Biosynthesis of Silver nanoparticles Using Rosaceae Petal extract and analysing its antimicrobial assay

Recent developments in nanoscience and nanotechnology have brought about a fundamental shift in the way we identify, treat, and prevent numerous diseases in all aspects of human life. Silver nanoparticles (AgNPs) are among the most significant and intriguing metallic nanoparticles employed in biomedical applications. AgNPs are very important for the domains of nanomedicine, nanoscience, and nanotechnology. Although numerous noble metals have been used for a wide range of applications, AgNPs have drawn special attention because of their potential for use in cancer treatment and diagnosis. The study showed an efficient method for the successful synthesis of AgNPs using petal extract from Rosaceae plants and characterizes them using a UV spectrometer and SEM. The produced AgNPs showed notable antibacterial activity against a variety of microbes, suggesting that they could find use as an antimicrobial agent in a number of different contexts. The work offers insightful information about how AgNPs might be used as a robust antibacterial agent against a variety of microbes

A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting : Identifying High-performing Active Region Flare Indicators

Solar flares create adverse space weather impacting space- and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single physical pathway. Studies indicate that multiple physical properties contribute to active region flare potential, compounding the challenge. Recent developments in machine learning (ML) have enabled analysis of higher-dimensional data leading to increasingly better flare forecasting techniques. However, consensus on high-performing flare predictors remains elusive. In the most comprehensive study to date, we conduct a comparative analysis of four popular ML techniques (k nearest neighbors, logistic regression, random forest classifier, and support vector machine) by training these on magnetic parameters obtained from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for the entirety of solar cycle 24. We demonstrate that the logistic regression and support vector machine algorithms perform extremely well in forecasting active region flaring potential. The logistic regression algorithm returns the highest true skill score of 0.967 +/- 0.018, possibly the highest classification performance achieved with any strictly parametric study. From a comparative assessment, we establish that magnetic properties like total current helicity, total vertical current density, total unsigned flux, R_VALUE, and total absolute twist are the top-performing flare indicators. We also introduce and analyze two new performance metrics, namely, severe and clear space weather indicators. Our analysis constrains the most successful ML algorithms and identifies physical parameters that contribute most to active region flare productivity.Peer reviewe

Fake Indian Currency Detection App

To identify counterfeit currency and report on the findings. Using a mobile camera, the model accepts the photograph. The extracted features from the scanning image are compared to a series of models. When a match is found, the outcome is outputted, indicating whether the match was true or not. Image resizing, image filtering, sobel edge detection, and template matching are the four algorithms used in this article. Even though printing false currencies is unlawful, counterfeit currencies continue to circulate in areas where there are no forms of verifying the currency's validity. The aim of this project is to avoid illicit notes from being distributed further. The project's aim is to identify false or counterfeit currency. It is accomplished by taking a sequence of steps in the same order each time. To begin, a cell phone is used to capture a picture of the currency note (camera). Second, the captured image is resized to or scaled down to 500 x 300 pixels. After that, a bilateral filter is used to eliminate noise from the signal. The features that determine a currency note's validity are then detected using the sobel operator. Correlation regression is used to match the characteristics of the note to those of an authentic note. Finally, features are listed and shown for the genuine note

Assessment of potential risk factors for COVID-19 among health care workers in a health care setting in Delhi, India -a cohort study.

IntroductionHealthcare workers (HCW) are most vulnerable to contracting COVID-19 infection. Understanding the extent of human-to-human transmission of the COVID-19 infection among HCWs is critical in managing this infection and for policy making. We did this study to estimate new infection by seroconversion among HCWs in recent contact with COVID-19 and predict the risk factors for infection.MethodsA cohort study was conducted at a tertiary care COVID-19 hospital in New Delhi during the first and second waves of the COVID-19 pandemic. All HCWs working in the hospital during the study period who came in recent contact with the patients were our study population. The data was collected by a detailed face-to-face interview, serological assessment for anti- COVID-19 antibodies at baseline and end line, and daily symptoms. Potential risk factors for seroprevalence and seroconversion were analyzed by logistic regression keeping the significance at pResultsA total of 192 HCWs were recruited in this study, out of which 119 (62.0%) were seropositive. Almost all were wearing Personal protective equipment (PPE) and following Infection prevention and control (IPC) measures during their recent contact with a COVID-19 patient. Seroconversion was observed among 36.7% of HCWs, while 64.0% had a serial rise in the titer of antibodies during the follow-up period. Seropositivity was negatively associated with being a doctor (odds ratio [OR] 0.35, 95% Confidence Interval [CI] 0.18-0.71), having COVID-19 symptoms (OR 0.21, 95% CI 0.05-0.82), having comorbidities (OR 0.14, 95% CI 0.03-0.67), and received IPC training (OR 0.25, 95% CI 0.07-0.86), while positively associated with partial (OR 3.30, 95% CI 1.26-8.69), as well as complete vaccination for COVID-19 (OR 2.43, 95% CI 1.12-5.27). Seroconversion was positively associated with doctor as a profession (OR 13.04, 95% CI 3.39-50.25) and with partially (OR 4.35, 95% CI 1.07-17.65), as well as fully vaccinated for COVID-19 (OR 6.08, 95% CI 1.73-21.4). No significant association was observed between adherence to any IPC measures and PPE adopted by the HCW during the recent contact with COVID-19 patients and seroconversion.ConclusionAlmost all the HCW practiced IPC measures in these settings. High seropositivity and seroconversion are most likely due to concurrent vaccination against COVID-19 rather than recent exposure to COVID-19 patients. Further studies using anti-N antibodies serology may help us find the reason for the seropositivity and seroconversion among HCWs