The first principles study is performed for the mechanical strength of Kevlar-29, and is based on density functional theory. The bond strength is investigated relative to the displacement of central nitrogen atom along X , Y and Z directions, respectively. The structural property analysis explains the asymmetric nature. A higher bond breaking energy is observed during compression along Z direction and vice versa for elongation. It is an insulator of forbidden energy gap which increases while compression and reduces during elongation. Crystal orbital overlap population reveals the higher strength of anti-bonding orbitals. It is mechanically stronger along the Z-axis and weaker along the Y-axis.

This study develops bagasse fiber (BGSEF) reinforced chemically functionalized polystyrene (CF-PS) composites (BGSEF/CF-PS) using CF-PS as a matrix, that has higher amount of functionality and requires processing upto 210°C. BGSEF/CF-PS have been processed by extrusion and injection moulding, retaining the stability of the thermally sensitive BGSEF. Relative to the CF-PS, all the BGSEF/CF-PS composite compositions show higher tensile and flexural properties, that increase with increasingly higher amounts of BGSEF in them. Relative to the CF-PS, the 10/90, 20/80 and 30/70 BGSEF/CF-PS composites show approx. 9.5%, 45% and 63% higher tensile strength, approx. 19%, 31% and 45% higher tensile modulus and approx. 20.5%, 22%, and 23% lower tensile elongation at break respectively, and show approx. 5%, 10%, and 16% higher flexural strength respectively, and approx. 6.5%, 16%, and 24% higher flexural modulus respectively. Upon water absorption saturation, the tensile and flexural properties of the wet composites decrease, relative to the dry composites. Adhesion between BGSEF and CF-PS in the composites, results from ester bonds and hydrogen bonds. The composites are thermally stable upto 290°C. These BGSEF/CF-PS composites by Palsule process show better performance than the BGSEF/PS composites by fiber treatment process.

This paper presents a SVM based computer-aided diagnosis (CAD) system for the characterization of clustered microcalcifications in digitized mammograms. First, the region of interest (ROI) in mammogram is enhanced using morphological enhancement (MORPHEN) method. Second, pixels in potential microcalcification regions are segmented out by using edge detection and morphological operations. Third, features based on shape, texture and statistical properties are extracted from each region. Finally, these features are fed to a SVM based classifier for identifying the clusters as either benign or malignant. The SVM with RBF kernel gave A z = 0.9803 with 97% accuracy and the SVM with polynomial kernel gave A z = 0.9541 with 95% accuracy.Index Terms-Computer aided diagnosis, contrast enhancement, microcalcification, support vector machine.

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