A Quadruple "E" Approach for Effective Cyber-Hygiene Behaviour and Attitude toward Online Learning among Higher-Education Students in Saudi Arabia amid COVID-19 Pandemic

The spread of SARS-CoV-2 (COVID-19) has made online learning more common worldwide than ever before. However, recent research showed that higher-education students in the Kingdom of Saudi Arabia (KSA) were exposed to cyber threats and attacks during online learning that affected their attitudes toward online learning, despite a high level of cybersecurity infrastructure and digital capabilities in KSA universities. There were several calls for enhancing higher-education students' cyber-hygiene awareness to improve their cybersecurity behaviours, develop healthy cyber-hygiene habits, and ensure positive attitudes toward online learning amid COVID-19. The current research developed an integrated cyber-hygiene model for improving this behaviour entitled the quadruple "E" approach (QEA), which includes four stages: educate (E1), explore (E2), execute (E3), and evaluate (E4). The research compares students' cyber-hygiene behaviour and attitude toward online learning pre- and post-implementation of QEA. A sample of 446 bachelor students distributed between females and males in four public KSA universities was adopted during the academic year 2021. The results showed statistically significant differences in students' cyber-hygiene behaviour and attitude toward online learning pre- and post-adoption of the QEA. Students showed more positive cyber-hygiene behaviour and attitudes toward online learning post-QEA adoption than pre-QEA implementation. In addition, female students have more positive behaviour and attitudes than their male counterparts post the adoption of QEA. The current research stimulates positive cyber-hygiene behaviour and enhances attitudes toward online learning in universities, which have implications for the sustainability of KSA higher education, particularly in relation to SDGs 4 and 10.

Fenofibrate and statins in patients with COVID-19

Statins, which are widely used to treat hypercholesterolemia, have anti-inflammatory and antioxidant effects, upregulate angiotensin-converting enzyme 2 (ACE2) receptors, which happen to be SARS-CoV-2's gateway into cells. This study aims to analyse the effects of Fenofibrate in comparison to Statins and a control group in patients with COVID-19. This is a retrospective open blind observational study of cohort of 300 patients experienced COVID-19 (symptoms' severity varied between patients). The participants were divided into three cohorts;a control group received standard COVID-19 treatment (n=100);a second group (n=100) of patients who were on Statins, in addition they received the standard treatment;and a third cohort for patients who were already taking Fenofibrate (TRICORR) as a medication to treat hyperlipidemia (n=100). Most symptoms (including cough, exertional dyspnoea, SOB, sore throat, sneezing, headache, tiredness, agitation, diarrhoea, joint pain, insomnia, myalgia, and fatigue) were less prevalent for patients who administered antihyperlipidemic drugs compared to the control group. Patients who were already taking Cholesterol-lowering medication presented with symptoms varied between mild to severe. Patients on Statins or Fenofibrate also showed less tachycardia and tachypnoea compared to those who were not on antihyperlipidemic drugs, and also the need for oxygen and ICU admission were less frequent. The length of stay in hospital was shorter in patients who were already on Statins or Fenofibrate. Both Statins and Fenofibrate have improved the outcome and the severity of symptoms for patients with Covid 19 infection.

DTI of the Olfactory Bulb in COVID-19-Related Anosmia: A Pilot Study.

This study aimed to assess the utility of DTI in the detection of olfactory bulb dysfunction in COVID-19-related anosmia. It was performed in 62 patients with COVID-19-related anosmia and 23 controls. The mean diffusivity and fractional anisotropy were calculated by 2 readers. The difference between the fractional anisotropy and mean diffusivity values of anosmic and control olfactory bulbs was statistically significant (P = .001). The threshold of fractional anisotropy and mean diffusivity to differentiate a diseased from normal olfactory bulb were 0.22 and 1.5, with sensitivities of 84.4% and 96.8%, respectively, and a specificity of 100%.

Fenofibrate and Statins in Patients with COVID-19

Statins, which are widely used to treat hypercholesterolemia, have anti-inflammatory and antioxidant effects, upregulate angiotensin-converting enzyme 2 (ACE2) receptors, which happen to be SARS-CoV-2's gateway into cells. This study aims to analyse the effects of Fenofibrate in comparison to Statins and a control group in patients with COVID-19. This is a retrospective open blind observational study of cohort of 300 patients experienced COVID-19 (symptoms' severity varied between patients). The participants were divided into three cohorts;a control group received standard COVID-19 treatment (n=100);a second group (n=100) of patients who were on Statins, in addition they received the standard treatment;and a third cohort for patients who were already taking Fenofibrate (TRICOR) as a medication to treat hyperlipidemia (n=100). Most symptoms (including cough, exertional dyspnoea, SOB, sore throat, sneezing, headache, tiredness, agitation, diarrhoea, joint pain, insomnia, myalgia, and fatigue) were less prevalent for patients who administered antihyperlipidemic drugs compared to the control group. Patients who were already taking Cholesterol-lowering medication presented with symptoms varied between mild to severe. Patients on Statins or Fenofibrate also showed less tachycardia and tachypnoea compared to those who were not on antihyperlipidemic drugs, and also the need for oxygen and ICU admission were less frequent. The length of stay in hospital was shorter in patients who were already on Statins or Fenofibrate. Both Statins and Fenofibrate have improved the outcome and the severity of symptoms for patients with Covid 19 infection. Copyright © RJPT All right reserved.

Face Masked and Unmasked Humans Detection and Tracking in Video Surveillance

Due to the spread of COVID-19, people wearing face masks became a regular occurrence worldwide. Moreover, there are nations where covering one's face is done for religious or cultural reasons, or even wear face masks for convenience. However, current face detection and tracking systems are hindered by face masks as the full facial features are no longer visible and therefore became less effective. In this paper, it is proposed to improve current face detection and long-term tracking technology by extracting the facial features of the top regions of the face, taking into account the eye, eyebrow, and forehead. The methodology contains two models, the face detector and the long-term object tracker. The face detection model uses a joint dataset from ISL-UFMD and MaskedFace-Net. The dataset is used to train a Keras sequential model. The object detection model uses pre-trained YOLOv4 weights and DeepSORT to identify people and uses the tracking-by-detection method to perform long-term tracking throughout the surveillance video. The final face detection model results show a testing accuracy of 93.33% and a loss of 26.92%, which are up to par and comparable with other state-of-the-art models. © 2022 IEEE.

Ensemble Federated Learning for Non-II D COVID-19 Detection

In light of the COVID-19 pandemic, the need for a chest X-ray scans classifier is crucial in order to diagnose patients and classify scans into normal, COVID-infected, and pneumonia. Federated learning was chosen for the classification as it uses a decentralized approach to train the model at the local servers belonging to each entity in various geographic locations. Therefore, information leakage that could happen from the traditional centralized approach of training is prevented, besides saving the huge cost of central storage. However, between the vast difference in the number of X-ray scans per data-silo (i.e. hospital), the dissimilar image-Acquisition techniques, and the diverse morphological structures of the human chest, non-IID (non-Independent and Identically Distributed) skews are introduced in the data. In this paper, real-world datasets of COVID and pneumonia scans are used to satisfy all the non-IID data skews. An experiment was then conducted to test the effect of these skews using five federated learning algorithms, FedAvg, FedProx, FedNova, SCAFFOLD, and FedBN, under the same metrics. The obtained accuracy values are 79.5%, 76.92%, 5.57%, 79.18%, and 84.4%, respectively. In this paper, we present the different effects of non-IID skews on the training process and discuss the different federated learning variations to mitigate the data heterogeneity. © 2022 IEEE.

Camera-based Human Counting for COVID-19 Capacity Restriction

In this paper, a Human Counting system is implemented for COVID-19 capacity restrictions. It was implemented using the deep learning model You Only Look Once version 3(YOLOv3) to detect and count the people in a room. The system also can monitor the social distancing between the people in the room while labeling each person as 'safe' or 'unsafe' depending on whether they respect the social distancing protocols that the World Health Organization recommended or not. To make the project user friendly, a Graphical User Interface (GUI) was implemented to allow the user to choose the source of their images that will be used as input to be processed by the system. An experiment was carried out to evaluate the performance of the system under different conditions and in different scenarios where the evaluation was done according to some metrics such as accuracy, precision and recall. The output results from this experiment were demonstrated in details and compared to a similar algorithm as both algorithms focused on people detection using images from an inclined camera. The results show an accuracy of 96% for detection and the number of people counted. © 2022 IEEE.

On Image to 3D Volume Construction for E-Commerce Applications

Ever since the corona pandemic started back in 2020 and the world stopped. However, e-commerce did not. In fact, one of the very few sectors that did rise during the COVID-19 pandemic was e-commerce. Being able to recover the three-dimensional shape of a specific object from one or multiple images is a difficult problem that has been attracting a lot of attention lately. Specifically after most shops have had to shut down to prevent the spread of COVID-19. A lot of existing solutions are available online but unfortunately, a global solution that deals with any object in any image does not exist yet. In this paper we try to discuss those solutions and how deep learning could be used to bridge the gap between the research and the industry. Finally we discuss the open challenges that are in this problem space and opportunities for future work. © 2021 IEEE.