Abstract: In this present work, five different composite samples with AA2024 as matrix and varying amounts of yttrium (0.1-0.5 wt.%) as reinforcement are developed through cold compaction. The microstructures of the developed composite samples revealed that the yttrium reinforcement caused grain refinement up to 0.3 wt.% and beyond which the refinement is not effective. The microstructure revealed Al2Cu precipitation which strengthened the composite up to 0.3 wt.% yttrium reinforcement. Upon further increase in yttrium reinforcement, the intermetallics and the precipitation coarsen and their corresponding strengthening effect decreases. The mechanical characterization revealed that the composite sample reinforced with 0.3 wt.% yttrium showed highest mechanical properties like 82 HV of hardness, 276 MPa Ultimate Tensile Strength (UTS), 229 MPa Yield Strength (YS) and an elongation (EL) of 18.9% respectively. However, the relative density of the developed composites decreased with the increase in yttrium reinforcement.
Abstract: In this paper, we propose a framework to help users to search and retrieve the portions in the lecture video of their interest. This is achieved by temporally segmenting and indexing the lecture video using the topic keywords. We use transcribed text from the video and documents relevant to the video topic extracted from the web for this purpose. The keywords for indexing are found by applying the non-negative matrix factorization (NMF) topic modeling techniques on the web documents. Our proposed technique first creates indices on the transcribed documents using the topic keywords, and these are mapped to the video to find the start and end time of the portions of the video for a particular topic. This time information is stored in the index table along with the topic keyword which is used to retrieve the specific portions of the video for the query provided by the users.
Abstract: The worldwide prevalence of H3N2 influenza virus
and its increasing resistance to the existing drugs necessitates for the
development of an improved/better targeting anti-influenza drug.
H3N2 influenza neuraminidase is one of the two membrane-bound
proteins belonging to group-2 neuraminidases. It acts as key player
involved in viral pathogenicity and hence, is an important target of
anti-influenza drugs. Oseltamivir is one of the potent drugs targeting
this neuraminidase. In the present work, we have taken subtype N2
neuraminidase as the receptor and probable analogs of oseltamivir as
drug molecules to study the protein-drug interaction in anticipation of
finding efficient modified candidate compound. Oseltamivir analogs
were made by modifying the functional groups using Marvin Sketch
software and were docked using Schrodinger-s Glide. Oseltamivir
analog 10 was detected to have significant energy value (16% less
compared to Oseltamivir) and could be the probable lead molecule. It
infers that some of the modified compounds can interact in a novel
manner with increased hydrogen bonding at the active site of
neuraminidase and it might be better than the original drug. Further
work can be carried out such as enzymatic inhibition studies;
synthesis and crystallizing the drug-target complex to analyze the
interactions biologically.