A Hybrid Deep Learning System to Detect Face-Mask and Monitor Social Distance

Coronavirus Disease 2019 (COVID-19) emerged towards the end of 2019, and it is still causing havoc on the lives and businesses of millions of people in 2022. As the globe recovers from the epidemic and intends to return to normalcy, there is a spike of anxiety among those who expect to resume their everyday routines in person.The biggest difficulty is that no effective therapeutics have yet been reported. According to the World Health Organization (WHO), wearing a face mask and keeping a social distance of at least 2 m can limit viral transmission from person to person. In this paper, a deep learning-based hybrid system for face mask identification and social distance monitoring is developed. In the OpenCV environment, MobileNetV2 is utilized to identify face masks, while YoLoV3 is used for social distance monitoring. The proposed system achieved an accuracy of 0.99. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Study of Coagulation Disorders and the Prevalence of Their Related Symptoms among COVID-19 Patients in Al-Jouf Region, Saudi Arabia during the COVID-19 Pandemic.

INTRODUCTION: The coronavirus (COVID-19) has affected millions of people around the world. COVID-19 patients, particularly those with the critical illness, have coagulation abnormalities, thrombocytopenia, and a high prevalence of intravascular thrombosis. OBJECTIVES: This work aims to assess the prevalence of coagulation disorders and their related symptoms among COVID-19 patients in the Al-Jouf region of Saudi Arabia. SUBJECTS AND METHODS: We conducted a retrospective study on 160 COVID-19 patients. Data were collected from the medical records department of King Abdulaziz Specialist Hospital, Sakaka, Al-Jouf, Saudi Arabia. The socio-demographic data, risk factors, coagulation profile investigation results, symptom and sign data related to coagulation disorders, and disease morbidity and mortality for COVID-19 patients were extracted from medical records, and the data were stored confidentially. RESULTS: Males represented the highest prevalence of COVID-19 infection at 65%; 29% were aged 60 or over; 28% were smokers; and 36% were suffering from chronic diseases, with diabetes mellitus representing the highest prevalence. Positive D-dimer results occurred in 29% of cases, with abnormal platelet counts in 26%. CONCLUSION: Our findings confirm that the dysregulation of the coagulation cascade and the subsequent occurrence of coagulation disorders are common in coronavirus infections. The results show absolute values, not increases over normal values; thus, it is hard to justify increased risk and presence based on the presented data.

An IoT Prototype for Monitoring Covid19 Patients Using Real Time Data from Wearable Sensor Through Android App

In the age of modern technology peoples are still facing a great challenges to manage and monitor the infected patients of COVID-19. Many systems have been implemented to track the location of infected person to reduce the spread of diseases. In today's world IoT with the health care system plays an important role specially in this COVID situation. In this research an IoT based monitoring system is designed to monitor and measure different signs of COVID-19 using wearable device. It also sends notification to the proper authority by monitoring the activity of infected patient. To determine the condition of patient, sensor data are analyzed which is passed from edge node, as body sensor are connected to IoT cloud via edge node. Three layered architecture is implemented in our proposed design, wearable sensor layer, Peripheral Interface (API) layer and Android web layer. Different layer have different work, at first health symptom is determined by analyzing data from IoT sensor layer. In next layer information is stored in the cloud database to take immediate actions. Finally android application layer is used to send notifications and alerts for the infected patient. To predict the health condition and alarming the situation both API and mobile application communicate with each other. The designed system has simple structure and helps the authority to find the infected person. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Twenty years, twenty publications and could have been more: Revisiting research collaboration with professor Bidyut Kanti Datta

Professor Bidyut Kanti Datta, a renowned professor of the Department of Pharmacy, Faculty of Pharmacy, University of Dhaka, died at the age of 73 on Friday 11 September 2020 in Canada (Canadian time 7.10 am and BD time 5.10 pm), as a consequence of COVID-19 infection followed by pneumonia. This article is a brief review of his research work where the authors of this article are immensely proud to be associated with. Prof Datta published more than 60 research articles in reputed journals, and the lead author (SDS) of this article, one of his former students from the University of Dhaka, is a co-author of 20 of those publications. These 20 publications resulted from a long-standing research collaboration that spanned over two decades, especially research involving various Bangladeshi species of the genus Polygonum L. of the family, Polygonaceae, and they demonstrate the breadth and depth of research activities that Prof Datta was involved in, and his long-standing commitment to research that underpinned and enriched his teaching offerings to hundreds of students he taught in higher education sector in Bangladesh.