109 research outputs found

    Study of Norway spruce cell wall structure with microscopy tools

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    The distribution and orientation of wood cell wall polymers play an important role in its physical, chemical and mechanical properties, and thus in the transformation into final products. Specifically, the orientation of cellulose elementary fibrils (EF) controls the performance of wood in almost every end use. Moreover, lignin is covalently linked to many of the cell wall polysaccharides, which imposes a serious technical challenge during the degradation of cellulose into value added products. Therefore, the deep understanding of the organization of the cell wall materials is imperative. In this study, normal Norway spruce wood was studied with a high-resolution cryo-transmission electron microscope (cryo-TEM). Both, two- (2D) and three-dimensional (3D) imaging techniques within TEM were applied on ultrathin wood sections to understand the wood structure. The defibration mechanisms in high-temperature thermomechanical pulping (HT-TMP) was also studied with a conventional TEM. Furthermore, the accessibility of cell wall lignin was studied with TEM and Raman microspectroscopy by analysing fresh and solvent extracted ultrathin sections of Norway spruce branch wood. The results showed that the organization of EFs varies from layer to layer and also within a single layer. In addition to the well-adopted concept of longitudinal EF angle in tangential plane, this study showed the presence of an out-of-plane EF angle relative to the cell wall plane. The S1 layer had a transverse EF orientation with a predominant radial lamellar structure of EF bundles. Both crossed and parallel EF orientations were detected in the S1-2 transition layer, which was supported by the defibration mechanisms in HT-TMP. EFs in the outer-S2 layer had a relatively high longitudinal EF angle and a large out-of-plane angle with respect to the tangential plane, which continued to decline inward and became almost axial in the inner-S2 layer. A transverse, out-of-plane EF orientation in the S3 transverse sections was observed. The models of the wood cell wall summarize most of the findings regarding the wood ultrastructure. Study of the lignin extracted ultrathin sections showed the change of lignin concentration in all cell wall layers during the extraction process. However, lignin obtained after extraction consists mainly of secondary wall lignin as this area contains most of the total cell wall lignin in conifer tracheids. The new observations on the wood cell wall structure may lead to a better understanding of the reactivity of cellulosic fibers in biochemical, chemical and mechanical treatments

    Identifying Long-Term Deposit Customers : A Machine Learning Approach

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    Majority of the revenue from the banking sector is usually generated from long term deposits by customers. It is for banks to understand customer characteristics to increase product sales. To aid this, marketing strategies are employed to target potential customers and let them interact with the banks directly, generating a large amount of data on customer characteristics and demographics. In recent years, it has been discovered that various data analysis, feature selection and machine learning techniques can be employed to analyze customer characteristics as well as variables that can impact customer decision significantly. These methods can be used to identify consumers in different categories to predict whether a customer would subscribe to a long-term deposit, allowing the marketing strategy to be more successful. In this study, we have taken a R programming approach to analyze financial transaction data to gain insight into how business processes can be improved using data mining techniques to find interesting trends and make more data-driven decisions. We have used statistical analysis like Exploratory Data Analysis (EDA), Principal Component Analysis (PCA), Factor Analysis and Correlations in the given data set. Besides, the study's goal is to use at least three typical classification algorithms among Logistic Regression, Random Forest, Support Vector Machine and K-nearest neighbors, and then make predictive models around customers signing up for long term deposits. Where we have gotten best accuracy from Logistic Regression which is 90.64 % as well the sensitivity is 99.05 %. Results were analyzed using the accuracy, sensitivity, and specificity score of these algorithms.acceptedVersionPeer reviewe
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