The effect of structure on the dilute solution properties of branched polysaccharides studied with SEC and AsFlFFF

Cereal arabinoxylans, guar galactomannans, and dextrans produced by lactic acid bacteria(LAB) are a structurally diverse group of branched polysaccharides with nutritional and industrial functions. In this thesis, the effect of the chemical structure on the dilute solution properties of these polysaccharides was investigated using size-exclusion chromatography(SEC) and asymmetric flow field-flow fractionation (AsFlFFF) with multiple-detection. The chemical structures of arabinoxylans were determined, whereas galactomannan and dextran structures were studied in previous investigations. Characterization of arabinoxylans revealed differences in the chemical structures of cereal arabinoxylans. Although arabinoxylans from wheat, rye, and barley fiber contained similar amounts of arabinose side units, the substitution pattern of arabinoxylans from different cereals varied. Arabinoxylans from barley husks and commercial low-viscosity wheat arabinoxylan contained a lower number of arabinose side units. Structurally different dextrans were obtained from different LAB. The structural effects on the solution properties could be studied in detail by modifying pure wheat and rye arabinoxylans and guar galactomannan with specific enzymes. The solution characterization of arabinoxylans, enzymatically modified galactomannans, and dextrans revealed the presence of aggregates in aqueous polysaccharide solutions. In the case of arabinoxylans and dextrans, the comparison of molar mass data from aqueous and organic SEC analyses was essential in confirming aggregation, which could not be observed only from the peak or molar mass distribution shapes obtained with aqueous SEC. The AsFlFFF analyses gave further evidence of aggregation. Comparison of molar mass and intrinsic viscosity data of unmodified and partially debranched guar galactomannan, on the other hand, revealed the aggregation of native galactomannan. The arabinoxylan and galactomannan samples with low or enzymatically extensively decreased side unit content behaved similarly in aqueous solution: lower molar mass samples stayed in solution but formed large aggregates, whereas the water solubility of the higher-molar-mass samples decreased significantly. Due to the restricted solubility of galactomannans in organic solvents, only aqueous galactomannan solutions were studied. The SEC and AsFlFFF results differed for the wheat arabinoxylan and dextran samples. Column matrix effects and possible differences in the separation parameters are discussed, and a problem related to the non-established relationship between the separation parameters of the two separation techniques is highlighted. This thesis shows that complementary approaches in the solution characterization of chemically heterogeneous polysaccharides are needed to comprehensively investigate macromolecular behavior in solution. These results may also be valuable when characterizing other branched polysaccharides.

Silvicultural decisions based on simulation-optimization systems

Forest management is facing new challenges under climate change. By adjusting thinning regimes, conventional forest management can be adapted to various objectives of utilization of forest resources, such as wood quality, forest bioenergy, and carbon sequestration. This thesis aims to develop and apply a simulation-optimization system as a tool for an interdisciplinary understanding of the interactions between wood science, forest ecology, and forest economics. In this thesis, the OptiFor software was developed for forest resources management. The OptiFor simulation-optimization system integrated the process-based growth model PipeQual, wood quality models, biomass production and carbon emission models, as well as energy wood and commercial logging models into a single optimization model. Osyczka s direct and random search algorithm was employed to identify optimal values for a set of decision variables. The numerical studies in this thesis broadened our current knowledge and understanding of the relationships between wood science, forest ecology, and forest economics. The results for timber production show that optimal thinning regimes depend on site quality and initial stand characteristics. Taking wood properties into account, our results show that increasing the intensity of thinning resulted in lower wood density and shorter fibers. The addition of nutrients accelerated volume growth, but lowered wood quality for Norway spruce. Integrating energy wood harvesting into conventional forest management showed that conventional forest management without energy wood harvesting was still superior in sparse stands of Scots pine. Energy wood from pre-commercial thinning turned out to be optimal for dense stands. When carbon balance is taken into account, our results show that changing carbon assessment methods leads to very different optimal thinning regimes and average carbon stocks. Raising the carbon price resulted in longer rotations and a higher mean annual increment, as well as a significantly higher average carbon stock over the rotation.

An Experimental Study of Home Gateway Characteristics

Many residential and small business users connect to the Internet via home gateways, such as DSL and cable modems. The characteristics of these devices heavily influence the quality and performance of the Internet service that these users receive. Anecdotal evidence suggests that an extremely diverse set of behaviors exists in the deployed base, forcing application developers to design for the lowest common denominator. This paper experimentally analyzes some characteristics of a substantial number of different home gateways: binding timeouts, queuing delays, throughput, protocol support and others.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Essays on Earnings Management (summary section only)

There is much literature developing theories when and where earnings management occurs. Among the several possible motives driving earnings management behaviour in firms, this thesis focuses on motives that aim to influence the valuation of the firm. Earnings management that makes the firm look better than it really is may result in disappointment for the single investor and potentially leads to a welfare loss in society when the resource allocation is distorted. A more specific knowledge of the occurrence of earnings management supposedly increases the awareness of the investor and thus leads to better investments and increased welfare. This thesis contributes to the literature by increasing the knowledge as to where and when earnings management is likely to occur. More specifically, essay 1 adds to existing research connecting earnings management to IPOs and increases the knowledge in arguing that the tendency to manage earnings differs between the IPOs. Evidence is found that entrepreneur owned IPOs are more likely to be earnings managers than the institutionally owned ones. Essay 2 considers the reliability of quarterly earnings reports that precedes insider selling binges. The essay contributes by suggesting that earnings management is likely to occur before high insider selling. Essay 3 examines the widely studied phenomenon of income smoothing and investigates if income smoothing can be explained with proxies for information asymmetry. The essay argues that smoothing is more pervasive in private and smaller firms.