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Background The mortality data of COVID cases around the world has less explored in relation to comorbidities.The dogma/question to be answered is that the virus perse resulting or any comorbidities contributing to such frightened deaths.The aim of the study is to describe the clinical and epidemiological characteristics of 201 deceased from Telangana. We retrospectively collected all consecutive death cases with laboratory‐confirmed COVID‐19 infection admitted from March to mid-June at Gandhi Hospital the nodal centre designated for COVID-19 in Telangana. Clinical history, comorbidities, laboratory findings and treatment were recorded for each patient.Results A total of 15394 patients with confirmed COVID‐19 test were admitted at Isolation wards between March 2020 and June 2020 and 253 death cases were reported till the submission of this paper. The mean age of death is 57.0 ys in our study, 40.7% (88) deaths were above 60 years and 71.4% (147) were male. Several comorbid conditions existed with COVID-19 death cases among which hypertension being the most common comorbidity (60.1%). Lymphopenia was observed in 46% cases while lymphopenia with comorbidity was recorded in 63% cases.Conclusion In this retrospective study, most of the COVID‐19 deceased patients were elderly male aged with an age range from 50-60 yrs and above. Mortality rate and severity are higher in males than females. Our study indicated the importance of understanding comorbid conditions in COVID‐19 cases especially Hypertension and Diabetes mellitus as they were more likely succumb to death.

Massively parallel processors such as graphics processesing units (GPUs) often face the challenge of resource underutilization due to varying resource proclivity of workloads. Running multiple applications on a GPU has been an efficient and known alternative to mitigate underutilization. This paper proposes a multi-application oriented framework that carries out dynamic optimizations based on the operational intensities of various applications. Our framework analyzes applications based on operational intensities to identify their bottleneck resources using Roofline model. We demonstrate that the proposed optimizations improve the utilization and system-wide throughput of the GPU co-running applications with irregular resource demands. The dynamic optimizations improve the performance by 14.8% on average and up to 72.4% over a state-of-the-art spatial multitasking technique.

<p><strong>Abstract.</strong> Near real time processing and feature extraction from high-resolution satellite images aids in various applications of remote sensing including segmentation, classification and change detection. The latest generation of satellite sensors are able to capture the data at a very high spatial, spectral and temporal resolution. The processing time required for such a huge data is also large. Disaster monitoring applications such as forest fire monitoring, earthquakes require fast/real time processing of high resolution data to enable response activities. In general, due to the large size of satellite data, the computational time of feature calculation and training neural network is found to be very high. Therefore in order to achieve the aim of near real time processing of such huge data, we developed a parallel implementation. The implementation is performed on NVIDIA’s Graphical Processing Unit. The performance improvement obtained is demonstrated by a GPU implementation on Resourcesat-1 data and compared with the traditional sequential implementation. The results show that the GPU implementation is found to achieve performance improvement in terms of execution time and speedup throughput as compared to the sequential implementation.</p>