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Your search keyword '"Karmakar P"' showing total 3,478 results
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3,478 results on '"Karmakar P"'

1. Tribological Performance Analysis of Electroless Nickel Coated Mild Steel: A Comparative Experimental Study

Author

Ghosh Sandip, Bose Bidesh, Dolui Chirantan, Karmakar Pritam, Pal Biplob, Kumar Ashutosh, and Hoque Saba Karim

Subjects
Environmental sciences, GE1-350
Abstract

In the pursuit of sustainable materials, electroless Nickel coating has emerged as a breakthrough in the development of novel materials with excellent tribological properties. In the present work, NiP and NiB coating has been performed over mild steel specimens for an hour. The tribological character for various loads of NiP and NiB-coated mild steel was determined through tribological tests. The wear behavior of both NiP and NiB-coated mild steel has been analyzed. The NiB-coated mild steel was found to show less coefficient of friction and wear rate than NiP-coated mild steel.

Published
2023
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2. Universal digital high-resolution melting for the detection of pulmonary mold infections.

Author

Goshia, Tyler, Aralar, April, Wiederhold, Nathan, Jenks, Jeffrey, Mehta, Sanjay, Karmakar, Aprajita, E S, Monish, Sharma, Ankit, Sun, Haoxiang, Kebadireng, Refilwe, White, P, Sinha, Mridu, Hoenigl, Martin, and Fraley, Stephanie

Subjects
HRM, IMI, dPCR, machine learning, Humans, Lung Diseases, Fungal, Fungi, Sensitivity and Specificity, Molecular Diagnostic Techniques, Transition Temperature, Bronchoalveolar Lavage Fluid, Machine Learning, Invasive Fungal Infections
Abstract

UNLABELLED: Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus, Mucorales, Lomentospora, and Fusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be >8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRMs speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes. IMPORTANCE: Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.

Published
2024

41. Generalised scalar-tensor theories of gravity and pressure profiles of galaxy clusters

Author

Haridasu Balakrishna S., Karmakar Purnendu, De Petris Marco, Cardone Vincenzo F., and Maoli Roberto

Subjects
Physics, QC1-999
Abstract

In the current proceedings, we summarise the results presented during the mm Universe@NIKA2 conference, taken from our main results in [1]. We test the Degenerate higher-order scalar-tensor(DHOST) theory as a generalised platform for scalar-tensor theory at galaxy cluster scales to predict in such static systems small scale modification to the gravitational potential. DHOST theory is not only a good alternative to ΛCDM for the background evolution but also predicts small-scale modification to the gravitational potential in static systems such as galaxy clusters. With a sample of 12 clusters with accurate Xray Intra Cluster Medium (ICM) data (X-COP project) and Sunyaev-Zel’dovich (SZ) ICM pressure (Planck satellite), we place preliminary constraints on the DHOST parameter (Ξ1) defining the deviation from GR. Moreover, we also collect a few supplementary analyses we have performed during the course: i) Gaussian process reconstruction without parametric assumptions, ii) PSZ-only data analysis not aided by the X-ray data. Finally, we present possible extensions to the current work which may benefit from future high sensitivity and spatial resolution observations.

Published
2022
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