Hosting a blockchain code on to a cloud network in order to create a prototype app in the Healthcare sector. Problem Statement short description in a short paragraph Who we are - A young start-up in the Health Tech space. What we plan to achieve – Create a blockchain application to store and share patient records with Doctors and Health care intermediaries. Where we are currently – The team has built the code and UI/UX designs for the application, but we need help to host the program on a network and create a prototype. What we need – We need young professionals to help solve the hosting issue and build the prototype. White Hour Solutions (WHS) LLP., is a young start-up looking to provide a solution for patient to record, store and share their Personal Health Records (PHRs). The PHRs would be interoperable between the patient and other Healthcare intermediaries. We plan to do this by providing an application which can be used through mobile phones, computers or tablets. We are currently at the phase where we have the codes developed and the designs for the app, but need help with hosting it on the cloud and building the prototype. All support and assistance will be provided for the same.

The ionospheric electron density is sensitive to the space weather parameters like Sunspot numbers, F10.7, Interplanetary magnetic field etc. and shows variation with respect to them. These parameters are detected and measured at L1 point (currently by ESA / NASA satellites) and are transmitted to earth. So, the information about these parameters are available before they actually influence the terrestrial ionosphere. Importantly, all these data are freely available over internet. The abovementioned parameters affect the ionospheric TEC, which in turn shows abrupt variations and deviations from the nominal characteristics. Therefore, these unconventionalities, which are typically observed during solar and geomagnetic storms, are necessary to be identified. This is also important, because the performance of the GNSS system, which is currently driving millions of applications, are also affected by the ionospheric variabilities. So, prediction of the TEC provides a handle to take necessary actions to avoid or mitigate the resultant impairments arising out of the above facts. For the purpose, the time sequence of space-weather parameters, along with the local time, location, season, date etc. can be related to the same for the vertical TEC and a definitive relationship can be established using Artificial Intelligence (AI) methods, like neural networks or other deep learning algorithms. Once trained with known dataset, the AI based algorithm may be able to predict the expected TEC variations which can be used by the concerned users. The schematic for the approach may be as the following. The problem requires the development of the proper AI algorithm to use the available space weather and other data to predict the TEC variations at one particular location on the earth, preferably over the geomagnetic equator.