This article analyses various patterns in the pollution levels of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] at various locations in Kolkata, India. Our analysis is based on a data set recorded by the Central Pollution Control Board of India and the West Bengal Pollution Control Board, comprising of about 15 years of irregular time series observations, due to the higher cost of precise and accurate measurements of pollution. We perform some exploratory analysis concerning the variations in trend, seasonal, and shift-specific seasonal levels. As well as a statistical model for forecasting pollution levels for two to three days in advance is also obtained, along with an analysis of the effects of festivities such as Kali Puja and Diwali on the level of the pollutants across different locations.
A basic algorithmic task in automated video surveillance is to separate background and foreground objects. Camera tampering, noisy videos, low frame rate, etc., pose difficulties in solving the problem. A general approach which classifies the tampered frames, and performs subsequent analysis on the remaining frames after discarding the tampered ones, results in loss of information. We propose a robust singular value decomposition (SVD) approach based on the density power divergence to perform background separation robustly even in the presence of tampered frames. We also provide theoretical results and perform simulations to validate the superiority of the proposed method over the few existing robust SVD methods. Finally, we indicate several other use-cases of the proposed method to show its general applicability to a large range of problems.
In this paper, we develop an extension of compartmental epidemiological models which is suitable for COVID-19. The model presented in this paper comprises seven compartments in the progression of the disease. This model, named as the SINTRUE (Susceptible, Infected and pre-symptomatic, Infected and Symptomatic but Not Tested, Tested Positive, Recorded Recovered, Unrecorded Recovered, and Expired) model. The proposed model incorporates transmission due to asymptomatic carriers and captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. In addition, the model allows estimating the number of undocumented infections in the population and the number of unrecorded recoveries. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. The results show that the testing rate of the asymptomatic patients is a crucial parameter to fight against the pandemic. The model is also shown to have a better predictive capability than the other epidemiological models.
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