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PurposeThe study aims to investigate the impact of workplace ostracism (WO) and fear of the COVID-19 pandemic on the family life of restaurant employees. This research is based on the conservation of resources (COR) theory and work-family interface model to understand the theoretical underpinnings of mistreatment in the food sector during the COVID-19 pandemic.Design/methodology/approachThe study utilized a survey with a structured questionnaire to collect time-lagged data from 238 restaurant employees in the central region of Punjab province in Pakistan. The collected data were analyzed using the SPSS tool with modern-day techniques like bootstrapping, process macro and SmartPLS.FindingsThe study reveals that perceived stress levels of the employees increase due to ostracism, leading to work-family conflict. Furthermore, the study found that employees who fear COVID-19 are less stressed by ostracism.Originality/valueThe study's significant contribution lies in demonstrating that the impact of ostracism in the workplace is quite different from what was expected. The results have shown that ostracism can reduce the perceived stress levels of employees, leading to a decrease in work-family conflict, especially in the presence of fear of COVID-19.
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The compounded Bell generalized class of distributions is proposed in this article as an alternative to the compounded Poisson generalized family of distributions. Some properties and actuarial measures are presented. The properties of a special model named Bell Weibull (BellW) are obtained such as the linear representation of density, rth moment, incomplete moment, moment generating function using Wright generalized hypergeometric function and Meijer's G function, the pth moment of order statistics, reliability, stochastic ordering, and residual and reversed residual life. Moreover, some commonly used entropy measures, namely, Rényi, Havrda and Charvat, and Arimoto and Tsallis entropy are obtained for the special model. From the inferential side, parameters are estimated using maximum likelihood estimation. The simulation study is performed to highlight the behavior of estimates. Some actuarial measures including expected shortfall, value at risk, tail value at risk, tail variance, and tail variance premium for the BellW model are presented with the numerical illustration. The usefulness of the proposed family is evaluated using insurance claims and COVID-19 datasets. Convincing results are obtained. © 2023 the author(s), published by De Gruyter.
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In this manuscript, we formulate a mathematical model of the deadly COVID-19 pandemic to understand the dynamic behavior of COVID-19. For the dynamic study, a new SEIAPHR fractional model was purposed in which infectious individuals were divided into three sub-compartments. The purpose is to construct a more reliable and realistic model for a complete mathematical and computational analysis and design of different control strategies for the proposed Caputo–Fabrizio fractional model. We prove the existence and uniqueness of solutions by employing well-known theorems of fractional calculus and functional analyses. The positivity and boundedness of the solutions are proved using the fractional-order properties of the Laplace transformation. The basic reproduction number for the model is computed using a next-generation technique to handle the future dynamics of the pandemic. The local–global stability of the model was also investigated at each equilibrium point. We propose basic fixed controls through manipulation of quarantine rates and formulate an optimal control problem to find the best controls (quarantine rates) employed on infected, asymptomatic, and "superspreader” humans, respectively, to restrict the spread of the disease. For the numerical solution of the fractional model, a computationally efficient Adams–Bashforth method is presented. A fractional-order optimal control problem and the associated optimality conditions of Pontryagin maximum principle are discussed in order to optimally reduce the number of infected, asymptomatic, and superspreader humans. The obtained numerical results are discussed and shown through graphs. © 2023 by the authors.
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Iris recognition is a robust biometric system-user-friendly, accurate, fast, and reliable. This biometric system captures information in a contactless manner, making it suitable for use during the COVID-19 pandemic. Despite its advantages such as high security and high accuracy, iris recognition still suffers from pupil deformation, motion blur, eyelids blocking, reflection occlusion and eyelashes obscure. If the pupillary boundary is not accurately segmented, iris recognition may suffer tremendously. Moreover, reflections in iris image may lead to an incorrect pupillary boundary segmentation. The segmentation accuracy can also be affected and reduced because of the presence of an unwanted noise created by the motion blur effect in iris image. Additionally, the pupillary boundary might change from circular shape to uneven or irregular shape because of the interference and obstruction in pupil region. Therefore, this work is carried out to determine an accurate, efficient and fast algorithm for the segmentation of pupillary boundary. First, the iris image is pre-processed with Wiener filter. Next, the respective iris image is assigned with a specific threshold. After that, the pixel property in iris image is computed to determine the pupillary boundary coordinates which are acquired from the measured pixel list and area in iris image. Finally, morphological closing is used to remove reflections in the inner region of pupil boundary. All experiments are implemented with CASIA v4 database and Matlab R2020a.
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Objective: To look for the psychiatric morbidity and associated socio-demographic factors among patients who tested positive and isolated for COVID-19. Study Design: Cross-sectional study. Place and Duration of Study: Combined Military Hospital, Malir Pakistan, from Mar to May 2020. Methodology: All patients who tested positive for COVID-19 and were admitted to the COVID-19 Ward without complications were included in the study. General Health Questionnaire-12 (GHQ-12) was administered to look for the presence of psychiatric morbidity. Results: Out of 61 patients included in the study, 45(73.7%) showed the presence of psychiatric morbidity, while 16(26.3%) did not show psychiatric morbidity when screened with GHQ-12. 43(70.4%) were male, while 18(29.6%) were female. The mean age of the patients was 35.21±2.355 years. The advanced age and female gender have a statistically significant relationship (pvalue<0.05) with the presence of psychiatric morbidity among patients of COVID-19. Conclusion: Many patients had psychiatric morbidity after being tested positive for COVID-19 and were isolated in the health facility. Female patients and patients aged more than 40 years were found to be more at risk of developing psychiatric morbidity among the patients admitted to COVID-19 ward. © 2022, Army Medical College. All rights reserved.
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The Covid-19 outbreak has caused disruptions in the education sector, making remote education the dominant mode for lecture delivery. The lack of visual feedback and physical interaction makes it very hard for teachers to measure the engagement level of students during lectures. This paper proposes a time-bounded window operation to extract statistical features from raw gaze data, captured in a remote teaching experiment and link them with the student's attention level. Feature selection or dimensionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores. The model trained using the optimal feature subset showed significant improvement in accuracy and computational complexity. For instance, a support vector classifier (SVC) led 2.39% improvement in accuracy along with approximately 66% reduction in convergence time. © 2022 IEEE.
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Background / Introduction: The novel coronavirus disease pandemic (COVID-19) has affected the world entirely. The government of Saudi Arabia adopted varieties of measures to mitigate the spread of the novel virus;one of the measures taken was to close all schools and universities across the kingdom and promoting online education. The aim of our study is to determine the prevalence of digital eye strain, the associated risk factors and the most prevalent associated symptoms among under graduated medical students at Majmaah University in Saudi Arabia. Objective(s): to study the prevalence of digital eye strain among undergraduate students in the college of medicine, and to identify the risk factors associated with digital eye strain, and to identify the preventive measures taken to avoid eye strain symptoms related to digital device use. Methodology: Observational descriptive study (Cross-sectional study) to evaluate Digital Eye Strain among undergraduate students in the college of medicine at Majmaah University, to determine the prevalence of DES, associated risk factors, and measures taken to relieve the symptoms. Data will be analyzed by researchers using SPSS version 20. Result(s): Our study showed that digital eye strain was positively associated in female gender more than male, also it was positively associated in people who have preexisting eye conditions like myopia. Regarding the incidence of digital eyestrain with the intensity, it has been shown that most of our participants had mild strain eyestrain (41%). Moreover, it was observed that headache was the most common complaints by our participants. Using the digital devices for more than 4 h/day, and takings a breaks during using the devices in frequency 60 minutes or more and not using antiglare screen were significant risk factors linked to sys strain symptoms (P<0.001, P=0.02, P=0.04) respectively .In regard the preventive measure taking to reduce the digital eye strain our study found that there was no significant association between practicing the rule of 20-20-20 and the prevalence of digital eye strain among participant using eye drops was significantly associated with low incidence of digital eye strain (P=0.01). Conclusion(s): In conclusion, digital eye strain is an emergent public health problem that is proportional to the duration of exposure to digital screens. It has also been associated with multiple digital devices among medical students most commonly iPads. Digital devices are mandatory in every institution and prevention of digital eye strains with the consequences must be included in the curriculum. Copyright © 2022 Ubiquity Press. All rights reserved.
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Objective: To explore the relationship between ethnicity and demographic factors with the time taken by patients to get negative on PCR for COVID-19. Study Design: Prospective Comparative Study. Place and Duration of Study: Combined Military Hospital, Malir Pakistan, from Mar to May 2020. Methodology: All patients who tested positive for COVID-19 with less than one week of exposure time and were admitted to the COVID-19 ward of Combined Military Hospital, Malir without any complications were included in the study. They were tested after every seven days with PCR. Time taken to get two consecutive negative tests were noted for each patient. Results: Out of 84 patients included in the study, 12(14.3%) tested negative on the 7th day, 34(40.4%) on the 14th day and 38(45.3%) tested negative after 14 days. 17(20.2%) were Sindhi, 13(15.5%) were Muhajir, 19(22.6%) were Punjabi, 25(29.7%) were Pathan, and 10(11.9%) were Kashmiris. Chi-square revealed that ethnicity and advancing age have a statistically significant relationship (p-value<0.05) with the time taken by patients to get negative on PCR for COVID-19. Conclusion: Ethnicity emerged as a significant factor in getting negative for COVID-19. Punjabis and Kashmiris required a shorter period to get negative than Sindhis and Pathans. Older age emerged as a factor requiring a longer period to get negative. © 2022, Army Medical College. All rights reserved.
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The elderly population is growing, and the health care system is experiencing a strain on services provided to the elderly. The recent COVID-19 pandemic has increased this strain and has resulted in an increased risk of exposure during visits to elderly homes. Increasing the desire to provide technological solutions to counteract this. Currently, there lack reliable real-time non-invasive sensing systems. This paper makes use of Radio Frequency sensing, where signal propagation is observed in Channel State Information (CSI) reports on Activities of Daily Living (ADLs). Real-time data has been collected for three classifications, “movement”, “empty room”, and “no activity”. A filter is applied to reduce the noise of the CSI data. Then the mean, max, min, kurtosis, skew and standard deviation features are extracted from the CSI data. A machine learning model provides classification for the real-time monitoring system allowing detection of abnormalities in the expected ADLs of the elderly. The timing of classifications gives insight into the real-time capabilities of the system. The Random Forest algorithm is chosen to create the machine learning model based on accuracy and timing capabilities. The model was able to achieve an accuracy of 100 % on new unseen testing data with an average classification time of 7.31 milliseconds. IEEE
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Paxlovid™ is a combination of Nirmatrelvir and Ritonavir antiviral pills with good oral bioavailability. In clinical studies, treatment of the patients infected with SARS-CoV-2 with Paxlovid™ within three to five days of the appearance of symptoms significantly reduced the hospitalization rate as well as mortality. It is the first oral antiviral treatment for the COVID-19 which received USFDA approval for EUA on 22nd December, 2021. Nirmatrelvir inhibits the replication of SARS-CoV-2 while another antiviral drug, Ritonavir, is given in combination to enhance the bioavailability of Nirmatrelvir. Molecular interaction studies have shown that Nirmatrelvir binds covalently with the catalytic triad of the active site of the viral protease enzyme (3CLPRO). It, therefore, acts by stopping the SARS-CoV-2 replication by its ability to block the translation of the viral genetic materials. Research studies conducted have proven the efficacy of this oral anti-viral drug in mild to moderate COVID-19 patients beside its ease of oral administration and good oral bioavailability. Alternative synthetic methods to scale up the synthesis of this potent molecule are needed to reduce the treatment cost of the COVID-19. Extensive clinical research on a larger group population is also underway for ensuring the safety and efficacy of this medication in the battle against the COVID-19 pandemic.
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COVID pandemic initiated in early 2019 and the origin from where it initiated was Wuhan city of China. It changed the whole world. A huge population died due to COVID-19 in spite of taking precautions. New treatments and vaccines are introduced for the treatment and prevention. Among successful treatments, antivirals were found effective against COVID-19. But there is a need to find derivatives, which could be more effective for the treatment of COVID-19. The current research is focused on computational studies on one of the antiviral, darunavir. A computational strategy, molecular docking and molecular dynamic simulation techniques is presented to discover the potent analogues of darunavir for inhibiting protease 3CLpro of SARS-CoV2. The newly discovered X-ray structure (PDB ID: 6LU7) was selected for docking study and generated analogues were docked. The docking results showed that the compounds were bound in the active site of receptor with good binding affinity. It was concluded that compounds D8 and D15 were have good binding affinity value of -9.85 and -8.95 kcal/mol, respectively and these compounds were selected for molecular dynamic simulation (MDS) study to check their stability in pocket of receptor. © 2022 Chemical Publishing Co.. All rights reserved.
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COVID-19 has now been sweeping the whole world, and fundamentally affecting our daily life. An effective mechanism to further fight against COVID-19 and prevent the spread of this pandemic is to alert people when they are in the vicinity of areas with a high infection risk, yielding them to adjust their routes and consequently, leave these areas. Inspired by the fact that mobile communication networks are capable of precise positioning, data processing and information broadcasting, as well as are available for almost every person, in this paper, we propose a mobile network assisted Risk arEa ALerting scheme, named REAL, which exploits heterogeneous mobile networks to alert users who are in/near to the areas with high risks of COVID- 19 infection. Specifically, in REAL scheme, all base stations (BSs) periodically estimate their serving users' locations, which are then analyzed by macro BSs (MBSs) to identify risk areas. Next, each MBS transmits the information about risk areas to small BSs (SBSs), which in their turn adjust the beamforming direction to cover these areas and send alerts to users located therein. Simulation results validate the effectiveness of the proposed REAL scheme. In addition, some key challenges associated with implementing REAL are discussed at the end. IEEE
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Peerconnect is a great solution for virtual event management. Peerconnect is an all-in-one event management platform for promoting and conducting online events on the same platform. The point of virtual event management software is to allow users to create hosts and manage the events at ease without having to depend on many software to accomplish different tasks. So, the event management software Peerconnect has established the pipeline for people who want to promote, manage and conduct virtual events. Organizers can create the events on the platform and can promote and sell the tickets to other users on the same platform. Organizers can host live chat rooms and live discussions with video calling capabilities as well. Peerconnect is a web application using which the user can get to know about the events which are conducted virtually. Once the user selects the event, then he has to register for it. Once the registration is successful, user can attend the event on the same platform. Once the registration is successful, users can successfully login and they can search for an event that they are interested in and filter them according to their interests. Once the user selects the event then they have to register for it. Once registration for the event is successful, user can join the event in just one click and attend the event on the same platform and can download the participation certificate as well. If a registered user misses an event, he can still watch the recorded session of that event anytime after its completion. The event organizer can create an organization and later post the event. During the event, the organizer can send files and use the chat feature to engage and clear doubts of the attendees. They can also share their screen so that it is visible to all the attendees. Organizers can also capture the attendance of those who are attending, based on which the attendees can be able to download their participation certificates. © 2022 IEEE.
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Smart building technologies transform buildings into agile, sustainable, and health-conscious ecosystems by leveraging IoT platforms. In this regard, we have developed a Persuasive Energy Conscious Network (PECN) at the University of Glasgow to understand the user-centric energy consumption patterns in an agile workspace. PECN consists of desk-level energy monitoring sensors that enable us to develop user-centric models that can be exploited to characterize the normal energy usage behavior of an office occupant. In this study, we make use of staked long short-term memory (LSTM) to forecast future energy demands. Moreover, we employed statistical techniques to automate the detection of anomalous power consumption patterns. Our experimental results indicate that post-anomaly resolution leads to 6.37% improvement in the forecasting accuracy. © 2022 IEEE.
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The SARS-CoV-2 has initiated in Wuhan city of China and then extend all around the world as a health emergency. It begins a new research area to produce potential drugs using data-driven approaches to identify potential therapies for the treatment of the virus. This is the time to develop specific antiviral drugs using molecular docking, quantum chemical approaches, and natural products. The protease inhibitors that constitute plant derivatives may become highly efficient to cure virus-prompted illnesses. A systematic study of isolated phytochemicals was executed then frontier molecular orbitals, docking score, molecular descriptors, and active sites were compared with favipiravir, dexamethasone, redeliver, and hydroxychloroquine which are being used against COVID19 nowadays. This is the first study on the phytochemicals of Daphne species to explore their anti-SARS-CoV-2 behavior by molecular docking and quantum chemical methods.
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COVID-19, the clinical syndrome produced by infection with SARS-CoV-2, can result in multisystem organ dysfunction, including respiratory failure and hypercoagulability, which can lead to critical illness and death. Musculoskeletal (MSK) manifestations of COVID-19 are common but have been relatively underreported, possibly because of the severity of manifestations in other organ systems. Additionally, patients who have undergone sedation and who are critically ill are often unable to alert clinicians of their MSK symptoms. Furthermore, some therapeutic measures such as medications and vaccinations can worsen existing MSK symptoms or cause additional symptoms. Symptoms may persist or occur months after the initial infection, known as post-COVID condition or long COVID. As the global experience with COVID-19 and the vaccination effort increases, certain patterns of MSK disease involving the bones, muscles, peripheral nerves, blood vessels, and joints have emerged, many of which are likely related to a hyperinflammatory host response, prothrombotic state, or therapeutic efforts rather than direct viral toxicity. Imaging findings for various COVID-19-related MSK pathologic conditions across a variety of modalities are being recognized, which can be helpful for diagnosis, treatment guidance, and follow-up. The online slide presentation from the RSNA Annual Meeting is available for this article. ©RSNA, 2022.
Subject(s)
COVID-19 , Musculoskeletal System , COVID-19/complications , Humans , Multimodal Imaging , SARS-CoV-2 , Post-Acute COVID-19 SyndromeABSTRACT
Purpose: With theoretical underpinnings in the conservation of resources theory, this research aims at understanding the link between workplace ostracism (WPO) and its effects on customers' interests in the context of COVID-19, with the mediation of stress and moderation of self-efficacy (SE). Design/methodology/approach: The study followed a time-lagged design. A sample of 217 frontline employees working in the food sector of southern Punjab, Pakistan responded to the study questions using survey method with structured questionnaires. A Statistical Package for the Social Sciences (SPSS) tool was utilized for data analysis with bootstrapping and PROCESS macro. Findings: The findings show that an important mechanism by which ostracism translates into customer service sabotage (CSS) is the increase in perceived stress levels of the employees. Additionally, SE was found to be an important personal resource that acts as a moderator in the said relationship. Practical implications: Employees with high SE sense less workplace stress even during a pandemic. Leadership should consider the stress-alleviating effect of SE for lessening the damaging influence of WPO on customers. Originality/value: The study fills an important empirical gap in the context of the COVID-19 pandemic, by showing that due to resource loss perceived by employees while being targeted by ostracism, they may decide to transfer their frustration towards organizational customers by sabotaging their service experience. © 2022, Ambreen Sarwar, Muhammad Ibrahim Abdullah, Muhammad Kashif Imran and Nazia Rafiq.
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Objective: To study the impact of covid-19 on medical education and anxiety level of medical students. Methodology: This prospective cross sectional study was done at Shifa College of mMedicine, Islamabad, Pakistan from 1st October 2020 to 15th November 2020. Medical students were randomly selected and a preformed questionnaire was circulated among them via Online Google forums. We used GAD-7 scale. Data were analyzed using SPSS version 23. Results: Out of 122 respondents, 69 (56.5%) were female. Mean age was 22.1 ± 1.7 years. Almost all students had online education during COVID-19, however, 64 (52.4%) of them faced communication problems during online education. Only 32 (26.2%) were satisfied with this method. In this study, 46 (37.7%) respondents had anxiety;38.0% had moderate to severe and 27.0% had mild anxiety. Most of the anxious respondents were significantly younger (21.6 vs. 22.5 years, p = 0.01). Females were significantly more anxious (69.6% vs. 30.4%) than males (p < 0.03). Conclusion: During Covid-19 pandemic, anxiety and stress levels have increased among medical student.
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Objective: To assess the importance of adenosine signaling in cardiovascular disorders (thrombosis, ischemia) and novel corona virus infection. Methodology: A specified web search was done to gather the relevant information using different scientific research forums and databases like WHO database, Pubmed and Google Scholar etc. Results: Adenosine receptors are P1 type of purinergic receptors and belong to G protein-coupled receptors (GPCRs), which is the largest family of integral membrane bound proteins receptors. Adenosine receptors are further classified into four subclasses known as A1, A2A, A2B, and A3. All four subclasses are being mediated by extracellular adenosine and perform a key role in a wide range of physiological functions such as immune system modulation, angiogenesis and sleep regulation. Adenosine receptors are thought to play a significant role in many pathophysiological conditions including cardiovascular disorders such as ischemia and thrombosis and novel corona virus infection making it a key target against these disorders. Conclusion: We suggest that modulation of adenosine receptor activity could increase the regenerative phase in these disorders by increasing the proliferation and differentiation rates of damaged tissue.
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With the advent of Coronavirus Disease 2019 (COVID-19), the world encountered an unprecedented health crisis due to the severe acute respiratory syndrome (SARS) pathogen. This impacted all of the sectors but more critically the transportation sector which required a strategy in the light of mobility trends using transportation modes and regions. We analyse a mobility prediction model for smart transportation by considering key indicators including data selection, processing and, integration of transportation modes, and data point normalisation in regional mobility. A Machine Learning (ML) driven classification has been performed to predict transportation modes efficiency and variations using driving, walking and transit. Additionally, regional mobility by considering Asia, Europe, Africa, Australasia, Middle-East, and America has also been analysed. In this regard, six ML algorithms have been applied for the precise assessment of transportation modes and regions. The initial experimental results demonstrate that the majority of the world's travelling dynamics have been contrastively shaped with the accuracy of 91.21% and 84.5% using Support Vector Machine (SVM) and Random Forest (RT) for different transportation modes and regions. This study will pave a new direction for the assessment of transportation modes affected by the pandemic to optimize economic benefits for smart transportation.