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This is quite evident that technological advancements are bringing automation almost in every activity specially core activities of the business in cost effective ways. The advent of internet technology provides the platform to perform procurement activities between business to business using electronic medium which is known as E-procurement. E-procurement is a key to optimise supply chain performance through cost reduction and process efficiency. Maintenance, Repair and Overhaul (MRO) sector of the aviation industry has not kept pace with the ever growing sector in India. Though this industry is expected to grow about $1.6 billion (spending each year) in the next 10 years from the current market size of about $700-800 million, it still remains largely unexplored. E-procurement focuses on acquisition of resources, especially MRO items, and increasingly materials and components. The purpose of this paper is to understand the facts that influence the implementation of e-procurement for MRO in an aviation industry. A cross-sectional survey is used as the methodology for the data collection. Conceptual model developed by the previous researchers was used to explain the readiness, benefits and barriers of e-procurement implementation in the MRO environment in India. [ABSTRACT FROM AUTHOR]

Osteoporosis is a common age-related disease characterized by reduced bone density and increased fracture-risk. Microstructural quality of trabecular bone (Tb), commonly found at axial skeletal sites and at the end of long bones, is an important determinant of bone-strength and fracture-risk. High-resolution emerging CT scanners enable in vivo measurement of Tb microstructures at peripheral sites. However, resolution-dependence of microstructural measures and wide resolution-discrepancies among various CT scanners together with rapid upgrades in technology warrant data harmonization in CT-based cross-sectional and longitudinal bone studies. This paper presents a deep learning-based method for high-resolution reconstruction of Tb microstructures from low-resolution CT scans using GAN-CIRCLE. A network was developed and evaluated using post-registered ankle CT scans of nineteen volunteers on both low- and high-resolution CT scanners. 9,000 matching pairs of low- and high-resolution patches of size 64×64 were randomly harvested from ten volunteers for training and validation. Another 5,000 matching pairs of patches from nine other volunteers were used for evaluation. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric for low-resolution data. Different Tb microstructural measures such as thickness, spacing, and network area density are also computed from low- and predicted high-resolution images, and compared with the values derived from true high-resolution scans. Thickness and network area measures from predicted images showed higher agreement with true high-resolution CT (CCC = [0.95, 0.91]) derived values than the same measures from low-resolution images (CCC = [0.72, 0.88]).