Prioritization of Awareness Campaigns for a Diseases through Fusion of Pentagonal Intuitionistic Fuzzy Number and Promethee

A novel method for assessing the effectiveness of enrichment evaluations PROMETHEE combining pentagonal intuitionistic fuzzy numbers (PIFNs) and preference rank-ing organization is presented in the present paper. PIFN suggests a new technique for multi -criteria group decision making (MCGDM) in which two characteristic values of membership and non-membership functions are involved. The key practicality of incorporating PIFN in decision -making is its effective capability of managing the vagueness and uncertainties of linguistic terms used during discussions. The designed algorithm is then applied to get an appropriate, cost-effective, and publicly accepted awareness campaign to be used to forewarn populaces about any virulent disease, which has not been studied before. Importantly, it is the only way to protect any huge population of a country from any fatal disease, i.e. to be timely aware of the disease's transmissibility, severity, and precautionary measures through any effectively ap-proachable source. Here, we consider alternative sources of campaigns, such as commercial advertisement on television, on social media, on bills /other government circulars, billboards, and door-to-door volunteering for guidance. These alternative campaigns are based on five generalized criteria, where the weight of each criterion is evaluated via the fuzzy analytical hier-archy process (F-AHP). After using the F-AHP for complex decisions based on acceptance and effectiveness, the F-PROMETHEE algorithm is applied to achieve the closest ideal alternative.

Sentimental Analysis of COVID-19 Vaccine Tweets Using BERT+NBSVM

The development of the vaccine for the control of COVID-19 is the need of hour. The immunity against coronavirus highly depends upon the vaccine distribution. Unfortunately, vaccine hesitancy seems to be another big challenge worldwide. Therefore, it is necessary to analysis and figure out the public opinion about COVID-19 vaccines. In this era of social media, people use such platforms and post about their opinion, reviews etc. In this research, we proposed BERT+NBSVM model for the sentimental analysis of COVID-19 vaccines tweets. The polarity of the tweets was found using TextBlob(). The proposed BERT+NBSVM outperformed other models and achieved 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Bibliometric Analysis of Pakistani Medical Journals Evidenced Exponential Growth of Coronavirus Disease–Related Publications

Since 2019, the novel coronavirus disease (COVID-19) has transformed into a global pandemic, and studies on COVID-19 are crucial in finding solutions to deal with the pandemic. The objective of this study is to present a bibliometric analysis of COVID-19–related literature published in Pakistani medical journals. In this study, the PakMediNet database was searched for COVID-19–related literature on February 11, 2022. The required bibliometric data of each publication were documented in Microsoft Excel. About 67% of the 480 articles found were original articles, among which the vast majority (99%) were published in 2021 and 2022. Hamzaullah Khan, Pakistan Journal of Medical Sciences, and the National University of Medical Sciences were the most prolific author, journal, and institution, respectively. The most cited publication received 491 citations. Psychology and public health were the most preferred subjects. Pakistani medical journals have published a significant number of publications on COVID-19. However, international contributions and citations of these publications were low. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

COVID-19 impact on dairy sector: The mediating role of knowledge sharing and trust on innovation capability

Background: In the current era, innovation has become the basis for the success of all industries. In reality, fast innovation facilitated by rapidly changing technological discoveries is critical to global economic progress. Aim: The primary goal of this article is to examine the effect of knowledge exchange and development of supervisory support, trust, training, information technology, and industrial cluster resources on innovation capabilities in the dairy sector of Pakistan. Setting: From a total of 520 small and medium enterprise (SMEs) dairy farms, 227 owners and managers were carefully chosen to participate in the survey. Method: The current study's research framework was based on the resources and diffusion of innovation perspective theories. The data were gathered from dairy farm owners and managers in Punjab, Pakistan. SmartPLS-SEM was used to examine the multivariate connection among the variables. Results: The current research finds that training and development, supervisory assistance, and industrial cluster resources strongly influence knowledge sharing. Furthermore, trust has a favourable influence on innovative capabilities. However, the mediation effect of knowledge sharing (KS) did not support information technology (IT) training and development (T&D) and innovative capabilities (IC). Conclusion: According to findings in the study, T&D as a form of learning connect employees through the sharing of new ideas, allowing the business to improve and the concept to be modified. This study found that supervisory assistance significantly impacts innovative capabilities and knowledge sharing. © 2023. The Authors. Licensee: AOSIS.

Fusion Based Self Diagnosis Expert System Empowered with Leven-berg Marquardt Algorithm

COVID-19 coronavirus disease is the latest virus in the new century. The World Health Organization- WHO organization announced that COVID-19 disease is a pandemic that leads to thousands of death in short time of spam. A quick and accurate diagnosis of COVID-19 shows an important role in its prevention. This study is based on a fusion-based Self-Diagnosis Expert System Empowered by the Leven-berg Marquardt Algorithm for the diagnosis of diseases. Leven-berg Marquardt has been implemented for the classification of different symptoms of the diseases and relates the results for their diagnosis. The MatLab software was used for the simulation purpose. The proposed fusion-based LB increased the accuracy in the training and validation process to be 10 times more efficient than the existing. The fusion technique achieved an overall accuracy of 98.86%, and 99.09% in all performance metrics which included TNR, precision, and FPR statistical parameters. © 2022 IEEE.

Comparative Analysis of E-Learning in Private and Public Sector Medical and Dental Institutes in Pakistan

Objective: The aim of this study was to analyze the public and private sector medical and dental institutions' management of online education amidst the COVID-19 pandemic in Pakistan and to understand whether their respective modes derived satisfaction of e-learning from students. Method(s): We conducted a survey of 371 students of various public and private sector medical and dental institutions across Pakistan, investigating their experience, confidence, and satisfaction regarding e-learning. The survey was carried out using 'Google Forms', which was sent via email to students. Result(s): In total, 371 responses were received. Despite the differences in resources and facilities, students of both sectors had not been confident in taking professional exams after the shift to online education: public (81.3%) and private (74.4%);very few students felt confident about their knowledge of basic medical sciences without labs/ practical work: public (10.8%) and private (10.3%);and more than 80% from both sectors also held the belief that e-learning is not sufficient to support academics. Conclusion(s): Private medical and dental institutes in Pakistan have better funding but students still felt under-confident. Public sector institutes lacked a developed IT department and had an irregular and erratic schedule of online lectures with limited engagement from professors. Many felt that online and traditional learning can be blended to bring forth a form of learning known as "blended learning". Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

Clinical Characteristics and Early Findings of Coronavirus Disease (Covid-19) at a Tertiary Care Teaching Hospital in Pakistan

Background: Severe acute respiratory syndrome-2 (SARS-CoV-2) emerged as a novel coronavirus and associated with the pandemic. In our study we observed the clinical characteristics, early findings, and its association with comorbidity. Method(s): A single center retrospective study was carried out in Mardan Medical Complex (MMC), Khyber Pakhtunkhwa (KP), Pakistan from May 21st, 2020 to June 30th, 2020. Altogether three thousand, one hundred and fifteen (n=3115) COVID-19 suspected patients were included in the current study. Briefly nasopharyngeal swab, sputum and blood were collected. The viral amplification was carried out by qualitative RT-PCR using commercially available kit and routine laboratory tests of all the suspected patients were performed. Result(s): Using RT-PCR total 19.8% (n=613/3115) confirmed cases of COVID-19 were observed. The majority were males' patients. The most common comorbidity was type-2 diabetes (T2DM);24.8% followed by cardiovascular diseases;6% and T2DM with cardiovascular disease 3.1%. Among the infected patient's leukocytosis was observed in 43% patients and 27.9% had abnormal findings on X-rays. The RNA detection efficacy from the sputum, nasopharyngeal swab, and blood specimens were 30%, 25.3% and 9.6% respectively. In total, 18.3% patients were critical, and 14.5% patients were on ventilator and the reported mortality rate were 5.2%. Conclusion(s): Overall, the COVID-19 patients observed in our study was comorbid and asymptomatic or with mild symptoms like fever, cough, and shortness of breath. Higher, RNA detection efficacy was observed from sputum. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

COVID-19 Data Analysis and Appropriate Vaccine Prediction using Machine Learning

COVID-19 highly contagious virus, it has wreaked devastation on the earth. To help the world overcome this challenging situation, scientists and professionals across many disciplines are working relentlessly to develop vaccines and prevention measures. Many people are getting sick because they don't know which of the discovered coronavirus vaccines are beneficial for the human body. The appropriate vaccine has been predicted by analyzing the types of diseases that people have in our data set and the types of diseases that people get after giving the first dose and second dose. From these data, we can predict what kind of vaccine will be appropriate for any disease and there will be no side effects in the first doses and no side effects in the second doses. Here three algorithms such as SVM, Random Forest, and Bagging Classifier of machine learning are used to get the appropriate vaccine. Finally, we can say that the vaccine made by machine learning will help reduce the death rate of the coronavirus and increase the immunity of our body. © 2022 IEEE.

IMPERATIVENESS AND IMPLICATIONS OF MODERN WORK PRACTICES ON EMPLOYEE PERFORMANCE IN MALAYSIAN CONSTRUCTION INDUSTRY

The construction industry plays a significant role in developing a country's economy. However, the success in this sector largely depends upon the performance of its human resources engaged in different functions. The rapidly developing countries like Malaysia are primarily focusing on improving their infrastructure, ultimately giving rise to a boom in the construction industry, though still, not many studies focused on performance management via a modern working environment. Nonetheless, the present COVID-19 pandemic has driven industries to implement new/modern working methods, but it has yet to be assessed whether the new practices have changed job performance. Dwelling on that, this study explores the modern working possibilities, including flexible work schedules, sabbaticals, and telecommuting, as critical factors affecting employee performance engaged in the construction industry. The data was collected via a survey in Malaysia and then statistically analyzed Structural Equation Modelling. The findings suggest flexible work practices and sabbaticals as critical factors deemed to improve employee performance especially during the recent pandemic of COVID-19. Furthermore, telecommuting is identified as another assertive factor affecting construction performance. Thus, this study will serve as the baseline to further explore the role of these important performance attributes on construction as well as other industries. © 2022, University of Cienfuegos, Carlos Rafael Rodriguez. All rights reserved.

Technology Enabling the New Normal: How Students Respond to Classes

This cross-sectional study investigates the online education intention of undergraduate students in the largest and oldest public university in Bangladesh during the COVID-19 pandemic. Under convenient sampling, 843 undergraduate students with rural and urban backgrounds participated in an online self-administered questionnaire. Partial least squares structural equation modelling (PLS-SEM) was employed to examine the hypothesized relationships. We found that students' online class intention is significantly influenced by their attitude towards online classes (AOC), perceived usefulness (PU), and facilitating conditions (FC). We further identified that external antecedents have significant indirect effects on the outcome variables. Our findings provide new insights and contribute to a learners' community on online classes during the COVID-19 pandemic. This study extends the technology acceptance model (TAM) and the theory of planned behavior (TPB) to depict the factors influencing undergraduate students' intention to attend online classes (IOC) during the COVID-19 pandemic.

COV-HM: Prediction of COVID-19 Patient's Hospitalization Period for Hospital Management Using SMOTE and Machine Learning Techniques

COVID-19 imposes burdens on hospitals. Evidence-based management and optimum resource allocation are essential. Understanding the time frame of support needs for COVID-19 patients staying in hospitals is vital for planning hospital resource allocation, especially in resource-constrained settings. Machine learning methods are being utilized in the approximation of the length of stay of a patient in the hospital. Four machine learning classifiers were used in this study to estimate the duration of hospitalization for patients in 11 different classes. Due to the dataset's imbalance, SMOTE was applied to eliminate the problem. The prediction accuracy of the K-Nearest Neighbors, Random Forest, Decision Tree, and Gradient Boosting classifiers was 73%, 69%, 58%, and 57%. The feature importance scores assist in the identification of vital features while building machine learning models. This research will assist responsible authorities in maintaining hospital services depending on the length of a patient's stay. © 2022 ACM.

In Silico Phytochemical Compounds Screening of Allium sativum Targeting the Mpro of SARS-CoV-2

The global pandemic of coronavirus disease is still widely spread across the world causing catastrophic effect in both human life and global economy. By the end of year 2021, it has caused a total of 5.437.636 deaths across the world. Indonesia has rich plant biodiversity including medicinal plants that may be used for combating the virus. One of the commonly used medicinal plants comes from Allium species and it has been proved to have antiviral activity. Conducting an in silico study, we screened bioactive compounds that came from Allium sativum to fight against coronavirus through the inhibition of 3CL-Pro, one of the major protease that have an active role for viral replication. Molecular docking of compounds from Allium sativum to 3CL-Pro resulting in the discovery of 5 compounds that have the best binding affinity to 3CL-Pro, which are squalene, 1,4-dihydro-2,3-benzoxathiin 3-oxide, 1,2,3-propanetriyl ester, trans-13-octadecenoic acid and methyl-11-hexadecenoate with binding affinity of -7, -6.5, -5.9, -5.7 and -5.6 kcal/mol, respectively. It is very likely that these compounds can be candidates for therapeutic agents and these candidates need to be studied further.

Tube thoracostomy surgery among COVID-19 positive patients in a tertiary care hospital in Bangladesh

Background: Acute respiratory distress syndrome requiring invasive mechanical ventilation may occur in COVID-19 patients. Barotrauma causes clinically severe pneumothorax, necessitating a chest tube thoracostomy. Acute respiratory syndrome coronavirus 2 is aerosolized during the process, hence specific precautions must be taken to minimize exposure risks to health care workers. Objectives: The objective of the study to diagnosis of Tube thoracostomy during the COVID-19 pandemic to detect and diagnose patients who are positive with the virus. Material & Methods: In Bangladesh, researchers from a tertiary care hospital's thoracic surgery section did a retrospective analysis. In total, we had 34 participants. All COVID-19 cases requiring thoracic surgery consultation and management that were admitted to the ICU between July 2020 and January 2022 were included in this study. Iatrogenic pneumothorax and other critical cases not associated with COVID-19 were also eliminated.

An Efficient Deep Learning Framework for Face Mask Detection in Complex Scenes

COVID-19 has caused a global health crisis that has infected millions of people across the globe. Currently, the fourth wave of COVID-19 is about to be declared as Omicron. The new variant of COVID-19 has caused an unprecedented increase in cases. According to World Health Organization, safety measures must be adopted in public places to prevent the spread of the virus. One effective safety measure is to wear face masks in crowded places. To create a safe environment, government agencies adopt strict rules to ensure adherence to safety measures. However, it is difficult to manually analyze the crowded scenes and identify people violating the safety measures. This paper proposed an automated approach based on a deep learning framework that automatically analyses the complex scenes and identifies people with face masks or without facemasks. The proposed framework consists of two sequential parts. In the first part, we generate scale aware proposal to cover scale variations, and in the second part, the framework classifies each proposal. We evaluate the performance of the proposed framework on a challenging benchmark data set. We demonstrate that the proposed framework achieves high performance and outperforms other reference methods by a considerable margin from experimental results. © 2022, IFIP International Federation for Information Processing.

Design and Implementation of an IoT-Based Healthcare Monitoring System

According to ways-to-die website, over 150,000 people die every day. And the most common cause of death, i.e., about 20% of all deaths, is heart diseases. So, the most crucial contribution from our side to lower this percentage can be to monitor the cardiac values as much as possible. There are conventional methods to measure patients' health and condition, but they are laborious;have possibilities of errors;and nocturnal monitoring has as well been very difficult. Moreover, since 2019, COVID19 has caused more than five million deaths all around the world, as stated by WHO. And it made the physical presence of doctors and caretakers almost impossible. So, we have designed an up-to-date IoT-based project that continuously monitors the patient's body temperature, heart-rate and oxygen saturation level;keep the data readings in display before the patient and in the screen of the doctor's mobile;and it also provides a non-touching handsanitizing system. The proposed design integrates NodeMCU, DS18B20 Temperature sensor, Max30100 Pulse-oximeter, and other required materials in a small box. The readings are as accurate as the conventional medical equipments while it just takes less than a minute of time to perform the whole procedure. The developed project has outperformed the conventional method by providing a safer, less complex, cost effective and faster service. © 2022 IEEE.

Evaluation of outcome of Coronavirus disease 2019 patients receiving RAAS inhibitors (OCRAS study): A prospective observational study of Bangladeshi hypertensive patients

Background: The fact that SAARS-Cov2 virus enters cells through ACE2 receptors and the Renin-Angiotensin-Aldosterone System Inhibitors (RAASi) upregulate the ACE2 receptors, there was speculation that use of RAASi may lead increased cellular entry of the virus. There was a pause for a brief period of the use of RAASi in COVID 19 patients. But clinically the speculation has been found to be incorrect. Different professional societies come up with the assertion to continue to use RAASi. As the hesitancy among the clinicians appears to continue and there is no first hand data regarding the safety of the use of RAASi in Bangladeshi population, the study was undertaken to evaluate the safety of RAASi in COVID 19 patients. Aims & Methods This study was a prospective, observational multi-center study to evaluate the outcome of COVID-19 patients receiving RAAS inhibitors. Adult Hypertensive patients (age ≥18 years) with diagnosed COVID-19 confirmed by RT-PCR test who have a history of taking either ACE inhibitor/ARB or any other anti-hypertensive medication. Evaluation of outcome was assessed by rate of hospitalization, requirement of oxygen therapy, requirement of high flow nasal cannula, admission to ICU and mortality between two groups. All statistical analyses were performed using SPSS for Windows, version 20.0 (SPSS Inc., Chicago, IL, USA). Results: We collected data from 147 Covid-19 positive patients confirmed by RT-PCR. Among them, 117 (79.6%) had a history of taking RAAS inhibitor and 30 had history of taking other antihypertensive medications. Of them, two-third patients had more than 50 years of age and more than half of the patients had overweight or obesity. Other than hypertension they had several comorbidities such as Diabetes Mellitus (45.4%), Ischemic Heart Diseases (35.4%), Asthma or COPD (15%) etc. Rate of hospitalization had no statistical difference between RAAS inhibitor group and other hypertensive group (48.7% vs 46.70% respectively;p-value-0.841). There was no statistical difference between two groups in terms of requirement of oxygen therapy (p-value-0.297), High Flow Nasal Cannula (p-value-0.430), intensive care unit (p-value-0.194) and death (p-value-0.383) also. Almost half and one-third of the patients had persistence of symptoms even after 14 days and 28 days respectively. Fatigue, cough, breathlessness, loss of appetite and taste were the most common symptoms among those. Conclusion: In our study we found that RAAS inhibitor treatment had no adverse effect on the outcome of COVID-19 patients compared with other antihypertensive drugs. Patients may continue receiving ACEIs and ARBs for the treatment of any indication for RAASi without an increased risk of worse outcomes.

Early rehabilitation can prevent long COVID pulmonary complications

The long-term sequelae of coronavirus disease 2019 (COVID-19) are only now beginning to be defined, but it is already known that the disease can have direct and indirect impacts mainly on the cardiorespiratory system. The aim of the narrative review is to derive concepts for the treatment based on the experience gained from the early rehabilitation in the treatment of patients with COVID-19, and to prevent long COVID respiratory complications in connection with currently available sources and experiences. An online literature search was conducted June 2020 to January 2021 using Medline, PubMed, Google scholar and manual search to retrieve meta-analyses, systematic reviews, randomized trials, guidelines, recommendations, state of the art, and other peer-reviewed studies investigating the relationship between COVID-19 and early Rehabilitation/mobilization or exercises. Thirty-four articles met the established criteria and the main findings were summarized and described, including indication, contraindication and recommendation for early rehabilitation and exercises prescription. after a detailed observation this review study can predict that long COVID pulmonary complications can be prevented in worth of early rehabilitation.

Surveillance of COVID-19 Using Geospatial Data: An Emergency Department Perspective

Background: The outbreak of coronavirus 2019 (COVID-19) which emerged in December 2019 spread rapidly and created a public health emergency. Geospatial records of case data are needed in real time to monitor and anticipate the spread of infection. Methods: This study aimed to identify the emerging hotspots of COVID-19 using a geographic information system (GIS)-based approach. Data of laboratory-confirmed COVID-19 patients from March 15 to June 12, 2020, who visited the emergency department of a tertiary specialized academic hospital in Dubai were evaluated using ArcGIS Pro 2.5. Spatiotemporal analysis, including optimized hotspot analysis, was performed at the community level. Results: The cases were spatially concentrated mostly over the inner city of Dubai. Moreover, the optimized hotspot analysis showed statistically significant hotspots (p < 0.01) in the north of Dubai. Waxing and waning hotspots were also observed in the southern and central regions of Dubai. Finally, there were nonsustaining hotspots in communities with a very low population density. Conclusion: This study identified hotspots of COVID-19 using geospatial analysis. It is simple and can be easily reproduced to identify disease outbreaks. In the future, more attention is needed in creating a wider geodatabase and identifying hotspots with more intense transmission intensity.

A close loop audit to assess awareness among non-consultant hospital doctors re use of personal protective equipment

Introduction: Personal protective equipments (PPEs) are like war uniforms in the fight against Corona Pandemic. The limited supply of PPEs warrant their proper use not only to avoid shortage of supplies but also to prevent any infectious spread to healthcare workers. This study aimed at analyzing awareness among non-consultant hospital doctors re proper use of PPEs Method: A questionnaire was devised using local available guidelines published by university Hospital Limerick. The study was done in 2 phases. In 1st phase 100 questionnaires were distributed to non-consultant hospital doctors(NCHDs). Results were analysed and after 1st phase and emails were sent with results and local guidelines and a zoom educational session was organized. In 2nd phase, questionnaire was redistributed in a week's time and results were re analysed to close the loop. Results: 200 NCHDs participated in the study,100 in each phase. Most common age group in two phases was 21-30 yrs. Awareness about PPEs use for Covid 19 increased significantly in 2nd phase across all domains (what is included in PPEs (100% from 91%), Sequence for putting on PPEs (52% to 88% p<0.05), steps for FIT test (57% to 74% p=0.247) and sequence for removing PPEs (47% to 81% p<0.05). Conclusions: Though PPEs donning and doffing sessions were organized by the hospital officially, Awareness about effective use of PPEs among NCHDs further improved after organizing a Zoom educational session and auditing.