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1.
2022 zh Conference on Human Factors in Computing Systems, zh EA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1846560

ABSTRACT

The digitization of financial transactions in both Global North and Global South has led to considerable shifts in how money is used, understood, and processed by users, banks, and fintechs. This shift from physical cash to digital media, accelerated by the COVID-19 push for digital transactions, has impacted how users perceive and use digital money and opened avenues for more data collection. This diverse panel proposes a discussion to understand the set of opportunities and challenges around the design of digital financial services (DFS) and data-driven decision-making in DFS. We will create a live working document starting before the panel to document the discussion, which develops during and after the panel. This live document will enable community to engage with a broader audience of researchers and industry, outlining processes, methods, and tools that researchers and practitioners have created to work with users to develop new equitable DFS and further exploration. © 2022 Owner/Author.

2.
2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; : 815-819, 2021.
Article in English | Scopus | ID: covidwho-1831747

ABSTRACT

The coronavirus pandemic (COVID-19) has unfolded hastily throughout the entire world. This pandemic disease can spread through droplets and can be airborne. Hence, the use of face masks in public places is crucial to stop its spread. The present study aims to develop a system that can identify masked or non-masked faces;whether it is a normal mask, transparent mask, or a face alike mask. The face mask detection system is developed with the help of Convolutional Neural Networks (CNN). The model compression technique of Knowledge Distillation has been used to make the machine lesser computation and memory intensive so that it is simple to install the model on a few embedded gadgets and cell computing platforms. Using the model compression technique and GPU systems will help boom the calculation velocity of the model and drop the storage space required for calculations. The experimental outcomes show that the developed detector is capable to classify diverse types of masks. Also, it can classify video images in real-time. Using the Knowledge Distillation on the baseline model can improve the testing accuracy from 88.79% to 90.13%. The proposed unique system can be implemented to assist in the prevention of COVID-19 spread and detect various mask types. © 2021 IEEE.

3.
2022 International Conference for Advancement in Technology, ICONAT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1788713

ABSTRACT

In this critical situations where people are fighting with dangerous pandemic disease;it is required to maintain the situation by indulging with social distancing or it can also be pronounced as physical distancing. Social or physical distancing may reflects to reduce the virus from spreading. There are several places where it should be followed properly to stop spreading COVID-19 like railway stations, malls, marts, airports and many more. It is advised to maintain at least 6 feet of social distancing as per the WHO guidelines. Various researches have been done to automatically detect the physical distancing violations but an ideal system should be available to detect it effectively with high level of accuracy. Here the system is based on PP-Yolo (PaddlePaddle - You only look once) and Tensorflow library. Tensorflow is an object detection or pattern recognition tool through which pedestrian can be detected automatically and then PP-Yolo classifies the distance between the pedestrians or classifying whether persons are following the physical distancing rule or not. Violation detection is bit challenging for any system because a crowd may have uncertain structures that can hardly classified distance among them. This challenge can be accepted through various researchers but not met the desired precision. Proposed system is intended to detect the physical distancing rule violations effectively and acquiring high level of accuracy with minimal false alarm rate. © 2022 IEEE.

4.
Journal of the Indian Chemical Society ; 99(5), 2022.
Article in English | Scopus | ID: covidwho-1788122

ABSTRACT

In the present work, we have designed three molecules, acyclovir (A), ganciclovir (G) and derivative of hydroxymethyl derivative of ganciclovir (CH2OH of G, that is D) and investigated their biological potential against the Mpro of nCoV via in silico studies. Further, density functional theory (DFT) calculations of A, G and D were performed using Gaussian 16 on applying B3LYP under default condition to collect the information for the delocalization of electron density in their optimized geometry. Authors have also calculated various energies including free energy of A, G and D in Hartree per particle. It can be seen that D has the least free energy. As mentioned, the molecular docking of the A, G and D against the Mpro of nCoV was performed using iGemdock, an acceptable computational tool and the interaction has been studied in the form of physical data, that is, binding energy for A, G and D were calculated in kcal/mol. It can be seen the D showed effective binding, that is, maximum inhibition that A and G. For a better understanding for the inhibition of the Mpro of nCoV by A, G and D, temperature dependent molecular dynamics simulations were performed. Different trajectories like RMSD, RMSF, Rg and hydrogen bond were extracted and analyzed. The results of molecular docking of A, G and D corroborate with the td-MD simulations and hypothesized that D could be a promising candidate to inhibit the activity of Mpro of nCoV. © 2022 Indian Chemical Society

5.
Lecture Notes on Data Engineering and Communications Technologies ; 116:611-650, 2022.
Article in English | Scopus | ID: covidwho-1782752

ABSTRACT

This online survey-based case study is a novel attempt to juxtapose the entwined experiences of teachers and students, their perspectives, and the effects of online teaching–learning during the COVID-19 Pandemic. The current work is an attempt to study the impact of the Pandemic on the mental health and emotional well-being of students and teachers across various parts of India. It presents a unique comparison of student responses at the beginning of the pandemic (in a previous study) with the recent data collected exactly one year after that, which shows a significant rise in negative emotional states, lower levels of satisfaction with personal progress, and much higher average screen-times. The novel dataset gathered for the current study has 572 responses of students with 51 unique attributes and 390 responses of teachers with 64 unique attributes were called “Covid-19 Go Away 2021” abbreviated as “C-19GA21” and was published on an open online data repository. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

6.
Blood ; 138(SUPPL 1):3525, 2021.
Article in English | EMBASE | ID: covidwho-1770434

ABSTRACT

Background - The WINDOW-1 regimen introduced first-line ibrutinib with rituximab (IR) followed by 4 cycles of R-HCVAD for younger mantle cell lymphoma (MCL) patients (pts) demonstrating 90% CR on IR alone and we aimed to improve the CR rate with the addition of venetoclax. We therefore investigated the efficacy and safety of IR and venetoclax (IRV) followed by risk-stratified observation or short course R-HCVAD/MTX-ARA-C as consolidation in previously untreated young patients with mantle cell lymphoma (MCL). Our aim was to use a triplet chemotherapy-free induction to reduce the toxicity, complications and minimize chemotherapy exposure in MCL pts. Methods - We enrolled 50 previously untreated pts in this single institution, single arm, phase II clinical trial - NCT03710772. Pts received IR induction (Part-1) for initial 4 cycles. Pts were restaged at cycle 4 and received IRV for up to eight cycles (Cycle 5 to Cycle 12) starting with ramp up venetoclax dosing in Cycle 5. All pts who achieved CR prior to cycle 12 continued to receive IRV for 4 cycles (maximum 12 cycles) and then moved to part 2. Pts were stratified into three disease risk groups: high, moderate and low risk categories from the baseline data for assignment to R-HCVAD/MTX-ARA-C as consolidation in part 2 (4 cycles, 2 cycles, or no chemotherapy for high, medium and low risk pts respectively). Briefly, low risk pts were those with Ki-67 ≤30%, largest tumor mass <3 cm, low MIPI score and no features of high risk disease (Ki-67 ≥50%, mutations in the TP53, NSD2 or in NOTCH genes, complex karyotype or del17p, MYC positive, or largest tumor diameter >5 cm or blastoid/pleomorphic histology or if they remain in PR after 12 cycles of part 1. Medium risk are pts which did not belong to low or high-risk category. Those who experienced progression on part 1 went to part 2 and get 4 cycles of part 2. Patient were taken off protocol but not off study, if they remained in PR after 4 cycles of chemotherapy, these patients were followed up for time to next treatment and progression free survival on subsequent therapies. After part 2 consolidation, all pts received 2 years of IRV maintenance. The primary objective was to assess CR rates after IRV induction. Adverse events were coded as per CTCAE version 4. Molecular studies are being performed. Results - Among the 50 pts, the median age was 57 years (range - 35-65). There were 20 pts in high-risk group, 20 pts in intermediate-risk group and 10 pts in low-risk group. High Ki-67 (≥30%) in 18/50 (36%) pts. Eighteen (36%) had high and intermediate risk simplified MIPI scores. Six (12%) pts had aggressive MCL (blastoid/pleomorphic). Among the 24 TP53 evaluable pts, eight pts (33%) had TP53 aberrations (mutated and/or TP53 deletion by FISH). Forty-eight pts received IRV. Best response to IRV was 96% and CR of 92%. After part 2, the best ORR remained unaltered, 96% (92% CR and 4% PR). The median number of cycles of triplet IRV to reach best response was 8 cycles (range 2-12). Fifteen pts (30%) did not receive part 2 chemotherapy, two pts (4%) received 1 cycle, 16 pts (32%) 2 cycles and 13 pts (26%) got 4 cycles of chemotherapy. With a median follow up of 24 months, the median PFS and OS were not reached (2 year 92% and 90% respectively). The median PFS and OS was not reached and not significantly different in pts with high and low Ki-67% or with/without TP53 aberrations or among pts with low, medium or high-risk categories. The median PFS and OS was inferior in blastoid/pleomorphic MCL pts compared to classic MCL pts (p=0.01 and 0.03 respectively). Thirteen pts (26%) came off study - 5 for adverse events, 3 for on study deaths, and 2 for patient choice, 2 patients lost to follow up and one for disease progression. Overall, 5 pts died (3 on trial and 2 pts died off study, one due to progressive disease and another due to COVID pneumonia). Grade 3-4 toxicities on part 1 were 10% myelosuppression and 10% each with fatigue, myalgia and rashes and 3% mucositis. One pt developed grade 3 atrial flutter on part 1. None had grade 3-4 bleeding/bruising. Conclusions - Chemotherapy-free induction with IRV induced durable and deep responses in young MCL pts in the frontline setting. WINDOW-2 approach suggests that pts with low risk MCL do not need chemotherapy but further follow up is warranted. This combined modality treatment approach significantly improves outcomes of young MCL pts across all risk groups. Detailed molecular analyses will be reported. (Figure Presented).

7.
Journal of Clinical and Diagnostic Research ; 16(3):OC10-OC15, 2022.
Article in English | EMBASE | ID: covidwho-1761186

ABSTRACT

Introduction: Computed Tomography (CT) chest plays an important role in triaging and managing patients of suspected COVID-19, especially in those where Coronavirus Disease 2019 (COVID-19) report is pending but CT chest has constraints of availability and cost. Chest X-ray (CXR) is a readily available investigation and is cheaper than a CT chest. Hence, any scoring on CXR which proves to be helpful in triaging and managing suspected COVID-19 patients will alleviate the dependency on CT chest. Modified Radiographic Assessment of Lung Edema Score (mRALES) and Brixia scores have been used to assess severity of disease and prognosis in COVID-19 confirmed cases. However, these two scores have never been used as a method to predict the confirmed COVID-19 pateints among the the suspected COVID-19 cases. Aim: To evaluate the role of mRALES and Brixia score along with clinical and laboratory parameters in predicting confirmed positive cases among suspected COVID-19 patients. Materials and Methods: This retrospective cross-sectional, observational study was conducted in Department of Medicine at Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS) and Dr. Ram Manohar Lohia Hospital, New Delhi, India, from 1st to 15th December 2020. Case records of patients admitted with severe acute respiratory illness (suspected COVID-19) were accessed and used to fill up a proforma where clinical and laboratory parameters were recorded. Chest radiographs (posteroanterior and anteroposterior) of the patients were evaluated to calculate mRALES and Brixia scores. Sensitivity, specificity, positive preditive value and negative predictive value were calculated. The p-value <0.05 was considered as statistically significant. Results: Out of the 113 patients, 62 were males and 51 females. The COVID-19 positivity rate was 15.04% (n=17). Mean age of patients was 52.64±15.63 years. Overall, the mean mRALES and Brixia scores were not statistically different between suspected (mRALES=3.94±2.51, Brixia=7.29±4.642), and confirmed COVID-19 (mRALES=4.25±2.56, Brixia=7.73±4.84) patients. However, in the subgroup of patients with history of obstructive airway disease, Brixia score was significantly higher among COVID-19 positive patients (7.09±4.70) as compared to COVID-19 suspected patients (0.53±4.31). Presence of low TLC {<9550/mm3 with sensitivity of 70.62%, specificity of 67.3%, Positive Predictive Value (PPV) of 26.7% and Negative Predictive Value (NPV) of 92.4%} and low ANC {< 7580/mm3 with sensitivity of 64.7%, specificity of 63.2%, PPV of 22.9% and NPV of 90.5%} significantly predicted the COVID-19 positivity among the suspected COVID-19 patients. Conclusion: mRALES and Brixia scores on CXR are not significantly different between suspected and confirmed COVID-19 patients and hence, cannot be used to judge who among suspected COVID-19 patients will turn out to be COVID-19 positive later. However, a TLC of less than 9550/ mm3 and an ANC of less than 7580/mm3 can predict COVID-19 positivity among suspected patients.

8.
Advanced Data Mining Tools and Methods for Social Computing ; : 51-66, 2022.
Article in English | Scopus | ID: covidwho-1750922

ABSTRACT

One of the most significant threats to today's global society is COVID-19. Due to the fear of the nCoV-19 virus and increasing infection and death rates, complete lockdowns are enforced in the whole world. Due to this contagious disease, physical communication is very difficult and risky, so the best option for communication is connection via digital media. With the constantly growing number of media platforms, India has shrunk due to the increasing communication and exchange of information. These digital platforms turned out to be most effective as regards quicker communication during the pandemic. The present study on the usage of social media during a time of pandemic addresses effective ways of usage of social media for public communication with emergency organizations, such as police, during lockdown. This information will help to identify people who are careless, cautious, and neutral towards this situation. Moreover, we discuss how to identify various emotions of people before, during, and after this crisis situation using naive Bayes and K-means clustering for clustering of tweets or comments on Twitter and Facebook and find trends using social media analytics. © 2022 Elsevier Inc. All rights reserved.

9.
Indian Journal of Community Health ; 32(1):19-24, 2020.
Article in English | GIM | ID: covidwho-1717500

ABSTRACT

Previously considered of meagre significance to the human race, coronaviruses have effectively evolved to jump the species barrier and cause widespread contagion in mankind. The SARS pandemic, the MERS situation in the middle - east and the ongoing COVID 201 9 epidemic are all attributed to this evolving virus. COVID 2019 is the seventh coronavirus isolated successfully and the third beta-coronavirus that causes a fatal illness in humans;the other two beta-coronaviruses being severe acute respiratory syndrome (SARS) CoV and middle east respiratory syndrome (MERS) CoV. Having a natural reservoir in bats these viruses infect humans through an intermediate host and then rapidly adapt and mutate for human to human transmissions. Four other known alpha coronaviruses cause only common cold in humans. Although mortality rate of COVID 2019 epidemic is lower at 2.5% than the previous two CoV outbreaks, that is, 9.6% in SARS and 34.4% in MERS, but rapid transmissibility points towards a sustained epidemic of epic proportions. In the absence of any specific treatment protocols and experimental vaccines still under research, management largely depends upon symptomatic therapy, strict infection control and quarantine measures. Restriction of human interactions with known animal sources of the virus as a measure of prevention is essentially required. Owing to huge genetic diversity and frequent genomic recombination, novel coronaviruses might emerge periodically, warranting the need for extensive research and development of effective treatments and vaccines.

10.
Journal of Vacuum Science & Technology B ; 40(2):6, 2022.
Article in English | Web of Science | ID: covidwho-1691449

ABSTRACT

The SARS-CoV-2 pandemic has had a significant impact worldwide. Currently, the most common detection methods for the virus are polymerase chain reaction (PCR) and lateral flow tests. PCR takes more than an hour to obtain the results and lateral flow tests have difficulty with detecting the virus at low concentrations. In this study, 60 clinical human saliva samples, which included 30 positive and 30 negative samples confirmed with RT-PCR, were screened for COVID-19 using disposable glucose biosensor strips and a reusable printed circuit board. The disposable strips were gold plated and functionalized to immobilize antibodies on the gold film. After functionalization, the strips were connected to the gate electrode of a metal-oxide-semiconductor field-effect transistor on the printed circuit board to amplify the test signals. A synchronous double-pulsed bias voltage was applied to the drain of the transistor and strips. The resulting change in drain waveforms was converted to digital readings. The RT-PCR-confirmed saliva samples were tested again using quantitative PCR (RT-qPCR) to determine cycling threshold (Ct) values. Ct values up to 45 refer to the number of amplification cycles needed to detect the presence of the virus. These PCR results were compared with digital readings from the sensor to better evaluate the sensor technology. The results indicate that the samples with a range of Ct values from 17.8 to 35 can be differentiated, which highlights the increased sensitivity of this sensor technology. This research exhibits the potential of this biosensor technology to be further developed into a cost-effective, point-of-care, and portable rapid detection method for SARS-CoV-2.

11.
2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1672766

ABSTRACT

COVID-19 might be devastatingly affecting our enterprises, public activities and individual prepping norms and principles but it has also sparked a digital revolution of innovation in different fields. The objective of this paper is to understand the in-depth role of the Internet of Things (IoT) in eHealth to mitigate the impact of COVID-19. This paper covers numerous applications of IoT in healthcare starting from research, telemedicine, teleconsultation via chatbots and virtual assistants providing instantaneous medical help online. Telemedicine and remote patient monitoring is the need of the hour to avoid direct contact with the patients which have been made possible via IoT and its associated tools like Artificial Intelligence, Machine Learning, Blockchain technology and Cloud Computing. With such high volumes and diversity of data being generated from IoT there is a strong need for connectivity and streaming analytics thus 5G technology and its applications have been discussed like smart 5G connected ambulances and smart 5G based hospitals. Long Range Radio is another promising technology which due to its low power operation and long-distance data transmission at higher speeds is turning out to be the defacto technology for IoT networks across the globe especially in areas with poor network coverage. Seeing the demand for both ventilators and skilled medical professionals due to lack of proper medical infrastructure worldwide, a review of IoT-based smart ventilators has also been carried out. The paper concludes with possible solutions to IoT challenges in healthcare by proposing a smart healthcare model design. Moreover keeping in mind the situation of Covid-19 Pandemic the module also comprises a UVC Disinfection box that would help in eliminating the risk of the virus entering our homes. © 2021 IEEE.

12.
Journal of Pharmaceutical Research International ; 33(58B):250-255, 2021.
Article in English | Web of Science | ID: covidwho-1626946

ABSTRACT

COVID-19 had a lot of lessons to tell, and one of the most interesting was how difficult it is to manage a deadly, fast-moving disease in a community that is closely tied together by mass transportation, mass consumption, and mass media. As the largest pandemic to have occurred in the "digital-mass society" period, COVID-19 demonstrates how quickly a virus can spread through the "masses" despite the many steps taken by public health authorities to limit its spread. The primary measure most in use by health officials during COVID-19 pandemic is preventing person to person spread of disease by creating a firewall between uninfected and infected masses. COVID-19 had a lot of lessons to tell, and one of the most interesting was how difficult it is to manage a deadly, fast-moving disease in a community that is closely tied together by mass transportation, mass consumption, and mass media. As the largest pandemic to have occurred in the "digital-mass society" era, COVID-19 offers an insight on how quickly a virus can spread through the "masses" despite the many steps taken by public health authorities to limit its spread. The material required for the review was taken from the databases of Pub Med, Web of science, the from the website of World Health Organization and the patients data of SMHRC and DMMC Wanadongari Nagpur.

13.
5th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021 ; 248:33-40, 2022.
Article in English | Scopus | ID: covidwho-1594397

ABSTRACT

The advancement of technology has created a huge scope and requirement for transforming the image into a high-visibility image. Here, transforming an image into a high-visibility image means to convert a low-visibility image into a high-visibility image. Super-resolution has many applications worldwide like in medical industries, surveillance, satellite photography, the study of the galaxy, etc. Also, COVID-19 is a monstrous threat to earth. Doctors are predicting whether the patient is having the coronavirus or not via X-Rays and CT-Scans. These scans sometimes miss little details because of the blurriness/low-visibility of the image. This problem can be overcome by using the Super-resolution convolutional neural network (SRCNN). The purpose of the study is the classification of whether the person has the coronavirus or not becomes very accurate by using the SRCNN model. For the transformation of a low-visibility image into a high-visibility image, SRCNN and for classifying whether the person is having coronavirus or not a convolutional neural network (CNN) is used. Our models are trained and tested on four datasets, which are Set5, Set14, COVID-chest-X-Ray dataset, and chest-X-Ray-pneumonia. Our results depict that after applying super-resolution on the X-Rays or the CT-Scans, the classification of COVID-19 attained an accuracy of 95.31% which is higher if compared to the classification of COVID-19 without image super-resolution that attained an accuracy of 92.19%. These were the results after running the model for 20 epochs. Hence, with the help of the SRCNN model, the classification of COVID-19 is much easier and accurate as compared to without the image super-resolution technique. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

14.
Neurology ; 96(15):2, 2021.
Article in English | Web of Science | ID: covidwho-1576506
16.
International Journal of Pharmaceutical Sciences and Research ; 12(12), 2021.
Article in English | EMBASE | ID: covidwho-1572952

ABSTRACT

Mucormycosis is an opportunistic fungal infection caused by a member of the order Mucorales. It is an angio-invasive fungal infection because of its propensity to invade blood vessel walls, resulting in catastrophic tissue ischemia (restriction in blood supply to tissues, causing a shortage of oxygen that is needed for cellular metabolism), infarct (tissue death that is necrosis) due to inadequate blood supply to the affected area. Mucorales fungi are distributed worldwide and found in soil and decaying organic substrates. The most common microbiologically confirmed infecting members of the order Mucorales are Rhizopus, Mucor, Cunninghamella bertholletiae, Apophysomyces elegans, Absidia, Saksenaea and Rhizomucor pusillus. The incidence of mucormycosis has increased significantly in patients with diabetes which is the commonest underlying risk factor globally. Recently, COVID-19 caused by SARS CoV-2 has further worsened the incidence of this disease. Diagnosis of mucormycosis remains challenging. The clinical approach to diagnosis has a low sensitivity and specificity;however, it helps raise suspicion and prompt the initiation of laboratory testing. Histopathology, direct examination, and culture remain essential tools, although the molecular methods are improving. The review highlights the current status on epidemiology, pathogenesis diagnosis and treatment regime available for mucormycosis.

17.
Lecture Notes on Data Engineering and Communications Technologies ; 54:151-164, 2021.
Article in English | Scopus | ID: covidwho-1565311

ABSTRACT

The COVID-19 outbreak has been treated as a pandemic disease by the World Health Organization (WHO). Severe diseases like Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS) are caused by members of a large family of viruses called coronavirus (CoV). A new strain was identified in humans in December 2019, named coronavirus (COVID-19). The highest affected countries are unable to predict the pace of the outbreak of COVID-19. So, AI is helpful to analyze the COVID-19 outbreak in the world. We have used the LSTM model to predict the outbreak of COVID-19 all over the world with limited epidemiological data. A variant of recurrent neural network (RNN) which has the capability of learning long-term dependencies with feedback connections, also known as long short-term memory (LSTM), is used in resolving the problems related to time series in deep learning. LSTM is capable of processing a single data point and an entire sequence of data related to any field. We observe that the LSTM model is useful to predict the ongoing outbreak so that authorities can take preventive action earlier. The LSTM model result shows that the growth rate of infected cases of COVID-19 increased exponentially every week. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

18.
Technology and Disability ; 33(4):319-338, 2021.
Article in English | Scopus | ID: covidwho-1551473

ABSTRACT

BACKGROUND: Users with Severe Speech and Motor Impairment (SSMI) often use a communication chart through their eye gaze or limited hand movement and care takers interpret their communication intent. There is already significant research conducted to automate this communication through electronic means. Developing electronic user interface and interaction techniques for users with SSMI poses significant challenges as research on their ocular parameters found that such users suffer from Nystagmus and Strabismus limiting number of elements in a computer screen. This paper presents an optimized eye gaze controlled virtual keyboard for English language with an adaptive dwell time feature for users with SSMI. OBJECTIVE: Present an optimized eye gaze controlled English virtual keyboard that follows both static and dynamic adaptation process. The virtual keyboard can automatically adapt to reduce eye gaze movement distance and dwell time for selection and help users with SSMI type better without any intervention of an assistant. METHODS: Before designing the virtual keyboard, we undertook a pilot study to optimize screen region which would be most comfortable for SSMI users to operate. We then proposed an optimized two-level English virtual keyboard layout through Genetic algorithm using static adaptation process;followed by dynamic adaptation process which tracks users' interaction and reduces dwell time based on a Markov model-based algorithm. Further, we integrated the virtual keyboard for a web-based interactive dashboard that visualizes real-time Covid data. RESULTS: Using our proposed virtual keyboard layout for English language, the average task completion time for users with SSMI was 39.44 seconds in adaptive condition and 29.52 seconds in non-adaptive condition. Overall typing speed was 16.9 lpm (letters per minute) for able-bodied users and 6.6 lpm for users with SSMI without using any word completion or prediction features. A case study with an elderly participant with SSMI found a typing speed of 2.70 wpm (words per minute) and 14.88 lpm (letters per minute) after 6 months of practice. CONCLUSIONS: With the proposed layout for English virtual keyboard, the adaptive system increased typing speed statistically significantly for able bodied users than a non-adaptive version while for 6 users with SSMI, task completion time reduced by 8.8% in adaptive version than nonadaptive one. Additionally, the proposed layout was successfully integrated to a web-based interactive visualization dashboard thereby making it accessible for users with SSMI. © 2021-IOS Press. All rights reserved.

19.
Electronic News ; 2021.
Article in English | Scopus | ID: covidwho-1542080

ABSTRACT

During the early weeks of the U.S. COVID-19 pandemic, society was battling an infodemic–defined as a “tsunami” of online misinformation. Through the lens of mediatization theory, this article examines 800,000 tweets to understand social media information and misinformation related to the COVID-19. Through multi-layered analysis, this article details prominent key words discussed on Twitter connected to pandemic trending hashtags in early-to-mid March 2020: #Covid19 and #Coronavirus. The most prominent word themes included: novelty of this virus and associated uncertainty and the spread of misinformation;severity and widespread reach of the virus;call for collective action;and expectations relative to government action. The article explains these findings through mediatization theory, applying how technology influences social media discussions. © The Author(s) 2021.

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