Who works on the ‘frontline'? Comparing constructions of ‘frontline' work before and during the COVID-19 pandemic

This article provides a comparative analysis of how frontline workers were constructed by the UK media prior to and during the COVID-19 pandemic in the UK. Both the News on the Web Corpus and the Coronavirus Corpus, as monitor corpora of web-based new articles, were utilised to identify changes in both the frequency and use of the word front*line from 2010 to 2021. Findings show that, following the outbreak of COVID-19, constructions of frontline work were more frequently associated with medical professions and became more figurative in nature. Our findings provide a counterpoint to claims that the COVID-19 pandemic led to an increased awareness of the critical nature of many types of ‘low-skilled' work not previously recognised as essential. The study also extends previous research which has traced changes in language and its deployment during the COVID-19 pandemic.

Multi-Joint Unit Network for COVID-19 Diagnosis using X-Ray and CT Images

The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.

COVlD-19 vaccine Distribution: Preparing for the Unexpected

In December 2020, two shipments of the vaccine experienced temperature excursions in which product was actually kept at overly cold temperatures (3). Urgent need to protect data One problem that vaccine developers and regulatory agencies need to address is the urgent need to protect data, says Nigel Thorpe, technology director with Secure Age, which specializes in enterprise data encryption using a public key infrastructure platform. For operators on the plant floor, the efforts required are fraught with potential error, especially during shift changes, says Jim Evans, director of Verista, Inc.'s vision, connectivity, and automation division. Raw materials The speed with which vaccines have been developed and are being distributed pose important questions centred around variability. If we're having a raw materials shortage when the vaccines haven't even been scaled up, what will happen when they get full approval?" he asks.

Key Factors Related to Cyber Security Affecting Consumer Attitude in Online Shopping: A Study in Bangladesh

Cybersecurity is an increasingly important factor in consumer attitudes toward online shopping. Online shopping has become an essential part of our lives in this digital era. As the popularity of online and e-commerce shopping continues to grow, so does the potential for cyber threats and attacks. As more and more consumers turn to online shopping, cyber threats such as hacking, identity theft, and credit card fraud have become more frequent. Therefore, understanding the factors of cybersecurity that affect consumer attitude is essential to build trust and creating a safe and sound shopping environment. This research explores the factors of cybersecurity that affect consumers' attitudes to shopping online and uses a survey to test several hypotheses related to influential cyber factors. Bangladesh is a developing country in Southeast Asia, and like many other countries, has experienced an increase in cyber threats and attacks in recent years. Consumers in Bangladesh face many of the same cyber threats, such as phasing attacks, malware, data breach, and other types of cyber security threats over online shopping. As a result of these cyber threats, online consumers are increasingly concerned about online security risks which may impact their willingness to engage in online shopping. Therefore, it is essential to identify critical factors of cyber security that impact consumers's attitudes toward online shopping to mitigate cyber risk and improve consumer trust in online shopping. This paper provides the result of a research study that will provide a better understanding of factors that influence consumer's trust and engagement with online and E-commerce platforms in Bangladesh) . © 2023 IEEE.

Tulsi Leaf Disease Detection using CNN

Since the start of the covid 19 pandemic, a wide range of medications have been produced and are currently being utilized to treat the disease. Tulsi, in addition to all of the chemical-based medications, is an herbal therapy that is particularly effective in the treatment of this ailment. Tulsi has been used to heal ailments and infections for millennia, particularly in India. Because we use tulsi for medicinal purposes, it's vital to monitor its health in order to reap the full benefits of its herbal properties. Plant diseases harm the health and growth of the plant. Disease detection in plants is crucial so that it can be treated before it spreads throughout the plant. To detect illnesses in tulsi leaves, we propose employing a model based on convolution neural networks. Image processing and CNN are widely employed. The prepared model extracts the image's key features and categorizes it into different disorders. The model has a 75 percent accuracy rate. © 2022 IEEE.

Developing a transfer risk assessment tool for the Midlands Critical Care Network

Introduction: Inter-facility critical care transfers are a high-risk activity, with a significant reported critical incident rate.1 The 2019 ICS Transfer of the Critically Ill Adult Patient guideline2 recommends a consultant-led risk assessment is performed in order to provide a rationale for the make-up of the transfer team. Prior to our project, there was no formalised risk assessment process at our unit. Objective(s): We wished to assess whether any 'informal' risk assessment process was already being performed prior to transfers. We then aimed to implement a clear assessment process, initially for our unit but ultimately for our critical care network. Method(s): We performed a baseline audit of adult inter-facility critical care transfers undertaken by a team from our unit between 1st December 2019 and 28th February 2020. Notes were analysed for evidence of any risk assessment performed in discussion with the responsible consultant We then locally piloted a new risk assessment tool for our Critical Care Network's transfer documentation. It included the required elements from ICS guidance, and followed a systems-based approach to facilitate completion in time-critical situations. Colour coding enabled easy identification of potential high-risk transfers and guided team formation. Initial re-audit of the new tool was performed between 16th September and 16th October 2020, after which it was implemented across the network. A further re-audit was performed between 1st October and 31st December 2021. Result(s): Fifteen transfers occurred during the initial audit period. All were clinical. No risk assessments were documented (0% compliance), although all were accompanied by a transfer-trained, airway competent doctor and all but one by an ODP. Our second audit cycle identified 10 transfers, of which 4 had risk assessments completed (40% compliance). All transfers had been undertaken with a dual doctor/ODP team. We identified that there was limited knowledge of the risk assessment process among clinicians, so introduced the topic into our unit's transfer training programme. Assessment completion was made a key performance indicator, fed back to team members following each transfer. Our final cycle covered 14 clinical transfers. Eight had a fully completed risk assessment (57% compliance), 2 had partially completed risk assessments (14% partial compliance), 4 had no risk assessment and 2 cases were excluded due to incomplete data. Conclusion(s): Our tool is now used for all inter-hospital transfers across the Midlands Critical Care Network. It enabled risk assessments to be performed appropriately for transfers originating from our unit. Introduction was initially hampered by limited training for clinicians during the first wave of the Covid pandemic, and compliance improved once this was implemented. The recent introduction of a regional critical care transfer service means that the majority of transfers undertaken by our unit's staff are now time-critical clinical transfers. This may contribute to the failure to complete risk assessments in some cases, however these assessments are likely to be of higher importance since such transfers may be higher risk. We now aim to collect feedback from transferring staff to identify any barriers to correct completion.

Building Resilient Supply Chains with Information Systems: Key Lessons from Médecins Sans Frontières Logistique During the COVID-19 Crisis

This research aims to analyze the resilience of humanitarian supply chains, with a focus on the role of information systems, through a case study of Médecins Sans Frontières Logistique during the COVID-19 pandemic. The empirical research methodology is based on a qualitative study, which includes semi-structured interviews with key actors and operators from the Médecins Sans Frontières Logistique during the COVID-19 crisis in 2020 and 2021. The paper highlights the crucial and inherent role of information systems on each of the four dimensions of humanitarian supply chain resilience: reorganization capacity, collaboration, flexibility, and humanitarian culture. Drawing on recent theoretical works on supply chain resilience as well as empirical results, the paper underscores the importance of information systems and proposes a conceptual model of the relationship between humanitarian supply chain resilience and the role of information systems. The value of this research is linked to its empirical and qualitative study of a Non-Governmental Organization logistics operation during an international crisis, which contributes not only to the literature on resilience, but also provides guidance for managers to target their actions responsively and proactively to enhance resilience over time.. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

A Long Short Term Memory Machine Learning Technique for Covid-19 Patient Count Prediction

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.

Scada Web-Based Instrumentation and Control Laboratory with Real-Time Remote Monitoring and Control System for Columban College

This paper provides a remote access control experiment for students who can't go to the campus because of the COVID-19 pandemic. This paper utilizes the SCADA (supervisory control and data acquisition) using LabView with the Internet of things technology to control the laboratory remotely in real-time. Remote access experiments of a Linear actuator, PID algorithm, Dynamics and Control of Second-order system response, and survey questionnaires were applied and used as an example to show how effective the research study is. The safety of the SCADA system was also considered by using the Virtual Private Network as the primary connection between the student and the server. The remote access laboratory will give a solution to the current problem of the academe for not providing a real-time laboratory equipment experiment. © 2022 IEEE.

Research on Key technologies of network public opinion warning based on improved Stacking algorithm

Online public opinion warning for emergencies can help people understand the real situation, avoid panic, timely remind people not to go to high-risk areas, and help the government to carry out epidemic work.In this paper, key technologies of network public opinion warning were studied based on improved Stacking algorithm. COVID-19, herpangina, hand, foot and mouth, varicella and several emergency outbreaks were selected as public opinion research objects, and rough set was used to screen indicators and determine the final warning indicators.Finally, the warning model was established by the 50% fold Stacking algorithm, and the training accuracy and prediction accuracy experiments were carried out.According to the empirical study, the prediction accuracy of 50% Stacking is good, and the early warning model is practical and robust.This study has strong practicability in the early warning of the online public opinion of the sudden epidemic. © 2023 SPIE.

Philosophy of education in a new key: Who remembers Greta Thunberg? Education and environment after the coronavirus

This paper explores relationships between environment and education after the Covid-19 pandemic through the lens of philosophy of education in a new key developed by Michael Peters and the Philosophy of Education Society of Australasia (PESA). The paper is collectively written by 15 authors who responded to the question: Who remembers Greta Thunberg? Their answers are classified into four main themes and corresponding sections. The first section, ‘As we bake the earth, let's try and bake it from scratch', gathers wider philosophical considerations about the intersection between environment, education, and the pandemic. The second section, ‘Bump in the road or a catalyst for structural change?', looks more closely into issues pertaining to education. The third section, ‘If you choose to fail us, we will never forgive you', focuses to Greta Thunberg's messages and their responses. The last section, ‘Towards a new (educational) normal', explores future scenarios and develops recommendations for critical emancipatory action. The concluding part brings these insights together, showing that resulting synergy between the answers offers much more then the sum of articles' parts. With its ethos of collectivity, interconnectedness, and solidarity, philosophy of education in a new key is a crucial tool for development of post-pandemic (philosophy of) education.

Being on the Frontline? Immigrant Workers in Europe and the COVID-19 Pandemic

This article provides the first systematic assessment of the impact of COVID-19 on the labor market for immigrant workers in Europe. The authors estimate that in 2020 extra-EU migrants were twice as likely and EU migrants were 1.6 times as likely to experience employment loss relative to comparable natives. To understand the determinants of these large gaps, the article focuses on three job characteristics—essentiality, temporariness, and teleworkability—and documents that migrants were overrepresented among essential, temporary, and low-teleworkable occupations at the onset of the pandemic. The authors estimate that pre-pandemic occupational sorting accounts for 25 to 35% of the explained migrant–native gap in the risk of employment termination, while sorting into industries accounts for the rest of the explained gap. More than half of this gap remains unexplained. Although major employment losses were averted thanks to the massive use of short-time work programs in Europe, migrant workers—particularly extra-EU migrants—suffered from high economic vulnerability during the pandemic. © The Author(s) 2023.

Post-Covid-19 Pandemic IT Project Management Skills and Challenges

Since December 2019, the COVID-19 pandemic has harmed social, financial, and work life. The novel coronavirus has caused problems in all business sectors, including Information Technology(IT). Many Projects have been stopped or delayed due to the impact of this pandemic. Most of the companies recognized the importance of IT to achieve competitive advantage and to survive. Companies are investing a lot of money in IT projects. More than 60% of IT project fail. Lack of professional IT project management is one of the main reasons for that fail. Managing IT projects is a complex problem. Crises such as COVID 19 and uncertainty are increasing the complexity and challenges in IT projects management. This paper investigates what are the IT project management Key success factors required during and after the Pandemic. Secondary data were collected from literature review in the field of IT project management. an online questionnaire was used to collect the primary data from 107 IT firms. 323 respondents were participated. It is concluded that the following Key success factors are required: e-communication, centralization of data, online project monitoring, designing new policies and guidelines for incorporating new work culture, gaining and establishing project risk management exercises, especially cybersecurity and data protection by gaining access to the latest tools, establishing a culture for strict usage of Personal Protective Equipment (PPEs) to help project managers complete projects safely, and top management support. © 2023 IEEE.

Emerging Key Elements of a Business Model for Sustaining the Cultural and Creative Industries in the Post-Pandemic Era

In this article, the challenges that cultural and creative industry (CCI) firms face in forming sustainable business models—issues heightened by the ongoing COVID-19 pandemic and the evolving consumer patterns that it has triggered—are addressed. The goal of this study was to identify a set of unique key elements crucial for the construction of a business model that aligns with the distinct characteristics of CCIs. To achieve this goal, an in-depth, long-term study using semi-structured interviews with proprietors in Eastern Taiwan was conducted. This region is home to unique, small-scale cultural and creative businesses and represents less than 5% of Taiwan's population. The semi-structured, in-depth interviews served as the data collection method, while content analysis was used for data interpretation. This approach allowed the current study to encapsulate a set of key elements that could inform the creation of a business model for CCI firms. The insights gathered by the study provide a robust framework for the development of sustainable CCI business models, offering valuable guidance for both existing businesses seeking to adapt and grow and new entrepreneurs entering the industry. This research also aims to stimulate further scholarly debate on the importance of a tailored, multidisciplinary business model for CCIs, given their unique needs and characteristics.

Enhancing compliance rate of hand hygiene before and after Covid-19: An interference analysis in Abu Dhabi, UAE hospitals

Hand hygiene (HH) is a crucial factor for reducing Healthcare Associated Infections (HAIs) in the hospital setting. The current analysis was created to create an intervention methodology to enhance hand hygiene compliance among hospital personnel in a healthcare setting. Covid-19 disease epidemic has additional stressed the need for world-wide development in hand hygiene compliance by the healthcare personnel. Healthcare Associated Infections have been a hot issue for several time periods and Healthcare Associated Infections are the most common adverse results due to the delivery of medical care and treatment. There is unanimity that hand hygiene is the extremely successful way to avoid healthcare associated infections. As healthcare systems fluctuate widely, prevention approaches must be designed appropriately. Hand hygiene, however, remains relevant in all settings, and World Health Organization (WHO) is strongly endorsing alcohol-based hand rubs to interrupt transmission. Nevertheless, very minimal compliance rate amongst the healthcare staff have been reported worldwide. Infected surfaces, especially those that are touched repeatedly by the patient's surroundings, act as reservoirs for pathogens and cause towards pathogen transmission. Therefore, healthcare disinfection requires a thorough approach whereby several strategies may be applied together, risk-based methodologies, to decrease the possibility of HAIs for the patients. In this paper more than 200 articles have been studied from 2016 to 2021 time period and various surveys have been conducted to analyze hand hygiene intervention and studied the various factors involving the patient's situation, medication management behavior of several units, and the type of healthcare employees during and before the Covid-19 pandemic. Based on this study, we evaluated overall hand hygiene compliance rate including the intake of hand wash liquid agent, alcohol-based hand rub (ABHR), the paper wipes, medical waste consumption and personal protective equipment's (gloves, masks etc.) before and after Covid-19 intervention to improve the hand hygiene compliance rate in Abu Dhabi hospitals. © 2023 Author(s).

Enhanced BB84 quantum cryptography protocol for secure communication in wireless body sensor networks for medical applications.

Wireless body sensor network (WBSN) is an interdisciplinary field that could permit continuous health monitoring with constant clinical records updates through the Internet. WBAN is a special category of wireless networks. Coronavirus disease 2019 (COVID-19) pandemic creates the situation to monitor the patient remotely following the social distance. WBSN provides the way to effectively monitor the patient remotely with social distance. The data transmitted in WBSN are vulnerable to attacks and this is necessary to take security procedure like cryptographic protocol to protect the user data from attackers. Several physiological sensors are implanted in the human body that will collect various physiological updates to monitor the patient's healthcare data remotely. The sensed information will be transmitted wirelessly to doctors all over the world. But it has too many security threats like data loss, masquerade attacks, secret key distribution problems, unauthorized access, and data confidentiality loss. When any attackers are attacking the physiological sensor data, there is a possibility of losing the patient's information. The creation, cancellation, and clinical data adjustment will produce a mass effect on the healthcare monitoring system. Present-day cryptographic calculations are highly resistant to attacks, but the only weak point is the insecure movement of keys. In this paper, we look into critical security threats: secure key distribution. While sharing the secret key between communicating parties in the wireless body sensor networks in the conventional method like via phone or email, the attackers will catch the private key. They can decrypt and modify more sensitive medical data. It can cause a significant effect like death also. So need an effective, secure key distribution scheme for transmission of human body health related data to medical professional through wireless links. Moreover, a new enhanced BB84 Quantum cryptography protocol is proposed in this paper for sharing the secret key among communicating parties in a secure manner using quantum theory. Besides, a bitwise operator is combined with quantum concepts to secure the patient's sensed information in the wireless environment. Instead of mail and phone via sharing secret key, quantum theory with the bitwise operator is used here. Therefore, it is not possible to hack the secret key of communication. The body sensor's constrained assets as far as battery life, memory, and computational limit are considered for showing the efficiency of the proposed security framework. Based on experimental results, it is proven that the proposed algorithm EBB84QCP provides high secure key distribution method without direct sharing the secret key and it used the quantum mechanism and bitwise operator for generating and distributing secret key value to communicating parties for sensitive information sharing in the wireless body sensor networks.

Metaheuristic secured transmission in Telecare Medical Information System (TMIS) in the face of post-COVID-19.

The outbreak of novel corona virus had led the entire world to make severe changes. A secured healthcare data transmission has been proposed through Telecare Medical Information System (TMIS) based on metaheuristic salp swarm. Patients need proper medical remote treatments in this Post-COVID-19 time from their quarantines. Secured transmission of medical data is a significant challenge of digitally overwhelmed environment. The objective is to impart the patients' data by encryption with confidentiality and integrity. Eavesdroppers can carry sniffing and spoofing in order to deluge the data. In this paper, a novel scheme on metaheuristic salp swarm based intelligence has been sculptured to encrypt electrocardiograms (ECG) for data privacy. Metaheuristic approach has been blended in cryptographic engineering to address the TMIS security issues. Session key has been derived from the weight vector of the fittest salp from the salp population. The exploration and exploitation control the movements of the salps. The proposed technique baffles the eavesdroppers by the key strength and other robustness factors. The results, thus obtained, were compared with some existing classical techniques with benchmark results. The proposed MSE and RMSE were 28,967.85, and 81.17 respectively. The time needed to decode 128 bits proposed session key was 8.66 × 1052 years. The proposed cryptographic time was 8.8 s.

Key market identification, mechanism transmission, and extreme shock during the risk spillover process: an empirical study of the G20 FOREX markets.

The role of the G20 in global governance has been increasingly prominent in the context of the extensive spread of coronavirus disease 2019 and the aggravation of financial risk contagion. Detecting the risk spillovers among the G20 FOREX markets is crucial to maintain financial stability. Therefore, this paper first adopts a multi-scale approach to measure the risk spillovers among the G20 FOREX markets from 2000 to 2022. Furthermore, the key markets, the transmission mechanism, and the dynamic evolution are researched based on the network analysis. We derive the following findings: (1) The magnitude and volatility of the total risk spillover index of the G20 countries are highly associated with extreme global events. (2) The magnitude and volatility of risk spillovers among the G20 countries are asymmetric in the different extreme global events. (3) The key markets in the risk spillover process are identified, and the USA always occupies a core position in the G20 FOREX risk spillover networks. (4) In the core clique, the risk spillover effect is obviously high. In the clique hierarchy, as the risk spillover effect is transmitted downward, the risk spillovers present the decrease trends. (5) The density, transmission, reciprocity, and clustering degrees in the G20 risk spillover network during the COVID-19 period are much higher than that in other periods.

Key frame extraction method for lecture videos based on spatio-temporal subtitles.

Affected by the Corona Virus Disease 2019 (COVID-19), online lecture videos have witnessed an explosive growth. In the face of massive videos, this paper proposes a method for extracting key frames of lecture videos based on spatio-temporal subtitles, which can efficiently and quickly obtain effective information. Firstly, the spatio-temporal slices of subtitle area of the video sequence are extracted and spliced along the time axis to construct the video spatio-temporal subtitle. Then, the video spatio-temporal subtitle is processed in binarization, and the projection method is used to construct the SSPA curve of the video spatio-temporal subtitle. Finally, a selection method for steady-state key frame is designed, that is, the key frame extraction is realized by combining curve edge detection and subtitle existence threshold, which ensures the robustness of the proposed method. The test results of 8 videos show that the average value of the comprehensive index F1-score of the key frame extracted by the algorithm can reach 0.97, the average precision is 0.97, and the average recall rate is 0.98. It can effectively extract the key frames in lecture videos, and compared with other algorithms, the average running time is reduced to 0.072 of the original, which is helpful to extract video information quickly and accurately.

Identification of key mutations responsible for the enhancement of receptor-binding affinity and immune escape of SARS-CoV-2 Omicron variant.

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised concerns worldwide due to its enhanced transmissibility and immune escapability. The first dominant Omicron BA.1 subvariant harbors more than 30 mutations in the spike protein from the prototype virus, of which 15 mutations are located at the receptor binding domain (RBD). These mutations in the RBD region attracted significant attention, which potentially enhance the binding of the receptor human angiotensin-converting enzyme 2 (hACE2) and decrease the potency of neutralizing antibodies/nanobodies. This study applied the molecular dynamics simulations combined with the molecular mechanics-generalized Born surface area (MMGBSA) method, to investigate the molecular mechanism behind the impact of the mutations acquired by Omicron on the binding affinity between RBD and hACE2. Our results indicate that five key mutations, i.e., N440K, T478K, E484A, Q493R, and G496S, contributed significantly to the enhancement of the binding affinity by increasing the electrostatic interactions of the RBD-hACE2 complex. Moreover, fourteen neutralizing antibodies/nanobodies complexed with RBD were used to explore the effects of the mutations in Omicron RBD on their binding affinities. The calculation results indicate that the key mutations E484A and Y505H reduce the binding affinities to RBD for most of the studied neutralizing antibodies/nanobodies, mainly attributed to the elimination of the original favorable gas-phase electrostatic and hydrophobic interactions between them, respectively. Our results provide valuable information for developing effective vaccines and antibody/nanobody drugs.