A novel computational approach for rapid screening of antibody therapeutics for efficacy against SARS-CoV-2 variants of concern including Omicron variant

Multiple severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) variants continue to evolve carrying flexible amino acid substitutions in the spike protein's receptor binding domain (RBD). These substitutions modify the binding of the SARS-CoV-2 to human angiotensin-converting enzyme 2 (hACE2) receptor and have been implicated in altered host fitness, transmissibility and efficacy against antibody therapeutics and vaccines. Reliably predicting the binding strength of SARS-CoV-2 variants RBD to hACE2 receptor and neutralizing antibodies (NAbs) can help assessing their fitness, and rapid deployment of effective antibody therapeutics, respectively. Here, we introduced a two-step computational framework with threefold validation that first identified dissociation constant as a reliable predictor of binding affinity in hetero-dimeric and -trimeric protein complexes. The second step implements dissociation constant as descriptor of the binding strengths of SARS-CoV-2 variants RBD to hACE2 and NAbs. Then, we examined several variants of concern (VOCs) such as Alpha, Beta, Gamma, Delta, and Omicron and demonstrated that these VOCs RBD bind to the hACE2 with enhanced affinity. Furthermore, the binding affinity of Omicron variant's RBD was reduced with majority of the RBD-directed NAbs, which is highly consistent with the experimental neutralization data. By studying the atomic contacts between RBD and NAbs, we revealed the molecular footprints of four NAbs (GH-12, P2B-1A1, Asarnow-3D11, and C118)-that may likely neutralize the recently emerged omicron variant-facilitating enhanced binding affinity. Finally, our findings suggest a computational pathway that could aid researchers identify a range of current NAbs that may be effective against emerging SARS-CoV-2 variants.

An Overview of Blockchain Technology and Its Adoption in Industry

The development of a cryptocurrency cannot occur without the use of a vital piece of technology known as blockchain. It is a distributed ledger that has progressed to the point where they are now even more applicable and beneficial. This is due to the passage of time and the development of many sectors. These days, blockchain technology is being employed in certain ways throughout all the industries. In this study, each and every significant attribute and weakness is discussed and analyzed. We have also conducted a literature analysis on blockchain-related topics and highlighted some of the most current sectors in which blockchain technology has found the greatest utility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assessment of 3D Printing for Surgical Instrument Manufacturing

Three-dimensional (3D) printing has emerged as a method for rapid prototyping and manufacturing of tools. In low-resource settings or field settings, the ability to perform surgeries is often limited by a lack of surgical instruments. On-demand manufacture of surgical instruments via 3D printing may offer a low-cost, reliable, convenient solution for provision of necessary care, particularly during trauma or emergency situations. The global coronavirus-19 disease pandemic has emphasized the need for rapid manufacturing of surgical instruments at the point of care, as the pandemic has often limited patient access to hospitals, due to measures to minimize the spread of infectious disease. Moreover, the ability to 3D print surgical instruments is a priority for enabling surgery during space missions. Recent progress has been made on 3D printing of commonly used surgical instruments from plastics. Important surgical tools such as forceps, scalpel handles, needle drivers, Army/Navy retractors, and hemostats have all been 3D printed, with typical print times on the order of hours. This paper assesses the current status of 3D printing of surgical instruments. The review will include 3D printing methods, raw materials, design times, print times, sterilization methods, and the types of surgical instruments that have been successfully printed. In addition, the results of mechanical testing and simulated surgical testing of 3D printed surgical instruments will be described. Finally, avenues for future work will be identified, including the need for faster print times, and the necessity for producing more intricate instruments via 3D printing. © 2022, Avestia Publishing. All rights reserved.

Surge in Adverse Events for Prescription Opioids and Opioid Overdose Treatments during the COVID-19 Pandemic

The opioid epidemic is one of the most pressing public health issues of our time, with hundreds of deaths daily due to opioid overdose. This research investigates the number of reported adverse events related to the use of prescription opioids and opioid overdose treatments during the COVID-19 pandemic, lending further insight into the impact the COVID-19 pandemic has had on the opioid epidemic. We hypothesized that adverse events for both prescription opioids and opioid overdose treatments rose during the COVID-19 pandemic, due to isolation and lack of access to healthcare services. Using data from the Food and Drug Administration Adverse Event Reporting System (FAERS), we analyzed the number of adverse drug events (ADE) in the years 2020 and 2021 compared to 2019, specifically for the medications Naloxone(G), Naloxone Hydrochloride(G), Oxycodone(G), Oxycodone Hydrochloride(G), and Oxycontin(P). We also analyzed the most commonly reported types of adverse reactions and the age of the reporters. The data reveals an alarming spike in the number of ADEs attributed to Naloxone(G) from 2019 to 2020, increasing by 148% and then another 29% in 2021. Similarly, the number of ADEs reported for Naloxone Hydrochloride(G) nearly rose four-fold from 66 to 246. For the prescription opioid Oxycodone(G), there was a 78% increase in ADEs from 2019 to 2020. More concerningly, there was a 434% spike in the number of ADEs for Oxycodone Hydrochloride(G) and more than thirteen-fold the number of cases in 2020 than 2019 for Oxycontin(P). Finally, we found the most commonly reported reactions were "overdose,” "drug dependence,” "drug withdrawal syndrome,” and "drug abuse”;the 18–64-year-old age group reported the majority of the cases. These results highlight the need to increase focus on the opioid epidemic, specifically monitoring the use of prescription opioids. © 2022, Avestia Publishing. All rights reserved.

An Agent-Based Model for Localized COVID-19 Transmission Dynamics and Intervention Impacts

Throughout the COVID-19 pandemic, disease-modeling has guided government health officials in choosing appropriate interventions. However, most current models simulate disease spread on a more generalized scale, lacking specificity for localities such as towns or counties, leading to one-size-fits-all policies being instituted on the country or state-wide-level. However, localities differ in many social determinants of health, which impact disease dynamics therefore necessitating models tailored to individual locations. This research aims to answer this question: What local factors affect COVID-19 outbreak severity and intervention effectiveness? To do this, a novel agent-based disease model was created using NetLogo to simulate contextualized COVID-19 disease dynamics at the local level. Model inputs include population demographic composition, area size, vaccination ratio, interventions (mask, test-and-isolate, or lockdown), and compliance rate. Agents representing the simulated local population are assigned specified traits, and become "susceptible”, "exposed”, "infected”, "recovered”, "quarantined”, or "dead” as they interact with other agents. The model was validated using data from state and local health agencies for Westchester County, NY (84.2% accuracy). A sensitivity analysis demonstrated that a higher elderly population, a lower young population, a lower vaccination rate, and weaker interventions were all factors that increased outbreak severity. A comparison of selective localities representing metric axes of high/low age and high/low vaccination was conducted for four different U.S. counties and showed that 1) any intervention would dramatically reduce locality variations and 2) interventions have higher impact in higher risk localities. This model enables local officials to better focus limited resources when making health related decisions, and a website (www.localcovidmodel.org) has been created for model access. © 2022, Avestia Publishing. All rights reserved.

Analysis of Malfunctions & Injuries Attributed to CT Scanners Before and After the Pandemic

Given the stresses placed on healthcare during the COVID-19 pandemic, and the critical role of computed tomography (CT) scanners in diagnosing cancers and other disorders, this project is designed to investigate the impact of the COVID-19 pandemic on reported malfunctions, injuries (and deaths) attributed to CT scans. Data were extracted from the Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. Yearly numbers of adverse event (AE) reports attributed to CT scanners including malfunctions, injuries, and deaths were recorded for the last 10 years (2012 to 2021). Monthly numbers of reports were also recorded for the 12 months immediately preceding the pandemic (2019/03 to 2020/03), as well as all the months after WHO declared COVID-19 a global pandemic (since 2020/03). It was found that the reported rates of injuries and malfunctions for CT scanners increased during the COVID-19 pandemic. The analysis also revealed unusual trends such as spikes in the malfunction rates from 2015 to 2018 compared to the preceding years, as well as in injuries and deaths. Manufacturers most responsible for these AE spikes included Philips, Superdimension, GE, Siemens, etc. The FDA Recall Database was further mined, and similar trends were identified in the yearly recalls over 2015- 2018, which correlated well with the malfunction rates (less apparent for injuries and deaths). While this project was originally centered around adverse pandemic-related effects on CT scanners, the important pre-pandemic findings warrant further research. These results might help prevent future AEs caused not only by CT scans but also by other medical devices. © 2022, Avestia Publishing. All rights reserved.

De Ritis Ratio A Potential Biomarker in Covid- 19 Admitted Patient's Survivors and Non Survivors

Dysregulation of liver enzymes aspartate transaminase (AST) and alaninetransaminase (ALT) are common in CO VID-19 patients. De Ritis ratio (ASTIALT) ratio is a noninvasive, cost-effective test however usefulness of De Ritis ratio in COVID-19 is unclear. Intraindividual variation of AS T and ALT are large leading to misclassification as normal on repeat testing compared to De Ritis ratio which may not be relatively large as predicted by the product of the individual variances. This was a retrospective study which included subject's male and female adults aged 18-50 years. Dyslipidemic, obese, hypothyroid, nephrotic syndrome, diabetes mellitus, pregnant women were excluded from the study. The study aimed to determine the levels of AST, ALT and De Ritis ratio and investigate association of De Ritis ratio between COVID-19 admitted patient's survivors and inhospital mortality in 500patients admitted to ESIC Medical College and Hospital, Faridabad.The De Ritis ratio was significantly lower in survivors than nonsurvivors (median: 0.94;IQR: 0.71-1.2 vs 1.53;IQR: 1.11-2.46,P= .04)whereas no significant differences was seen in ALT and AST concentrations. In ROC Curve analysis, the AUC value of the De Ritis ratio was 0.80(95% CI 0.56 to 0.65, P < 0.0001) with sensitivity and specificity of 70.64% and 70.27%, respectively as compared toAST (0.60) and ALT (0.64).De Ritis ratio along with correlation with inflammatory markers can be used as a significant biomarker in prognosis and management of COVID-19 admitted patients without incurring any additional cost.

COVID-19: Insights into Structure-Function Relationship, Drug Targets and Drug Design Strategies for Novel-Coronavirus 2019

Background: Novel coronavirus (2019-nCov) imposed deadly health calamity with unexpected disastrous situation alarming the globe for urgent treatment regimes. World Health Organization (WHO) termed the coronavirus disease as COVID-2019 on February 11, 2020 and announced its outbreak as pandemic on 11 March 2020. The first infection was noticed in Wuhan, Hubei province, China, in December 2019, and it is believed that the corona-virus is transmitted to humans through bats as a reservoir involving human to human transfer. However, the proper intermediary transmission channel is yet to be unestablished. Method(s): Elderly populations and patients with concomitant symptoms are more at risk as compared to middle-aged patients as it may progress to pneumonia followed by severe acute respiratory syndrome (SARS) and multi-organ failure. Morbidity rates estimated in patients are less, i.e., 2-3%, but the dearth of a specific treatment strategy to prevent coronavirus infection is a major concern. Result(s): Currently, anti-viral and anti-malarial drugs are in practice for the management of COVID-19 disease along with plasma therapy in the absence of a potent vaccine. Besides, home isolation and social distancing are the precautionary measures adopted by many countries to minimize the spread of infection. Various studies have been conducted, and numerous are still going on to establish specific treatment for COVID-19. Conclusion(s): In this review, we summarized information on the structural components of COVID19 virus with special emphasis on the virus genome, life cycle, the importance of protease enzyme, the role of spike proteins in viral replication, validated drug targets, ongoing effective treatments for COVID-19 management and the latest research on drug design to develop anti-CoV drugs.Copyright © 2021 Bentham Science Publishers.

Title: Effect of COVID-19 on Mental Health: The perspectives

The year 2020 saw the emergence of a novel, highly contagious, coronavirus disease (COVID-19) that originated in the Wuhan province of China and spread across the globe. This led to a worldwide pandemic. The World Health Organisation (WHO), within a month of cases being detected, declared the illness as a -public health emergency of international concern". COVID-19 caused by SARS-CoV-2 not only affected the public health resulting in neurological manifestations (headache, dizziness, or cerebrovascular symptoms), but also initiated a plethora of mental health issues like anxiety, depression and suicidal tendencies. Having spread to over 200 countries, this virus has been a dire cause of concern for primarily two reasons: the threat they possess to the physiological and psychosocial health of the individuals;and the fear, anxiety and panic that has arisen as a result of the pandemic. Most nations, including India, underwent a complete lockdown with stringent norms of social distancing, self-isolation, and quarantine (for infected patients). As the nation tried to manage the situation, guidelines were set up for all its citizens by providing personal protective equipment (PPE), instilling practices like wearing a protective mask, gloves and frequent sanitisation in order to curb the spread of disease and safeguard public health. This review discusses the influence of COVID-19 on the mental health of the general population, focusing on the adolescent, pregnant and elderly;its proposed mechanism of action, and possible strategic interventions to protect the people, offer supportive measures to enhance quality of life, and prevent the spread.Copyright © 2020 Ubiquity Press. All rights reserved.

Addressing Grief and Bereavement in Covid-19 Pandemic

Losses that took place during the covid 19 pandemic are recognised as "bad deaths”. They are characterized by physical discomfort, difficulty breathing, social isolation, psychological distress and ineffective care. The experience of grief has been even more challenging during the covid times as compared to the usual grief prior to the pandemic due to the lack of resources that assist in coping. Lack of social support, uncertainty about the future, lack of routine, and absence of mourning rituals deny the bereaved with the basic opportunity to grieve adaptively. Enhancing advance care planning may help dying patients to receive effective care. Virtual funeral services, extending support through telephonic conversations, online psychotherapy and encouraging continuing bonds with the deceased assist individuals who experience grief and bereavement. The current chapter focuses on expanding awareness about the nature of grief during the pandemic and understanding effective measures to mitigate complicated grief across various subgroups of the society who experienced grief as a result of the covid-19 pandemic. © The Author(s) 2023.

Alteration in Liver Enzymes and Lipid Parameters in COVID-19 Affected Patients and its Association with Infammatory Markers

Introduction: Coronavirus disease 2019 has challenged the global healthcare system since 2019. Cytokine storm due to the release of pro-inflammatory cytokine scan lead to systemic inflammation reaction. Dysregulation of lipid profile and liver enzymes Aspartate Transaminase (AST) and Alanine Transaminase (ALT) are reported in COVID-19 patients. De Ritis ratio (AST/ALT) ratio is a non-invasive, costeffective test however its usefulness in COVID-19 is unclear. Aim(s): To determine serum host serum lipid levels and serum levels of AST, ALT and De Ritis ratio in admitted patients and its correlation with inflammatory markers. Materials and Methods :It was a retrospective study conducted from June 2020 to December 2020, included 500 COVID-19 admitted patients. AST, ALT, Total Cholesterol, Triglycerides, Low Density Lipoprotein, High Density lipoprotein, Ferritin, Procalcitonin, hsCRP estimated in Autoanalyzer and Interleukin-6 by ELISA. Result(s): A significant increase in Serum Triglycerides and decrease in HDL-C was observed with no remarkable finding in other lipid parameters. A statistically significant (p<0.05) correlation was observed between TG (positive), HDL (negative)and inflammatory markers such as hsCRP, PCT, Ferritin, IL-6. The De Ritis ratio was significantly lower in survivors than non-survivors whereas no significant differences was seen in ALT and AST concentrations. In ROC Curve analysis, the AUC value of the De Ritis ratio was 0.80(95% CI 0.56 to 0.65, p<0.0001) with sensitivity and specificity of 70.64% and 70.27%, respectively as compared to AST (0.60) and ALT (0.64). Conclusion(s): Liver enzymes and lipid profile are cost-effective and easily accessible in all laboratories. Its correlation with inflammatory markers can be used as a significant biomarker in prognosis and management of COVID-19 admitted patients without incurring any additional cost.

A Robust NIfTI Image Authentication Framework Based on DST and Multi-Scale Otsu Thresholding

Telemedicine has been intensely promoted in the present pandemic situation of COVID-19 to maintain a strategic distance from the infected person. Several medical tests were used to detect the coronavirus, including antigen, RT-PCR, and a lung CT scan. Only a lung CT-Scan can detect the coronavirus and provide information about the lung infection. As a result, digital imaging plays a critical role in the current pandemic situation. Teleradiology allows for the communication of digital medical images of patients over the internet for diagnosis. A lung CT-Scan test is currently being performed on billions of people to detect COVID-19. These images were sent via the internet for diagnosis and research purposes. The NIfTI image file (.nii extension) was created by the CT-Scanner and contains multiple slices of the lungs. As a result, radiologists determine that the received image has not been tempered during transmission, posing a critical authenticity problem when transmitting these images over the internet. As a result, the researchers are more concerned about the integrity and authenticity of these images in teleradiology. This paper proposes a blind, robust watermarking scheme for lung CT-Scan NIfTI images to address this issue. We use Otsu's image segmentation algorithm in the proposed scheme to identify the slice with the least amount of medical information for watermark embedding. The proposed scheme employs the Discrete Shearlet Transform (DST), Lifting Wavelet Transform (LWT), and Schur decomposition to embed the encrypted watermark. Watermarks are encrypted using the Affine Transform. The experimental results show that watermarked slice has been tainted by the addition of various sorts of noise, including salt-and-pepper noise, compression, Gaussian noise, speckle noise, and motion blur. After an attack, a watermark is retrieved, and the NC values of extracted watermarks are 0.99623 for Salt and pepper noise, 0.96964 for Gaussian noise, 0.99014 for Speckle noise. The proposed scheme was put to the test with a variety of attacks and produced significant results.

Online Educating System and Musculoskeletal Issues in Students: A Concern

Sudden change in education system from offline to online educating system due to pandemic (Covid 19) had a drastically impact on way of teaching methodologies in school and colleges. Black board teaching is replaced by a online teaching by using of various app or software program like-blackboard, Google meet and zoom etc. It was a new experience for teachers and as well as for student. Instant transformation of in-person teaching to e-learning has increased the academic demand of school student's entails rigorous work (assignments, tests and writing examinations). These academic tasks involve the constant use of the computers;as such students spend most of their times in computer laboratories. Several reasons could be advanced to explain the prevalence of musculoskeletal problems among the school students. Copyright © 2022, Anka Publishers. All rights reserved.

Robots for Connection: A Co-Design Study with Adolescents

Adolescents isolated at home during the COVID19 pandemic lockdown are more likely to feel lonely and in need of social connection. Social robots may provide a much needed social interaction without the risk of contracting an infection. In this paper, we detail our co-design process used to engage adolescents in the design of a social robot prototype intended to broadly support their mental health. Data gathered from our four week design study of nine remote sessions and interviews with 16 adolescents suggested the following design requirements for a home robot: (1) be able to enact a set of roles including a coach, companion, and confidant;(2) amplify human-to-human connection by supporting peer relationships;(3) account for data privacy and device ownership. Design materials are available in open-access, contributing to best practices for the field of Human-Robot Interaction. © 2022 IEEE.

Recognizing Students' Attention in a Virtual Class through Facial Expressions using Machine Vision

The COVID-19 has resulted in schools to pause face-to-face classes in traditional classrooms and shifted to online classes in virtual classrooms across the globe. Unfortunately, students' ability to pay attention in class is uncertain. Hence, this study developed models that detect student attention in a virtual class through facial expression. In this study, for every 15-second video segment, statistical values such as the mean, median, and variance of Facial Action Units of each volunteer participant were extracted. Same video segments were labeled independently by three domain experts with attentive or inattentive to form the dataset. Such dataset was then split into 80-20 training-testing split ratios. Results showed that the model developed by the Decision Tree classifier using the Information Gain split criterion gave the best performance with an accuracy of 90.00% and a kappa of 0.796. The presented rate of the accuracy implies a high percentage of correctly predicted observations, while the high kappa value implies a very strong agreement between our human annotators and the machine in labeling students' level of attention. © 2022 IEEE.

Conclusions

This chapter presents the usage of data science, which further helps in exploring the global pandemic COVID-19. This disease suppresses an overwhelming burden, not only to healthcare systems but to the world's economy too. In this era of techniques and technologies, it is believed that data science can better utilize scarce healthcare resources. In this chapter, we provide an introduction of data science and its applications, which helps in combating different aspects of COVID-19. Publicly available datasets related to disease are used as community resources. Different kinds of datasets are used to analyze various aspects of pandemic at different scales. These different kinds of datasets can be audio, video, textual, speech, and sensor data. More than hundreds of research articles are also studied to prepare a bibliometric study. Apart from grabbing all the advantages from datasets, this paper highlights a few challenges, such as surety of correct data, need of multidisciplinary collaboration, new data modality, security issues, and availability of data. © 2021 Elsevier Inc. All rights reserved.

COVID-19 and its impact on cancer, HIV, and mentally ill patients

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) and its disease, COVID-19 is a global pandemic creating an unprecedented medical as well economic havoc across the world. Despite the wide spread global infection rates, the death rate is low for COVID-19. However, COVID-19 patients with other comorbid conditions face severe health complications irrespective of their gender or age. As the management of COVID-19 patients is taking up health resources, it is getting difficult to treat patients suffering from other dreadful diseases like cancer, HIV, and mental health issues. In this chapter, we discuss the effects of COVID-19 and management of cancer patients of main cancer subtypes (e.g., breast, lung, blood cancers), and patients affected with HIV and mental health issues. Finally, we also add a perspective on Ayurvedic treatment and its efficacy on COVID-19 patients. © 2022 Elsevier Inc. All rights reserved.

Utilizing ML and DL Algorithms for Alert Classification in Intrusion Detection and Prevention Systems: A Detailed Review

Intrusion detection/prevention systems have attracted much interest in recent years due to increased online connectivity. In recent years due to COVID pandemic and due to the increased number of online users, online data has become more and more exposed to different types of attacks. Hence, in order to keep data safe, it has become quite important to detect/prevent such attacks. An IDS is a sensor that is used for the observation of such attacks on the nodes or the network itself, and in this way, it tries to keep the information safe from possible attacks. However, accurately identifying such attacks so that they can be prevented effectively is a concern. This accuracy is measured by the number of false positive & false negative in a dataset. These days ML/DL algorithms are being significantly utilized for improving the accuracy of different systems (e.g., health care, stock market, forecasting etc.). Considering its importance, the work presented here studies the impact of using ML/DL algorithms on the accuracy of IDS/IPS. The impact of these algorithms is studied by using evaluation metrics for classification of network assaults in the intrusion detection system using different datasets. These algorithms are subject to further changes for improving the accuracy parameters based on evaluation metrics. © 2022 IEEE.

Sharded Blockchain-based Online Diagnostic System for Suspected Patients During Pandemics

During pandemics, diagnostic tests are essential to provide quick treatment of patients and limit the disease spread. The high demand for testing resources can stress the healthcare system. Thus, a remote collection of symptoms and reporting the results via an automated diagnostic system is highly desirable. However, such a system is challenged by privacy and scalability issues. Hence, we propose a sharded blockchain-based system that (a) introduces a set of shards that distributes the testing load among a group of local nodes (LNs), hence, offering high scalability for country-wide adoption, (b) uses ring signatures and unique random identifiers to ensure the anonymity of the users and the unlinkability of test requests, hence, supporting privacy-preservation, (c) deploys a detection strategy at the LNs based on deep neural networks, which is implemented on smart contracts, hence, enabling autonomous diagnosis, and (d) provides healthcare entities with authorized access to the symptoms and test results, hence, enabling efficient data sharing that supports future research. We provide an implementation of the proposed system and our experimental results demonstrate the high scalability and privacy of the system while achieving a testing accuracy up to 90%. We present a case study for U.S. wide deployment showing that a total daily test request of 2, 407, 462 can be performed and reported in 11 minutes compared to 63 days in absence of sharding. Moreover, sharding decreased the user storage requirement to be 0.18 MB at maximum instead of 723 MB without sharding.

Current State of Engineering Education in India: Student Teacher Scenario in Online Mode

The international expansion of abrupt face-to-face training via online media has been necessitated by the emergence of the COVID-19 pandemic. Reacting quickly and thoughtfully to prevent content-based and hand-crafted and design-based engineering from being harmed by the pandemic is imperative. We performed a case study at Amity University, Noida, Uttar Pradesh to assess the state online engineering Studies. For the 2020–21 academic year, 52 faculty members and academic department members 60 students from various departments in engineering groups worked on in the conducted research and provided feedback on their experiences with online teaching in order to help raise awareness of the difficulties they encounter. Our findings indicate numerous obstacles affecting online engineering education including issues with equipment and technological difficulties, learning and teaching challenges, anxiety and relationship issues and physical safety. To provide another example, a large percentage of students demonstrated low interest in the classroom, difficulties staying focused, and expressed mental weariness after having taken several online classes. A review of many studies revealed that online exams were linked to an increase in the teachers’ perceptions of cheating. It is critical these findings were derived from this study be shared with other instructors to support effective and selective development of online engineering teaching techniques ahead of COVID-19 and following the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.