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The aim of this study was to evaluate the impact of the most frequent Asn501 polar uncharged amino acid mutations upon important structural properties of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) Surface Glycoprotein RBD–hACE2 (human angiotensin-converting enzyme 2) heterodimer. Mutations N501Y, N501T and N501S were considered and their impact upon complex solubility, secondary motifs formation and intermolecular hydrogen bonding interface was analyzed. Results and findings are reported based on 50 ns run in Gromacs molecular dynamics simulation software. Special attention is paid on the biomechanical shifts in the receptor-binding domain (RBD) [499-505]: ProThrAsn(Tyr)GlyValGlyTyr, having substituted Asparagine to Tyrosine at position 501. The main findings indicate that the N501S mutation increases SARS-CoV-2 S-protein RBD–hACE2 solubility over N501T, N501 (wild type): (Formula presented.), (Formula presented.). The N501Y mutation shifts (Formula presented.) -helix S-protein RBD [366-370]: SerValLeuTyrAsn into π-helix for t > 38.5 ns. An S-protein RBD [503-505]: ValGlyTyr shift from (Formula presented.) -helix into a turn is observed due to the N501Y mutation in t > 33 ns. An empirical proof for the presence of a Y501-binding pocket, based on RBD [499-505]: PTYGVGY (Formula presented.) 's RMSF peak formation is presented. There is enhanced electrostatic interaction between Tyr505 (RBD) phenolic -OH group and Glu37 (hACE2) side chain oxygen atoms due to the N501Y mutation. The N501Y mutation shifts the (Formula presented.) hydrogen bond into permanent polar contact;(Formula presented.);(Formula presented.). © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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The Corona-virus H19 pandemic is quickly spreading throughout the globe. Every three to four times, waves occur and have a major effect on people's lives. Other illnesses including covid disorders are misdiagnosed in this setting. There is no reliable statistics on the total number of covid patients in the nation, and no system exists to track them. This prevents the patients from receiving the necessary care and treatment. The number of patients in a given dataset may be determined with more precision using AI methods. In this article, we show how to forecast how many patients will be included in the Covid-19 database by using an adaptive method. Python spyder is used to run the simulation. . © 2023 IEEE.
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The objective of this research paper is to investigate the impact of COVID-19 on the factors influencing on-time software project delivery in different Software Development Life Cycle (SDLC) models such as Agile, Incremental, Waterfall, and Prototype models. Also to identify the change of crucial factors with respect to different demographic information that influences on-time software project delivery. This study has been conducted using a quantitative approach. We surveyed Software Developers, Project Managers, Software Architect, QA Engineer and other roles using a Google form. Python has been used for data analysis purposes. We received 72 responses from 11 different software companies of Bangladesh, based on that we find that Attentional Focus, Team Stability, Communication, Team Maturity, and User Involvement are the most important factors for on-time software project delivery in different SDLC models during COVID-19. On the contrary, before COVID-19 Team Capabilities, Infrastructure, Team Commitment, Team Stability and Team Maturity are found as the most crucial factors. Team Maturity and Team Stability are found as common important factors for both before and during the COVID-19 scenario. We also identified the change in the impact level of factors with respect to demographic information such as experience, company size, and different SDLC models used by participants. Attentional focus is the most important factor for experienced developers while for freshers all factors are almost equally important. This study finds that there is a significant change among factors for on-time software project delivery before and during the COVID-19 scenario. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
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The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs' criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system's features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student's applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system's use case. © 2022 IEEE.
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The novel Coronavirus disease 2019 (COVID-19) presents a major threat to public health but can be prevented by safe and effective COVID-19 vaccines. Vaccine acceptance among healthcare workers (HCWs) is essential to promote uptake. This study, aimed to determine the COVID-19 vaccination uptake and hesitancy and its associated factors among HCWs in Tanzania. We employed a convergent-parallel mixed-methods design among 1368 HCWs across health facilities in seven geographical zones in Tanzania in 2021. We collected quantitative data by using an interviewer-administered questionnaire and qualitative data, using in-depth interviews and focus group discussions. Participants in the quantitative aspect were conveniently selected whereas those in the qualitative aspect were purposively selected based on their role in patient care, management, and vaccine provision. Stata software version 16.1 was used in the analysis of quantitative data and thematic analysis for the qualitative data. Multiple logistic regression was used to assess the determinants of COVID-19 vaccine uptake. The median age of 1368 HCWs was 33, and the interquartile range was 28–43 years;65.6% were aged 30+ years, and 60.1% were females. Over half (53.4%) of all HCWs received the COVID-19 vaccine, 33.6% completely refused, and 13% chose to wait. HCWs aged 40+ years, from lower-level facilities (district hospitals and health centers), who worked 6+ years, and with perceived high/very high risk of COVID-19 infection had significantly higher odds of vaccine uptake. The qualitative data revealed misinformation and inadequate knowledge about COVID-19 vaccine safety and efficacy as the key barriers to uptake. Nearly half of all HCWs in Tanzania are still unvaccinated against COVID-19. The predominance of contextual influence on COVID-19 vaccine uptake calls for interventions to focus on addressing contextual determinants, focusing on younger HCWs' population, short working duration, those working at different facility levels, and providing adequate vaccine knowledge. [ FROM AUTHOR] Copyright of COVID is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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In order to repurpose currently available therapeutics for novel diseases, druggable targets have to be identified and matched with small molecules. In the case of a public health emergency, such as the ongoing coronavirus disease 2019 (COVID-19) pandemic, this identification needs to be accomplished quickly to support the rapid initiation of effective treatments to minimize casualties. The utilization of supercomputers, or more generally High-Performance Computing (HPC) facilities, to accelerate drug design is well established, but when the pandemic emerged in early 2020, it was necessary to activate a process of urgent computing, i.e., prioritized and immediate access to the most powerful computing resources available. Thanks to the close collaboration of the partners in the HPC activity, it was possible to rapidly deploy an urgent computing infrastructure of world-class supercomputers, massive cloud storage, efficient simulation software, and analysis tools. With this infrastructure, the project team performed very long molecular dynamics simulations and extreme-scale virtual drug screening experiments, eventually identifying molecules with potential antiviral activity. In conclusion, the EXaSCale smArt pLatform Against paThogEns for CoronaVirus (EXSCALATE4CoV) project successfully brought together Italian computing resources to help identify effective drugs to stop the spread of the SARS-CoV-2 virus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The COVID-19 pandemic elicited a surge in the use of digital tools to replace "classic” manual disease tracking and contact tracing across individuals. The main technical reason is based on the disease surveillance needs imposed by the magnitude of the spread of the SARS-CoV-2 virus since 2020, particularly how these needs overwhelmed governments around the world. Such developments led to stark variations across countries in terms of legal approaches towards the use of digital tools, including self-reporting software and mobile phone apps, for both disease tracking and contact tracing. Against this backdrop, in this article I highlight some of the normative challenges posed by the digitalization of disease surveillance, underscoring its almost non-existent regulation under international law. I look back at the historical emergence of the epidemiological principles underlying this procedure, by referring to John Snow's trailblazing work in cholera control. I emphasize how the COVID-19 pandemic prompted both technical and normative shifts related to the digitalization of these procedures. Furthermore, I refer to some of the overarching obstacles for deploying international law to tackle future tensions between the public health rationale for digitalized disease tracking and contact tracing, on the one hand, and normative concerns directly related to their legality, on the other hand. Lastly, I put forward conclusions in light of the current juncture of international health law reforms, and how they so far display limited potential to herald structural changes concerning the legality of the use of digital tools in disease surveillance.
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The COVID-19 pandemic highlighted an urgent need for streamlined drug development processes. Enhanced virtual screening methods could expedite drug discovery via rapid screening of large virtual compound libraries to identify high-priority drug candidates. The EXSCALATE4CoV (EXaSCale smArt pLatform Against paThogEns for CoronaVirus) consortium (E4C) research team developed EXSCALATE (EXaSCale smArt pLatform Against paThogEns), the most complex screening simulation to date, containing a virtual library of >500 billion compounds and a high-throughput docking software, LiGen (Ligand Generator). Additionally, E4C developed a smaller virtual screen of a "safe-in-man” drug library to identify optimal candidates for drug repurposing. To identify compounds targeting SARS-CoV-2, EXSCALATE performed >1 trillion docking simulations to optimize the probability of identifying successful drug candidates. Ligands identified in simulations underwent subsequent in vitro experimentation to determine drug candidates that have anti-SARS-CoV-2 agency and have probable in-human efficacy. While many compound candidates were validated to have anti-SARS-CoV-2 properties, raloxifene had the best outcome and subsequently demonstrated efficacy in a phase 2 clinical trial in patients with early mild-to-moderate COVID-19, providing proof of concept that the in silico approaches used here are a valuable resource during emergencies. After its emergence in 2019, the SARS-CoV-2 coronavirus spread internationally at a rapid pace, leading to the designation of COVID-19 as a pandemic in March 2020. In addition to a devastating impact on public health, COVID-19 has resulted in extensive negative social and economic effects in every corner of the globe. When the pandemic arrived, the medical and scientific communities identified an urgent need to establish more rapid therapeutic and vaccine development processes for COVID-19. However, it was clear that any new measures needed to be implemented in a way that also supported rapid mobilization to fight potential future pandemics. Therapeutic discovery is a complicated and prolonged process, often taking 10–15 years to complete all stages, and typically involves a linear workflow starting with in silico investigations, followed by increasingly complex and correspondingly expensive in vitro, in vivo, and clinical studies. In the context of the pandemic, the importance of the in silico stage increased because of the capacity of exascale computational methods to identify and prioritize small molecule (and biological) agents with the greatest therapeutic potential. Better in silico-generated starting points for drug-discovery efforts increase the likelihood of success in downstream laboratory-based experimental stages and can contribute to vitally needed reductions in costs and time to market for new therapies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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In the wake of the COVID-19 pandemic, many studies have begun to address what some refer to as the "new normal,” comprising hybrid arrangements of employees working from home and working at the office with varying schedule arrangements. While many of the studies to date addressed how employees coped with work-from-home, we sought to investigate how employees dealt with a transition to the new normal of hybrid arrangements. To shed light on this topic, we conducted a survey-based case study at one office location of a large, multinational software corporation. The site sought to transition employees fully working from home to working two days remotely and three predefined days in their shared workspace. Our survey results indicated a substantial decline in work satisfaction since the beginning of this transition, which can be explained by diverse work preferences. Furthermore, some software developers felt frustrated during this transition time;they described challenges they underwent and proposed potential solutions. In this paper, we present our lessons learned in this case study and describe some actionable recommendations for practitioners facing such transitions. © 2023 IEEE Computer Society. All rights reserved.
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This study aims to analyze the factors influencing unemployment and determine the role of stakeholders in reducing unemployment in Central Java Province, Indonesia. Data encompasses 35 regencies and municipalities of the Central Province from 2007 to 2020, with the total sample being 490. The employed sequential mixed method includes two analytical tools, namely panel data and vector regression with mactor software;the latter is used to examine the convergence among actors. Six main actors in reducing unemployment are identified, namely Regional Development Planning Agency (Badan Perencanaan Pembangunan Daerah/Bappeda), Department of Labor, Department of Education, community leaders;job training center, and Indonesian Chamber of Commerce (Kamar Dagang dan Industri Indonesia/Kadin). The results of the first analysis show that the variables of economic growth, Human Development Index (HDI) and school enrollment have negative and significant effect on unemployment. The results of convergence analysis highlight the importance of the Department of Labor in linking the supply and demand sides of labor in Central Java.
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Hampton Roads Sanitation District (HRSD) provides wastewater conveyance and treatment services for 1.7 million people in southeast Virginia. Since 2017, HRSD has used Virtual Reality (VR) design reviews on more than 20 projects because of how accessible VR makes designs to every level within an organization, including the operations and maintenance staff responsible for maintaining the completed project. However, VR is not necessarily appropriate for all projects. This paper uses a recent HRSD project to show how HRSD approaches the use of VR, to what extent it is used, and how HRSD focuses on the operation and maintenance aspects of the designs during reviews. The paper also highlights features of the VR software, design-review best practices, limitations of two-dimensional design, how standard details can be incorporated into the model, and the added value from use of the internet-based, real-time reviews during the COVID-19 pandemic, when in-person meetings were impossible. Copyright © 2022 Water Environment Federation.
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The concept of IoT in the current world where speed, accuracy and efficiency are of a high importance, can do wonders if implemented in a structured manner, into a machine, project, hardware, idea which can improve technology. So, IoT has its application in the Events. Events can be of many types and there is a need of man power to handle the events efficiently. People gather in huge numbers if there is a political event, whereas there is limited audience in a cultural show or less people in a marriage function. Any of such events, if handled smartly, can ease the tasks of humans, as well as provide speed and accuracy and ensure proper event management and organization. This project demonstrates a hardware for the entry-exit of people for any event, through the technology of Radio Frequency Identification (RFID), Wireless Fidelity (Wi-Fi), and main heart as ESP 8266 Controller. The software simulation in Cisco Packet Tracer shows a general event organization related to a hotel or government-based area, where different sections are integrated to control and handle the event in a smart way. The use of RFID indicates the contactless operation for monitoring the attendee entry-exit, due to the current COVID-19 protocols. So, such systems are safe and smart to execute. © 2022 IEEE.
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This introductory paper first summarizes the major accomplishments of the literature on data modeling, modeling with software and the integration of statistical and computational thinking in statistical modeling, including how these collective efforts have helped the field evolve. Next, challenges that the field must address and general suggestions for future research are discussed. Finally, it is important to note that the papers in this special issue were in their final editing stages in early to mid-2020, at which time there were an unprecedented confluence of global crises: Covid-19, Civil Unrest over Racism, and Climate Change. Although the papers were written prior to Covid-19, it would be remiss not to discuss some of the important themes cultivated in this special issue in light of current events, particularly around the relationship between the non-neutral nature of data and ideas of data, context and chance in statistical modeling. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Objective: To study the mechanism of Runfei Ningshen Decoction in the treatment of insomnia caused by corona virus disease 2019(COVID-19) by using network pharmacology and molecular docking analysis. Methods: The chemical components and targets of Chinese medicinal materials of Runfei Ningshen Decoction in TCMSP, Batman, and CTD databases were searched. The relevant targets of novel coronavirus pneumonia and insomnia in Disgenet, GeneCards, CTD, and Malacards databases were searched. The component-target-disease network was established by using Cytoscape 3.2.1 software;The protein-protein intereation(PPI) network was constructed in string database. The common targets were enriched by using Cluster Profiler software package in R language software platform. The molecular docking of core targets related to insomnia caused by COVID-19 was carried out by using Discovery Studio 4.0 software. Results: 349 medicinal ingredients in Runfei Ningshen Decoction, 1 904 targets, 1 505 new coronavirus pneumonia-related targets, and 1 337 insomnia-related targets were collected. When the intersection of Venn diagrams were used, 404 common targets were obtained for the 2 diseases. 250 targets were intersected with the 2 diseases, and 33 core targets were screened out by the analysis of the interaction network between targets. Pathway enrichment analysis showed that Runfei Ningshen Decoction mainly acts on AKT1, INS, TP53, IL-6, key targets such as AKT1, INS, TP53, IL-6, JUN, CASP3, TNF, CAT, PTGS2 and CXCL8, which are involved in the important pathway processes such as human cytomegalovirus infection, fluid shear stress, and AGE-RAGE signaling pathways in complications of atherosclerosis and diabetes. The results of molecular docking showed that the core target has a high affinity with beta-sitosterol, 1-methoxy phaseolin, 3'-hydroxy-4'-O-methylglycyrrhizin, and anhydroicariin. The prescription treatment of insomnia caused by COVID-19 may be through the targets such as PTGS2, AR, PPARG, NOS2, HSP90 AA1 and so on. Conclusion: Runfei Ningshen Decoction can treat insomnia caused by COVID-19 by inhibiting IL-6 and TNF-a.
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Due to the impact of Covid-19, many students all over the world have faced some educational issues. Therefore, many educational institutes focused on shifting their learning process to E-learning system. To provide a complete E-learning system, the performing of virtual and remote Laboratory experiments is needed. In this paper, a generic and flexible online authoring tool for the Laboratory Learning System (LLS) is presented. The LLS system is a platform that provides teachers and students with a flexible environment for virtual and remote controlled labs using the proposed authoring tool. The heart of the LLS system is the authoring tool which facilities the ease and flexibility of designing various laboratory experiments which includes a number of pages, and each page has a number of steps with many draggable components. Furthermore, the proposed authoring tool is the first authoring tool that provides general and reusable virtual laboratory resource (VLR) for automatically managing laboratory software and hardware resources. To support the new VRL feature of the authoring tool, the LLS supports the ability to remotely control the laboratory equipment while performing laboratory experiments and also has the capability to run any type of simulation tool for virtually simulated labs. The proposed authoring tool is designed considering all the needed components with well-defined interfaces to achieve an effective and flexible Laboratory learning system. © 2022 IEEE.
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The Covid-19 virus has substantially transformed many aspects of life, impacted industries, and revolutionized supply chains all over the world. System dynamics modeling, which incorporates systems thinking to understand and map complex events as well as correlations, can aid in predicting future outcomes of the pandemic and generate key learnings. As system dynamic modeling allows for a deeper understanding of the manifestation and dynamics of disease, it was helpful when examining the implications of the pandemic on the supply chain of semiconductor companies. This tutorial describes how the system dynamics simulation model was constructed for the Covid-19 pandemic using AnyLogic Software. The model serves as a general foundation for further epidemiological simulations and system dynamics modeling. © 2022 IEEE.
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Context: many tech companies have had to adapt their software development processes to the reality of forced remote work due to COVID-19. Later, many of these companies transitioned to hybrid work, interspersing remote work with face-to-face work. Goal: this article aims to understand the challenges and impacts of this transition to software quality and to create hybrid software development teams. Method: an empirical study was carried out in the technology department of a Brazilian multinational company, using a multivocal literature review combined with a field study with semi-structured interviews and discursive textual analysis. Results: this study identified three main dimensions of the impact of the transition from remote to hybrid model: people, processes, and organization. In each of them, specific items were identified and will be detailed in this article. Conclusions: permanent or hybrid remote work models have enormous challenges and require studies in the specific scenarios of each company. It is necessary to understand these challenges to propose solutions that simultaneously facilitate the work of teams and guarantee the quality of the projects. © 2022 ACM.
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Internet of Medical Things (IoMT) is on-demand research area, generally utilized in most of medical applications. Security is a challenging problem in decentralized platform while handling with medical data or images. An effective deep learning-based blockchain framework with reduced transaction cost is proposed to enhance the security of medical images in IoMT. The proposed study involves four different stages like image acquisition, encryption, optimal key generation, secured storing. The input images initially are collected in the image acquisition stage. Then, the collected medical images are encrypted using coupled map lattice (CML). This encryption process assists to preserve the input medical images from the attackers. In order to provide more confidentiality to the encrypted images, optimal keys are generated using opposition-based sparrow search optimization (O-SSO) algorithm. These encrypted images are stored using distributed ledger technology (DLT) and smart contract based blockchain technology. This blockchain technology enhances the data integrity and authenticity and allows secured transmission of medical images. After decrypting the image, the disease is diagnosed in the classification stage using proposed Recurrent Generative Neural Network (RGNN) model. The proposed study used python tool for simulation analysis and the medical images are gathered from CT images in COVID-19 dataset. © 2022 IEEE.
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Many faculty were forced to teach online with inadequate training or resources, affecting student academic performance because of COVID-19. The purpose of this qualitative, descriptive, embedded, single-site case study is to describe teaching online and the use of Microsoft Teams as a student engagement tool as perceived by the faculty in a rural middle school in North Carolina. This study explored how middle school teachers used Microsoft teams to engage students, perceived benefits and challenges, and best practices. Microsoft Teams was chosen because it was one of the most searched for online conference tools by educators during the pandemic. Questions were developed for an online survey, interviews, and a focus group. Data were collected from 19 teachers and administrators in one middle school. Using the adolescent community of engagement framework, interview data were analyzed with categorizing and connecting strategies. Findings indicated that teachers engaged students through MS Teams features of Livestream, chat box, recordings, student-teacher interaction, and class discussion. Using MS Teams included allowing students to be connected to the class, asking questions live, and allowing learning to continue during the pandemic, providing an emotionally safe environment. MS Teams provided interactive features of livestream and the chat box. The challenges of using MS Teams included unprepared teachers who had inadequate training, lacked time to plan and prepare and disengaged students. Students did not join livestreams because they had trouble logging in, navigating the tool, and attending was not required. Recommended practices include training teachers and students in using the features of MS Teams and incorporating interaction and collaboration into lessons. Future research could be repeated in another middle school in a non-pandemic environment in an online or hybrid course. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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Spread prevention actions (SPAs) during Coronavirus pandemic period, such as increased hand-washing, temporary lock-downs, preventions in transportation, and the reduction of recreational and industrial activities may change the routines in social behaviors. Accordingly, SPAs can be effective on the quality and quantity of raw municipal wastewater. This research evaluates the aforementioned hypothesis and recommends solutions for the proper operation of wastewater treatment plants (WWTPs). Methods: For this purpose, the quantity and quality of sewage in 23 municipal WWTPs in Isfahan province, as the study area, were surveyed and compared from 2015 to 2020. SPSS software (version 23) was used for statistical analysis. Results: Results indicated that the annual growth rate of sewage discharged in the spring and summer of 2020 (period of SPAs) in the study area is 24% more than the average of annual growth rate in long-term (2015-2019). This increase is 45% in small WWTPs, while it is only 5% in large WWTPs. Results also revealed that the concentration of chemical oxidation demand (COD) of sewage was reduced 24% on average in this period. In addition, the biodegradability of wastewater is increased in large WWTPs mainly due to the decrease of industrial activities. Conclusion: Therefore, SPAs in the pandemic period of Coronavirus could increase the quantity of municipal wastewater and reduce its COD concentrations. These variations may provide more appropriate operational conditions for waste stabilization ponds rather than activated sludge units.