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1.
Indian Journal of Critical Care Medicine ; 26(4):514-517, 2022.
Article in English | Web of Science | ID: covidwho-1818517

ABSTRACT

Several vaccines were developed and rolled out at an unprecedented rate in response to the coronavirus disease-2019 (COVID-19) pandemic. Most vaccines approved globally by WHO for emergency use to combat the pandemic were deemed remarkably effective and safe. Despite the safety, rare incidences of vaccine-induced thrombosis and thrombocytopenia (VITT), sometimes known as vaccine-induced prothrombotic thrombocytopenia (VIPIT), have been reported. We report a case of young female with prothrombotic conditions and suspected VITT who developed catastrophic cerebral venous sinus thrombosis (CVST) and progressed to brain death. We highlight hurdles of organ retrieval from a brain-dead patient with suspected SARS-CoV-2 vaccine-induced immune thrombotic thrombocytopenia. There is limited data and lack of substantial evidence regarding transplantation of organs from brain-dead patients with suspected VITT.

2.
2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 ; : 56-59, 2022.
Article in English | Scopus | ID: covidwho-1788619

ABSTRACT

COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization on March 11, 2020. To solve the global problem of analysis of different variants of COVID-19 genome sequences, there is a need to develop intel-ligent, scalable machine learning techniques that can process and analyze important COVID-19 protein data by utilizing the Big Data framework. For this, we have first proposed a feature extraction approach for COVID-19 protein data named Scalable Distributed Co-occurrence-based Probability-Specific Feature extraction approach (SDCPSF). The proposed SDCPSF approach is executed on the Apache Spark cluster to preprocess the massive COVID-19 protein sequences. The proposed SDCPSF represents each variable-length COVID-19 protein sequence with fixed length six dimensions numeric feature vectors. Then the extracted features are used as input to the kernelized fuzzy clustering algorithms, i.e., KSRSIO-FCM and KSLFCM, which efficiently performs clustering of big data due to its in-memory cluster computing technique and thus forms clusters of COVID-19 genome sequences. Furthermore, the performance of KSRSIO-FCM is compared with another scalable clustering algorithm, i.e., KSLFCM, in terms of the Silhouette index (SI) and Davies-Bouldin index (DBI). © 2022 IEEE.

4.
10th International Conference on System Modeling and Advancement in Research Trends, SMART 2021 ; : 462-467, 2021.
Article in English | Scopus | ID: covidwho-1722931

ABSTRACT

In this paper, we present a method for automatically detecting mask elements from faces and synthesizing the affected region with fine details while preserving the original structure of the face. Wearing face masks appears to be a promising approach for reducing COVID-19 spread. Effective recognition technologies are crucial in this situation to keep people's faces hidden in limited places. As a result, in order to train deep learning models to distinguish between those wearing masks and those who aren't, a huge dataset of masked faces is required. A technique has been devised for researching COVID-19 related behaviours and contamination processes. A potential method for minimising COVID-19 transmission through health education has been identified. With 96.70% accuracy, the suggested approach recognised faces with and without masks. They will give you a decent detection result with or without a mask. © 2021 IEEE.

5.
Journal of Global Information Management ; 30(5):21, 2022.
Article in English | Web of Science | ID: covidwho-1635362

ABSTRACT

The study aims to identify social, intellectual, and conceptual structures along with key areas, contributors, current dynamics, and suggest future research directions in the field of engagement with e-learning systems. An objective analysis of a sample of 358 articles taken from the Web of Science database, supported by subjective assessments based on the research, focused on the integration of management into e-learning domain. Citations and page rank metrics were used to identify the most influential papers along with most influential authors. To understand the intellectual structure of the research area, a co-citation network was developed. The study may help to explore effective ways of delivering education during a crisis, while also taking a sustainable approach to the promotion of education through online methods. By understanding the behavior of learners towards various forms of content delivery, policy makers at national level can develop a framework to implement it nationwide.

6.
3rd International Conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020 ; 806:103-109, 2022.
Article in English | Scopus | ID: covidwho-1626473

ABSTRACT

Face recognition is a method of identifying or verifying the identity of an individual using their face but what if this recognition method could be extended further to suit the needs of the current scenario. Given this COVID pandemic, this paper fits best by recognizing the people wearing masks. The research has been done by creating our own dataset using images from our friends and relatives followed by doing image augmentation by performing operations like rotating by some angle, changing brightness and contrast, zooming in and out, etc. Then, face with the mask is extracted from the given image with the help of MTCNN to get a bounding box, width, and the height of the face, and then, segmentation has been done by reducing the height by a factor of 2. FaceNet pretrained model has been used to represent the faces on a 128-dimensional unit hyper-sphere and get the embeddings for further classification. Many different algorithms like linear Discriminant analysis, SVM, ridge classifier, K-neighbors classifier, logistic regression, Naive Bayes, XGBoost, Ada Boost, random forest classifier, and decision tree classifier have been used for experimentation. After testing this, good accuracy was obtained as can be seen in the result section of this paper. The scope of this paper is quite vast as it covers many practical applications in real-scenario like detecting the presence of a particular person from an image or even from video by capturing faces frame by frame. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

7.
Applied Economics ; : 16, 2021.
Article in English | Web of Science | ID: covidwho-1585635

ABSTRACT

This paper provides a comparative analysis of how the energy-sector stocks of 20 regional blocs (Americas, Australasia, BRIC, Southeast Asia, Scandinavia, Southern Europe, Far East, Europe, European Union, Emerging Europe, Asia, G7, G12, Economic and Monetary Union (EMU), CCARBNS, Latin America, North America, PIIGS, Asia-Pacific and NORCS) are connected from 5 July 1994 to 21 April 2020. It uses various techniques: Diebold and Yilmaz (2014)(DY 2014, hereafter) spillover indices and TVP-VAR, LASSO-VAR. Our main results are as follows: First, the DY approach results show that the biggest net contributor of volatility is the CCARBNS region, followed by the G12 and G7 regions, while the biggest receiver of volatility is the Southeast Asia region. Second, the TVP-VAR and LASSO-VAR results reveal that Scandinavia, Far East, and America's regions are net receivers of energy shocks, with net transmitters being CCARBNS, G7, G12 and Emerging European regions. Third, during the 2007-2008 financial crisis and recent COVID-19 outbreak, energy stock market spillovers have reached unprecedented high levels. Fourth, the world policy uncertainty greatly influenced the magnitude of volatility spillovers across regional energy stock markets.

8.
Journal of Global Operations and Strategic Sourcing ; 2021.
Article in English | Scopus | ID: covidwho-1566146

ABSTRACT

Purpose: A major component in managing pandemic outbreaks involves testing the suspected individuals and isolating them to avoid transmission in the community. This requires setting up testing centres for diagnosis of the infected individuals, which usually involves movement of either patient from their residence to the testing centre or personnel visiting the patient, thus aggregating the risk of transmission to localities and testing centres. The purpose of this paper is to investigate and minimize such movements by developing a drone assisted sample collection and diagnostic system. Design/methodology/approach: Effective control of an epidemic outbreak calls for a rapid response and involves testing suspected individuals and isolating them to avoid transmission in the community. This paper presents the problem in a two-phase manner by locating sample collection centres while assigning neighbourhoods to these collection centres and thereafter, assigning collection centres to nearest testing centres. To solve the mathematical model, this study develops a mixed-integer linear programming model and propose an integrated genetic algorithm with a local search-based approach (GA-LS) to solve the problem. Findings: Proposed approach is demonstrated as a case problem in an Indian urban city named Kolkata. Computational results show that the integrated GA-LS approach is capable of producing good quality solutions within a short span of time, which aids to the practicality in the circumstance of a pandemic. Social implications: The COVID-19 pandemic has shown that the large-scale outbreak of a transmissible disease may require a restriction of movement to take control of the exponential transmission. This paper proposes a system for the location of clinical sample collection centres in such a way that drones can be used for the transportation of samples from the neighbourhood to the testing centres. Originality/value: Epidemic outbreaks have been a reason behind a major number of deaths across the world. The present study addresses the critical issue of identifying locations of temporary sample collection centres for drone assisted testing in major cities, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust epidemic resistance. © 2021, Emerald Publishing Limited.

9.
Management Decision ; ahead-of-print(ahead-of-print):27, 2021.
Article in English | Web of Science | ID: covidwho-1550706

ABSTRACT

Purpose This study aims to evaluate the impact of perceived cause- related marketing (perceived-CRM) on the repurchase intention (CRIN). Besides, brand image (BIMA) and customer satisfaction (CSAT) connect this relationship as mediating variables. Especially, the role of perceived corporate social responsibility (perceived-CSR) contributed to this nexus between perceived-CRM and BIMA, perceived-CRM and CSAT in emerging economies. Design/methodology/approach The paper follows a quantitative approach. Based on a comprehensive literature review on perceived-CSR, perceived-CRM , BIMA, CSAT and repurchase intention, the authors evaluate the impact of those constructs on repurchase intention in an emerging market. The study sample was composed of 395 responses covering customers of consumer goods. The study uses the Smart PLS-SEM version 3.3.2 to analyze the data. Findings The findings revealed significant contributions to the extant CRM literature in some ways. This study's outcomes contribute to extending the existing literature on CRM and CSR. Specifically, the extension focuses on the mediating and moderating effects of BIMA, CSAT and perceived-CSR, respectively, in the relationship between perceived-CRM and CRIN. Moreover, the novelty of this study lies in providing a new approach to the influence of perceived-CRM on CRIN, with the mediating of BIMA, CSAT and moderating effects perceived-CSR integrated into a conceptual model. Practical implications From a management perspective, the contribution of this study plays a very important role in strategic planning to enhance competitive advantage and improve business performance on a sustainable basis. This sustainability is founded on an insight into how changes in contextual factors affect the perception and consumer behavior of millennials in fast-moving consumer goods (FMCG) market, especially in a context of Covid-19 global crisis. It is important to emphasize that genuineness and transparency in all activities and communications are a prerequisite in today's sensitive context. The application of acquired insight into practice will help businesses operating in the consumer sector improve brand reputation and CSAT. As a result, this leads to enhanced competitive advantage of the business in the market, improved market performance and ultimately to an improvement in the overall performance of the enterprise. Originality/value This is the first study that explores the moderating role of perceived CSR on the nexus between perceived-CRM with brand image (BIMA) and CSAT to the best of our knowledge. Besides, the study also discovers the mediating role of BIMA and CSAT between perceived-CRM and repurchase-intention in an emerging economy. Findings in this study provided additional evidence to the increasingly important roles of perceived-CRM and perceived-CSR in creating win-win relationships with customers, aiming to solve specific social causes jointly. Further, the perceived-CRM and perceived-CSR mechanisms help businesses enhance their intangible assets and competitive advantages through enhanced BIMA and stronger CRIN. In the current context, the business environment is changing rapidly due to many factors that lead to increased competition at a global level. Therefore, improving competitive advantage is a mandatory condition for businesses to survive and develop sustainably.

10.
Indian Journal of Medical and Paediatric Oncology ; 42(04):311-318, 2021.
Article in English | Web of Science | ID: covidwho-1550392

ABSTRACT

Introduction There has been an exponential rise in number of coronavirus disease 2019 (COVID-19)-positive infections since March 23, 2020. However, cancer management cannot take a backseat. Objective The aim of this study was to identify any difference in the complication and mortality rates for the cancer patients operated during the ongoing COVID-19 pandemic. Materials and Methods This was a retrospective study of a prospectively maintained database of five centers situated in different parts of India. Variables such as demographics, intraoperative, and postoperative complications were compared between COVID-19 (group A-March 23, 2020-May 22, 2020) and pre-COVID time period (group B-January 1 to January 31, 2020). Results One-hundred sixty-eight cancer surgeries were performed in group B as compared with 148 patients who underwent oncosurgeries in group A. Sixty-two percent lesser cancer surgeries were performed in the COVID-19 period as compared with the specific pre-COVID-19 period. There was no significant difference in age group, gender, comorbidities, and type of cancer surgeries. Except for the duration of surgery, all other intraoperative parameters like blood loss and intraoperative parameters were similar in both the groups. Minimally invasive procedures were significantly lesser in group A. Postoperative parameters including period of intensive care unit stay, rate of infection, need for the change of antibiotics, and culture growth were similar for both the groups. While minor complication like Clavien-Dindo classification type 2 was significantly higher for group A, all other complication rates were similar in the groups. Also, postoperatively no COVID-19-related symptoms were encountered in the study group. A subset analysis was done among the study groups between those tested preoperatively for COVID-19 versus those untested showed no difference in intraoperative and postoperative parameters. No health-care worker was infected from the patient during the time period of this study. Conclusion Our study shows that there is no significant difference in the incidence of postoperative morbidity and mortality rates in surgeries performed during COVID-19 pandemic as compared with non-COVID-19 time period.

11.
Global Finance Journal ; 51, 2022.
Article in English | Scopus | ID: covidwho-1549801

ABSTRACT

This study has been inspired by the emergence of socially responsible investment practices in mainstream investment activity as it examines the transmission of return patterns between green bonds, carbon prices, and renewable energy stocks, using daily data spanning from 4th January 2015 to 22nd September 2020. In this study, our dataset comprises the price indices of S&P Green Bond, Solactive Global Solar, Solactive Global Wind, S&P Global Clean Energy and Carbon. We employ the TVP-VAR approach to investigate the return spillovers and connectedness, and various portfolio techniques including minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio to test portfolio performance. Additionally, a LASSO dynamic connectedness model is used for robustness purposes. The empirical results from the TVP-VAR indicate that the dynamic total connectedness across the assets is heterogeneous over time and economic event dependent. Moreover, our findings suggest that clean energy dominates all other markets and is seen to be the main net transmitter of shocks in the entire network with Green Bonds and Solactive Global Wind, emerging to be the major recipients of shocks in the system. Based on the hedging effectiveness, we show that bivariate and multivariate portfolios significantly reduce the risk of investing in a single asset except for Green Bonds. Finally, the minimum connectedness portfolio reaches the highest Sharpe ratio implying that information concerning the return transmission process is helpful for portfolio creation. The same pattern has been observed during the COVID-19 pandemic period. © 2021 Elsevier Inc.

13.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; : 8348-8352, 2021.
Article in English | Web of Science | ID: covidwho-1532691

ABSTRACT

Hospital workers are known to work long hours in a highly stressful environment. The COVID-19 pandemic has increased this burden multi-fold. Pre-COVID statistics already showed that one in every three nurses reported burnout, thus affecting patient satisfaction and the quality of their provided service. Real-time monitoring of burnout, and other underlying factors, such as stress, could provide feedback not only to the clinical staff, but also to hospital administrators, thus allowing for supportive measures to be taken early. In this paper, we present a context-aware speech-based system for stress detection. We consider data from 144 hospital workers who were monitored during their daily shifts over a 10-week period;subjective stress readings were collected daily. Wearable devices measured speech features and physiological readings, such as heart rate. Environment sensors, in turn, were used to track staff movement within the hospital. Here, we show the importance of context-awareness for stress level detection based on a bidirectional LSTM deep neural network. In particular, we show the importance of hospital location and circadian rhythm based contextual cues for stress prediction. Overall, we show improvements as high as 14% in F1 scores once context is incorporated, relative to using the speech features alone.

14.
Journal of the Indian Chemical Society ; 98(10):11, 2021.
Article in English | Web of Science | ID: covidwho-1510018

ABSTRACT

COVID-19 is considered as a major public health problem caused by the SARS CoV-2. This Viral infection is known to induce worldwide pandemic in short period of time. Emerging evidence suggested that the transmission control and drug therapy may influence the preventive measures extensively as the host surrounding environment and pathogenic mechanism may contribute to the pandemic condition earlier in COVID-19 disease. Although, several animals identified as reservoir to date, however human-to-human transmission is well documented. Human beings are sustaining the virus in the communities and act as an amplifier of the virus. Human activities i.e., living with the patient, touching patient waste etc. in the surrounding of active patients or asymptomatic persons cause significant risk factors for transmission. On the other hand, drug target and mechanism to destroy the virus or virus inhibition depends on diversified approaches of drugs and different target for virus life cycle. This article describes the sustainable chemical preventive models understanding, requirements, technology adaptation and the implementation strategies in these pandemic-like situations. As the outbreak progresses, healthcare models focused on transmission control through disinfections and sanitization based on risk calculations. Identification of the most suitable target of drugs and regional control model of transmission are of high priority. In the early stages of an outbreak, availability of epidemiological information is important to encourage preventive measures efforts by public health authorities and provide robust evidence to guide interventions. Here, we have discussed the level of adaptations in technology that research professionals display toward their public health preventive models. We should compile a representative data set of adaptations that humans can consider for transmission control and adopt for viruses and their hosts. Overall, there are many aspects of the chemical science and technology in virus preventive measures. Herein, the most recent advances in this context are discussed, and the possible reasons behind the sustainable preventive model are presented. This kind of sustainable preventive model having adaptation and implementation with green chemistry system will reduce the shedding of the virus into the community by eco-friendly methods, and thus the risk of transmission and infection progression can be mitigated.

15.
European Journal of Molecular and Clinical Medicine ; 8(4):1130-1136, 2021.
Article in English | EMBASE | ID: covidwho-1489349

ABSTRACT

Introduction: While countries including India, have taken strong measures to contain the spread of Covid-19 through better diagnostics and treatment, vaccines will provide a lasting solution by enhancing immunity and containing the disease spread. Understanding the adverse effects pattern will help make aware citizens, dismiss false rumours and reduce vaccine hesitancy. Hence the present study describes the pattern of adverse effects reported following COVID-19 vaccination among beneficiaries in Government Medical College, Raigarh, Chhattisgarh, India. Methods: A cross-sectional, observational study of pattern of adverse effects reported following COVID-19 vaccination was conducted among 540 beneficiaries in Government Medical College, Raigarh, Chhattisgarh, India from January 2021 to April 2021. Data was collected through online survey which included questions pertaining to immediate & late post vaccination experience. Results: Majority (33%) participants were 18-30 age group and (58%) were male. Overall (73%) respondents reported atleast one post-vaccination symptom. General weakness & tiredness (73.4%), pain at injection site (62%), bodyache (48%), chills (43%) & fever (39%) were the most prevalent symptoms. The frequency of symptoms among 18-30 age group was (85.9%) & more likely to be reported by women (83.2%) compared to men (65.1%). Around (36.9%) beneficiaries who had one or more comorbidities showed post vaccination symptoms. Around (82.1%) of Covishield beneficiaries developed atleast one or more symptoms post vaccination, while (60.3%) of Covaxin beneficiaries developed the same. Conclusion: Nearly two-thirds of study participants reported mild symptoms following vaccination. General weakness & tiredness, pain at injection site, bodyache, chills & fever were the most prevalent symptoms. Symptoms were more common among younger individuals. More Covishield beneficiaries developed atleast one or more symptoms post vaccination compared to Covaxin beneficiaries.

16.
Atmosphere ; 12(10), 2021.
Article in English | Scopus | ID: covidwho-1470788

ABSTRACT

A series of experiments was undertaken on an intercity train carriage aimed at providing a “proof of concept” for three methods in improving our understanding of airflow behaviour and the accompanied dispersion of exhaled droplets. The methods used included the following: measuring CO2 concentrations as a proxy for exhaled breath, measuring the concentrations of different size fractions of aerosol particles released from a nebuliser, and visualising the flow patterns at cross-sections of the carriage by using a fog machine and lasers. Each experiment succeeded in providing practical insights into the risk of airborne transmission. For example, it was shown that the carriage is not well mixed over its length, however, it is likely to be well mixed along its height and width. A discussion of the suitability of the fresh air supply rates on UK train carriages is also provided, drawing on the CO2 concentrations measured during these experiments. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

17.
Open Biol ; 11(10): 210213, 2021 10.
Article in English | MEDLINE | ID: covidwho-1462586

ABSTRACT

The etiopathogenesis of COVID-19 and its differential geographic spread suggest some populations are apparently 'less affected' through many host-related factors that involve angiotensin-converting enzyme 2 (ACE2) protein, which is also the entry receptor for SARS-CoV-2. The role of ACE2 has been well studied in COVID-19 but not in the context of malaria and COVID-19. We have previously suggested how malaria might intersect with COVID-19 through ACE2 mutation and here we evaluate the currently available data that could provide a link between the two diseases. Based on the existing global and Indian data on malaria, COVID-19 and the suggested ACE2 mutation, the association could not be examined robustly, neither accepting nor refuting the suggested hypothesis. We strongly recommend targeted evaluation of this hypothesis through carefully designed robust molecular epidemiological studies.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , COVID-19/complications , COVID-19/epidemiology , Malaria/complications , Malaria/epidemiology , Alleles , Genetic Predisposition to Disease , Genetic Variation , Geography , Global Health , Humans , India , Mutation
18.
20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2021 ; 12896 LNCS:704-709, 2021.
Article in English | Scopus | ID: covidwho-1437123

ABSTRACT

The socio-cultural polarization induced due to information and communication technology because of the selective online exposure during COVID-19 has been a major cause of concern around the globe. In this paper, we use random network theory-based simulation technique to investigate the temporal dynamics of opinion formation on YouTube videos. Our findings reveal that as the pandemic unfolded, the degree of polarization in the online discourse has increased with time. This study is significant for understanding that online discourse on sociocultural issues can lead to polarization particularly in crisis situations such as a pandemic and exacerbate the social divide. © 2021, IFIP International Federation for Information Processing.

19.
Heliyon ; 7(2), 2021.
Article in English | CAB Abstracts | ID: covidwho-1409310

ABSTRACT

This paper examines the time-frequency relationship between the number of confirmed COVID-19 cases, temperature, exchange rates and stock market return in the top-15 most affected countries by the COVID-19 pandemic. We employ Wavelet Coherence and Partial Wavelet Coherence on the daily data from 1st February, 2020 to 13th May, 2020. This study adds to the literature by implementing the Wavelet Coherence technique to explore the unexpected outbreak effects of the global pandemic on temperature, exchange rates and stock market returns. Our results reveal (i) there is evidence of cyclicality between temperature and COVID-19 cases, implying that average daily temperature has a significant impact on the spread of the COVID-19 disease in most of the countries;(ii) strong connectedness at low frequencies display that COVID-19 cases have a significant long-term impact on the exchange rate returns and stock markets returns of the most affected countries under study;(iii) after controlling for the effect of stock market returns and temperature, the co-movements between the confirmed COVID-19 cases and exchange rate returns becomes stronger;(iv) after controlling for the effect of exchange rate returns and temperature, the co-movements between the confirmed COVID-19 cases and stock market returns become stronger. Apart from theoretical contribution, this paper offers value to investors and policymakers as they attempt to combat the coronavirus risk and shape the economy and stock market behavior.

20.
International Journal of Agricultural and Statistical Sciences ; 16(Suppl. 1):965-977, 2020.
Article in English | CAB Abstracts | ID: covidwho-1391348

ABSTRACT

The sudden spread of COVID-19 across the globe during 2020 severely impacted efficient use of inputs for production in all the sectors in most of the developing countries including India. This paper tries to evaluate technical efficiency of paddy producers by applying the model of stochastic frontier analysis (SFA) under time invariant fixed effect application. To measure the level of technical inefficiency of farmers with family and hired labour with the help of primary data collected from 200 farmers in a field survey in the state of Haryana. Maximum likelihood estimator (MLE) was deployed to estimate the model of production under the assumption of Cobb-Douglas model of production function with stochastic frontier analysis for the variables affecting the technical efficiency. Results show that the important variables that impact the technical efficiency are the cost of labour, seed, fertilizers, machine labour and irrigation charges. Average technical efficiency of the surveyed paddy farmers that used both family and hired labour are 0.73 (73 per cent) and 0.69 (69 per cent) during COVID-19 and before Covid-19 respectively. Farmers have enhanced their level of technical efficiency during COVID-19 by optimally utilizing family labour. This shows that the small agricultural land holders can reduce the level of input requirement by 41 percent for giving the same level of output if their cultivation practices are technically more efficient.

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