ABSTRACT
Objective: The purpose of present study was to evaluate the impact of peripheral neuromodulation through vagus nerve stimulation on headache in Post COVID-19 survivors. Method(s): Thirty Post COVID-19 survivors from both genders (17 females and 13 males) aged from 20 to 40 years who suffered from Post COVID-19 headache were recruited and randomized into two groups of equal number. Subjects in group A (study group) received transcutaneous vagus nerve stimulation as well as the designed physiotherapy program whereas subjects in group B (control group) underwent placebo transcutaneous vagus nerve stimulation on vagus nerve in addition to the same designed physical therapy program. The treatment was carried-out for 5 sessions each week for four weeks. Visual analogue scale (VAS) was used to measure the intensity of headache pain whereas the headache disability index (HDI) was used to measure the disability resulted from headache. Result(s): The findings showed significant decline in VAS and HDI post treatment in study group (A) and control group (B) in comparison with that pretreatment (p<0.001). Between-group analysis showed no significant difference between the two groups pretreatment (p>0.05), whereas there was significant decline in VAS and HDI in study group in comparison with that of the control group posttreatment (p<0.05). Conclusion(s): peripheral neuromodulation is more effective in managing headache in post COVID-19 survivors. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
Online exams have been the standard approach adopted by universities and institutes because of the COVID-19 pandemic which forces the world to go towards distance learning and online exams. But with this approach come the challenges such as online exam proctoring which is considered one of the most difficult challenges to solve. It is a must to ensure the academic honesty and credibility of the online exam. Existing proctoring techniques require a few proctors to observe a huge number of students to detect cheating students, and due to it is time-consuming and labor-intensive, we implemented multi-modalities to detect the student's activity during the online exam using a webcam and sent a report to the proctor for the suspected student. Those modalities are head-pose, object detection and eye-gaze estimation. This proposed solution is tested and evaluated on 29 students with a total of four exam sessions to ensure the effectiveness of our proposed solution. The events' detection accuracy of the multi-modalities experiment was 95.69%. © 2022 IEEE.
ABSTRACT
Online learning has emerged as powerful learning methods for the transformation from traditional education to open learning through smart learning platforms due to Covid-19 pandemic. Despite its effectiveness, many studies have indicated the necessity of linking online learning methods with the cognitive learning styles of students. The level of students always improves if the teaching methods and educational interventions are appropriate to the cognitive style of each student individually. Currently, psychological measures are used to assess students' cognitive styles, but about the application in virtual environment, the matter becomes complicated. The main goal of this study is to provide an efficient solution based on machine learning techniques to automatically identify the students' cognitive styles by analyzing their mouse interaction behaviors while carrying out online laboratory experiments. This will help in the design of an effective online laboratory experimentation system that is able to individualize the experiment instructions and feedback according to the identified cognitive style of each student. The results reveal that the KNN and SVM classifiers have a good accuracy in predicting most cognitive learning styles. In comparison to KNN, the enlarged studies ensemble the KNN, linear regression, neural network, and SVM reveal a 13% increase in overall total RMS error. We believe that this finding will enable educators and policy makers to predict distinct cognitive types in the assessment of students when they interact with online experiments. We believe that integrating deep learning algorithms with a greater emphasis on mouse location traces will improve the accuracy of our classifiers' predictions.
ABSTRACT
COVID-19 pandemic has led to a great interest in online learning systems. However, the lack of suitable online laboratory learning systems has posed a particular challenge for sectors that need laboratory experimentation activities as in engineering and science domains. This paper presents a simple but efficient technique for providing intelligent virtual tutor that can assist students in online laboratory experimentation environment. The proposed technique is based on analyzing and modelling the student's mouse interaction behavior for identifying the difficulties that the student faced during conducting the lab's experiment, and hence providing the suitable assistance. The different levels of difficulties will be detected using the trajectory of mouse movement activities. The obtained results verify accurate and very fast operation for identifying the student's difficulties.
ABSTRACT
A contagious respiratory disease caused by COVID19 has spread out from China to worldwide, on 30 January 2020;World Health Organization (WHO) declared officially the COVID19 is pandemic disease. In this study, computational study was performed to evaluate the effectiveness of chemical compounds (M1 & M2) against lsysomal protease of SAR-CoV-2. The molecular docking results showed that the two molecules (M1& M2) have pretty good potential affinity to bind with preferred active site of A1 subunit of lysosomal protease of SAR-CoV-2, where the compounds (M1, M2) showed highest functional score (-12.5, -21.6 Kcal/mol) with appropriate orientation and full fitness (-1271, -1308) inside of the active site compared with Chloroquine and Hydroxychloroquine (-12.3, -10.5 Kcal/mol) respectively. The results of ADME toxicity profile of compounds (M1, M2) were computed and compared with Chloroquine and Hydroxy chroloquine. Table (1) showed the two molecules (M1, M2) meet the drug likeness parameters Both compounds have high Pharmacokinetics with ability to inhibit CYP1A2, CYP2C19 and CYP2C9 with high ability to absorption in gastrointestinal (GIA), effluated in central nerve system (CNS) and brain-blood barrier permeability (BBB). Based on the computational study results, the molecules (M1 & M2) have pretty potential inhibitor candidate for Lysosomal protease of SAR-CoV-2. Two benzo (b) thiophene containing triazole moity especially 3-(3-chloro-1-benzothien-2-yl)-4H-1,2,4-triazol-3-N-piperidine (M1) and 3-(chloro-1-benzothien-2-yl)4H-1,2,4-triazole-3-N-pyrole (M2) were synthesized and succefully characterized by FT-IR spectrum.
ABSTRACT
The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.