Study of Mechanical behavior for Tamarind Shell Powder and Coconut Coir Fiber Epoxy Composite for Aerospace application
G. Purushotham, Yathin K.L

Abstract
Now-a-days, the natural fibres from renewable natural resources offer the potential to act as a reinforcing material for polymer composites alternative to the use of glass, carbon and other man-made fibres. Among various fibres, coir is most widely used natural fiber due to its advantages like easy availability, low density, low production cost and satisfactory mechanical properties. For a composite material, its mechanical behavior depends on many factors such as fiber content, orientation, types, length etc.
Natural fibre composites (NFC) are gaining interest in manufacturing because they address some of the environmental problems of traditional composites: use of non-renewable resources, and large impacts related to their production and disposal. Since natural fibres are not yet optimized for composite production, it is crucial to identify the most appropriate applications, and determine the optimal fibre/ matrix ratio. Results from various experiments help identify the application with the largest reduction in environmental burden and show that the fibre/matrix combination with the lowest environmental burden also has the best mechanical properties.
Attempts have been made in this research work to study the effect of fiber loading and orientation on the physical and mechanical behavior of coconut fiber and tamarind shell powder reinforced epoxy based hybrid composites which is prepared by hand-layup method with different weight proportions.
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Predictive Analysis for Gold Pricing in Indian Context
Baba Gnanakumar Perbettan

Abstract
As blockchain technology has been perceived positively by the investors and financial intermediaries, we test it on the Gold trading platform. This research aims to identify the distributed networks need to build the blockchain for gold trading in Indian perspective by using machine learning algorithm. The study concluded that the automated data capturing system must from RBI, BSE, MCX for the national level network; for inter-national network data capturing from World Gold Council is needed. As Indian Gold prices are associated with only US-Dollar, Euro, Canadian Dollar, Swiss Franc, Chinese Renminbi, Thai- baht, Vietnamese dong, Egyptian pound, Korean won and Australian dollar, the predictive algorithm must include these currency prices, while integrating the Indian Gold price.
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Innovative Heuristics Modeling for Dynamic Supply Chain Risk Management
Radhakrishnan Perumalsamy and Jeyanthi Natarajan

Abstract
Organizations of all types are increasingly carrying out business operations to accomplish their business objectives. Efficient and effective risk analysis and management significantly improves the organization’s competitive edge as well as enhances ultimate service provided to the customer. Escalating events around the world have increased the awareness of how detrimental risk can be to the business. Supply chain risk assessment and mitigation involve critical processes that must be implemented to address major supply chain risk. Efficient supply chain risk analysis and management is a complex process in our effort to deal with the complexity of managing risk as it happens in its different forms from multiple sources. In this paper, an optimization methodology utilizing the Particle Swarm Optimization algorithm is proposed to generate essential predictive analytics to maintain dynamic supply chain risk analysis and management towards effective business performance management.
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Extent of Human Resource Disclosure in Annual Reports of Companies Listed on Indian Stock Exchange
Ahmed Hussain, Debansu Das

Abstract
The purpose of the study was to measure and analyse the extent of human resource information in corporate annual reports in India. For this purpose annual reports of 50 companies for year 2015-16 were examined. 50 sample companies were selected following simple random sampling from top 500 companies listed on Indian Stock Exchanges. Based on a disclosure checklist of 76 human resource information items and using an unweighted disclosure index, the study measures the extent of human resource disclosure both at the aggregate level as well as in respect of eight categories of human resource information. The result shows that the extent of overall human resource disclosures varies from 30% to 89%. Minimum disclosure score of 30% is very poor. But the maximum disclosure score of 89% is fairly high. However, it reveals that none of the sample companies has disclosed 100% information items of our disclosure checklist. Mean disclosure score of 54.64% indicates an inadequate average level of disclosure. The study reveals a wide variation in the extent of overall disclosure with range and standard deviation being 59% and 11.58% respectively. So far as disclosure in respect of different human resource information is concerned, mean disclosure score is the highest (63%) for ‘Information related to Human Resource Policy’ followed by disclosure score (62%) for ‘Financial Information on Human Resource’ and (56%) for ‘General Information about Human Resource’. The lowest mean disclosure score is 22% for ‘Occupational Hazards, Health and Safety Issues’. Maximum level of disclosure under different categories of human resource information varies from 80% to 100% while minimum level of disclosure under different categories varies from 0% to 30%. On analyses of research findings, the paper concludes that there exists scope for improvement of human resource reporting by Indian companies.
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Data Lake-An Optimum Solution for Storage and Analytics of Big Data in Cardiovascular Disease Prediction System
Ekta Maini, Bondu Venkateswarlu, Arbind Gupta

Abstract
Cardiovascular diseases are the biggest reason of deaths across the world. An innovation change is being experienced by the healthcare industry as healthcare organizations change their plans of action to increase operational efficiencies to reduce expenses. To incorporate this change, analytics needs to be an integral component of IT strategy. A data lake pools data from multiple sources and applies analytical models to provide a new approach to information management, reporting, and predictive analytics to help create advanced analytic insights, deploy evidence-based care strategies, and improve patient engagement outcomes. This paper explains how healthcare organizations can build and develop their data analytics infrastructure, data science skills, and data governance processes necessary for a high-performing data lake. The huge data can be stored efficiently using Azure Data Lake and data analytics can be carried out to fascinate learning patterns which can be used to develop a support system to help the medical practitioner to detect the chances of heart diseases at an early stage and take suitable decisions accordingly.
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