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While the world was still busy battling active COVID-19 infections, a large subset of patients started showing prolonged symptoms or developing complications following an initial recovery from COVID-19. Post covid complications range from mild symptoms such as fatigue, headache, shortness of breath to serious, life threatening conditions like opportunistic infections, deep venous thrombosis, pulmory embolism, pneumothorax and lung fibrosis. A single center, prospective, observatiol study was carried out in a tertiary respiratory care institute in North India from June 2021 to August 2021 where 224 cases of previously treated COVID-19/ongoing symptomatic COVID-19 (those patients who were manifesting symptoms beyond 4 weeks), were enrolled and followed up for a period of 3 months to estimate the prevalence of persistent symptoms, complications and any risk factors associated with it. Data alysis was done using SPSS software version 21. Univariate and multivariate alysis done among risk factors and outcome variables. ROC was done on predictor variables and area under curve (AUC) calculated. p value less than 0.05 was considered significant. Among the 24.6% symptomatic patients at follow up, the most common symptom was fatigue (51.8%) followed by dyspnea (43.8%) and anxiety (43.3%). Among the complications of COVID-19, the most common according to our study was fibrosis (15.2%), followed by pulmory thromboembolism (PTE) (12.1%), echocardiographic abnormalities (11.2%) and pulmory mucormycosis (5.4%). Female gender, presence of comorbidities, requirement of non-invasive or invasive ventilation during hospital stay emerged as independent risk factors for complications following COVID-19. This study brings forth the huge morbidity burden that COVID-19 brought upon seemingly cured individuals and lists the risk factors associated with persistence of symptoms and complications. This would help to better streamline health resources and standardize follow up guidance of COVID-19 patients.

Today, even a small street vendor in India provides the customer an option to pay electronically, using their wireless device. The businesses are aware that consumers are increasingly using smartphones to make payments for goods and services. Two types of mobile payments have been introduced by Indian retailers: wallet based and UPI (unified payments interface) based. With the government encouraging its cashless economy drive, it is backing UPI based mobile payment apps. Since earlier researchers studied the mobile payment adoption intention empirically, this study attempts to provide a mathematical model for adoption. The Bass model is used to study time based adoption pattern. Regression analysis was used to estimate the model parameters on BHIM app dataset, a UPI based government initiative. Findings show that the data fits the model well and the effect of coefficient of imitation is greater than that of innovation. Finally, discussions based on the results and implications for practitioners are provided. Future studies may use other extended versions of Bass model.

IntroductionIt is hypothesized that bronchoalveolar lavage (BAL) neutrophilia, Krebs von den Lungen-6 (KL-6), and C-reactive protein (CRP) predict the severity of chronic fibrosing interstitial lung diseases (CF-ILDs).MethodsThis cross-sectional study enrolled 30 CF-ILD patients. Using Pearson’s correlation analysis, BAL neutrophils, KL-6, and CRP were correlated with forced vital capacity (FVC), diffusing lung capacity for carbon monoxide (DLCO), six-minute walk distance (6MWD), partial pressure of oxygen (PaO2), computed tomography fibrosis score (CTFS), and pulmonary artery systolic pressure (PASP). Using the receiver operator characteristic (ROC) curve, BAL KL-6 and CRP were evaluated against FVC% and DLCO% in isolation and combination with BAL neutrophilia for predicting the severity of CF-ILDs.ResultsBAL neutrophilia significantly correlated only with FVC% (r = -0.38, P = 0.04) and DLCO% (r = -0.43, P = 0.03). BAL KL-6 showed a good correlation with FVC% (r = -0.44, P < 0.05) and DLCO% (r = -0.50, P = 0.02), while BAL CRP poorly correlated with all parameters (r = 0.0-0.2). Subset analysis of BAL CRP in patients with CTFS ≤ 15 showed a better association with FVC% (r = -0.28, P = 0.05) and DLCO% (r = -0.36, P = 0.04). BAL KL-6 cut-off ≥ 72.32 U/ml and BAL CRP ≥ 14.55 mg/L predicted severe disease with area under the curve (AUC) values of 0.77 and 0.71, respectively. The combination of BAL neutrophilia, KL-6, and CRP predicted severity with an AUC value of 0.89.ConclusionThe combination of BAL neutrophilia, KL-6, and CRP facilitates the severity stratification of CF-ILDs complementing existing severity parameters.

The moored buoy network in the Indian Ocean revolutionized the observational programs with systematic time-series measurement of in situ data sets from remote marine locations. The real-time meteorological and oceanographic data sets significantly improved the weather forecast and warning services particularly during extreme events since its inception in 1997. The sustenance of the network requires persistent efforts to overcome the multitude of challenges such as vandalism, biofouling, rough weather, corrosion, ship time availability, and telemetry issues, among others. Besides these, the COVID-19 pandemic constrained the normal functioning of activities, mainly by delaying the maintenance of the network that resulted in losing a few expensive buoy system components and precious data sets. However, the improvements in the buoy system, in-house developed data acquisition system, and efforts in ensuring the quality of measurements together with “best practice methods” enabled 73% of the buoy network to be functional even when the cruises were reduced to 33% during the COVID-19 lockdown in 2020. The moored buoys equipped with an Indian buoy data acquisition system triggered high-frequency transmission during the Super cyclone Amphan in May 2020, which greatly helped the cyclone early warning services during the COVID-19 pandemic. The COVID-19 lockdown points toward the reliability and enhanced utility of moored buoy observations particularly when other modes of measurements are limited and necessitates more such platforms to better predict the weather systems. The present study analyzed the enhancement of the buoy program and improvisation of the buoy system that extended the life beyond the stipulated duration and enabled the high-frequency data transmission during cyclones amid the COVID-19 lockdown. The recommendations to better manage the remote platforms specifically in the event of a pandemic based on the operational experience of more than two decades were also presented.

In this day and age, software is an indispensable part of our per diem endeavours, thereby keeping a check on exploitable vulnerabilities has become a vital function of a software firm. The motivation of this paper is to have better understanding of vulnerabilities, creating a tool for the industry practitioners to identify a critical vulnerability that could be detrimental for the firm’s assets. In this article, 1999 vulnerabilities related to Google Chrome was analysed to understand the behaviour of vulnerabilities. The identification of trends and patterns using topic modelling technique lead to extraction of topics. The extricated topics were then implemented in 10 classifiers to foresee the criticality of the vulnerability. The resulting performances were also assessed with the classifiers without implementing topic modelling techniques. A 10-fold validation was conducted on the suggested prediction model.

This paper investigates the attributes related to the software development process (SDP) that affect software reliability (SR). In addition, the impact of management review (MR) on SR during testing period is studied. An interactive path model is developed to examine interrelationships between SDP factors, MR and SR. Partial Least Square is used for examining the consistency of factors within the model and to test predictive validity based on the hypothesis developed for relationships among factors. The survey-based research study is conducted to validate the model by collecting data from software professionals working at different job positions. The statistical results reveal that there is a direct positive influence of SDP factors on SR and MR positively moderates the relation between testing and SR. This means that management’s frequent assessment of the testing process, together with better planning and execution of SDP components, improves SR.