The COVID-19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India-specific COVID-19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short-term memory, Bi-directional long short-term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.

Herein, the authors review the self-regulation system secured by well-designed hybrid materials, composites, and complex system. As a broad concept, the self-regulated material/system has been defined in a wide research field and proven to be of great interest for use in a biomedical system, mechanical system, physical system, as the fact of something such as an organisation regulating itself without intervention from external perturbation. Here, they focus on the most recent discoveries of self-regulation phenomenon and progress in utilising the self-regulation design. This paper concludes by examining various practical applications of the remarkable materials and systems including manipulation of the oil/water interface, cell out-layer structure, radical activity, electron energy level, and mechanical structure of nanomaterials. From material science to bioengineering, self-regulation proves to be not only viable, but increasingly useful in many applications. As part of intelligent engineering, self-regulatory materials are expected to be more used as integrated intelligent components.

With the advent of intelligence, more and more machines and devices involve the creation of complex structures. In the intelligent manufacturing industries, moldings including injection molding, blow molding, compression molding and others play critical roles in manufacturing the highly precise parts required for building intelligent machines (such as computers, cell phones, robots, etc.). The performance of the clamping mechanism directly affects the quality of the microstructure of injection products. The design of injection molding mold clamping mechanism is based on the microstructure characteristics of the trip of toggle lever mechanism ratio, speed ratio, and force amplification ratio. These are used to study the main performance parameters, such as analysis, as well as for the establishment of the physical model of the clamping mechanism. The model is based on the microstructure of injection of hyperbolic elbow clamping mechanism kinematics simulation. Simulation results and the theoretical calculation contrast analysis shows that the maximum dynamic template speed is 215.34 mm/s. The dynamic templates and crosshead speed ratio is 2.15, therefore the design of injection molding mold clamping mechanism for microstructure provides favorable technical support. The method described here is important to build complicated molds required to build highly precise parts to build intelligent machineries.