We quantified the effects of wood density (chip specific gravity) and wood chemical composition (cellulose, hemicellulose, and lignin) on the kraft pulp yield of 13-year-old loblolly pine trees (Pinus taeda) grown as part of a genetic selection study. Both bleachable (kappa No. 30) and linerboard grade (kappa No. 100) pulps were made from 18 trees selected for combinations of wood specific gravity and cellulose:hemicellulose:lignin ratios. Statistical analysis indicated that digester pulp yield correlated significantly with wood xylan content and cellulose-to-lignin ratio but was not strongly correlated to wood specific gravity. Near infrared (NIR) spectra were collected from wood samples and correlated with the total kraft pulp yields. The analyses for both kappa No. 30 and kappa No. 100 pulps provided strong calibration statistics, suggesting that papermakers can use NIR spectroscopy to esti-mate the bleachable and linerboard grade pulp yields of P. taeda whole-tree samples.
A large numbers of metrics have been proposed for measuring properties of object-oriented software such as size, inheritance, cohesion and coupling. The coupling metrics presented in this paper exploring the difference between inheritance and interface programming. This paper presents a measurement to measure coupling between object (CBO), number of associations between classes (NASSocC), number of dependencies in metric (NDepIN) , number of dependencies out metric (NDepOut) , Number of children (NOC) and Depth of Inheritance Tree (DIT) in object oriented programming. A measurement is done for C# inheritance and interface programs. The metric values of class inheritance and interface prove which program is good to use and beneficial for C# developers.
The effects of wood properties on the strength of bleachable and linerboard grade kraft pulps from 13-year-old loblolly pine (Pinus taeda) trees were investigated. Eighteen trees were selected based on breast height wood cores to represent specified ranges of specific gravity and lignin content. Air-dry density and stiffness (modulus of elasticity [MOE]), tracheid coarseness, radial diameter, tangential diameter, specific surface area, wall thickness, and microfibril angle (MFA) were estimated using SilviScan wood analysis technology and near infrared reflectance (NIR) spectroscopy. NIR spectra collected in 10 mm sections from the surface radial strips correlated very well with air-dry density, MFA, MOE, and tracheid wall thickness and were used to develop whole tree predictions. In addition, chemical composition, fiber properties, and handsheet strength were measured for both pulp grades. Statistical analysis indicated that wood density, wood fiber coarseness, and pulp fiber length had the greatest effects on sheet properties.
This paper aims to recognize the diagnosis of the thyroid disease and then categorize the type of thyroid disease a patient may be suffering from (i.e., hyperthyroidism or hypothyroidism). The project implementation is being done by using python and Kaggle is the platform from which the dataset has been taken. At present many machine learning algorithms have been used to detect thyroid disease like but our goal is to implement the machine learning algorithm which has higher accuracy and which takes less time in detecting the disease along with the type of thyroid. We have trained the dataset taken from Kaggle over various machine learning techniques. We have also attempted to reduce the number of parameters required to detect the disease.
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