Fast signal processing implementation techniques for detection and classification of power quality (PQ) disturbances are the need of the hour. Hence in this work, a parallel computing approach has been proposed to speed up the feature extraction of PQ signals to facilitate rapid building of classifier models. Considering that the Fourier, the one-dimensional discrete wavelet, the time-time and the Stockwell transforms have been used extensively to extract pertinent time-frequency features from nonstationary and multi-frequency PQ signals, acceleration approaches using data and task parallelism have been employed for parallel implementation of the above time-frequency transforms. In the first approach, data parallelism was applied to the Stockwell transform and the time-time transform-based feature extraction methods separately to alleviate capability problems. Also, data parallelism was applied to Fourier and wavelet-based feature extraction methods independently to alleviate capacity problems. Secondly, a combination of task and data parallelism was applied to speed up S-transform based three-phase sag feature extraction. Experiments were conducted using shared-memory and distributed memory architectures to try out the effectiveness of the proposed parallel approaches. The performances of these parallel implementations were analysed in terms of computational speed and efficiency in comparison with the sequential approach.

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In transportation systems based on e-vehicles, the energy demand is met with the integration of renewable energy sources while maintaining the voltage profile and mitigating the active and reactive power losses. Vehicle-to-grid optimization technique is used to ensure this integration. Minimum active and reactive power losses are achieved when e-vehicles are integrated with the renewable energy sources in a hybrid mode. A machine learning framework with nested learning is used to ensure optimal methodology to trigger vehicular movement and monitoring of the SoC battery level. When the HEV operates, there is a high possibility for battery degradation, leading to loss of its capacity. To determine the optimal policy, the TD( λ ) learning algorithm is incorporated. This algorithm is known to showcase high performance and a high convergence rate in a non-Markovian environment. The output is simulated to record the readings observed which is aimed at optimizing the total operation cost and reduction in battery replacement. The results show that for shorter drives, the battery replacement cost is more and it is optimally possible to increase the battery life by 21% using the proposed work. Similarly, the recordings indicate that the proposed work shows a significant reduction of about 8%–10% in the operating cost when compared with the RL and rule-based policy.

COVID-19 infections have imposed immense pressure on the healthcare system of most countries. While the initial studies have identified better therapeutic and diagnostic approaches, the disease severity is still assessed by close monitoring of symptoms by healthcare professionals due to the lack of biomarkers for disease stratification. In this study, we have probed the immune and molecular profiles of COVID-19 patients at 48-hour intervals after hospitalization to identify early markers, if any, of disease progression and severity. Our study reveals that the molecular profiles of patients likely to enter the host-immune response mediated moderate or severe disease progression are distinct even in the early phase of infection when severe symptoms are not yet apparent. Our data from 37 patients suggest that at hospitalization, IL6 (>300pg/ml) and IL8 levels (>200pg/ml) identify cytokine-dependent disease progression. Monitoring their levels will facilitate timely intervention using available immunomodulators or precision medicines in those likely to progress due to cytokine storm and help improve outcomes. Additionally, it will also help identify cytokine-independent progressive patients, not likely to benefit from immuno-modulators or precision drugs.

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