ABSTRACT
The agent responsible for the COVID-19 pandemic was the newly discovered coronavirus SARS-CoV-2. A trimeric spike protein on the SARS-CoV-2 virion binds to the ACE2 receptor on host cells. In this study we performed a structure-based virtual screening and molecular docking of existing drugs against a high-resolution structure of the SARS-CoV-2 spike protein-ACE2 receptor complex. The 2.5-A crystal structure of the C-terminal domain of the SARS-CoV-2 spike protein (residues 319-541) in complex with human ACE2 (SARS-CoV-2-S-CTD/hACE2) (PDB ID: 6LZG) was used as the target for screening 4,374 FDA-approved drugs from the ZINC15 database using PyRx software. Molecular docking was performed using BIO VIA Discovery Studio Visualizer. The top twenty highest affinity drugs had binding energies of -7.0 to -8.8 kcal/mol. The highest affinity drug was the selective vasopressin V2-receptor antagonist Tolvaptan, for which molecular docking identified drug-amino acid residue interactions with ACE2. Other drugs displaying binding energies better than -8.0 kcal/mol were Nizoral, Amaryl, Accolate, Sorafenib, Glipizide and Azelastine. The predicted interactions of these highest affinity drugs with residues in ACE2 were at positions that could disrupt the spike protein-ACE2 complex, so these drugs have the potential to be repurposed as inhibitors of the SARS-CoV-2 virus.
ABSTRACT
COVID-19 was traced on 17th November 2019 in China the first case was reported on 31st December 2019 in the China city of Wuhan province of Hubei. The COVID-19 pandemic is a worldwide pandemic virus which is severely affected on acute respiratory system in human beings. The pandemic has raised several issues in Pakistan but the major issue faced the socio-economic issues. The outbreak disease of COVID-19 has severely affected Pakistan's economy. On a large scale Industries were closed and labour was unemployed. The major victim of the COVID-19 outbreak is micro small and medium size industries. Many employed of the industries administrator and labourers are unemployed. COVID-19 has created negative impacts on the livelihood of the Pakistani population. Many employed labourers lost their job. The purpose of the study finds the economic issues of industrial labour in the period of COVID-19 in the District of Faisalabad Punjab. The data was collected from different industrial labourers in District Faisalabad. 150 respondents were selected from the different industries of Faisalabad. The research planned to analyze the study of socio-economic impacts of covid-19 on industries' labour in District Faisalabad. Three industries were selected from these industries area Sheikupura Road and canal road Faisalabad. The areas of industries are Masood textile mills Chenab textile and Rafhan maize mill Faisalabad. Then 50 respondents were selected from each area through a purposive sampling technique. Data was gathered through interviewing schedule. Data were analyzed by using statistical techniques. The majority of my respondents were male and these are the head of the family that age is under 25-30 and 35-35. The data include 22.7% of respondents having 1-2 rooms 42.7% of respondents having 2-4 rooms 24% of respondents having 4 to 6 4ooms and 10.7% of respondents having above six rooms. This indicates that 83.3% of respondents lost their jobs during COVID-19 and 16.7% of respondents did not lose their job and continued their work as before during COVID-19. Copyright © 2022, Anka Publishers. All rights reserved.
ABSTRACT
When COVID-19 prevailed, the educational system was shifted to online rather than traditional to facilitate the learning process. This study aimed at exploring the impacts of online learning techniques on the students' Cumulative Grade Point Average (CGPA). A total of 155 randomly selected students currently studying M. Phill education at the University of Agriculture Faisalabad, Pakistan participated in this study. Data were collected through validated, pre-tested and reliable questionnaires. Collected data were analyzed using Statistical Package for Social Sciences (SPSS). Findings unveiled that online learning techniques improved the learning abilities, personality traits and teaching styles as perceived by the respondents which further improved the CGPA of students. Within the effects on learning abilities, enabling students to judicious use of technology, multimedia, observation and clearing the concepts were major improvements which helped students to attain an increase in CGPA. As for as effects on personality traits were concerned, social interaction enhanced communication skills and improvement in understanding, social skills and confidence led the students to get high CGPA. Moreover, online learning improved the teaching styles by integrating video lectures, immediate results assessment and easy access to the technology were key drivers of the increase in CGPA. This study suggested a hybrid educational system at the University of Agriculture Faisalabad for effective learning in students.
ABSTRACT
The digital insurgency is troublemaking the association between banks and their customers and new features constantly seem to upgrade client experience. A digital transformation procedure comprises of innovative technology application developments, just as company aspects, for example, C-suite executives uphold, digital transformation strategy, product innovations, HR systems and organizational changes. The practical implications of this research study show digital bank customers are more satisfied than traditional bank customers during COVID-19, digital banking services are cheaper than traditional banking services, Product innovation plays major role in digital transformation and also note that most of the customers are preferred to use digital bank services rather than internet banking, POS terminals, Credit Cards, Debit Cards & ATM Machines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Over the years, private housing prices skyrocketed and the high demand to own private housing remained unmet. Prices influenced the perception of affordability and would also affect the demand for private housing. The urgency to understand the intertwined relationships between housing cost (prices), affordability, and demand especially in the COVID-19 pandemic situation is high. Hence, this research aimed to quantify the relationship or impact of rising prices on the private housing affordability and its demand. The results from this research could conclude that soaring prices lowered the affordability of buyers and delay the purchase of private property shortly. However, the demand to purchase a private property was higher with rising prices suggesting that higher prices indicate more wealth and potential to own a more valuable asset. Affordability is a temporary barrier to own private properties. This implied that the wealth effect from properties likely outweighed that of the consumption or income substitution effect. This understanding of the relative impacts between housing cost, affordability, and demand would contribute significantly to policymaking in providing signals and advice for policymakers to priorities social mobility or investment return from the property market. Copyright © 2022 Rangaswamy, Chong and Nawaz.
ABSTRACT
The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social media data has drawn many researchers to extract valuable information and understand the user's emotions during the pandemic time. The research looks at the data collected from the micro-blogging site Twitter for the tourism sector, emphasizing sub-domains hospitality and healthcare. The sentiment of approximately 20,000 tweets have been calculated using Valence Aware Dictionary for Sentiment Reasoning (VADER) model. Furthermore, topic modeling was used to reveal certain hidden themes and determine the narrative and direction of the topics related to tourism healthcare, and hospitality. Topic modeling also helped us to identify inter-cluster similar terms and analyzing the flow of information from a group of a similar opinion. Finally, a cutting-edge deep learning classification model was used with different epoch sizes of the dataset to anticipate and classify the people's feelings. The deep learning model has been tested with multiple parameters such as training set accuracy, test set accuracy, validation loss, validation accuracy, etc., and resulted in more than a 90% in training set accuracy tourism hospitality and healthcare reported 80.9 and 78.7% respectively on test set accuracy.
ABSTRACT
The world social insurance frameworks have been vibrated with COVID-19 pandemic, regardless of Industry all are profoundly influenced and searching for endurance in COVID-19 pandemic and to encourage administrations for their investors and clients. Advanced education is one of the additionally affected with COVID-19 pandemic, presently searching for a choice to encourage benefits through computerized innovation. Particularly advanced education libraries are searching for to give satisfactory administrations with the goal that the understudy network will be fruitful in their evaluations, powerful information gaining to fulfill Industry needs. The current investigation endeavored toward this path, made snappy fast audit by considering databases Pubmed, Baidu, Scopus and Google Scholar and introduced computerized based methodology for libraries benefits, this will profit for colleges, libraries and understudy networks to beat the emergency of COVID-19 pandemic. © 2020 Innovare Academics Sciences Pvt. Ltd. All rights reserved.