The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods.

During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories.

The adverse impacts of high temperature during the summer season on the rabbit industry have gained increased global attention. In this study, the comparative effects of biological (BIO) and chemical (CH) nanoselenium (nano‐Se) combined with vitamin E on the growth and immune performances of rabbits were observed. A total of 200 white male rabbits of similar age (90 days) were divided into five treatment groups (T0, T1, T2, T3, and T4), 40 animals in each treatment. The rabbits in the first treatment group (T0) was fed basal diet; (T1) basal diet supplemented with 35 mg biological synthesized nanoselenium/kg diet; (T2) basal diet with 35 mg biological nanoselenium/kg diet+150 mg Vit. E/kg; (T3) basal diet+35 m chemically synthesized nanoselenium/kg diet; and (T4) basal diet+35 mg of chemical nanoselenium/kg diet+150 mg Vit. E/kg. The duration of this experiment was 63 days. The body weight of each rabbit was recorded weekly. Results revealed a significant (P < 0.05) increase in live body weight (LBW), total body gain (TBG), and feed conversion ratio (FCR) of rabbits treated with BIO‐Se+Vit. E (T2) compared to the other groups. Selenium concentrations in the kidneys and liver were significantly higher (P < 0.05) in animals fed with BIO‐Se+Vit. E (T2). The concentrations of serum urea, glutamyl transferase (GGT), and triglycerides (TG) were lower in untreated (T0) and treated groups (T1, T2, T3, and T4). From the results of this study, it can be concluded that biological nano‐Se gave maximum improvement for the parameters under study compared to the chemically synthesized nanoselenium by playing a role in alleviating heat stress, increasing the growth performance, and enhancing the immunity of growing white male rabbits. Further addition of Vit. E is an alternative method to maximize productivity with no adverse effects during the fattening period of growing white male rabbits.