Search

Low-cost diagnostic tools for point-of-care immunoassays, such as the paper-based enzyme-linked immunoassay (ELISA), have become increasingly important, especially so in the recent COVID-19 pandemic. ELISA is the gold-standard antibody/antigen sensing method. This paper reports an easy-to-fabricate nitrocellulose (NC) paper plate, coupled with a desktop scanner for ELISA, which provides a higher protein immobilization efficiency than the conventional cellulose paper-based ELISA platforms. The experiments were performed using spiked samples for the direct ELISA of rabbit IgG with a limit of detection (LOD) of 1.016 μg/mL, in a measurement range of 10 ng/mL to 1 mg/mL, and for the sandwich ELISA of sperm protein (SP-10) with an LOD of 88.8 ng/mL, in a measurement range of 1 ng/mL to 100 μg/mL. The described fabrication method, based on laser-cutting, is a highly flexible one-step laser micromachining process, which enables the rapid production of low-cost NC paper-based multi-well plates with different sizes for the ELISA measurements. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive summary of evidence to explore and position the role of serology testing in the context of coronavirus disease 19 (COVID-19) immunization and policy response in the Asia-Pacific (APAC) region. The document builds on a review of academic literature and existing policies followed by a process of discussion, validation, and feedback by a group of six experts. Six countries and territories—Australia, Hong Kong, India, Indonesia, Thailand, and Taiwan—were sampled to highlight the differing contexts and scenarios in the region. The review includes an overview of (1) the impact of the COVID-19 pandemic, including the emergence of Variants of Concern (VOCs), especially Omicron, (2) the introduction of immunization, (3) the available testing options and potential use of serology testing, (4) the landscape of guidelines and recommendations for their use, and (5) the barriers and challenges to implementing serology testing as a tool to support COVID-19 immunization. Based on the findings, the co-authors propose a set of recommendations to resolve knowledge gaps, to include the use of serology testing as part of the policy response, and to ensure adequate means of implementation. This paper's target audience includes members of the academic community, medical societies, health providers and practitioners, and decision-makers. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the urgent need for rapid and accurate diagnostic methods for various infectious diseases, including SARS-CoV-2. Traditional RT-PCR methods, while highly sensitive and specific, require complex equipment and skilled personnel. In response, we developed an integrated RT-LAMP-MS assay, which combines rapid reverse transcription loop-mediated isothermal amplification (RT-LAMP) with microscanning (MS) technology for detecting SARS-CoV-2. The assay uses magnesium pyrophosphate formed during LAMP amplification as a visual marker, allowing direct observation via microscopy without the need for additional chemical indicators or probes. For the SARS-CoV-2/IC RT-LAMP-MS assay, the sample-LAMP reagent mixture was added to a microchip with SARS-CoV-2 primers and internal controls, then incubated at 62 °C for 30 min in a heat block, followed by amplification analysis using a microscanner. In clinical tests, the RT-LAMP-MS assay showed 99% sensitivity and 100% specificity, which is identical to the RT-LAMP results and comparable to the commercial AllplexTM SARS-CoV-2 assay results. Additionally, the limit of detection (LOD) was determined to be 10−1 PFU mL−1 (dynamic range: 103~10−1 PFU mL−1). The assay delivers results in 30 min, uses low-cost equipment, and demonstrates 100% reproducibility in repeated tests, making it suitable for point-of-care use in resource-limited settings. [ABSTRACT FROM AUTHOR]

Surveillance of meningococcal disease (MD) is crucial after the implementation of vaccination strategies to monitor their impact on disease burden. Adolescent vaccination could provide direct and indirect protection. Argentina, Brazil, and Chile have introduced meningococcal conjugate vaccines (MCV) into their National Immunization Programs (NIP), while Uruguay has not. Here, we analyze the epidemiology of MD and vaccination experience from these four South American countries to identify needs and plans to improve the current vaccination programs. Methodology: Descriptive study of MD incidence rates, serogroup distribution, case fatality rates (CFR), and MCV uptakes during the period 2010–2021 in Argentina, Brazil, Chile, and Uruguay. Data were extracted from national surveillance programs, reference laboratories, NIPs, and Pubmed. Results: MD overall incidence from 2010 to 2021 have a decreasing trend in Argentina (0.37 [IQR = 0.20–0.61]), Brazil (0.59 [IQR = 0.54–1.22]), and Chile (0.45 [IQR = 0.40–0.77]), while a significant increase in Uruguay (0.47 [IQR = 0.33–0.69]) was found from 2016 to 2019. During the COVID-19 pandemic, all countries sharply reduced their MD incidence. The highest incidence rates were observed among infants, followed by children 1–4 years of age. No second peak was evident in adolescents. A reduction in serogroup C, W, and Y cases has occurred in Argentina, Brazil, and Chile after introduction of MCV, serogroup B becoming predominant in all four countries. Median CFR was 9.0%, 21%, 19.9%, and 17.9% in Argentina, Brazil, Chile, and Uruguay, respectively. Median uptake of MCV for Argentina and Brazil were 66.6% and 91.0% for priming in infants; 54.7% and 84.5% for booster in toddlers; and 47.5% and 53% for adolescents; while for Chile, 95.6% for toddlers. Conclusions: Experience after the implementation of MCV programs in South America was successful, reducing the burden of MD due to the vaccine serogroups. High vaccine uptake and the inclusion of adolescents will be crucial in the post-pandemic period to maintain the protection of the population. The increase in the proportion of serogroup B cases emphasizes the importance of continuous surveillance to guide future vaccination strategies. [ABSTRACT FROM AUTHOR]

The global impact of the COVID-19 pandemic has been profound, placing significant challenges upon healthcare systems and the world economy. The pervasive presence of illness, uncertainty, and fear has markedly diminished overall life satisfaction. Consequently, sentiment analysis has gained substantial traction among scholars seeking to unravel the emotional and attitudinal dimensions of this crisis. This research endeavors to provide a bibliometric perspective, shedding light on the principal contributors to this emerging field. It seeks to spotlight the academic institutions associated with this research domain, along with identifying the most influential publications in terms of both paper volume and h-index metrics. To this end, we have meticulously curated a dataset comprising 646 papers sourced from the ISI Web of Science database, all centering on the theme of sentiment analysis during the COVID-19 pandemic. Our findings underscore a burgeoning interest exhibited by the academic community in this particular domain, evident in an astonishing annual growth rate of 153.49%. Furthermore, our analysis elucidates key keywords and collaborative networks within the authorship, offering valuable insights into the global proliferation of this thematic pursuit. In addition to this, our analysis encompasses an n-gram investigation across keywords, abstracts, titles, and keyword plus, complemented by an examination of the most frequently cited works. The results gleaned from these endeavors offer crucial perspectives, contribute to the identification of pertinent issues, and provide guidance for informed decision-making. [ABSTRACT FROM AUTHOR]

This article investigates the impact of the COVID-19 pandemic on logistics and supply chain processes through a two-phase analysis. First, a literature review maps the existing studies, published from 2021 to 2023 (101 papers), offering a view of the multiple challenges faced by supply chains during the pandemic emergency. The literature analysis makes use of descriptive statistics, thematic classifications and cross-analyses to provide a detailed overview of the issues raised by the COVID-19 pandemic and of the related implications. Second, a case study targeting a logistics operator was conducted, to derive practical insights into the real-world implications of pandemic disruptions. The study highlights the importance of proactive risk management strategies and the role of Industry 4.0 technologies to improve supply chain resilience and sustainability. Hence, this research contributes to a deeper understanding of the impact of the COVID-19 pandemic on logistics and supply chain processes and offers valuable guidance for companies, especially those working in the transport and logistics field. [ABSTRACT FROM AUTHOR]

An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R-Track algorithm; 75% (n = 15) were qRT-PCR positive. The S-Facts Algorithm identified 7.75% (n = 11) positive oncology staff members, and 81.82% (n = 9) were qRT-PCR positive. Oncology patients (n = 369) were evaluated, and 1.36% (n = 5) were positive for the Algorithm used. The five patients (100%) were confirmed by qRT-PCR. (4) Conclusions: The proposed early detection tools have proved to be a low-cost and efficient tool in a country where qRT-PCR tests and vaccines are insufficient for the population. [ABSTRACT FROM AUTHOR]

Protests during the 2021 Climate Conference in Glasgow exemplified our dilemma. The establishment perpetuates old thinking, while young people demand a new approach to mitigate the impact of climate change. The authors agree with the young people, and as a solution we propose to replace the current fragmentary approach with a new holistic one. The passive house approach that was conceptualized by the University of Illinois and built in Canada in 1977 showed us that energy consumption can be reduced about half of that used in the traditional design. Seventeen years later, a European passive house was built in Darmstadt. In 2008, a demonstration house in Syracuse, NY, showed that integrated passive measures produced energy use by about half of the NY state code for 2004. At the same time, some advanced houses in the USA showed total energy use of about 70 kWh/(m2∙y). In 2008, at the first Building Enclosure Science and Technology Conference, two equally important objectives for 2030 were proposed by the Lawrence Berkeley National Laboratory: (1) a 90% reduction of energy use in new buildings and (2) 50% for the retrofitting of existing buildings, i.e., to the level achieved in the 1980s. The first objective has recently been achieved in small buildings while the large residential buildings remain on the level obtained in the 2000s. Yet, the retrofitting of existing buildings (the second objective) has been a dismal failure. This paper acknowledges progress in hydronic heating and cooling involving electric heat pumps and hybrid solar panels, building automatics used for operation of HVAC, and modification of air distribution systems that comes from experience with the SARS-CoV-2 pandemic. Furthermore, it highlights that to accelerate energy efficiency and carbon emission reductions, there must be broad public-private educational programs with demonstrations of a new generation of retrofitting. Economically and ecologically retrofitted buildings will create a new approach to real estate investment. [ABSTRACT FROM AUTHOR]

A review of the top 10 cited papers in the journal in 2018-2020 covering such topics as bank profitability, cryptocurrency market trading, and corporate social responsibility is presented.

The pandemic of COVID-19 and its widespread transmission have made us realize the importance of early, quick diagnostic tests for facilitating effective cure and management. The primary obstacles encountered were accurately distinguishing COVID-19 from other illnesses including the flu, common cold, etc. While the polymerase chain reaction technique is a robust technique for the determination of SARS-CoV-2 in patients of COVID-19, there arises a high demand for affordable, quick, user-friendly, and precise point-of-care (POC) diagnostic in therapeutic settings. The necessity for available tests with rapid outcomes spurred the advancement of POC tests that are characterized by speed, automation, and high precision and accuracy. Paper-based POC devices have gained increasing interest in recent years because of rapid, low-cost detection without requiring external instruments. At present, microfluidic paper-based analysis devices have garnered public attention and accelerated the development of such POCT for efficient multistep assays. In the current review, our focus will be on the fabrication of detection modules for SARS-CoV-2. Here, we have included a discussion on various strategies for the detection of viral moieties. The compilation of these strategies would offer comprehensive insight into the detection of the causative agent preparedness for future pandemics. We also provide a descriptive outline for paper-based diagnostic platforms, involving the determination mechanisms, as well as a commercial kit for COVID-19 as well as their outlook. [ABSTRACT FROM AUTHOR]

Background: Social innovation is often used as a mechanism to jump-start public–private partnerships to leverage resources to achieve social impact; the analysis of sustainability and the impact of corporate social responsibility (CSR) cannot be emphasized enough. Due to advances in the information and communication technology industry in Taiwan, this paper aims to explore whether these advancements drive CSR as a form of social innovation to meet health needs in Taiwan. Methodology: This paper uses a case study to look at CSR programs in the health sector in Taiwan. Corporations with diverse missions and different CSR approaches that are available on the internet are selected. The analysis of the case study takes a qualitative, exploratory approach to shed light on current initiatives. Results: The majority of CSR programs in Taiwan are private sector activities that emerged during the COVID-19 pandemic; current CSR activities in Taiwan are driven by awards, public relations, and external interests. Corporations in Taiwan have the potential to address the health care gaps of urban–rural health utilization among Taiwanese indigenous communities. It is recommended for corporations to (1) develop partnerships with public health experts or to (2) employ CSR personnel with health care backgrounds who can navigate the intersection between health, business, and policies to develop CSR strategies. Conclusions: Further evaluation of the projects mentioned in this paper to assess the direct and indirect impact on health outcomes could provide a more comprehensive understanding of the field of CSR in the health sector in Taiwan. [ABSTRACT FROM AUTHOR]

Play involves diverse meaning-making for young children and grandparents. This paper derives from a larger intergenerational family practices project conducted in Australia during 2021–2022 against the backdrop of the COVID-19 pandemic. The research team undertook intergenerational group interviews and filmed family play activities in addition to collecting videoclips and photos of young children's play from six families. In this paper, vignettes from two families are described and given meaning. Rogoff's social learning three planes theory involving the intertwined and interdependent—'personal', 'interpersonal', and 'community'—planes is used to analyse the data. Our findings show the important role that grandparents play in facilitating children's learning including through the creation of linguistic and cultural heritage play spaces. We argue that intergenerational play practices can offer a means to integrate linguistic and cultural experiences in young children's lives by providing a space and time for social interaction and learning. [ABSTRACT FROM AUTHOR]

Technological advancements for diverse aspects of life have been made possible by the swift development and application of Internet of Things (IoT) based technologies. IoT technologies are primarily intended to streamline various processes, guarantee system (technology or process) efficiency, and ultimately enhance the quality of life. An effective method for pandemic detection is the combination of deep learning (DL) techniques with the IoT. IoT proved beneficial in many healthcare domains, especially during the last worldwide health crisis: the COVID-19 pandemic. Using studies published between 2019 and 2024, this review seeks to examine the various ways that IoT-DL models contribute to pandemic detection. We obtained the titles, keywords, and abstracts of the chosen papers by using the Scopus and Web of Science (WoS) databases. This study offers a comprehensive review of the literature and unresolved problems in applying IoT and DL to pandemic detection in 19 papers that were eligible to be read from start to finish out of 2878 papers that were initially accessed. To provide practitioners, policymakers, and researchers with useful information, we examine a range of previous study goals, approaches used, and the contributions made in those studies. Furthermore, by considering the numerous contributions of IoT technologies and DL as they help in pandemic preparedness and control, we provide a structured overview of the current scientific trends and open issues in this field. This review provides a thorough overview of the state-of-the-art routing approaches currently in use, as well as their limits and potential future developments, making it an invaluable resource for DL researchers and practitioners and it is a useful tool for multidisciplinary research. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal Biomedicines, discussing a special issue on the latest research in post-COVID conditions. The COVID-19 pandemic has led to a significant increase in research, with a focus on managing acute cases and preventing the spread of the virus. However, there is now a growing concern about the development of long-lasting symptoms after the acute infection, known as long-COVID or post-COVID-19 condition. This special issue includes fourteen papers covering topics such as the treatment of post-COVID-19 condition, the impact of SARS-CoV-2 infection on neonates, risk factors for severe COVID-19, and the phenotyping of post-COVID pain. The papers discuss various treatment strategies, the potential transmission of the virus during pregnancy, risk factors for severe disease, and different types of post-COVID pain. Overall, this special issue provides valuable insights into the understanding and management of post-COVID conditions. [Extracted from the article]

Many scholars have understood conspiracy theories as sense-making mechanisms. Among them, a particular strand further inspected them in parallel with religion and magic. This comparison bears the risk of framing conspiracy theories as irrational interpretations and anachronisms with respect to contemporary ways of explaining reality. This contribution takes off from the reflections of those who have highlighted this risk. It tries to confront the possibility of using the concept of enchantment without implying a normative judgment on the irrationality of conspiracy theories. This paper carries out this effort by closely inspecting Max Weber's texts. I argue that Weber's notion of enchantment and disenchantment allows for a punctual use of both, devoid of normative implications. After setting out this non-normative notion of enchantment, this paper examines the characteristics of the enchanted worldview and its usefulness in reading conspiracy theories. Finally, this paper supports this effort using the identified characteristics to systematise a reading of conspiracy-based accounts of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The real estate market constitutes one of the major sectors that was influenced by the COVID-19 pandemic. It is therefore useful to understand the ways in which COVID-19 has changed the dynamics of the real estate market, establishing new trends in different market segments. The health crisis has upset the consolidated "human normality" by spreading new behaviors and involving almost all sectors. Among economic fields, the identification of temporary and permanent impacts on the real estate market is fundamental to assess the consequences worldwide and to evaluate countries' resilience ability. The aim of the research is to provide a systematic literature review on the existent academic insights in order to identify the aspects that have been mainly addressed regarding the real estate market's impacts from COVID-19 and to grasp how the real estate market dynamics have changed. Thus, through five structured steps—from the formulation of research questions formulation to an analysis and discussion of the selected papers for drafting overall conclusions—102 research papers published in the period between the second half of 2020 and the first half of 2023 collected from Scopus and Google Scholar were reviewed and examined. The findings reveal that (i) the housing market has been the most analyzed, (ii) the period between 2021 and 2022 has been the most scientifically prolific, (iii) the US real estate market has been the most studied, (iv) American authors have been the most active on the subject, (v) and the "article" type of research paper has been the most published. These results provide the basis for future research developments on COVID-19 and real estate market dynamics, supporting the implementation of recovery plan strategies and decision-making processes of the market operators for improving the sector. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected nearly all strata of life, including scientific activities. Implementation of nationwide lockdowns, closures of universities, and other measures significantly limiting social mobility precluded field and laboratory research from being pursued at a pre-pandemic pace. On the other hand, working from home could translate into higher productivity of researchers in terms of publication output, ultimately affecting the journals' metrics. This effect has been well evidenced in the biomedical field, but whether it also occurred in aquatic sciences has not been explored. Therefore, this study aimed to analyze and compare various metrics of journals classified in the Scopus evaluations in 2019 (encompassing 2016–2019, the pre-pandemic period) and 2022 (covering 2019–2022, the pandemic period) within three subjects: aquatic science, oceanography, and water science and technology. In general, the number of journals classified into the considered categories increased in 2022 by 12%, and the majority (78%) experienced an increase in the CiteScore metric. The total published articles and received citations also significantly increased in 2022 by 17.5% and 63.5%, respectively, and were even higher in the case of open access journals—69% and 145%, respectively. The overall share of cited articles in the considered period was higher by 5% than in the 2019 Scopus evaluation and by 7% in the case of open access periodicals. However, source normalized impact per paper (SNIP) and the SCImago journal rank (SJR) values decreased by 7% and 8%, respectively. The highest share of citations was generated by Water Research, Water, and the Journal of Hydrology, and the highest number of articles was published by Water, Frontiers in Marine Science, and Desalination and Water Treatment. Only five of the COVID-19-related papers published in journals with the highest CiteScore and total number of citations received more than 200 citations, but their contribution to citations received by the journals was not high. In summary, the present study's findings indicate the high potential of the field dedicated to water sciences to adapt in a period challenged by an infectious disease pandemic. However, they do not exclude the adverse effect that this period could have on the activities of individual scientists and research teams. [ABSTRACT FROM AUTHOR]

Introduction: Dermatologists had to face several challenges during the COVID-19 pandemic. In this scenario, a large amount of data has been produced and published. Objectives: We present a literature analysis of publications on COVID-19 in the dermatology field in the first year of the pandemic. Methods: The research was carried out by searching the PubMed database using keywords related to "COVID-19" combined with the keyword "Dermatology" in the "affiliation" search field and collecting articles published from February 2020 to December 2020. Results: A total of 816 publications from 57 countries were retrieved. Overall, publications increased notably along the timespan considered in this study and appeared to be closely linked to pandemic progression in different countries. In addition, article types (i.e., commentaries, case reports, original research) appeared to be strictly influenced by the pandemic's progression. However, the number and category of these publications may raise questions regarding the scientific relevance of the messages reported. Conclusions: Our analysis provides a descriptive quantitative analysis and suggests that publications do not always respond to real scientific needs but are sometimes linked to a need/opportunity for publication. [ABSTRACT FROM AUTHOR]

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO 2 , mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process. [ABSTRACT FROM AUTHOR]

This document discusses the challenges faced in mathematical modeling and controlling COVID-19 epidemics. It highlights the global impact of the pandemic on the economy and health, as well as the uneven effects on different age groups. The availability of vaccines led to mass vaccination campaigns, prompting researchers to implement new strategies and innovative approaches. The document also mentions various journals that have published special issues on COVID-19, covering topics such as forecasting, vaccination strategies, and control measures. The special issue presented in this document consists of eleven papers that analyze clinical characteristics, variant spread, economic recovery, mortality rates, non-pharmaceutical interventions, and coordinated actions. The papers propose mathematical models and control strategies to minimize disease spread and prioritize vaccination. The document emphasizes the multidisciplinary nature of the research, involving fields such as applied mathematics, data science, engineering, biology, and economics. Overall, this special issue provides valuable insights into the mathematical modeling and control of COVID-19 epidemics. [Extracted from the article]

A bibliometric study was performed to explore the financial and economic implications of the COVID-19 pandemic. The SCOPUS database was sourced, and VOSviewer version 1.6.20 was used to generate visualizations. Articles published between 2020 and 2024 were targeted, resulting in 1257 papers used in the analysis. The comprehensive bibliometric analysis conducted in this paper was guided by the keywords "COVID-19", "pandemic", "financial crisis", "financial impact", and "economic impact", which revealed critical insights that contribute to the body of knowledge on the financial and economic implications of the COVID-19 pandemic. The outputs belong to topical areas of economics, finance, business, and management. Keyword mapping and clustering methods were employed to analyze links between the financial crisis, economic impact, and COVID-19 themes. A co-occurrence network analysis identified key thematic clusters, including economic and financial analysis; COVID-19 research and impact; social, environmental, and corporate responsibility; regional studies and disease-related research; and economic challenges and policy responses. This study reveals an annual publication decline of 62.94% and an average citation rate of 20.13 per document. The findings suggest an abundance of global collaboration networks and authorships. This study contributes to a better understanding of the multifaceted financial and economic impact of the pandemic from a bibliometric perspective, offering a foundation for future research and the application of financial strategies and effective crisis management. [ABSTRACT FROM AUTHOR]

This paper focuses on returns comovements in global stock portfolios including the US Dollar as a defensive asset. The main contribution is the selection of a large set of macroeconomic and financial variables as potential drivers of these comovements and the emphasis on the predictive accuracy of proposed econometric models. One-year US Expected Inflation stands out as the most important predictor, while models including a larger number of variables yield significant predictive gains. Larger forecast errors, due to parameters instabilities, are documented during major financial crises and the COVID-19 pandemic period. Some research directions to improve the forecasting power of econometric models are discussed in the concluding section. [ABSTRACT FROM AUTHOR]

Polypharmacy, the use of multiple and potentially inappropriate medications, is an increasing problem among older adults. The global polypharmacy prevalence is 34.6% in patients with COVID-19, and polypharmacy in COVID-19 increases with age. The present paper proposes that polypharmacy in older adults with COVID-19 and other comorbid conditions is linked to the medication outcome reporting bias of randomized controlled trials. Outcome reporting bias can occur when treatment efficacy is reported as relative risk reductions, which overestimates medication benefits and exaggerates disease/illness risk reductions compared to unreported absolute risk reductions. The comorbidities common in patients with COVID-19 include high blood pressure, cardiovascular disease, dementia or cerebrovascular disease, and diabetes. Accordingly, the present paper reassesses the relative and absolute risk reductions in clinical trials from a small convenience sample of antihypertension, statin, anticoagulant, and antihyperglycemic medications. Examples demonstrate a wide gap between reported relative risk reductions and unreported absolute risk reductions in medication clinical trials. This paper concludes that medication clinical trial outcome reporting bias is an important upstream factor that contributes to biased medication benefits and poor clinical decision making, leading to polypharmacy in older adults with COVID-19 and other comorbid conditions. Public health campaigns are urgently needed to educate the public about the link between polypharmacy and medication outcome reporting bias. [ABSTRACT FROM AUTHOR]

We examine the financial performance of the forest products industry in the initial phase of the COVID-19 pandemic, employing data for publicly trading companies in the industry globally. We first examine the market investor reaction to the declaration of a pandemic by the World Health Organization (WHO) in March 2020 by conducting an event-study analysis. Then, we analyze medium-term changes in stock returns and their systematic risk by an econometric estimation of the capital asset pricing model. Our event-study analysis of the forest products industry shows that the forestry subsector was impacted more than the paper subsector when the WHO declared the pandemic. The effect was most prominent in North America. We find that the systematic risk for the forestry subsector tended to increase during 2020, until October. Again, this effect was most clear in North America. Conversely, the impact on the paper subsector was more stable. [ABSTRACT FROM AUTHOR]

For many years, HCI research has been known to suffer from a replication crisis, due to the lack of openly available datasets and accompanying code. Recent research has identified several barriers that prevent the wider sharing of primary research materials in HCI, but such material does, in fact, exist. Interested in the field of mobile text entry research, and largely hindered by the lack of access to participants due to the COVID-19 pandemic, the exploration of a recently published open gaze and touch dataset became an appealing prospect. This paper demonstrates the numerous problems and the extent of required effort related to understanding, sanitising and utilising open data in order to produce meaningful outcomes from it, through a detailed account of working with this dataset. Despite these issues, the paper demonstrates the value of open data as a means to produce novel contributions, without the need for additional new data (in this case, an unsupervised learning pipeline for the robust detection of gaze clusters in vertically distinct areas of interest). Framing the experience of this case study under a dataset lifecycle model intended for ML open data, a set of useful guidelines for researchers wishing to exploit open data is derived. A set of recommendations is also proposed, about the handling of papers accompanied by data, by conferences and journals in the future, Finally, the paper proposes a set of actions for the mobile text entry community, in order to facilitate data sharing across its members. [ABSTRACT FROM AUTHOR]

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide a systematic insight into the socio-economic aspects of the last two economic crises in Slovenia: the Economic crisis between 2009 and 2013, and the COVID-19 crisis in 2020. A three-stage territorial model was developed as a theoretical tool for this study. The data for the analyses came from various statistical sources and from the available literature. The socio-economic aspects of both economic crises were analysed in 11 categories and at three territorial levels: macro (national), meso (regional) and micro (locational). Both economic crises differ fundamentally in many aspects. Compared to the Economic crisis, the COVID-19 crisis was much shorter and less severe, and had relatively little impact on the socio-economic structure of Slovenia and its regions. Both economic crises had some common features: reduction of interregional disparities and different development paths of regions during the crisis, as well as strong economic growth in the first year of recovery. The proposed model can be extended by additional territorial levels and by adding additional social and political-geographical aspects. [ABSTRACT FROM AUTHOR]

Investigating the significant "roles" within financial complex networks and their stability is of great importance for preventing financial risks. On one hand, this paper initially constructs a complex network model of the stock market based on mutual information theory and threshold methods, combined with the closing price returns of stocks. It then analyzes the basic topological characteristics of this network and examines its stability under random and targeted attacks by varying the threshold values. On the other hand, using systemic risk entropy as a metric to quantify the stability of the stock market, this paper validates the impact of the COVID-19 pandemic as a widespread, unexpected event on network stability. The research results indicate that this complex network exhibits small-world characteristics but cannot be strictly classified as a scale-free network. In this network, key roles are played by the industrial sector, media and information services, pharmaceuticals and healthcare, transportation, and utilities. Upon reducing the threshold, the network's resilience to random attacks is correspondingly strengthened. Dynamically, from 2000 to 2022, systemic risk in significant industrial share markets significantly increased. From a static perspective, the period around 2019, affected by the COVID-19 pandemic, experienced the most drastic fluctuations. Compared to the year 2000, systemic risk entropy in 2022 increased nearly sixtyfold, further indicating an increasing instability within this complex network. [ABSTRACT FROM AUTHOR]

Cities were at the center of the COVID-19 pandemic due to the concentration of affected populations and economic activities that needed to be revived to support global recovery. While cities offer strong economic and social benefits due to density, proximity, and global connectivity, the pandemic had a tremendous impact on their vital functions. It resulted in lost lives and livelihoods and deepened economic and social divides. Furthermore, the pandemic exacerbated many existing environmental challenges in cities. This presented an opportunity to tackle these interlinked challenges in an integrated manner. Evidence suggests that many city leaders integrated environmental sustainability as an important element to complement their emergency responses. Drawing from experiences in cities around the world, particularly those participating in the Sustainable Cities Program supported by the Global Environment Facility (GEF), this paper describes how integrated solutions were applied to tackle the COVID-19 pandemic. Consistent with a Healthy Planet Healthy People concept, a framework is proposed for sustainable urban transformation and to build cities that are resilient to shocks and stresses. With global environmental benefits at the core, the framework highlights the importance of integration, inclusion, and innovation as key approaches in steering the future green growth and prosperity of cities. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the social media posts of prefectural governors in Japan during the COVID-19 pandemic. It investigates the correlation between social media activity levels, governors' characteristics, and engagement metrics. To predict citizen engagement of a specific tweet, machine learning models (MLMs) are trained using three feature sets. The first set includes variables representing profile- and tweet-related features. The second set incorporates word embeddings from three popular models, while the third set combines the first set with one of the embeddings. Additionally, seven classifiers are employed. The best-performing model utilizes the first feature set with FastText embedding and the XGBoost classifier. This study aims to collect governors' COVID-19-related tweets, analyze engagement metrics, investigate correlations with governors' characteristics, examine tweet-related features, and train MLMs for prediction. This paper's main contributions are twofold. Firstly, it offers an analysis of social media engagement by prefectural governors during the COVID-19 pandemic, shedding light on their communication strategies and citizen engagement outcomes. Secondly, it explores the effectiveness of MLMs and word embeddings in predicting tweet engagement, providing practical implications for policymakers in crisis communication. The findings emphasize the importance of social media engagement for effective governance and provide insights into factors influencing citizen engagement. [ABSTRACT FROM AUTHOR]

This paper examines the writings of European travelers, chaplains, and resident doctors on pandemics in the Mediterranean regions from the 16th to the 19th centuries. Using French comparative literary theory, the article highlights how Muslim communities in Egypt, Turkey, Aleppo, and Mecca were stereotyped based on their belief in predestination, their failure to avoid contamination, and their lack of social distancing during plague outbreaks. This paper argues that travelers were influenced by Renaissance humanism, Ars Apodemia, religious discourses, and texts, such as plague tracts, model town concepts, the book of orders, and tales, and that they essentialized Mediterranean Islamicate societies by depicting contamination motifs supposedly shaped by the absence of contagion theory in prophetic medicines. Regarding plague science, this paper concludes that Christian and Muslim intellectuals had similar approaches until the Black Death and that Arabs were eclectic since the Abbasid period. This paper further maintains that the travelers' approaches fostered chauvinism and the cultural hegemony of the West over the Orient since the Renaissance and Enlightenment periods, driven by eschatology, conversion, and power structure narratives. [ABSTRACT FROM AUTHOR]

This article analyses the ongoing processes in the organisational field of Italian schools in light of the innovations induced by digital education policies. Specifically, it focuses on the relationship between actors and digital policies concerning the experience of distance learning (DL) that characterised the period of the COVID-19 pandemic. The paper reflects on DL outcomes regarding the three expectations that have often characterised the rhetoric associated with the promotion of digital educational policies, namely: the raising of learning levels, the development of digital competences and the increase in school inclusion. Through an analysis of a series of empirical studies exploring the point of view of the paper, this paper highlights what progress has been made in the digital schooling in Italy and what are still its main limitations. The results of the study show both the limits of the effectiveness of educational policies constructed with a top-down approach and highlight the potential for policy recalibration offered by a reorganisation of the decision-making process through the active involvement of all the actors in the educational system. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to a surge in online studio education, which has presented a significant challenge to traditional design studio teaching methods that rely on face-to-face interactions between instructors and students. It is contended that online studio education enhances the accessibility of design studio pedagogy, making it possible for students to learn from anywhere in the world. However, it also challenges the development of tactile skills, which are crucial in design education. Additionally, online studio education can render certain aspects of traditional design studio pedagogy obsolete, while bringing back elements of design history and theory that may have been overlooked in traditional studio teaching. It can also be argued that online studio education has the potential to reverse the traditional power dynamics between instructors and students, resulting in more democratic and collaborative forms of learning that can empower students. As the literature on the effects of online studio education is growing, there is a need to understand how the shift from the material space and its affordances to an online environment affects the core components of an architectural design studio. To understand the effects of this new medium, this research employed Marshall McLuhan's tetradic approach, a hermeneutic tool to perform a critical interpretation of any medium by examining four simultaneous effects: how it enhances a human sense, what it makes obsolete, what forgotten aspect it retrieves, and how it flips into its opposite at its extremes. A literature review was conducted to analyze the effects of online studios from a tetradic framework and identify the major discussions of the impact of online studio education. The methodology involves a two-part literature review. This study specifically focused on peer-reviewed, empirical research published after 2020, and the authors used search terms related to online architectural studios during the pandemic. The process identified 176 records of peer-reviewed empirical studies for further analysis and 20 papers were read and included in the review, defining repeating topics/themes and organized under four categories pertaining to the founding archetypes of an architectural design studio: (a) setting and communication, (b) actors, (c) outputs, and (d) dynamics. This process was followed by organizing the findings and interpreting them within the tetradic framework to develop a comprehensive understanding of the consequences of the online design studio. Overall, this research aims to provide a detailed and nuanced analysis of the impact of online studio education on design studio pedagogy, conceptualizing McLuhan's tetrad as a basis for the analysis, and therefore aiming to enrich our understanding of the post-COVID-19 era of learning architecture by examining the dramatic change in the medium and its effects. [ABSTRACT FROM AUTHOR]

This article provides both stylized facts and estimations of the endogenous nexus of the financial fragility hypothesis (FFH) with public social spending (PSS) for a paradigmatic Eurozone member country. The sample period 1995–2022 includes three major economic crises, the global financial crisis 2007–2009, the European debt crisis 2010–2015 and the COVID-19 pandemic one in 2020–2022. Within the context of the financialization literature, this paper is founded, for the first time, as far as we know, on the "financial fragility hypothesis", combining the effects of both Minsky's "financial instability", as it has been extended for open economies, and the "Eurozone fragility one". Similar to the relevant literature, the findings show that the PSS is associated, in a long-term steady state (cointegration), with the financial fragility process, starting, firstly, from the hedge-financing structure with high profitability of firms, when PSS decreases; secondly, to hyper-speculative financing with risky options, supported by bank credit and openness, indebtedness or discretionary fiscal policy, when PSS rises; thirdly, to the hyper-speculative or even Ponzi financing structures with over-indebtedness (leverage) from the global capital market, inflated asset prices and internationalized fragility, when PSS also rises, and so on. Our conclusion validates Minsky's famous saying, "stability breeds instability", also in the architecturally incomplete Eurozone. Policy implications are straightforward and discussed. [ABSTRACT FROM AUTHOR]

During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration. [ABSTRACT FROM AUTHOR]

This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML) models enhance their predictive value. We find payments data with a factor model can help improve accuracy up to 25% in predicting GDP, retail, and wholesale sales; and nonlinear ML models can further improve the accuracy up to 20%. Furthermore, we find the retail payments data are more useful than the data from the wholesale system; and they add more value during crisis and at the nowcasting horizon due to the timeliness. The contribution of the payments data and ML models is small and linear during low and normal economic growth periods. However, their contribution is large, asymmetrical, and nonlinear during crises such as COVID-19. Moreover, we propose a cross-validation approach to mitigate overfitting and use tools to overcome interpretability in the ML models to improve their effectiveness for policy use. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic is the greatest crisis of our time, having claimed over 2 million lives and shocking the global economy. Scientists and governments have suggested the idea of a digital COVID-19 certificate, to identify vaccinated persons easily. This paper assesses the positions of stakeholders on COVID-19 vaccination certificates, their presentation, and their importance. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was applied in this study. Search terms with Boolean and/or operators were combined to increase relevant results. Four large digital databases were used for the search. Inclusion and exclusion criteria were used to screen 298 collated studies. Two reviewers independently assessed search results, extracted data, and assessed the quality of the included studies. It is essential to re-examine digital COVID-19 vaccination certificates, considering their benefits, such as real-time detection of fake vaccination certificates and identifying and mapping non-vaccinated areas for strategic vaccination planning. The use of a single electronic platform globally will ease verification processes while bringing economies back to their feet. Digital COVID-19 vaccination certificates may provide balance in this pandemic era. With digital COVID-19 certificate exceeding documentation purposes, it is important to recognise factors such as global economy and human rights, boosting free movements of persons. [ABSTRACT FROM AUTHOR]

This paper addresses the scientific production of pharmaceutical communication in Spain around the COVID-19 crisis, in which information overload, amplified by the digital media, evidenced the relevance of communication in the digital society. The research observes the evolution and characteristics of such studies, identifying scientific fields and disciplines related to communication, thematic lines, agents and publics. To this end, it proposes an exploratory review study adjusted to the PRISMA protocol with a search strategy including three databases (Scopus, WOS and Dialnet) and whose filtration produced a final population of 56 publications on Spanish pharmaceutical communication between 2018 and 2022. The results point to a greater production of scientific papers around the year of the pandemic. These papers were published by university institutions in health sciences journals, although differences in authorship by gender were detected. Most of them are empirical papers, with a predominance of mixed content analyses. The field of public relations stands out, but terminological confusion was also detected. This leads to a reflection on its causes and solutions in favour of the transparency and accountability in pharmaceutical communication. [ABSTRACT FROM AUTHOR]

Flexible sensors have attracted extensive attention because of their promising applications in the fields of health monitoring, intelligent robots, and electronic skin, etc. During the COVID-19 epidemic, noncontact control of public equipment such as elevators, game consoles, and doors has become particularly important, as it can effectively reduce the risk of cross-infection. In this work, a noncontact flexible temperature sensor is prepared via a simple dip-drying progress, in which poly(3,4-ethylenedioxythiophene):poly(4-styrene sulfonate) (PEDOT:PSS) and printer paper served as the sensing material and the flexible substrate, respectively. We combined the highly sensitive temperature-responsive property of PEDOT:PSS with the good hygroscopicity of printer paper. The prepared sensor shows high sensitivity and good stability in noncontact sensing mode within the temperature range of 20–50 °C. To prove the practicability of the noncontact temperature sensor, a 3 × 2 sensing array is prepared as a noncontact human-machine interface to realize the interaction between player and "Pound-A-Mole game" and a Bluetooth car. These two demos show the sensor′s ability to perceive nearby temperature changes, verifying its application potential as a noncontact human-machine interaction interface. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Brain Sciences titled "Neurological and Psychiatric Disorders in the COVID-19 Era." The issue aims to expand knowledge about the impact of COVID-19 on neurological and psychiatric disorders and how the outbreak has affected their management. The issue includes six original scientific papers. One study found an association between pulmonary embolism in COVID-19 patients and neurological complications such as stroke and seizures. Another study reviewed the possible mechanisms of SARS-CoV-2 damage in the body, including neurological disorders. A systematic review identified risk factors for depressive symptoms during COVID-19-related restrictions. Another study evaluated long-term neuropsychiatric sequelae in COVID-19 survivors and found that a significant portion still experienced symptoms. A review focused on the impact of COVID-19 on neurology training, and the final paper examined emotional distress in COVID-19 patients admitted to a rehabilitation unit. The special issue aims to provide new insights for future research initiatives. [Extracted from the article]

In the present economic context, one of the most important topics of discussion is that regarding sustainable development. According to the agenda developed by the United Nations, one of the most important objectives for the present decade is represented by the list of the Sustainable Development Goals. The Sustainable Development Goals can be divided into five pillars: people, planet, prosperity, partnership and peace. One of the first stipulated goals of the UN agenda is the eradication of poverty and famine. We consider that a significant influence on the eradication of poverty is represented by the development of technology. In this paper, the authors aim to establish a connection between the rate of technological development and the poverty headcount rate. To measure the digital development of the analyzed countries, we decided to compose an index of digital development by taking into account indicators made available by the International Telecommunication Union and the poverty headcount ratio, as was calculated by the World Bank database. This empirical study is of interest for the implications that it has in shaping governmental policies regarding easing the access to digital technology. The method used to quantify the influence of digital development on poverty was the panel data GMM vector autoregressive model for a dataset composed of 35 countries for the period between 2005 and 2018. The results indicate that an increase in digital development will lead to a reduction in the poverty headcount rate. These results imply that by increasing access to technology, countries could help reduce their level of poverty. In this paper, we will also analyze the way in which adopting digital development leads to better economic performance when faced with the COVID-19 pandemic. The results of the present study are of great interest to the scientific community and the public due to the implications of digital development in the field of economics and the combined effect of this phenomenon and the COVID-19 pandemic. We thus conclude that by encouraging digital development and through adopting new technologies, the government can lead to the eradication of poverty. This seems counterintuitive due to the fact that investment in shelter and primary goods can be seen as one of the primary ways of developing the economy. We conclude that better and more consistent results regarding the reduction of poverty can be obtained by increasing the digital development of a country. [ABSTRACT FROM AUTHOR]

Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities' preparedness for future pandemics. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence in recent years, intelligent evaluation of college students' growth by means of the monitoring data from training processes is becoming a promising technique in the field intelligent education. Current studies, however, tend to utilize course grades, which are objective, to predict students' grade-point averages (GPAs), but usually neglect subjective factors like psychological resilience. To solve this problem, this paper takes mechanical engineering as the research object, and proposes a new machine-learning-driven GPA prediction approach to evaluate the academic performance of engineering students by incorporating psychological evaluation data into basic course scores. Specifically, this paper adopts SCL-90 psychological assessment data collected in the freshman year, including key mental health indicators such as somatization, depression, hostility, and interpersonal sensitivity indicators, as well as professional basic course scores, including mechanical principles, mechanical design, advanced mathematics, and engineering drawing. Four representative machine learning algorithms, Support Vector Machine (SVM), CNN-CBAM, Extreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART) that include deep and shallow models, respectively, are then employed to build a classification model for GPA prediction. This paper designs a validation experiment by tracking 229 students from the 2020 class from the School of Mechanical and Electrical Engineering of Henan University of Science and Technology, China. The students' academic performance in senior grades is divided into five classes to use as the prediction labels. It is verified that psychological data and course data can be effectively integrated into GPA prediction for college students, with an accuracy rate of 83.64%. Meanwhile, this paper also reveals that anxiety indicators in the psychological assessment data have the greatest impact on college students' academic performance, followed by interpersonal sensitivity. The experimental results also show that, for predicting junior year GPAs, psychological factors play more important role than they do in predicting sophomore GPAs. Suggestions are therefore given: the current practice in existing undergraduate teaching, i.e., only conducting psychological assessments in the initial freshman year, should be updated by introducing follow-up psychological assessments in each academic year. [ABSTRACT FROM AUTHOR]

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry. [ABSTRACT FROM AUTHOR]

The problem addressed in this study is the profound impact of the COVID-19 pandemic on the tourism economies of Kosovo (KOS) and Albania (AL), which led to economic–financial stagnation and price increases. The aim was to analyze the financial frontier challenges facing the tourism industry during COVID-19 and beyond and propose effective strategies for shaping a sustainable future for countries within Europe with great potential for tourism development in the current decade. The survey was conducted in 102 locations, including cities, municipalities, regions, villages, and neighborhoods in both countries over the years 2020–2023, while data analysis was performed using a cluster analysis (K-means and hierarchical) and the multidimensional scaling method (Alscal). The results highlighted (a) the severe impact of COVID-19 on both the population and businesses in the tourism sector, which will persist beyond the pandemic, (b) the indispensable role of government intervention in alleviating the financial crisis, (c) the need for innovative approaches and accurate financial management by both the country and businesses to attract tourists, and (d) the importance of control and management for financial sustainability. This paper is of significant importance to tourism destinations as it provides insights into the severe impact of COVID-19 on both the population and businesses in the tourism economies. By highlighting the indispensable role of government intervention, the need for innovative approaches and accurate financial management, and the importance of control and management for financial sustainability, the study offers valuable guidance for tourism destinations in navigating the current crisis and attracting tourists. Furthermore, the paper emphasizes the need for future studies to explore opportunities for long-term financial resilience and growth, contributing to the development of sustainable tourism destinations. [ABSTRACT FROM AUTHOR]

This paper presents the findings of a national survey of over 3400 teachers across all phases of education in England, 10 weeks into school closure. In this paper we report on the teachers' experiences and their testimonies of the effects of the lockdown on their wellbeing as they navigate the situation, trying to cope with teaching and adjusting to the use of new technology to deliver lessons in the confines of their home. We present their voices as they related their experiences and emotions in real time as the event unfolded. Teachers reported an increased workload with a proportionately large amount of time spent on administrative duties and planning, but less on actual teaching. On average, these teachers spent 15 h a week marking during the lockdown. Under half of the respondents said they felt happy and cheerful often, while only 17% said they did not feel this way often. Almost all teachers said they felt that what they were doing was important and worthwhile. The most common emotions expressed by teachers were fear, isolation, neglect, anxiety, and confusion. The inconsistency and lack of clarity of government guidelines added to teachers' anxiety. The negative media portrayal of teachers did not help. The majority of teachers reported little previous experience in online teaching and only a quarter were confident in using edtech to deliver remote teaching. Only a third of teachers said they were adequately supported for online teaching. This has implications for future teacher development and initial teacher training. However, there were equally numerous positive experiences reported by teachers. Active support from parents and consistent school-home communication were seen as important in ensuring students' continued engagement with learning. These lessons are beneficial in supporting the long-term sick and school refusers post-lockdown. [ABSTRACT FROM AUTHOR]

The development of antimicrobial polymeric materials has evolved into one of the more promising methods for preventing the growth of microbes and mitigating the spread of infectious diseases in several applications including the health and food packaging sectors. The outbreak of global pandemics, and particularly the recent COVID-19 pandemic, further strengthen the importance of developing such solutions. This review paper presents a fundamental understanding of how antimicrobial polymers are developed, describes the possible surface modification approaches to render polymers with antimicrobial properties, highlights the potential mechanism of action against a range of microorganisms (bacterial, viral, and fungal), and details some of the international standard protocols and procedures to evaluate the antimicrobial properties of modified materials (such as plastics and textiles). In addition, this review paper discusses the toxicity of antimicrobial additives when used in healthcare and food packaging applications. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has required countries to implement different containment strategies to limit its spread, like strict or weakened national lockdown rules and the application of age-stratified vaccine prioritization strategies. These interventions have in turn modified the age-dependent patterns of social contacts. In our recent paper, starting from the available age-structured real data at the national level, we identified, for the Italian case, specific virulence parameters for a two-age-structured COVID-19 epidemic compartmental model (under 60, and 60 years and over) in six different diseases transmission scenarios under concurrently adopted feedback interventions. An interpretation of how each external scenario modifies the age-dependent patterns of social contacts and the spread of COVID-19 disease has been accordingly provided. In this paper, which can be viewed as a sequel to the previous one, we mainly apply the same general methodology therein (involving the same dynamic model) to new data covering the three subsequent additional scenarios: (i) a mitigated coordinated intermittent regional action in conjunction with the II vaccination phase; (ii) a super-attenuated coordinated intermittent regional action in conjunction with the II vaccination phase; and (iii) a last step towards normality in conjunction with the start of the III vaccination phase. As a new contribution, we show how meaningful updated information can be drawn out, once the identification of virulence parameters, characterizing the two age groups within the latest three different phases, is successfully carried out. Nevertheless, differently from our previous paper, the global optimization procedure is carried out here with the number of susceptible individuals in each scenario being left free to change, to account for reinfection and immunity due to vaccination. Not only do the slightly different estimates we obtain for the previous scenarios not impact any of the previous considerations (and thus illustrate the robustness of the procedure), but also, and mainly, the new results provide a meaningful picture of the evolution of social behaviors, along with the goodness of strategic interventions. [ABSTRACT FROM AUTHOR]

This paper addresses the role of the U.S. dollar in fostering global economic growth during the post-war period. The existing literature lacks a comprehensive understanding of the true implications of the U.S. dollar's status as a reserve currency and a dearth of studies examining its impact. In this study, we explore the dynamic long-run and short-run relationships between cross-border U.S. dollar claims, global GDP, and global trade while gauging the impact of the Global Financial Crisis (GFC) and the COVID-19 pandemic. In doing so, we use ARDL methodology for a data set that spans the period of 1980 to 2022. The estimation results reveal a robust long-run relationship between U.S. dollar claims, global GDP and global trade and no clear evidence of asymmetric effects. Our findings are of great significance for monetary authorities, emphasising the need for a nuanced understanding of the implications of the U.S. dollar's conducive role in shaping global economic dynamics and fostering growth. [ABSTRACT FROM AUTHOR]