets with frictions. This is achieved by introducing an agent-based model of a financial market where finitely many agents trade a financial security, the price of which evolves according to price impacts generated by trades. It is shown that, if the number of agents is large, then under certain assumptions the price process of security, which is a pure-jump process, can be approximated by a one-dimensional diffusion process. In a slightly extended model, in which agents may exhibit herd behavior, the approximating diffusion model turns out to be a stochastic volatility model. Finally, it is shown that when agents' tendency to herd is strong, logarithmic returns in the approximating stochastic volatility model are heavy-tailed. The remaining papers are related to no-arbitrage criteria and superhedging in continuous-time option pricing models under small-transaction-cost asymptotics. Guasoni, Rásonyi, and Schachermayer have recently shown that, in such a setting, any financial security admits no arbitrage opportunities and there exist no feasible superhedging strategies for European call and put options written on it, as long as its price process is continuous and has the so-called conditional full support (CFS) property. Motivated by this result, CFS is established for certain stochastic integrals and a subclass of Brownian semistationary processes in the two papers. As a consequence, a wide range of possibly non-Markovian local and stochastic volatility models have the CFS property.

Small firm internet adoption: A market oriented approach

Fundamental to the development of new customer value offerings via web-based commerce is a small firm's ability to strategically acquire and exploit knowledge. The focus of this paper is the empirical testing of a normative web-based commerce adoption model developed from a review of the extant literature related to electronic marketing, the Internet and the diffusion of new innovations. A preliminary test of the model's theoretical contentions lent support to its overall focus, but found that the firm's existing learning capabilities were diminished during the adoption of web-based commerce. Consequently, sub-optimal adoption outcomes were associated with insufficient knowledge development.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Modeling Efficient Classification as a Process of Confidence Assessment and Delegation

In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort. We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard. The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.