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PurposeThis study aims to examine the factors influencing user satisfaction with unified payment interface (UPI)-based payment systems during the COVID-19 pandemic in India. The study also aimed to examine whether the user satisfaction with UPI-based payment systems during the COVID-19 pandemic will transform into their continuance intention post-COVID-19 pandemic.Design/methodology/approachThe study was performed in three phases, i.e. pre-testing (for developing questionnaire), pilot study (using exploratory factor analysis to ensure unidimensionality) and the main study. The main study was based on the feedback from a sample of 369 internet users who first used the UPI-based payment system during the COVID-19 pandemic. Data generated were analysed using the structural equation modelling approach.FindingsThe study findings suggest that the users who are satisfied with UPI-based transactions during the COVID-19 pandemic are likely to continue their use of this payment mode in future. Factors such as post-adoption perceived value, perceived usefulness and post-adoption perceived risk were observed to be key constructs in explaining user satisfaction and continued intention for UPI-based payment systems.Originality/valueThe study is one of the pioneering studies, in the sense that it investigated the continuance intention of UPI-based payment systems, which, surprisingly, did not gain much attention from past researchers.
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In this paper, we explored the impact of course design elements that aim to support and sustain students' engagement during a 12-week online course. The course we analyzed targeted higher education, master-level students of Computer Science and Educational Technologies, and took place fully online during the COVID-19 pandemic. The course was facilitated by a Learning Management System (LMS), and due to the circumstances, the instructor's primary goal was to motivate students to actively participate during the course duration. To that end, the instructor implemented a course design focused on integrating elements such as interactive activities, short quizzes, hidden "easter eggs,” and real-time webinars. To study the impact of these elements on students' activity, we carried out an exploratory analysis of students' activity as recorded by the log files of the LMS and the qualitative feedback that students provided to the instructor. Our results suggest that the course design supported sustaining students' engagement. The level of students' activity varied for the learning materials and resources, but we confirmed a high usage of the quizzes over the course duration. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda.
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PurposeThis study aims to investigate the multiple influence paths or underlying mechanisms of entrepreneurial leadership (EL) on adaptive innovation from the perspectives of organizational learning and resource management, drawing on complex adaptive system theory.Design/methodology/approachWith a questionnaire survey of 317 senior and middle managers from different firms in China, structural equation modeling was used to test the hypothesized conceptual model, and bootstrapping method was employed to examine the multiple mediating effects.FindingsResults indicate that EL has a significant and positive effect on adaptive innovation. This relationship is partially mediated through exploitative learning, exploratory learning, resource bricolage and boundary-spanning integration, respectively. The impact of EL on adaptive innovation is also sequentially transmitted through exploitative learning and resource bricolage or exploratory learning and boundary-spanning integration.Originality/valueAdaptive innovation has become a firm competition strategy to cope with dynamic changes in current uncertain environment where EL can play its effectiveness to engage firms in such innovation activities. However, the question of why and how EL drives adaptive innovation has yet to be discussed. This study highlights the innovation effectiveness of EL and the triggering process of adaptive innovation, and contributes to several countermeasures for firms to implement leadership and innovation practices responding to uncertain environment.
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Aims: (1) To describe how occupational possibilities of young adults experiencing a first episode of psychosis changed during the pandemic;(2) to describe how mental health practitioners, working in two first episode clinics, strived to assist their clients in maintaining a meaningful daily life during the COVID-19 pandemic. Method(s): This exploratory study used an interpretative descriptive design. Young adults (n = 6), attending a specialized clinic, participated in two semi-directed interviews: they explored how their daily lives changed during the pandemic, their (re)actions in light of the changes and which occupations were (im)possible to pursue. A focus group discussion with the practitioners will allow to explore the adjustments made to clinical practices to enrich young adults' daily lives. The interviews and focus groups were transcribed and analysed independently by two members of the research team, using a thematic analysis approach. Result(s): Preliminary results suggest that if some occupational possibilities were lost, many were transformed or newly envisioned by young adults. They engaged in occupations within existing niches. The presentation will also include the perspective of mental health practitioners, as focus groups will be concluded in early Winter 2023. Conclusion(s): This presentation offers a novel perspective on the daily lives of young adults during the pandemic, focusing on the nature of their occupational engagement, whilst considering how the context shaped their occupational possibilities. Likewise, it will document the innovative practices and challenges experienced by practitioners, as they strived to expand possibilities for their clients and to support their transition into adulthood.
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Transgender and gender diverse (TGD) individuals experience high levels of minority stress, as well as a high prevalence of suicidality and self-harm. The current study investigates if emotion regulation mediates the relationships of minority stressors with self-harm and suicidality. TGD adult primary care patients (N = 115) completed a survey including measures of minority stressors, emotion dysregulation, self-harm, and suicidality. Emotion regulation mediated the relationship between victimisation and suicidality. Emotion regulation did not mediate the relationship between victimisation and self-harm. TGD individuals' suicide risk may be increased when they experience victimisation through increased emotion dysregulation.
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The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.
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Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals. © 2022 IEEE.
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Aims: Peers4Rs (Remembering Resilience, Respect and Recovery) was established within the Early Psychosis Intervention Programme (EPIP) in 2010 to promote client advocacy and recovery. Despite recommendations for peer support in early intervention services, there is a general lack of research on its impact. Current research evaluating peer support are heterogeneous in relation to interventions and outcomes, limiting research quality. This exploratory study aims to evaluate oneto- one peer support intervention on recovery processes in people with early psychosis in Singapore, during the COVID-19 period. Method(s): Data from 26 clients with first-episode psychosis (FEP), aged between 15 and 44 years, who were accepted into the service between 2021 and 2022, was included in the analysis. One-to-one peer support intervention was delivered over phone/video call or inperson by certified Peer Support Specialists (PSSs). The 22-item selfreport Process of Recovery Questionnaire (QPR) was administered at pre- and post-intervention. Paired-sample t-test was run. Result(s): Pre- and post- scores were compared before and after completing the one-to-one peer support intervention. On average, post scores (M = 62.77, SD = 7.60) were higher than pre scores (M = 49.31, SD = 12.07). This improvement, 13.46, 95% CI [9.26, 17.67], was statistically significant, t (25) = 6.59, p < .001, Cohen's d = 1.34. Conclusion(s): Based on preliminary results, clients with FEP grew in their recovery process through the one-to-one peer support intervention in EPIP. This finding is promising, given small sample sizes and limits in mode of contact during the pandemic, lending support to further discussions.
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Objectives: Acute respiratory distress syndrome (ARDS) often results in high mortality and morbidity. Hemoadsorption therapy, such as CytoSorb©, is being increasingly used to target the underlying hyperinflammation that occurs with ARDS. This review aims to evaluate the available data on the use of CytoSorb in combination with veno-venous extracorporeal membrane oxygenation (V-V ECMO) in severe ARDS cases, and to assess its effects on inflammatory, laboratory, and clinical parameters, as well as on patient outcomes. Method(s): A systematic literature review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) statement. Whenever possible, an analysis of changes in relevant biomarkers and clinical parameters was performed. Result(s): CytoSorb© therapy was associated with significant reductions in circulating levels of C-reactive protein and interleukin-6 (p = 0.039 and p = 0.049, respectively), as well as an increase in PaO2/FiO2 levels (p = 0.028). There was also a trend towards reduced norepinephrine dosage (p = 0.067). Mortality rates in patients treated with CytoSorb©tended to be lower than in the control groups, but these studies had high heterogeneity and low power. In an exploratory analysis of 90-day mortality in COVID19 patients receiving V-V ECMO, the therapy was associated with a reduced risk of death. Conclusion(s): Overall, the reviewed data suggests that CytoSorb© therapy can effectively reduce inflammation and potentially improve survival in ARDS patients treated with V-V ECMO. Therefore, early initiation of CytoSorb ©in conjunction with ECMO may offer a promising approach to enhance lung rest and promote recovery in patients with severe ARDS. A randomised trial is warranted to confirm our findings.
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The negative consequences of the COVID-19 pandemic on mental health have been widely reported, but less is known about how the impact of COVID-19 on others in one's social circle shapes these high distress levels. This study examines associations between social COVID-19 exposure-knowing someone who had a COVID-19 infection-and psychological functioning, as well as whether socio-demographic factors moderate these relationships. In June 2020, respondents (N = 343) from clinics in Tampa, Florida, U.S.A. reported whether they had social COVID-19 exposure, anxiety, depression, and stress, and other COVID-19-related concerns. Social COVID-19 exposure was associated with increased anxiety, stress, and concerns about a family member getting sick, and concerns about drinking and substance use. Several associations between exposure and psychological functioning were stronger in women, younger people, and people with lower income, implying these groups face elevated psychological risks due to the pandemic, and should be prioritized in mental health recovery efforts.
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COVID-19 has created many complications in today's world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situation enables us to dig deeper into mortality records and find meaningful patterns to save many lives in future. Based on the article from the New Indian Express (published on January 19, 2021), a whopping 82% of people who died of COVID-19 in Tamil Nadu had comorbidities, while 63 percent of people who died of the disease were above the age of 60, as per data from the Health Department. The data, part of a presentation shown to Union Health Minister Harsh Vardhan, show that of the 12,200 deaths till January 7, as many as 10,118 patients had comorbidities, and 7613 were aged above 60. A total of 3924 people (32%) were aged between 41 and 60. Compared to the 1st wave of COVID-19, the 2nd wave had a high mortality rate. Therefore, it is important to find meaningful insights from the mortality records of COVID-19 patients to know the most vulnerable population and to decide on comprehensive treatment strategies.
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Introduction/Aim: Post COVID conditions are highly heterogenous and pose significant challenges to healthcare systems. The aim of this study was to identify and characterise symptom clusters at 6-months following COVID illness. Method(s): Symptom burden was assessed in a COVID respiratory clinic 6-months following diagnosis. K-mean cluster analysis was utilised to identify clusters and validated assessment tools for dyspnoea [MMRC], mood [PHQ-4], fatigue [FSS] and pain [WPI]were used to characterise clusters. Result(s): 58 patients (median age 59 years, 31 males) attended 6-month follow up. Cluster 4 represented patients experiencing high symptom burden with high fatigue, pain, depression and anxiety scores. A high proportion of Cluster 2 reported no symptoms but had high dyspnoea scores. Results for PHQ-4, FSS and WPI are presented as means (95%confidence interval) Cluster (n) Symptoms MMRC >1 (n%) PHQ-4 FSS WPI 1(8) anosmia, headache, fatigue, memory, concentration 3(5.1) 1(1,2) 31(17,45) 5(-1,10) 2(42) Isolated Dyspnoea 15(25.9) 1(1,2) 22(18,26) 1(0,1) 3(3) fatigue, nasal congestion, chest pain 3(5.1) 5(-10,21) 36(-26,98) 7(-11,24) 4(4) high symptom burden 4(6.9) 7(0,13) 51(36,66) 12(2,21) Conclusion(s): This exploratory analysis identified 4 possible post COVID condition phenotypes with unique symptom profiles. Larger scale phenotyping may facilitate a streamlined and customised approach to managing this evolving chronic and highly heterogenous clinical condition.
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Introduction: Since March 2020, a number of SARS-CoV-2 patients have frequently required intensive care unit (ICU) admission, associated with moderate survival outcomes and an increasing economic burden. Elderly patients are among the most numerous, due to previous comorbidities and complications they develop during hospitalization [1]. For this reason, a reliable early risk stratification tool could help estimate an early prognosis and allow for an appropriate resources allocation in favour of the most vulnerable and critically ill patients. Method(s): This retrospective study includes data from two Spanish hospitals, HU12O (Madrid) and HCUV (Valencia), from 193 patients aged > 64 with COVID-19 between February and November 2020 who were admitted to the ICU. Variables include demographics, full-blood-count (FBC) tests and clinical outcomes. Machine learning applied a non-linear dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) [2];then hierarchical clustering on the t-SNE output was performed. The number of clinically relevant subphenotypes was chosen by combining silhouette and elbow coefficients, and validated through exploratory analysis. Result(s): We identified five subphenotypes with heterogeneous interclustering age and FBC patterns (Fig. 1). Cluster 1 was the 'healthiest' phenotype, with 2% 30-day mortality and characterized by moderate leukocytes and eosinophils. Cluster 5, the severe phenotype, showed 44% 30-day mortality and was characterized by the highest leukocyte, neutrophil and platelet count and minimal monocytes and lymphocyte count. Clusters 2-4 displayed intermediate mortality rates (20-28%). Conclusion(s): The findings of this preliminary report of Eld-ICUCOV19 patients suggest the patient's FBC and age can display discriminative patterns associated with disparate 30-day ICU mortality rates.
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PurposeLocalism refers to a concept that encourages local production, consumption and promotion of goods. It is a movement to encourage consumers and businesses to purchase from locally owned, independent businesses that has grown rapidly in the past decade. However, localism remains understudied by researchers. This study aims to provide a more comprehensive understanding of the localism movement by capturing the dynamism of fashion localism in the context of the Sri Lankan fashion retail sector.Design/methodology/approachAdopting a qualitative exploratory approach, the authors conducted a series of in-depth interviews with 12 fashion practitioners.FindingsBased on the findings, the authors propose a conceptual model of fashion localism consisting of eight themes: fashion localism design approach, locally sourced staples, land ethic, employee development, community development, consumer, regulations and limitations and future opportunities.Originality/valueThis research sheds some light on localism literature by capturing the dynamism of fashion localism. In particular, to the best of the authors' knowledge, this research is the first study in marketing to propose a conceptual model of fashion localism. This research further points out certain managerial implications by illustrating a few practical approaches to the concept of localism within the Sri Lankan fashion retail sector.
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Introduction/Aim: Accumulating evidence indicates that an early, robust type 1 interferon (IFN) response to SARS-CoV-2 is critical for COVID-19 outcomes. Our objective was to examine the prophylactic potential of IFN treatment to limit viral transmission Methods: A cluster-randomised clinical trial was undertaken to determine effects of IFNbeta-1a treatment on SARS-CoV-2 household transmission (clinicaltrials.gov: NCT04552379). Index cases were identified from confirmed SARS-CoV-2 cases in Santiago, Chile, with 341 index cases and 831 household contacts enrolled. Households received 125 mug subcutaneous pegylated-IFNbeta-1a on days 1, 6, & 11 (172 households, 607 participants), or standard care (169 households, 565 participants). Primary outcomes included: (1) duration of viral shedding in infected cases (IC-INF), (2) transmission to treatment-eligible household contacts (EHC-ITT) at day 11. Result(s): Of 1172 individuals randomised, 53 individuals withdrew from the study (IFNbeta-1a = 35, SOC = 18). Eighty-two households (IFNbeta-1a = 36, SOC = 46) where the index case was SARS-CoV-2 negative on days 1 & 6, or with no SARS-CoV-2 negative contacts at recruitment, were excluded from exploratory analyses. Treatment with IFNbeta-1a: had no effect on duration of viral shedding in the IC-INF population (primary outcome 1), had no effect on transmission of SARS-CoV-2 at day 11 in the EHC-ITT population (primary outcome 2) but an effect was observed in a sensitivity analysis at day 6 (EHC-ITT: OR = 0.493, 95% CI = 0.256-0.949), reduced duration of hospitalisation in the IC-INF population and incidence of hospitalisation in per-protocol index cases (secondary outcome 3). In exploratory frequentist analysis, a significant effect of IFNbeta-1a treatment on SARS-CoV-2 transmission by day 11 (OR = 0.55, 95% CI = 0.36-0.99), and a Bayesian analysis identified a significant reduction in the odds of transmission (OR = -0.85, 95% credible interval = -1.59--0.16). Conclusion(s): Ring prophylaxis with IFNbeta-1a had no effect on duration of viral shedding but reduces the probability of SARS-CoV-2 transmission to uninfected, post-exposure contacts within a household.
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Introduction: During the COVID-19 pandemic the number of solid organ transplants (SOT) lessened globally due probably to decreased donation and detour of resources to other prioritized activities [1, 2]. The aim of this paper is to analyze the SOT behavior during the COVID- 19 pandemic in this city. Method(s): An exploratory analysis was performed on the data of the Statistics on donation and transplant of organs and tissues in Bogota from 2018 until the third trimester of 2022 [3]. Result(s): 416 SOT from 365 organ donors were performed in Bogota during 2018. The first COVID-19 case in Colombia was documented in March 2020. During the following two years there was a decline in the number of performed SOT and total organ donors, as shown in Table 1. During 2020 there was a drastic reduction on the total SOT, compared with 2018. During 2021 there was a slight recovery in the total SOT, and in the first three trimesters of 2022 there was a drastic increase in the total SOT with 380 procedures performed and 380 organ donors. There was a steeper reduction in the number of heart and lung transplants during 2020 and 2021. The SOT waiting lists remained stable from 2018 to 2022, with 1804 patients in 2021, up to 1950 patients in 2022. Conclusion(s): The reduction in SOT might be due to COVID-19 in donors, reduced incidence of brain death and lessened capability to preserve viable organ donors. The SOT waiting list didn't increase possibly caused by high mortality due to end-stage organ failure. The SOT increase during 2021 coincides with a decrease in COVID-19 lethality in Bogota. This decline in SOT was observed globally during 2020, whilst the rapid recovery in SOT and availability of organ donors during 2022 is a phenomenon that has not been described yet to our knowledge.
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This paper combines available NLP technologies for Serbian languages and traditional data science methods in order to analyze collected dataset on the news headlines related to the COVID-19 pandemics. As an addition to NLP technologies for the Serbian language, a specialized database was created in an attempt to enhance the research within the field. Within the paper, the database was exploratory analyzed, and perspectives of the work with the data were thoroughly explored. © 2023 IEEE.
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Introduction: Adversarial attacks are a great threat to deep learning (DL) as they can generate imperceptible perturbations in images which severely affects model performance. More worryingly, recent works have shown that medical DL models are vulnerable to such attacks. The DL process flow is susceptible to various kinds of adversarial attacks. Specifically, causative attacks occur before a model is built, during training, and exploratory attacks occur after model training, during the inference phase. Furthermore, these attacks can be exploited to compromise overall model accuracy, or influence results on specific targeted classes. This research aims to study the impact of causative and exploratory attacks for non-targeted and targeted purposes, on medical DL models built for image classification tasks. Method(s): Warping Based Backdoor Attack and Universal Adversarial Pattern Attack were selected due to their superior performance in generating imperceptible adversarial samples for non-targeted and targeted attacks. DL models were produced from both original and perturbed ISIC-2019 dermoscopic and COVID-NET chest X-ray image datasets. These models were subsequently evaluated on their classification performance. Result(s): Experiments on models achieving above 90.0% accuracy revealed that both causative and exploratory attacks could lower model accuracy by at least 45.0%. In the best-case adversarial attack scenario, model accuracy was reduced by up to 99.0%. Conclusion(s): These results provide a better understanding on the damaging nature of causative and exploratory adversarial attacks as well as vulnerability of medical DL models. The findings can serve as a starting point towards building effective defence approaches that are vital for medical systems utilising DL algorithms.
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Introduction: COVID-19 causes a major inflammatory response, which may progress to shock and multiple organ failure. We explored whether continuous renal replacement therapy (CRRT) using adsorption membrane (oXiris) could reduce the inflammatory response in critically ill COVID-19 patients with acute renal failure (ARF) [1, 2]. Method(s): Case-control study including 24 critically ill COVID-2019 patients requiring RRT using an oXiris filter. We measured cytokines before and during treatment as well as relevant clinical endpoints. The control group was selected among COVID-19 patients included into our ongoing RECORDS trial (NCT04280497) who received RRT without adsorbing filters. Result(s): 24 severe COVID-19 patients, admitted to the intensive care unit (ICU) and treated with CRRT using the oXiris filter between March and April 2020 (20 males and 4 females);median age 67. The average time from COVID-19 symptoms to initiation of oXiris treatment was 18 +/- 7 days, and from ICU admission to initiation of oXiris treatment 9.5 +/- 7.8 days and from ARF to oXiris treatment was 3 +/- 5 days. The average length of treatment was 152.8 +/- 92.3 h. Treatment was associated with cytokine decreases for IL-1beta (p = 0.00022), MCP-1 (p = 0.03), and MIP-1 alpha (p = 0.03). The SOFA scores also showed a reduction over 48 h of therapy without reaching statistical significance. Our study found no significant effect of hemodynamic status. The average ICU stay length was 14 +/- 5 days and the mortality rate was 79% in the Oxiris group. We compared the mortality across the two matched groups, there was no evidence of any difference in mortality (Fig. 1). Conclusion(s): In our study, CRRT using the oXiris filter seemed to effectively remove IL-1 beta, MCP-1, and MIP-1 alpha in COVID-19 patients. These exploratory results should be confirmed in a randomized controlled study.
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Aim. Describe the psychological distress and the psychosocial concerns during the third wave of the pandemic in oncological patients and family caregivers and analyze the evolution of these variables in cancer patients taking as references April and December of 2020. Method. The researchers developed a self-administered questionnaire. It was composed by items developed ad hoc to assess sociodemographic characteristics and social concerns and the Kessler K-6 scale to measure psychological distress. Results. The proportion of oncological patients and family caregivers who showed clinical levels of distress was higher than that the ones of non-oncological population during December 2020. Clinical distress was higher in oncological patients during the December 2020, compared to April levels. This increase was especially significant among women and younger patients in both assessment moments. The main social concerns at the third wave for oncological patients and family caregivers were: the fear of contracting COVID-19, restlessness and fear of the future, suspension of the social contacts, the absence of face-to-face contact and inactivity in the streets and shops. Conclusions. The results of this study highlight the need to provide the proper care to oncological patients and family caregivers due to the presence of socio-emotional needs, and to develop strategies that allow them to be covered from psychological impact of COVID-19.Copyright © 2023, Universidad Compultense Madrid. All rights reserved.