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For many, Covid-19 is a short-term, mildly debilitating disease. But some people are still struggling with monthly symptoms with persistent inflammation, chronic pain and shortness of breath. The situation of "long-term cowardice" has become so debilitating that it is now common for some to say that they are tired even if they walk a short distance. So far, the focus has been on saving lives from the plague. But now there are growing concerns about people facing the long-term consequences of the COVID epidemic. The fundamental question, with the uncertainty of whether those with chronic goiter, or all those affected, will fully recover is raised. In this paper a smart monitoring model was proposed to keep monitoring the COVID patient's health conditions. The smart method keep on watching the different changes reflected in the body conditions and ensure the changes in the database. In case any emergency is raised, then these smart monitoring tools inform the information to the doctors. This can very much helpful for the patients to communicate with the doctors. [ABSTRACT FROM AUTHOR]

The article discusses the rise of "team science" in academia, where research and academic publications are increasingly collaborative efforts. This trend has led to a significant increase in the number of multi-authored papers, with some papers having thousands of authors. However, this raises questions about the ethics of authorship and the transparency of individual contributions. The article highlights the need for clear standards and communication practices regarding authorship roles and contributions. While other disciplines have developed various methods for communicating author contributions, the journal Information Polity invites authors to be more explicit about their co-authors' inputs without imposing a single system. This promotes research ethics and transparency in the field. [Extracted from the article]

This paper discusses the organization of students' internship in new circumstances caused by COVID-19 pandemic. The focus of the research is on the IT companies in eastern part of Croatia and their perspective on how they managed students' internship during and after the lockdown. Some of these IT companies successfully cooperate with the Department of Information Sciences, Faculty of Humanities and Social Sciences at the University of Osijek. Since 2015 the Department enrolls students in graduate study of Information Technology track. From that time students used to do their internship and later find employment in IT companies in the region. The well based cooperation with local IT companies was interrupted in 2020 by the COVID-19 lockdown and semi-lockdown. In such a situation, teachers and IT companies' managers have been trying to find out the proper ways in which students could fulfill their tasks via online or hybrid mode. To learn more about the experience from the perspective of IT companies, we interviewed 18 of them. The paper points out the observed advantages and disadvantages as well as suggestions for setting a framework for future internship programs. Employers are thinking about continuing to perform online or hybrid internships, and it is clear to them that one of the answers is well defined and regulated model of internships in the online environment. [ABSTRACT FROM AUTHOR]

BACKGROUND: In March 2020, with the scope to reduce the spread of COVID-19, most national governments around the world canceled in-person education and moved to online learning. Therefore, teachers and students had to adapt a new way of teaching. Most of Italian teachers never had such an experience before and encountered difficulties in effectively carrying out this process on their own. Difficulties that can naturally increase anxiety and stress, leading, in situations perceived as extreme, to burnout syndrome. OBJECTIVES: This paper endeavored to verify levels of job stress and burnout of Italian teachers caused by the COVID-19 pandemic using the validated Maslach Burnout Inventory-General. This study aimed to measure the association among the three main dimensions of burnout and the variables of teachers' personal and working lives that changed due to COVID-19. METHOD: The aim of this paper was to verify burnout state and to measure the association among the three dimensions of burnout and the personal and working lives of Italian teachers using structural equation model analysis. The analysis was conducted in December 2021 and considered the situation in which the Italian teachers (from primary to middle and upper school) are working since March 2020. RESULTS: The results showed that teachers were emotionally exhausted; they did not feel able to fully fulfill their task towards the students. This involved a high absenteeism, a lower quality of work performance and the impossibility of making an objective evaluation of the students with an inevitable flattening of the class level. In contrast, the study shows that teachers who experienced few problems had relatively low levels of burnout. CONCLUSION: The findings brought out some proposals to reduce the risk of burnout and increase the individual well-being of schoolwork organization with positive effects on the lives of students: to strengthen social identity, to avoid a full-time online connection, to promote a psychological support service and to promote resilience training. [ABSTRACT FROM AUTHOR]

Scholarly publishing is considered one of the most frustrating endeavors among academicians. For learners, it symbolizes the culmination of studies, whereas for academicians, it signifies promotion and acknowledgment. This paper highlights and discusses some of the breakthroughs and hindrances scholars, especially doctorate students, undergo in their scholarly pursuits. It traverses the intricate journey of scholarly publishing, exploring the myriad frustrations students face along this intellectual path. The paper concludes by providing insights and recommendations that can contribute to a more equitable and sustainable academic publishing process. [ABSTRACT FROM AUTHOR]

Dear HSM readers, On behalf of the Editorial Board of I Human Systems Management i and as a guest editor of this issue, I am glad to introduce the special issue "Interaction between Human and ICT". This article investigates the impact of information communication technology (ICT) on entrepreneurial self-efficacy (ESE), social networking (SN) and facilitation to micro-entrepreneurs during COVID-19 pandemic in Pakistan. Ninth paper, "Cognitive-affective appraisal of technostressors by ICT-based mobile workers and their impacts on technostrain", authored by Shirish Anuragini, aims to explain the relative importance of the cognitive and affective processes used amongst ICT-based mobile workers when coping with technostressors. [Extracted from the article]

The data revolution has resulted in discussions in the statistical community on the future of official statistics. Will official statistics survive as a brand, or will such statistics drown in the flow of data and statistics from new sources and actors, including misused statistics and fake news? The COVID-19 pandemic has been an additional driver for discussion. There is a need to maintain the quality of official statistics and highlight the value of such statistics for the users as a basis for – and supplement to – other statistics and information. It is at the same time important to implement new developments to improve and keep up the relevance of official statistics. Key pillars today are statistical legislation, quality frameworks and core values defining requirements for official statistics. Possibilities are linked to new statistics, use of new data sources and possible extended roles of the statistical institutes within coordination, collaboration, and data stewardship. The paper addresses these issues in the light of trends in official statistics since the UN Fundamental Principles of Official Statistics were formulated about 30 years ago. Quality challenges for statistics and dilemmas in defining the roles of statistical institutes are considered. The paper includes examples from Statistics Norway. [ABSTRACT FROM AUTHOR]

Next to the already mentioned topics this issue covers a lot of other themes, a first paper stemming for the recent ISI World Statistics Conference, a set of Covid related articles, some articles on population and social statistics, agricultural statistics and some remaining papers from the cancelled 2020 European Statistics Quality conference. Ger Snijkers, Tim Punt, José Gómez Pérez and Sofie de Broe, in ' I Exploring sensor data for agricultural statistics: the fruit is not hanging so low as we thought i ' argue that in theory sensor data could be a valuable new source for official statistics, for example in agricultural statistics, where new business processes are heavily data-driven with farmers using machines with sensors for precision agriculture. The opening article in the Economist ' I Data and the economy i ' [4] argues about the need for good data and statistics and that, taking the example of Central Bank statistics,[5] (but of course this need for good data holds true for all kind of statistics) much of the required data still are not adequately available and the traditional statistical systems lag behind with survey and even with integrated data. [Extracted from the article]

In terms of the inclusivity of democracy, both the opportunities and risks of using digital media have been highlighted in the literature. Empirical research into the use of digital media and the inclusivity of citizen participation, however, is limited. More specifically, we have a limited understand of the relation between the 'richness' of digital media and the inclusivity of citizen engagement The introduction of digital participation by governments during the COVID-19 pandemic presents a highly interesting situation for studying the relation between digital participation and the inclusivity of citizen engagement. This paper presents an assessment of five cases in the Netherlands to explore to what extent the media richness of digital participation affects the inclusivity of citizen engagement. Our findings present few indications for the exclusion of citizens and, in contrast, highlight the participation of more citizens when access was facilitated through digital media. The overall picture that emerges is that especially lean digital tools, webinars, enhance inclusivity in terms of access but only richer tools, interactive platforms, also facilitate participation as interaction with citizens. To our surprise, we found that hybrid forms of participation fall short in providing opportunities for inclusive interaction. We end the paper by recommending governments to realize both broad and in-depth inclusive democracy by sequential use of lean media – webinars – to boost the access to participation and rich media – platforms and offline meetings – for interactions with citizens. [ABSTRACT FROM AUTHOR]

BACKGROUND: During an outbreak such as COVID-19, hospital staff needs to be in close contact with all types of patients visiting the hospital and the risk of cross-infection is extremely high. Payment and medication pickup is a mandatory part of a patient's hospital visit, with direct contact between healthcare workers and patients, and long waiting times in the hospital area, which can easily lead to the spread of disease infection. OBJECTIVE: This paper designed the prototype of a contactless smart medicine cabinet based on RFID technology and optimized the patient consultation and medication pickup process to address these problems. METHODS: We conducted a 50-day field observation of patients for consultation and medication pickup at the First Hospital in H city, Jiangsu Province, China, and randomly timed 1600 sets of data from Surgery (ophthalmology) and Internal patients, then we designed the prototype of a contactless smart medicine cabinet based on RFID technology, optimized the patient consultation and medication pickup process, comparing the traditional and intelligent models using AnyLogic. RESULTS: The results show that this contactless medicine cabinet was able to reduce the time taken by patients in consultation and medicine pickup by 18.74 minutes, increasing the overall efficiency of the consultation by 32.20%. The simulation revealed that this contactless intelligent medication pickup model was able to reduce the time taken by patients in consultation and medicine pickup, increasing the overall efficiency of the consultation, effectively reducing the frequency of contact between healthcare workers and patients, and reducing the risk of disease infection. CONCLUSION: The proposed technical model provides a new idea to solve the problems of long queues, low efficiency and high risk of infection for patients to consult and get medicine during epidemics. Especially within hospitals it has important theoretical and practical implications for epidemic prevention and control as well as future hospital management. [ABSTRACT FROM AUTHOR]

This paper proposes a new epidemic prediction model that hybridizes several models, such as the autoregressive integrated moving average model (ARIMA), random forest (RF), and response surface method (RSM). The modeling process based on ensemble empirical mode decomposition (EEMD) is particularly suitable for dealing with non-stationary and nonlinear data. ARIMA's timeliness and difference have strong deterministic information extraction ability. RF is robust and stable, with fast speed, and strong generalization ability. Under the adjustability and correspondence of the response surface, the comprehensiveness of the model is well demonstrated. Taking the United States as an example, the proposed ARIMA-RF-RSM model is used to explore the development mechanism of the early epidemic according to the data of the early epidemic of coronavirus disease 2019 (COVID-19). The proposed model has high prediction accuracy (mean absolute percentage error (MAPE) is 1.97% and root mean square error (RSME) is 7.24%). It helps to take effective prevention and control measures in time. In addition, the model has universal applicability to the analysis of disease transmission in relevant areas. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has demonstrated the increasing need of policymakers for timely estimates of macroeconomic variables. A prior United Nations Conference on Trade and Development (UNCTAD) research paper examined the suitability of long short-term memory artificial neural networks (LSTM) for performing economic nowcasting of this nature. Building off those findings, in this paper, the LSTM's performance during the COVID-19 pandemic is compared and contrasted with that of the dynamic factor model (DFM), a commonly used methodology in the field. Three separate variables, global merchandise export values and volumes and global services exports, were nowcast with actual data vintages and performance evaluated for the second, third, and fourth quarters of 2020 and the first and second quarters of 2021. In terms of both mean absolute error and root mean square error, the LSTM obtained better performance in two-thirds of variable/quarter combinations, as well as displayed more gradual forecast evolutions with more consistent narratives and smaller revisions. [ABSTRACT FROM AUTHOR]

During the 2 nd phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, open government data (OGD) was often used as a valuable crisis management resource. Unfortunately, there is limited research that explores how OGD can be used during times of crisis as a crisis management tool. To ensure that OGD can be used effectively in future crises, there is a need to understand how it may be used and what benefits its usage may bring. This paper brings new insight into this topic by conducting a comparative exploratory case study of three Central and Eastern European (CEE) countries – Czech Republic, Estonia and Latvia, where OGD was used at different levels to help manage different aspects of the COVID-19 pandemic. As a result of this research, three contributions are made: (1) it integrates OGD into previous crisis management literature, offering new and initial conceptual propositions; (2) it demonstrates how OGD enables the co-creation of new services that create public value during times of crisis; and (3) it provides empirical examples of OGD-driven co-created services. [ABSTRACT FROM AUTHOR]

This paper addresses the critical role of health literacy during the COVID-19 infodemic and provides recommendations for decision-makers regarding how health literacy can be advanced as an empowering resource to mitigate the harmful effects of future infodemics. Based on a comprehensive literature review, key areas concerning health literacy are identified that help provide a strategic response during an infodemic. A framework for systemic health literacy capacity and policy advice is presented to inform and guide decision-makers on managing an infodemic with health literacy strategies. The way forward includes emphasizing the rights to access information and a broader view of how health literacy can help build back better in the aftermath of the COVID-19 pandemic. Finally, the paper includes a call to action for decision-makers to integrate systemic health literacy responsiveness into public health emergency strategies to counter a future infodemic's diffusion. [ABSTRACT FROM AUTHOR]

The preface summarizes six contributions within a special issue of Information Services and Use in tribute to Andrew Pleasant, Ph.D., 1962–2022. Five topic-oriented manuscripts within the special issue are devoted to health literacy and health communication issues during the COVID-19 pandemic. A sixth paper focuses on Dr. Pleasant's career and contributions. In addition to manuscript summaries, the preface introduces two health literacy/health mass communication topics: a constructive response to the 'infodemic'; and the value of health narratives. These topics were of interest to Dr. Pleasant during the pandemic and augment some of the issues raised within the special tribute issue's other contributions. [ABSTRACT FROM AUTHOR]

During the COVID-19 crisis many European governments have implemented major policy measures to support companies and households coping with the sharp decline in economic activity. Correctly interpreting and recording these measures in the national accounts was a major challenge for compilers who had to ensure not only timely but also independent, reliable, and comparable statistics in exceptional circumstances. High quality official statistics are fundamental for public finance monitoring, both at quarterly and at annual level. They provide informative basis for policy makers and economic analysts and play a central role in the context of the European fiscal surveillance process. This paper discusses how the Italian Statistical institute (ISTAT) managed to quickly respond to the sudden increase in the need for public finance information in the aftermath of government interventions to contrast the consequences of COVID-19 crisis. In particular, the paper focuses on one of the first fiscal actions Italian government enforced immediately following the outbreak that is deferrals of tax obligations. According to OECD (2021) this is the main fiscal relief European countries have introduced in response to Covid crisis. The urgency of giving appropriate trace of these actions in the framework of national accounts has raised a significant methodological issue for compilers, and its solution can be exemplary of an innovative approach to provide data during extraordinary periods remaining as compliant as possible with the conventional processes and codified rules. The approach is based on the combination of new data sources with already existing data, on a strengthen interaction among data providers and government agencies to assure a speedier access to administrative data (Ministry of Finance and Revenue Agency), and on a strict collaboration with European institutions (Eurostat). [ABSTRACT FROM AUTHOR]

BACKGROUND: During the COVID-19 pandemic, it has been essential for some workplaces to stay open. Considering the rapid spread of the virus, interior architectural re-designing of these essential workplaces such as markets, banks, and drugstores is crucial for prevention. The employees, as well as the customers in these workplaces, have a high infection rate. Some precautions need to be taken urgently to prevent the spread of the disease. Some workplaces may have already performed their action plan whereas others have not. OBJECTIVE: Some practical, rapid, and cost-efficient preventive precautions are presented in this paper for employers to take action in their workplaces. METHODS: Two new proposals are advised to be carried out. The contents of these newly designed barriers will be introduced. RESULTS: Some practical and cost-efficient ideas are given within this report. CONCLUSION: All the preventions proposed in this paper are claimed to reduce the spread of COVID-19 and may save lives around the country as well as the world. [ABSTRACT FROM AUTHOR]

The economic downturn due to lockdown measures at the beginning of the COVID-19 crisis raised the question whether any adaptations to the short-term statistics (STS) were needed to ensure accurate and relevant output. We limit ourselves to STS on turnover and related variables like volume of production. We looked into the different stages of the production process – from data collection to output – and anticipated a number of potential lockdown effects. With respect to output relevance, there was an increased interest in faster and specific output. With respect to the output accuracy, we took measures to check whether the anticipated effects really occurred and measures to mitigate the consequences. Examples of such measures are the calculation of an additional editing score function, alternative imputations and extensions of the regular analysis step. In this paper we give an overview of the anticipated effects, the subsequent measures that we took, we evaluate to what extent the anticipated effects occurred in practice and we mention some unforeseen effects. We end this paper by discussing to what extent the developed measures are also useful to keep after the economy has recovered. [ABSTRACT FROM AUTHOR]

We analyze overall mortality in the U.S. as a whole and several states in particular in order to make conclusions regarding timing and strength of COVID pandemic effect from an actuarial risk analysis perspective. No effort is made to analyze biological or medical characteristics of the pandemic. We use open data provided by CDC, U.S. state governments and Johns Hopkins University. In the first part of the paper, we suggest time series analysis (ARIMA) for weekly excess U.S. mortality in 2020 as compared to several previous years' experience in order to build a statistical model and provide short-term forecast based exclusively on historical mortality data. In the second half of the paper, we also analyze weekly COVID cases, hospitalizations and deaths in 2020 and 2021. Two midwestern states, Minnesota and Wisconsin, along with geographically diverse Colorado and Georgia, are used to illustrate global and local patterns in the COVID pandemic data. We suggest vector autoregression (VAR) as a method of simultaneous explanatory and predictive analysis of several variables. VAR is a popular tool in econometrics and financial analysis, but it is less common in problems of risk management related to mortality analysis in epidemiology and actuarial practice. Efficiency of short-term forecast is illustrated by observing the effect of vaccination on COVID development in the state of Minnesota in 2021. [ABSTRACT FROM AUTHOR]

Government communications typically use social media for federal agencies, national policies, and emergency cases. However, it is crucial to understand the use of these platforms within local government agencies. This investigation fills this gap and includes social media data during three months of the COVID-19 pandemic and compares it with previous trends. The research question that leads this research is: How has government social media use and communication behavior changed due to the COVID-19 pandemic? This research analyzes a study case applied to a local Mexican government agency named ISSEMyM. We analyze the data during the period between October 2017 and June 2020. Findings reveal a consistent use of Twitter and Facebook for communication by the ISSEMyM government agency. Also, it indicates an increase in engagement and communication flow over three years. We found that official local government communications remained unchanged during the whole period. We found no change in interaction, number of followers, and message structure during this period; however, the COVID-19 pandemic radically alters interactions, followers, and user engagement. [ABSTRACT FROM AUTHOR]

BACKGROUND: This paper focuses on the concept of career construction based on the theory of conservation of resources to understand the overall effect of career capital on career success from both a subjective and objective manner through the mediating effect of perceived employability. OBJECTIVE: This study attempts to explain how different integrated aspects of career capital, including human, social, and psychological (antecedents), influence both subjective career success and objective career success (outcome) through the mediating effect of perceived employability (mediator). METHODS: Time-lagged data of 331 employees from the telehealth medical billing service companies based in Pakistan were analyzed through a structural equation modeling technique using SmartPLS software. RESULTS: The main results confirmed that career capital positively affects perceived employability and career success while perceived employability positively mediates the relationship between career capital and career success. CONCLUSION: This research responded to prior calls by explaining the positive mediating role of perceived employability (as a mediator) in explaining the positive influence of career capital on career success using different various dimensions of career capital and career success. This research included the contextual issues by testing the model in the telehealth sector of Pakistan. The findings suggested that context or occupation matters in the relationship between career capital and career success. [ABSTRACT FROM AUTHOR]

BACKGROUND: During the COVID-19 pandemic, office workers were obliged to work from home (WFH). Alongside known positive aspects of home-based telework, it is associated with reduced health and productivity impacts. Its success depends on employee and environmental characteristics. OBJECTIVE: This paper fills the gap in knowledge on the mediating role of health between personal and environmental factors and employee productivity, when obliged to work from home full-time. It covers health in full (physical, mental, and social) unlike other WFH studies. METHODS: Two large survey-based datasets (gathered April 27th - November 20th, 2020) were analysed resp. with a path model and descriptive analyses. The data provide experiences on health and productivity of resp. 25,058 and 18,859 Dutch office workers from different public organisations, who were obliged to work from home during the COVID-19 lockdowns. RESULTS: In general, the workers in the sample perceived their health to be quite good. Path analysis revealed that gender, age, education, the at-home workspace, the presence of children in the household, and perceived organisational support were significantly related to self-perceived productivity. However, most of these effects were found to be mediated by physical, mental, and/or social health indicators. Possible explanations for health issues from the descriptive analyses were sedentary behaviour, unsuitable furniture, having to be at home, social isolation and changed content and frequency of contact with colleagues. CONCLUSION: Findings imply that specifically engagement and organisational support of teleworkers are most relevant to steer on to ensure productivity while WFH. [ABSTRACT FROM AUTHOR]

BACKGROUND: During the 1990s, voluntary teleworking became more grounded, because of expected advantages as increased productivity and comfort. However, COVID-19 obliged employees to work from home (WFH), even in unsuitable houses, which might have reduced their mental health. A holistic overview of methods and measures of the physical home-workspace characteristics and mental health is currently lacking. Insights in the potential influence of the physical home-workspace on mental health are also not yet holistically examined. OBJECTIVE: The aim of this study is to provide insights in previously studied relationships between the physical home-workspace and mental health and to identify measures for both using a systematic scoping review. METHODS: This study used the PRISMA method to systematically review existing literature. RESULTS: Most studies focussed on noise, acoustics, and privacy, in relation to productivity, concentration, and sleep quality. Only a few studies used objective measures for physical home-workspace characteristics. CONCLUSION: The list of relevant measures can be used by academics to examine relationships between the home-workspace and mental health further. Workplace managers can use it to help employees in optimizing their home-workspace. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has exposed the inaccessibility of government information and communication technologies (ICTs) for members of the disability community. Organizational learning around ICT accessibility can be impacted by factors influencing strategies and assumptions or values and norms. Using data collected over two time periods in 2021, we study how the accessibility of US state health agencies COVID-19 information and vaccine websites improve over time. We examine how time, state policies, and partisanship influence organizational learning around website accessibility. Our analysis determines that the longer a COVID-19 related website exists on the Internet, the less accessible the website. We also find that more extensive internal state accessibility policies are more correlated with websites that meet fundamental accessibility requirements. Additionally, we find that partisanship plays an unexpected role in meeting fundamental accessibility demands, although both state policies and politics do not influence if an ICT meets the best practices standards of accessibility. Our paper initiates a discussion around the factors that influence organizational learning about government website accessibility and points to future research questions where the primary ICT function is not influenced by a rapidly evolving pandemic. [ABSTRACT FROM AUTHOR]

Masked face recognition embarks the interest among the researchers to find a better algorithm to improve the performance of face recognition applications, especially in the Covid-19 pandemic lately. This paper introduces a proposed masked face recognition method known as Principal Random Forest Convolutional Neural Network (PRFCNN). This method utilizes the strengths of Principal Component Analysis (PCA) with the combination of Random Forest algorithm in Convolution Neural Network to pre-train the masked face features. PRFCNN is designed to assist in extracting more salient features and prevent overfitting problems. Experiments are conducted on two benchmarked datasets, RMFD (Real-World Masked Face Dataset) and LFW Simulated Masked Face Dataset using various parameter settings. The experimental result with a minimum recognition rate of 90% accuracy promises the effectiveness of the proposed PRFCNN over the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper investigates from a comparative perspective the development of e-government in the field of social services and benefits for the case of Romania. The analysis takes into account the global context of the COVID-19 pandemic, where there has been an increased usage of ICT technology and new circumstances for delivering social services. Therefore, the analysis will examine whether there has been an impetus for developing e-government social services in Romania. Research questions address whether there is a difference in the availability of electronic delivery of social services and benefits during the pandemic period and examine potential differences between types of services and benefits, counties/regions and types of institutions (central, regional/county, local – mayoralties/urban and rural municipalities). Additionally, informative procedures available in 2021 are examined. The analysis revealed that there is no standardized set of available electronic procedures from similar institutions. The most eloquent case is the one of deconcentrated institutions, County Agencies for Payments and Social Inspection, which are subordinated to the same central level institution – Ministry of Labor. However, the study outlines a development on the total number of available procedures for social services and benefits. Significant improvements are needed to standardize the same procedures from different institutions, irrespective of their type of affiliated territory. [ABSTRACT FROM AUTHOR]

Conjoint Analysis is an analytical technique used in market research to quantify consumer preferences for a product or service. The result of the technique provides us with different combinations of attributes that consumers value the most. The technique uses different features of a product or service and their corresponding levels to model and quantifies the customers' buying decision process. Using Conjoint Analysis, one can study the trade-offs undertaken by the consumer and use the derived insights for design implementation. The sale of Bluetooth Earphones in India witnessed a sudden spike during the COVID 19 pandemic. The consequential market, thriving with competition, saw market leaders attempt to strengthen their positions further and new players try to enter the market. The paper seeks to understand the existing consumer preferences for different features of Bluetooth Earphones using Conjoint Analysis. The outcome of the analysis provides market stakeholders with a yardstick to guide their design decisions and formulate sustainable long-term business strategies. [ABSTRACT FROM AUTHOR]

Among modern cities developing in a large-scale, extensive and unbalanced manner, smaller cities are relatively lagged behind due to relatively underdeveloped infrastructure, inadequate capital and technology talents, and insufficient attention from the national government, and thus they are more vulnerable when hit by unexpected disasters. The rampant pandemic of coronavirus disease 2019 (COVID-19) has made it even clearer that small cities must be equipped with stronger abilities to timely identify and prevent potential disease outbreaks. This paper takes Zhaodong City as an example to study how to better locate spaces with cluster infection risks in small cities. It combines spatial syntax, points of interest (POI), and geographical information system (GIS), and adopts hotspot analysis, average nearest neighbour analysis, kernel density estimation and other methods, to identify and locate potentially vulnerable spaces in neighbourhoods with relatively frequent people-to-people contact and thus higher disease transmission risks. Results show that there are three point-space, four line-space, and one plane-space with high risk of outbreaks in Zhaodong City, verifying the efficacy of the identification method for small cities. [ABSTRACT FROM AUTHOR]

Nowadays, chatbots have become popular tools in such a way that they are used in different sectors like commercial, elderly care, tourism, and education. The COVID-19 pandemic has forced many students and teachers to suspend face-to-face classes. Therefore, schools and governments have found it necessary to continue education remotely, using the resources provided by the Internet. This fact has created a greater interest in educational chatbots, so several projects have been proposed to develop these academic tools, each following its way of implementation and addressing issues from different points of view. This paper presents a proposal for chatbot classification, following the Systematic Mapping Study and an iterative method to review and classify educational chatbots. We also discuss the resulting categories and their characteristics and limitations and possible uses by developers and researchers. [ABSTRACT FROM AUTHOR]

Government control over the financing of official statistics has long been seen as threat to their integrity. This paper reviews the personal experience of the author over 40 years working in and around official statistics in the United Kingdom. It notes that as a state function, official statistics must be funded by the state and respect the accountability arrangements necessary for state funding to be seen as legitimate. It considers that the critical element is strong protection from partisan manipulation for political ends. The paper concludes that the COVID pandemic has accelerated a trend for recognition of the value of official statistics and the consequent need for commensurate state funding. [ABSTRACT FROM AUTHOR]

The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused millions of infected cases globally and still poses an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 649824. Recently, some mathematical models have been constructed for better understanding the coronavirus infection. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. So at the current position it is a challenge for the world to understand and control the spreading of COVID-19. Therefore, the aim of our paper is to develop some novel techniques, namely complex Pythagorean fuzzy weighted averaging (abbreviated as CPFWA) operator, complex Pythagorean fuzzy ordered weighted averaging (abbreviated as CPFOWA) operator, complex Pythagorean fuzzy hybrid averaging (abbreviated as CPFHA) operator, induced complex Pythagorean fuzzy ordered weighted averaging (abbreviated as I-CPFOWA) operator and induced complex Pythagorean fuzzy hybrid averaging (abbreviated as I-CPFHA) operator to analysis the spreading of COVID-19. At the end of the paper, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested approach along with a sensitivity analysis, showing the feasibility and reliability of its results. [ABSTRACT FROM AUTHOR]

BACKGROUND: Stroke is the main reason for disabilities worldwide leading to motor dysfunction, spatial neglect and cognitive problems, aphasia, and other speech-language pathologies, reducing the life quality. To overcome disabilities, telerehabilitation (TR) has been recently introduced. OBJECTIVE: The aim of this review was to analyze current TR approaches for stroke patients' recovery. METHODS: We searched 6 online databases from January 2018 to October 2021, and included 70 research and review papers in the review. We analyzed TR of 995 individuals, which was delivered synchronously and asynchronously. RESULTS: Findings show TR is feasible improving motor function, cognition, speech, and language communication among stroke patients. However, the dose of TR sessions varied significantly. We identified the following limitations: lack of equipment, software, and space for home-based exercises, insufficient internet capacity and speed, unavailability to provide hands on guidance, low digital proficiency and education, high cognitive demand, small samples, data heterogeneity, and no economic evaluation. CONCLUSIONS: The review shows TR is superior or similar to conventional rehabilitation in clinical outcomes and is used as complementary therapy or as alternative treatments. More importantly, TR provides access to rehabilitation services of a large number of patients with immobility, living in remote areas, and during COVID-19 pandemic or similar events. [ABSTRACT FROM AUTHOR]

BACKGROUND: The COVID-19 pandemic has shocked society worldwide. Multiple efforts went immediately into developing treatments for managing and vaccines for preventing the disease. National and international initiatives emerged to deal with the deep economic and social inequalities in our world today. OBJECTIVE: To show that the global inequalities that have become salient during the COVID-19 pandemic and specially with the global vaccination campaign, are the result of a highly privatized system of research and development (R&D), which is first and foremost profit-driven, and where epistemic and social concerns are not prioritized. METHODS: Philosophical analysis. RESULTS: After exploring the current organization of scientific research, as well as its impact in the development of research at a global scale, the paper shows the way in which this highly privatized organization of research has permeated scientific research on COVID-19, identifying who is currently benefiting from pandemic science, and who on the contrary is suffering the consequences of this organization. Using the COVAX mechanism as an example, the paper argues that international efforts to counteract the power of commercially-driven science has not rendered the expected results. CONCLUSIONS: The current organization of science ought to be shaken and restructured if we aim to be better prepared to address the global challenges of the future with the relevant scientific and technological development. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of how the COVID-19 pandemic affected the operations of National Statistical Offices (NSOs), how NSOs responded and adjusted to the disruptions, and how they are transitioning to a post-pandemic equilibrium. The paper uses four rounds of the Global COVID-19 survey of NSOs conducted by The World Bank and the United Nations Statistical Division (UNSD), in coordination with the UN Regional Commissions. The paper highlights the heterogeneity of the COVID-19 disruptions among NSOs and provides evidence that statistical operations have been hardest hit in low- and lower middle-income countries. We find that NSOs with weaker Information and Communications Technology (ICT) infrastructure, and those more impacted by declines in funding tended to be more affected and lag in their recovery trajectory. These NSOs tend to be concentrated in low- and lower-middle income countries. The paper concludes that without targeted action the effects of the pandemic will exacerbate and widen pre-existing data production inequalities, despite the best efforts of NSOs that have in most cases responded to the pandemic with the adoption of innovative solutions and have ingeniously used this crisis as an opportunity to accelerate the modernization of the national statistical systems. [ABSTRACT FROM AUTHOR]

The statistical modeling of the coronavirus pandemic is also flattened - yet the number of monthly publications is huge, exceeding the "normal pre-pandemic" level in 25-30 times. This special MASA issue is intended for the problems of statistical modeling of pandemics in general, and the Coronavirus COVID-19 one particularly. And here we see our contribution to the whole mankind efforts to survive the pandemics with the tools which address this crisis and can help to cure in the post-pandemic world as well. [Extracted from the article]

BACKGROUND: While the health and economic impact of COVID-19 is becoming better known among the general population, little is known about the impact of the pandemic and recession on employees with intellectual and developmental disability (IDD). PURPOSE: The purpose of this paper is to examine the impact of the COVID-19 pandemic and subsequent recession on individuals with IDD. METHODS: The present paper described the impact of COVID-19 pandemic and recession on the employment status, hours worked, and hourly wage of 156 individuals with IDD who work in competitive integrated employment for February to July 2020. These findings are compared with a similar group receiving services from the same agency in February to July 2019. RESULTS: Findings indicate that these employees were not able to work from home and experienced furlough or lay off instead. Further, the number of hours worked monthly was also significantly affected. Wages were not affected by the pandemic and recession. Employees in some industries, notably health care and distribution and supplies, were less affected than employees in other industries like food service, retail, and entertainment. Finally, and surprisingly, black, indigenous, and other people of color worked more hours monthly than their white peers in both 2019 and 2020. CONCLUSION: Implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

BACKGROUND: SARS-CoV-2 (COVID-19) is considered a highly infectious and life threatening disease. OBJECTIVE: The present paper aims to evaluate various aspects of preventive measures and clinical management of the scheduled visits for orthodontic patients to the dental clinics during the outbreak of COVID-19, and to assess how orthodontists dealt with this challenge. METHODS: Orthodontists in private and public clinics were invited to fill a questionnaire that addressed infection control protocols and concerns about clinical management of patients in the clinics during the pandemic. Frequncies and percentages of the responses were obtained and compared using Chi-square tests. RESULTS: About 77% of those working in private clinics, and 63% of those working in private and governmental clinics performed room disinfection following each patient. In case of patients needing urgent management and have active infection, 56% of the participants provided care under high infection control measures; on the contrary, 64% provided symptomatic intervention including orthodontic wax, analgesics and sometimes mouthwash. CONCLUSION: The COVID-19 pandemic has had a negative impact on the orthodontic treatments. All pre-cautionary measures have to be made available in the clinic in order to minimize the spread of viral infection with continuous dental health care training. [ABSTRACT FROM AUTHOR]

This paper offers an overview of some of the highlights of the 2023 NISO Plus Annual Conference that was held virtually from February 14–February 16, 2023. This was the fourth such conference and the third to be held in a completely virtual format due to the global impact of COVID-19. These conferences have resulted from the merger of NISO and the National Federation of Abstracting and Information Services (NFAIS) in June 2019, replacing the NFAIS Annual Conferences and offering a new, more interactive format. As with prior years, there was no general topical theme (although the importance of metadata was a common thread throughout), but there were topics of interest for everyone working in the information ecosystem - from the practical subjects of standards, archiving, digital preservation, Open Science, and Open Access. With speakers and attendees from around the world and across multiple time zones and continents, it truly was a global conversation! [ABSTRACT FROM AUTHOR]

During COVID-19 pandemic, researchers have used innovative technologies for fast tracking the development to end this pandemic. Virtual Reality (VR) has offered an imperative role for fighting this pandemic, through audiovisual-based virtual communication. Virtual reality modeling language (VRML), as an international standard of virtual reality, has developed rapidly. VRML expanded the function of script node by introducing Java and script programs written in java script language. This paper presents a VRML method. Libraries and platoons are virtualized to meet the normal use of users. In principle, any text editing system can be used for VRML programming, but some editing systems have few related functions and are not suitable for large-scale VRML Scene Design. The VRML algorithm proposed in this paper can be applied to large buildings. The VRML algorithm proposed in this paper is compared with the traditional algorithm. The VRML algorithm proposed in this paper is superior to the traditional algorithm in the aspects of realism, interactivity, design rationality and execution speed. The practicability of the VRML algorithm is proved. It provides help for people who are inconvenient to go out during the protection period of covid-19. [ABSTRACT FROM AUTHOR]

Perinatal death, a global health problem, can be prevented with simple resuscitation interventions that help the baby breathe immediately at birth. Latter-day Saint Charities (LDSC) and Safa Sunaulo Nepal (SSN) implemented a program to scale-up Helping Babies Breathe (HBB) training in Karnali Province, Nepal from January 2020-February 2021. The interventions were implemented using a hybrid approach with on-site mentoring in the pre/post COVID period combined with remote support and monitoring during the COVID period. This paper reports overall changes in newborn outcomes in relation to the unique implementation approach used. A prospective cohort design was used to compare outcomes of birth cohorts in 16 public health facilities in the first and last three months of program implementation. Results showed significant decreases in intrapartum stillbirths (23%), and neonatal deaths within (27%) and after (41.3%) 24 hours of life. The scale-up of HBB training resulted in 557 providers receiving training and mentoring support during the program period, half trained during the COVID period. Increased practice sessions, review meetings and debriefing meetings were reported during the COVID period compared to pre/post COVID period. The evaluation is suggestive of the potential of a hybrid approach for improved perinatal outcomes and scaling-up of newborn resuscitation trainings in health system facing disruptions. [ABSTRACT FROM AUTHOR]

The study outlines the main challenges in measuring external imbalances via a "current account (CA) model", from which a CA "norm" may be derived and against which the actual CA is appraised. The selection of both the dependent and explanatory variables and technical issues in the estimation procedure are amongst the concerns raised. The impact on CA modelling of global trends, such as the expansion of MNEs, and of the COVID-19 pandemic is also discussed. The paper does not prescribe fully-fledged solutions, but rather provides awareness and hints on how to possibly tackle at least some of these challenges. [ABSTRACT FROM AUTHOR]

BACKGROUND: Many companies are forced to restructure themselves by right sizing due to unexpected fall in demand for their products and services created by the COVID-19 pandemic. COVID 19 not only affected the health of human beings but also their wealth across the world. Global economic parameters are showing a sign of positive growth with decreased number of COVID 19 cases across the world. Many companies are in a dilemma to rehire their former employees or to hire the new candidates to meet the increased demand. OBJECTIVES: The objectives of study are i) to analyze the key drivers for boomerang hiring and ii) to develop a conceptual process for boomerang hiring. METHODS: An exploratory methodology was designed to identify the key drivers of boomerang hiring by studying the various successful stories of those companies which had rehired their former employees. Various papers were reviewed to develop the process for boomerang hiring. RESULTS: Study showed that knowledge about the culture of the company, cost of hiring, morale booster for the existing employees, and customer retention, are the key drivers for boomerang hiring. This hiring process requires special skills from HR Managers, as this decision will impact long term success of the company. CONCLUSION: The process of boomerang hiring cannot be standardized as each organization culture is different and companies cannot have the same strategy for each candidate as every individual is different. Boomerang hiring will work as the right strategy during pandemic situation as former employees would have built relations with the customers. The customers will be happy to see the former employees who had served them better. [ABSTRACT FROM AUTHOR]

Nowadays, Biometric systems are prevalent for personal recognition. But due to pandemic COVID 19, it is difficult to pursue a touch-based biometric system. To encourage a touchless biometric system, a less constrained multimodal personal identification system using palmprint and dorsal hand vein is presented. Hand based Touchless recognition system gives a higher user-friendly system and avoids the spread of coronavirus. A method using Convolution Neural Networks(CNN) to extract discriminative features from the data samples is proposed. A pre-trained function PCANeT is used in the experiments to show the performance of the system in fusion scheme. This method doesn't require keeping the palm in a specific position or at a certain distance like most other papers. Different patches of ROI are used at two different layers of CNN. Fusion of palmprint and dorsal hand vein is done for final result matching. Both Feature level and score level fusion methods are compared. Results shows the accuracy of upto 98.55% and 98.86% and Equal error rate (EER) of upto 1.22% and 0.93% for score level fusion and feature level fusion, respectively. Our method gives higher accurate results in a less constrained environment. [ABSTRACT FROM AUTHOR]

Effectively identifying deviations in real-world medical time-series data is a critical endeavor, essential for early surveillance of disease outbreaks. This paper demonstrates the integration of time-series anomaly detection techniques to develop surveillance systems for disease outbreaks. Utilizing data from Sweden's telephone counseling service (1177), we first illustrate the trends in physical and mental symptoms recorded as contact reasons, offering valuable insights for outbreak detection. Subsequently, an advanced anomaly detection technique is applied incrementally to these time-series symptoms as univariate and multivariate approaches to assess the effectiveness of a machine learning-based method on early detection of the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

BACKGROUND: School closures and service disruptions related to the COVID-19 pandemic significantly impacted students' postschool transitions. Students with disabilities who were also members of historically marginalized groups including immigrant students, multilingual students, students of color, and those experiencing poverty, were disproportionately negatively impacted by pandemic-limited services. OBJECTIVE: This paper examined the impact of the pandemic on the transition experiences of secondary students receiving both special education and English learner services. METHOD: We collected and analyzed data from ethnographic interviews with 26 students, their parents, and teachers. A close analysis of a representative case illustrates how transition education and planning were affected by challenges introduced by the COVID-19 pandemic for some of the nation's most vulnerable students. RESULTS: Despite postsecondary education goals and high parent expectations, evidence of minimal information sharing between school and family, specific plans for goal actualization, and interruptions to service delivery negatively impacted goal attainment, tipping precariously positioned transition plans toward missed opportunities. CONCLUSION: The pandemic accentuated pre-existing inequities in transition and vocational rehabilitation (VR) services. Implications for practice and research are discussed, including the importance of supported family engagement, enhanced self-determination skills, and integrated VR services into high school special education programming. [ABSTRACT FROM AUTHOR]