Oil spills have huge and immediate economically, socially, and environmentally adverse impacts. Current methods to remediate oil spills do not provide a sustainable solution, in terms of cost, ease of deployment, and further impact on the environment. Here we report an oil spill remediation solution in form of an oleophilic, hydrophobic, and magnetic (OHM) sponge that is economical, efficient, and ecofriendly; thereby promising a potentially industry-adaptable approach. The OHM sponge can not only selectively remove the oil from oil/water interface but also recover the oil by a simple squeezing process. Furthermore, the OHM sponge can be reused for many cycles. The OHM sponge works effectively in diverse and extreme aquatic conditions (pH, salinity) and can absorb a variety of oils and oil-based compounds. The selective absorption/desorption, recovery, high absorption capacity, and reusability under one platform open new prospects for potentially sustainable water and environmental remediation applications.

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Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this).

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In this project we are going to implement the use of up-to-date technology in sensing the very low variations in frequency or voltage magnitude of a generator in a Power grid in which there may be many generators working in synchronism with the grid in terms of phase sequence, voltage magnitude and frequency. In today’s practical Power grid as we all know many generators or power source are working together and to maintain stability between all, the detection and isolation of the sources falling out of synchronism, is of crucial significance as otherwise it would have caused the entire system to fail. Hence various techniques have been developed in industries and power plants (especially solar power plants) to keep all the generators and sources in synchronism with the Power Grid and in case of and failure detect and isolate the failed generator out of the grid and hence maintain a stable operation of the Power System.