Engineering the next-generation tin containing beta titanium alloys with high strength and low modulus for orthopedic applications

Metastable beta Ti alloys are the new emerging class of biomaterial for load bearing orthopedic applications. However, these alloys in the single beta phase microstructure have insufficient strength for use in load bearing applications. It is imperative to strengthen these alloys by carefully designed thermo-mechanical processing routes that typically involve aging treatment. In this investigation two newly designed Sn based beta Ti alloys of composition Ti-32Nb-(2, 4) Sn are evaluated. The effects of Sn content on the mechanical properties and biological performance of these alloys processed through designed thermo-mechanical processing route are investigated. The increase in the Sn content led to a reduction in the elastic modulus of the alloy. An increase in the Sn content increased the aspect ratio of the a precipitates, which led to a significant strengthening in the alloy while keeping the elastic modulus low. In addition, the corrosion behavior of the alloy was evaluated in simulated body fluid. The Sn containing beta alloys have an excellent corrosion resistance as desired for an implant material. The corrosion resistance improved with an increase in Sn content. These alloys were also observed to have excellent cytocompatibility as they not only supported the attachment and proliferation of human mesenchymal stem cells but also their osteogenic differentiation in vitro. The combination of high strength, low elastic modulus, superior corrosion resistance and biocompatibility underscores the promise of Sn containing beta Ti alloys for use in orthopedic applications

Caption Generation from Keywords using Probabilistic Dual-LSTM Networks and Dynamic Programming

Generation of natural languages has always been a keystone in AI research. In many modern challenges virtual assistants or caption generation it is often required to generate natural language texts from a set of annotations in the absence of a proper schema or language model. In this work we have used the principles of dynamic programming on probabilistic dual-LSTM Networks to generate sentences from a set of keywords. Validation against human judges showed that the system is able to provide quite satisfactory description of images from a set of keywords