Performance of engineered cementitious composite containing stone slurry powder waste

Most of the materials used in engineered cementitious composite are fine in size to achieve ductile nature.Stone slurry powder(SSP)is an inert material obtained from stone industries as by-product which may cause hazardous impact on environment.In this research work,partial replacement of silica sand(SS)and fine sand(FS)by SSP with different contents(25%and 50%each)for making engineered cementitious composite has been explored.The performance was evaluated on the basis of strength,tensile strain,mid span deflection capacity,ultra-sonic pulse velocity and microstructure.Mechanical strength was found to be increased at 25%SSP in both replacements;whereas,strength decreased slightly at 50%replacement.Tensile strain,mid span deflection and quality of concrete were enhanced with increase in SSP content.Using SSP formed denser cementitious composite can help to save the natural resources and contribute in making green cementitious composite.

Big Data Analytics for Healthcare Industry:Impact,Applications,and Tools

In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data. The health industry sector has been confronted by the need to manage the big data being produced by various sources,which are well known for producing high volumes of heterogeneous data. Various big-data analytics tools and techniques have been developed for handling these massive amounts of data, in the healthcare sector. In this paper, we discuss the impact of big data in healthcare, and various tools available in the Hadoop ecosystem for handling it. We also explore the conceptual architecture of big data analytics for healthcare which involves the data gathering history of different branches, the genome database, electronic health records, text/imagery, and clinical decisions support system.

A Novel Clustering Technique for Efficient Clustering of Big Data in Hadoop Ecosystem

Big data analytics and data mining are techniques used to analyze data and to extract hidden information.Traditional approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values.

Cutis laxa presenting as recurrent ileus

Cutis laxa(CL)is a rare connective tissue disorder characterized by phenotypic appearance of loose and redundant skin.CL can be congenital or acquired.Congenital forms include autosomal dominant,autosomal recessive and X-linked recessive.Apart from cutaneous abnormalities,CL can present with visceral involvement.In this article,we report a case of CL presenting as recurrent ileus.