Working from home during lockdown: The impact on performance and wellbeing

The experience of working from home changed drastically with the arrival of COVID-19. Compared to pre-pandemic experiences, key differences included the vast number of people involved, its involuntary nature, the suddenness of its implementation, its lengthy duration, and the presence of others at home. The demands of this form of remote work during lockdown have partly been mitigated by the resources employees have accessed. This study aimed to investigate the factors impacting employee performance and wellbeing while compulsorily working from home during New Zealand's first nationwide lockdown. We analyzed qualitative data gathered from employees in two organizations. The resulting aggregate dimensions across both demands and resources include organizational factors, furniture and technology factors, and individual factors. Given the ongoing nature of COVID-19 we identify new research directions for investigating remote work, and practical implications focusing on suitable home furniture and technology, plans for future remote work, and supporting employees. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Impact of breastfeeding on the incidence and severity of respiratory syncytial virus (RSV)-associated acute lower respiratory infections in infants: a systematic review highlighting the global relevance of primary prevention.

BACKGROUND: Respiratory syncytial virus (RSV) is the principal cause of acute lower respiratory infections (ALRI) among infants worldwide, and an important cause of morbidity, hospitalisation and mortality. While infants are universally exposed to RSV, most mortality occurs among normal term infants from low-income and middle-income countries. Breastfeeding has been suggested to have a protective effect against RSV infection. This study aims to determine the association of breastfeeding on the frequency and severity of RSV-associated ALRI among infants. METHODS: A systematic review was conducted using keywords and Medical Subject Headings on MEDLINE, PubMed, Google Scholar, EMBASE, MedRxiv and Cochrane Central Register of Controlled Trials. Full-text articles published in English from 2000 to 2021 that studied exclusively or partially breastfed infants who developed RSV-associated ALRI <12 months of age were included. Covidence software-based evidence extraction and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol guidelines were followed. Quality of evidence was analysed using UK National Service Framework grading and the risk-of-bias assessment using Robvis. RESULTS: Among 1368 studies screened, 217 qualified full-text review and 198 were excluded based on pre-agreed criteria. Nineteen articles published from 12 countries that included 16 787 infants from 31 countries (of which 8 middle-income) were retained for analysis. Results indicate that non-breastfeeding practices pose a significant risk for severe RSV-associated ALRI and hospitalisation. Exclusive breastfeeding for >4-6 months significantly lowered hospitalisation, length of stay, supplemental oxygen demand and admission to intensive care units. CONCLUSION: In the context of no effective or standardised treatment for established RSV-associated ALRI, available evidence suggest that breastfeeding is associated with lower frequency and severity of RSV-associated ALRI, based on observational studies of variable grades of evidence and risk-of-bias. With both exclusive and partial breastfeeding benefiting infants who develop RSV-associated ALRI, breastfeeding should be promoted globally as an adjunct primary prevention; in addition to emerging immunoprophylaxis and maternal immunisation strategies.

Design and Implementation of IoT-Based Automatic Oxygen Flow Control in Response to the Covid-19 Crisis

Internet of Things (IoT) technology has brought a revolution in several ways to a common person's life by making everything smart and intelligent. During the Covid-19 crisis, health workers around the world needed to monitor patients' health and needed to provide sufficient oxygen, when necessary, as Covid-19 was responsible for many respiratory cases. Health workers were at high risk of being contaminated while treating Covid-19 patients. The study of this paper is to propose an IoT-based automatic oxygen flow control in response to the Covid-19 crisis. The proposed approach helped to real-time monitoring of SpO2, heartbeat, oxygen quantity of oxygen cylinder, and control of the flow of oxygen based on SpO2 value. A health worker can monitor a patient's health-related parameters and control the flow of oxygen without any physical contact with it. Also, provides an alarm to the health worker when SpO2 is below the threshold and re-measuring oxygen quantity of oxygen cylinder with the help of our developed android app. Implementation of IoT-based low-cost pulse oximeter and IoT-based pressure gauge helps to monitor and control different health parameters. The IoT-based system may potentially be valuable during the Covid-19 pandemic for accurate oxygen flow distribution and for saving people's lives. © 2022 IEEE.

Machine Learning Algorithms and Frameworks in Ransomware Detection

Ransomware has been one of the biggest cyber threats against consumers in recent years. It can leverage various attack vectors while it also evolves in terms of finding more innovative ways to invade different cyber security systems. There have been many efforts to detect ransomware within the workforce and academia leveraging machine learning algorithms, which has shown promising results. Accordingly, there is a considerably large body of literature addressing various solutions on how ransomware threats can be detected and mitigated. Such large and rapidly growing scientific and technical materials start to make it difficult in knowing the actual ML algorithm(s) being used. Hence, the aim of this paper is to give insight about ransomware detection frameworks and those ML algorithms which are typically being used to extract ever-evolving characteristics of ransomware. In addition, this study will provide the cyber security community with a detailed analysis of those frameworks. This will be augmented with information such as datasets being used along with the challenges that each framework may be faced with in detecting a wide variety of ransomware accurately. To summarize, this paper delivers a comparative study which can be used by peers as a reference for future work in ransomware detection. Author

Gynaecological Laparoscopic Surgeries in the Omicron Era: Improvising the Management Skills After Lessons Learnt From the Delta Wave.

Background During the peak of the Omicron wave, elective laparoscopic surgeries were restricted; however, semi-emergency and emergency cases were managed despite the limited resources and manpower. We conducted this study to assess the types of gynaecological laparoscopic surgeries performed, the difficulties faced during the Omicron wave, and how we could implement the lessons learnt from the previous Delta wave for better management of gynaecological cases in the Omicron wave. Methodology We conducted a prospective cohort study over a period of three months involving 105 patients who underwent laparoscopic surgeries. Based on the decision regarding surgical incision time, the surgeries were sub-classified into immediate, urgent, and expedited. The surgical outcome and satisfaction rates among the patients were assessed through various parameters. Results Most of the women (81.9%) were pre-menopausal. Diabetes and chronic hypertension were the predominant medical co-morbidities. Three patients had a history of cardiac valve replacement which required switching warfarin to unfractionated heparin in the pre-operative period. Nearly three-fourthsof the study patients were doubly vaccinated against coronavirus disease 2019 (COVID-19) (77; 73.4%). A total of 14 (13.3%) patients had a history of COVID-19 infection in the past two weeks prior to the current admission. Immediate, urgent, and expedited surgeries comprised 11.4%, 22.8%, and 65.8% of total surgeries, respectively. On assessing the ease of pre-operative preparation according to the five-point Likert scale, immediate, urgent, and expedited surgeries were rated with a mean score of two, four, and five, respectively. The mean duration of surgery in the immediate and urgent groups was 37.6 and 44.2 minutes, respectively. The expedited group comprising mostly laparoscopic myomectomies and hysterectomies required an average duration of 92.6 minutes. The mean rating of patient satisfaction measured by the Likert scale was four, five, and five, respectively, in the three subgroups. Pre-operative patient preparation during the Omicron wave was faster, thereby decreasing the decision to incision interval compared to the Delta wave. Conclusions The lessons learnt from the previous Delta wave were used to modify the existing hospital policies in the Omicron wave. More number of vaccinated ground staff, less stringent intubation and extubation protocols during surgery, and lesser duration of post-operative stay helped modify our existing hospital policies for better patient care and satisfaction.

Gynaecological Laparoscopic Surgeries in the Omicron Era: Improvising the Management Skills After Lessons Learnt From the Delta Wave

Background During the peak of the Omicron wave, elective laparoscopic surgeries were restricted;however, semi-emergency and emergency cases were managed despite the limited resources and manpower. We conducted this study to assess the types of gynaecological laparoscopic surgeries performed, the difficulties faced during the Omicron wave, and how we could implement the lessons learnt from the previous Delta wave for better management of gynaecological cases in the Omicron wave. Methodology We conducted a prospective cohort study over a period of three months involving 105 patients who underwent laparoscopic surgeries. Based on the decision regarding surgical incision time, the surgeries were sub-classified into immediate, urgent, and expedited. The surgical outcome and satisfaction rates among the patients were assessed through various parameters. Results Most of the women (81.9%) were pre-menopausal. Diabetes and chronic hypertension were the predominant medical co-morbidities. Three patients had a history of cardiac valve replacement which required switching warfarin to unfractionated heparin in the pre-operative period. Nearly three-fourthsof the study patients were doubly vaccinated against coronavirus disease 2019 (COVID-19) (77;73.4%). A total of 14 (13.3%) patients had a history of COVID-19 infection in the past two weeks prior to the current admission. Immediate, urgent, and expedited surgeries comprised 11.4%, 22.8%, and 65.8% of total surgeries, respectively. On assessing the ease of pre-operative preparation according to the five-point Likert scale, immediate, urgent, and expedited surgeries were rated with a mean score of two, four, and five, respectively. The mean duration of surgery in the immediate and urgent groups was 37.6 and 44.2 minutes, respectively. The expedited group comprising mostly laparoscopic myomectomies and hysterectomies required an average duration of 92.6 minutes. The mean rating of patient satisfaction measured by the Likert scale was four, five, and five, respectively, in the three subgroups. Pre-operative patient preparation during the Omicron wave was faster, thereby decreasing the decision to incision interval compared to the Delta wave. Conclusions The lessons learnt from the previous Delta wave were used to modify the existing hospital policies in the Omicron wave. More number of vaccinated ground staff, less stringent intubation and extubation protocols during surgery, and lesser duration of post-operative stay helped modify our existing hospital policies for better patient care and satisfaction.

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to an SS labeled as either supportive, informative, or similar (sharing experiences) using the principles of natural language inference. Evaluation by 21 domain experts indicates the efficacy of incorporated knowledge and shows the efficacy the matching system.

Face Authentication from Masked Face Images Using Deep Learning on Periocular Biometrics

Nowadays, identity theft is an alarming issue with the growth of e-commerce and online services. Moreover, due to the Covid-19 pandemic, society has been pushed towards the usage of masks for people to safely interact with one another. It is hard to recognize a person if the face is mostly covered, even more so to artificial intelligence who have more difficulty identifying a masked individual. To further protect personal information and to develop a secure information system, more comprehensive bio-metric approaches are required. The currently used facial recognition systems are using biometrics such as periocular regions, iris, face, skin tone and racial information etc. In this paper, we apply a deep learning-based authentication approach using periocular biometric information to enhance the performance of the facial recognition system. We used the Real-World Masked Face Dataset (RMFD) and other datasets to develop our system. We implemented some experiments using CNN model on the periocular region information of the images. Hence, we developed a system that can recognize a person from only using a small region of face, which in this case is the periocular information including both eyes and eyebrows region. There is only a focus on the periocular region with our model in the view of the fact that the periocular region of the face is the main reliable source of information we can get while a person is wearing a face mask. © 2022, Springer Nature Switzerland AG.

Deep Features for COVID-19 Detection: Performance Evaluation on Multiple Classifiers

Humanity has faced the greatest difficulties in recent years in COVID-19. These diseases are caused by significant alveolar damage and progressive respiratory failure. To address this issue, healthcare facilities needed rapid testing methods to identify COVID-19 patients and treat them immediately. In this paper, we developed a rapid testing strategy using machine and deep learning architecture with three different categories of chest x-ray images, such as COVID-19, normal, and pneumonia, were considered to identify the COVID-19 affected images. It is very difficult to diagnose COVID-19 from the pool of chest x-ray images, as pneumonia and COVID-19 affected x-ray images closely resemble each other. For this issue, feature extraction plays an important role. Here we considered deep features which were extracted from deep learning models such as VGG19 and InceptionResnetV2. These deep features were classified using different machine learning algorithms. It was observed that 96.81% accuracy was obtained after classification using MLP. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covishield (TM) (ChAdOx1) Vaccine Effectiveness and Epidemiological Risk Factors of COVID-19 Infection among Frontline Workers during Second Wave of COVID-19 Pandemic, New Delhi: A Case-Control Study

Background: Frontline workers were the first cohorts vaccinated with Covishield (TM) (ChAdOx1 nCoV-19) vaccine with dose-interval of 4-6 weeks. We evaluated vaccine effectiveness (VE) of Covishield and studied epidemiological risk factors associated with COVID-19 during second wave of COVID-19 pandemic. Methods: We conducted a 1:3 case-control community-based study, as per WHO protocol. We identified case-patients from COVID-19 surveillance system and recruited controls from the same community as per the WHO protocol. Information was obtained through questionnaire;and all potential confounders were identified to evaluate VE. Results: We enrolled 243 case-patients and 712 controls. Adjusted VE of fully vaccinated was 74% (95% confidence interval [CI]: 53%-86%) against infection and 91% (95% CI: 78%-97%) against moderately severe disease. Pre-infection high-risk exposure events such as ccontact with COVID-19-positive patient, visit to a crowded place, and attending social-gathering in confined space were significantly associated with contracting infection, with odds ratios 10.1 (95% CI: 5.6-18.3), 6.0 (95% CI: 1.8-20.2) and 3.9 (95% CI: 1.4-10.5) respectively. The use of double-mask and past COVID-19 infection was 60% and 70% protective, respectively. Conclusion: Covishield vaccine is highly effective against infection and mainly against disease-severity during high-transmission settings. We recommend three-layer shield to minimize breakthrough and re-infections comprising of vaccination, double-masking, and avoiding "pre-infection high-risk exposure events."

Modelling of Air Pollution Dispersion in Thiruvananthapuram Corporation

Air is a crucial element of the earth’s ecosystem, and even minor changes in its composition can have a wide range of effects on the survival of creatures on earth. Deterioration of air quality is an important issue faced by many cities in India. Modelling of air pollution is a numerical method for describing the causal relationship between emissions, meteorology, atmospheric concentrations and deposition. The current study prepared annual and monthly air pollution dispersion maps at sensitive areas of Thiruvananthapuram Municipal Corporation, which is the administrative spot in the city of Thiruvananthapuram, the capital of Kerala. ADMS-Urban model was used in conjunction with GIS to produce the dispersion maps. The study has demonstrated a methodology for the development of emission inventory, dispersion modelling and mapping. Dispersion modelling and trend analysis were used to investigate the concentration of the pollutants and their intensity of dispersion in relation to meteorological conditions in the study area such as wind speed, wind direction, temperature and humidity. The present study calculates emission concentration of nitrogen dioxide (NO2), sulphur dioxide (SO2), suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM), from various monitoring stations and industries within the study area from the year 2016–2020. It was found that concentration of pollutants lie within the Central Pollution Control Board limits. Also, trend analysis of pollutant concentration was done separately for the year 2020 and there was a significant reduction (>50%) in pollution concentration due to the lockdown scenario created by COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion - toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.

Change ubiquity: employee perceptions of change prevalence from three countries

Purpose>The purpose of this paper is to address assumptions about the prevalence of change in human resource management (HRM) and organizational change literature, providing evidence from employee perceptions across three countries. The results indicate change was commonplace even before the pandemic disruptions of 2020.Design/methodology/approach>Given this study's exploratory, employee perspective, a cross-sectional self-report survey was used. Three survey panel samples were collected in 2017: US (n = 718), Australia (n = 501) and New Zealand (n = 516). Analysis of variance was used to test whether the prevalence of change differed significantly between countries or specific groups of employees. An analysis of comments on change types and emotional response provides further insights.Findings>The paper provides evidence of the ubiquity of change: 73% of employees are experiencing change at work and 42% perceived it as moderate to massive, with little variation between countries. Employees commonly experience more than one change, with those experiencing large amounts of change reporting predominantly negative emotional impacts.Research limitations/implications>The research provides a snapshot across three countries during a prosperous and relatively stable period, providing a point of comparison for the turbulent times we have faced in 2020” as the publication date will be 2021 the current text may not work as well. Since change can be arduous, the authors recommend that HRM researchers consider change prevalence as a contextual factor, and practitioners heed employee reactions to change, particularly during periods of significant change.Originality/value>In providing foundational evidence of change ubiquity in contemporary workplaces, this paper enables more accurate discussions regarding change.

Detection of COVID-19 Using Deep Transfer Learning-Based Approach from X-Ray and Computed Tomography(CT) Images

Clinical authorities need technological support aided with artificial intelligence for early diagnosis and slowing the spread of pandemic diseases. The outbreak of COVID-19 disease caused by the newly discovered SARS-CoV-2 virus was reported by the officials in Wuhan City, China, in December 2019. Since then the virus had a disrupting impact on the health of people accompanied by psychological, financial, and social distress. In this paper, a deep learning-based approach for early detection of COVID-19 has been proposed. Five deep neural network architectures have been trained through transfer learning based on the available X-ray and computed tomography image dataset. The chosen architectures have given quite promising results in terms of accuracy. Thus, the proposed experiment provides an efficient tool for the early detection of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Towards the Development of Triboelectricity-Based Virus Killer Face Mask for COVID-19: Role of Different Inputs

Since the early detection of COVID-19 infection in December 2019, the number of infected persons has been increasing day by day. In this present scenario, people worldwide are reorganizing their life taking safety precautions like doing frequent sanitization, wearing face masks, and avoiding social gathering to protect themselves from getting infected as the proven vaccine or lifesaving drugs are yet to be discovered. However, deficiency of face mask and their reusability have become a key issue because the used masks need to be discarded after some time. In this background, we propose the design of a self-powered (no external power source) face mask which does not require to be sterilized. The proposed mask is comprised of two differently charged tribo-series materials with outer electrocution layer. Different combinations of tribo-series (+ and −) materials have been chosen based on their triboelectric properties to generate static electricity. Nanofibers have been considered for their ability to generate a sufficient amount of triboelectricity. Multilayer of electrospun nanofiber-based tribo-materials such as polyvinylidene fluoride (PVDF)-nylon and PVDF-poly(ethyl methacrylate) has been used due to the effective air filtration property of nanofibers and generating tribo electricity. In addition, the generated charge via utilization of contact electrification and electrostatic induction is amplified using a suitable energy harvesting circuit. The design of an outer electrocution layer has been made keeping a few nm distances in between the tribo-layers and the electrocution layer to avoid short-circuiting. Metallic nonwoven fabric has been taken in practice to design the outer electrocution layer. In this practice, the harvesting of triboelectric energy has been done using a suitable charging circuit which can generate sufficient voltage (few volts) to trigger the outer electrocution layer. During the wearer’s inhalation and exhalation, the inner tribo-layers produce triboelectric charges due to mechanical agitation between the layers. Additionally, acoustic or air vibration during talking and different facial expressions of the volunteer will also take part in the generation of effective triboelectric power. The viruses get electrocuted once the droplets containing viruses come in contact to the mask’s outer layer. In addition, the fitting comfort and the breathing permeability of the proposed mask are also ensured. In this chapter, we shall explain the face mask’s design and present the analysis results of different physiological inputs for the efficacy of the mask for killing the deadly virus. © 2022, Springer Nature Switzerland AG.

Computational Modeling of Chloroquine Analogues for Development of Drugs Against Novel Coronavirus (nCoV)

The pandemic of coronavirus disease 2019, known as COVID-19, has challenged the global health;unfortunately still we do not have any specific therapeutic agents to treat this disease. The existing drugs are used only for symptomatic relief;among them chloroquine and its analogues have shown apparent and promising effectiveness in the treatment of COVID-19-related pneumonia. Due to unknown etiology of SARS-CoV-2, the mode of action of chloroquine and its derivatives has not been clear, but based on its different positive analysis results, on March 28, 2020, the U.S. Food and Drug Administration (FDA) allowed chloroquine phosphate and hydroxychloroquine sulfate to treat hospitalized COVID-19 patients. Globally, various researchers are continuously working on these compounds to improve their efficacy and affinity to SAR-CoV-2 targets and also trying to design new chloroquine derivatives with better response. In the present time, computational modeling approaches (homology modeling, molecular docking, molecular dynamic simulation, quantitative structure-activity relationship or QSAR, pharmacophore, etc.) are being proved very helpful for the easy identification of novel inhibitors against different SARS-CoV-2 targets. Chloroquine and its analogues are being explored by various researchers for repurposing of these drugs against SARS-CoV-2 by implementing different computational methodologies. In this chapter, we have presented recently published reports on the in silico modeling of chloroquine analogues for the design and identification of novel drugs against SARS-CoV-2. The chapter also provides the reader with a general idea about a successful computational drug discovery research in this particular area of applications.

Computational Modeling of RdRp Inhibitors for the Development of Drugs against Novel Coronavirus (nCoV)

Corona virus disease 2019, known as COVID-19, is a type of viral infection, which may cause acute respiratory infection and severe pneumonia, for which there is no specific therapeutic treatment. The available drugs are used only for symptomatic relief. Among all the targets, RNA-dependent RNA polymerase (RdRp) has been proved as an optimistic drug target against severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) to manage its replication process. There are number of molecules which have been approved by the FDA as RdRp inhibitors such as ribavirin, remdesivir, sofosbuvir, etc. The reported inhibitors for RdRp enzyme is being used by the various researchers for repurposing of drugs against SARS-Cov-2 RdRp enzyme by implementing different computational methodologies. Molecular modeling and cheminformatics approaches have been used for the design and identification of novel molecules having medicinal applications in various areas. The preliminary studies in computational drug discovery (CDD) and in silico approaches are important parts of the modern drug discovery practices, and they are frequently applied in the identification of new drugs or for the prediction of biological activity of chemical series. Now, in the ongoing corona pandemic, in silico modeling is being proved very helpful for easy identification of novel inhibitors. The current chapter presents recent reports on the application of computational modeling approaches for the design and identification of ligands against RdRp associated with SARS-CoV-2. Here, we will illustrate recently published computational studies for the identification or development of novel RdRp inhibitors applying different computational approaches, encompassing homology modeling, molecular docking, virtual screening, and molecular dynamics simulations. The chapter will also provide the reader an overall idea about a successful computational drug discovery research in this particular area of applications.