An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown.

There have been considerable losses in terms of human and economic resources due to the current coronavirus pandemic. This work, which contributes to the prevention and control of COVID-19, proposes a novel modified epidemiological model that predicts the epidemic's evolution over time in India. A mathematical model was proposed to analyze the spread of COVID-19 in India during the lockdowns implemented by the government of India during the first and second waves. What makes this study unique, however, is that it develops a conceptual model with time-dependent characteristics, which is peculiar to India's diverse and homogeneous societies. The results demonstrate that governmental control policies and suitable public perception of risk in terms of social distancing and public health safety measures are required to control the spread of COVID-19 in India. The results also show that India's two strict consecutive lockdowns (21 days and 19 days, respectively) successfully helped delay the spread of the disease, buying time to pump up healthcare capacities and management skills during the first wave of COVID-19 in India. In addition, the second wave's severe lockdown put a lot of pressure on the sustainability of many Indian cities. Therefore, the data show that timely implementation of government control laws combined with a high risk perception among the Indian population will help to ensure sustainability. The proposed model is an effective strategy for constructing healthy cities and sustainable societies in India, which will help prevent such a crisis in the future.

Texture Enhancement of Medical Images for Efficient Disease Diagnosis with Optimized Fractional Derivative Masks.

For the past two decades, fractional-order derivatives have been used to model many systems in science and engineering with more accuracy than existing integer-order derivatives. Many of these applications have been employed in the image processing field. It is undeniable that an image enhancement algorithm is very much desirable for medical image analysis to diagnose various kinds of diseases more efficiently. These requirements demand that the image should be of high quality. Hence, accurate edge-detection and denoising models are required in medical image processing, improving, and enhancing the contrast of an image to attain a better texture and avoid noise. In this study, we employ and compare the conventional methods and recent and most popular fractional-order-based methods for medical image analysis texture enhancement. To make a fair comparison, the fractional-order operators are optimized for all images with gray wolf optimizer while considering the performance metric mean squared error. The results showed that fractional differential-based operators perform better than conventional integer-order operators for texture enhancement of medical images.