ABSTRACT
Due to the COVID-19 outbreak, industries have gained a thrust on contactless processing for computing technologies and industrial automation. Cloud of Things (CoT) is one of the emerging computing technologies for such applications. CoT combines the most emerging cloud computing and the Internet of Things. The development in industrial automation made them highly interdependent because the cloud computing works like a backbone in IoT technology. This supports the data storage, analytics, processing, commercial application development, deployment, and security compliances. Now amalgamation of cloud technologies with IoT is making utilities more useful, smart, service-oriented, and secure application for sustainable development of industrial processes. As the pandemic has increased access to computing utilities remotely, cyber-attacks have been increased exponentially. This paper reviews the CoT's contribution to industrial automation and the various security features provided by different tools and applications used for the circular economy. The in-depth analysis of security threats, availability of different features corresponding the security issues in traditional and non-traditional CoT platforms used in industrial automation have been analysed. The security issues and challenges faced by IIoT and AIoT in industrial automation have also been addressed.
ABSTRACT
COVID-19 originated in Wuhan city, China, in 2019 erupted a global pandemic that had put down nearly 3 million lives and hampered the socio-economic conditions of all nations. Despite the available treatments, this disease is not being controlled totally and spreading swiftly. The deadly virus commences infection by hACE2 receptor and its co-receptors (DPP4) engagement with the viral spike protein in the lung alveolar epithelial cells, indicating a primary therapeutic target. The current research attempts to design an in-silico Bispecific antibody (BsAb) against viral spike glycoprotein and DPP4 receptors. Regdanvimab and Begelomab were identified to block the D614G mutated spike glycoprotein of SARS-CoV-2 and host DPP4 receptor, respectively. The designed BsAb was modified by using KIH (Knobs into Holes) and CrossMAb techniques to prevent heavy chain and light chain mispairings. Following the modifications, the site-specific molecular docking studies were performed, revealing a relatively higher binding affinity of BsAb with spike glycoprotein and DPP4 co-receptor than control BsAb. Also, for blocking the primary entry receptor, hACE2, an anti-viral peptide was linked to the Fc region of BsAb that blocks the hACE2 receptor by linker cleavage inside the infected host. Thus, the designed BsAb and anti-viral peptide therapy could be a promising triumvirate way to obstruct the viral entry by blocking the receptor engagement.
ABSTRACT
Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.