Search

Your search keyword '"Das, Puja"' showing total 163 results
Start Over Author "Das, Puja" Publication Year Range Last 10 years
163 results on '"Das, Puja"'

1. Finer resolutions and targeted process representations in earth systems models improve hydrologic projections and hydroclimate impacts

Author

Das, Puja and Ganguly, Auroop R.

Subjects
Physics - Geophysics
Abstract

Earth system models inform water policy and interventions, but knowledge gaps in hydrologic representations limit the credibility of projections and impacts assessments. The literature does not provide conclusive evidence that incorporating higher resolutions, comprehensive process models, and latest parameterization schemes, will result in improvements. We compare hydroclimate representations and runoff projections across two generations of Coupled Modeling Intercomparison Project (CMIP) models, specifically, CMIP5 and CMIP6, with gridded runoff from Global Runoff Reconstruction (GRUN) and ECMWF Reanalysis V5 (ERA5) as benchmarks. Our results show that systematic embedding of the best available process models and parameterizations, together with finer resolutions, improve runoff projections with uncertainty characterizations in 30 of the largest rivers worldwide in a mechanistically explainable manner. The more skillful CMIP6 models suggest that, following the mid-range SSP370 emissions scenario, 40% of the rivers will exhibit decreased runoff by 2100, impacting 260 million people.

Published
2024

2. Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting

Author

Das, Puja, Posch, August, Barber, Nathan, Hicks, Michael, Vandal, Thomas J., Duffy, Kate, Singh, Debjani, van Werkhoven, Katie, and Ganguly, Auroop R.

Subjects
Physics - Atmospheric and Oceanic Physics
Abstract

Precipitation nowcasting, critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting but have struggled with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from physics-free machine learning (ML) methods and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists' judgment but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here, we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High-Resolution Rapid Refresh (HRRR) model, the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. For grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with a median CSI of 0.04 for HRRR. However, despite hydrologically relevant improvements in point-by-point forecasts from NowcastNet, caveats include the overestimation of spatially aggregated precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists, and river managers suggests the possibility of improved flood emergency response and hydropower management.

Published
2024
Full Text
View/download PDF

3. SFRSeg-Net: Synovial Fluid Region Segmentation from Rheumatoid Arthritis Affected Small Joints Using USG for Early Detection

Author

Das, Puja, Roy, Sourav Dey, Sangma, Kaberi, De, Asim, Bhowmik, Mrinal Kanti, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Antonacopoulos, Apostolos, editor, Chaudhuri, Subhasis, editor, Chellappa, Rama, editor, Liu, Cheng-Lin, editor, Bhattacharya, Saumik, editor, and Pal, Umapada, editor

Published
2025
Full Text
View/download PDF

4. Testing neutrino mass hierarchy under type-II seesaw scenario in $U(1)_X$ from colliders

Author

Das, Arindam, Das, Puja, and Okada, Nobuchika

Subjects
High Energy Physics - Phenomenology
Abstract

The origin of tiny neutrino mass is a long standing unsolved puzzle of the Standard Model (SM), which allows us to consider scenarios beyond the Standard Model (BSM) in a variety of ways. One of them being a gauge extension of the SM may be realized as in the form of an anomaly free, general $U(1)_X$ extension of the SM, where an $SU(2)_L$ triplet scalar with a $U(1)_X$ charge is introduced to have Dirac Yukawa couplings with the SM lepton doublets. Once the triplet scalar developes a Vacuum Expectation Value (VEV), light neutrinos acquire their tiny Majorana masses. Hence, the decay modes of the triplet scalar has a direct connection to the neutrino oscillation data for different neutrino mass hierarchies. After the breaking of the $U(1)_X$ gauge symmetry, a neutral $U(1)_X$ gauge boson $(Z^\prime)$ acquires mass, which interacts differently with the left and right handed SM fermions. Satisfying the recent LHC bounds on the triplet scalar and $Z^\prime$ boson productions, we study the pair production of the triplet scalar at LHC, 100 TeV proton proton collider FCC, $e^-e^+$ and $\mu^-\mu^+$ colliders followed by its decay into dominant dilepton modes whose flavor structure depend on the neutrino mass hierarchy. Generating the SM backgrounds, we study the possible signal significance of four lepton final states from the triplet scalar pair production. We also compare our results with the purely SM gauge mediated triplet scalar pair production followed by four lepton final states, which could be significant only in $\mu^- \mu^+$ collider., Comment: 14 pages, 7 figures, extended analysis and muon collider corrected

Published
2024

5. Ex vivo functional whole organ in biomedical research: a review

Author

Subbiahanadar Chelladurai, Karthikeyan, Selvan Christyraj, Jackson Durairaj, Rajagopalan, Kamarajan, Vadivelu, Kayalvizhi, Chandrasekar, Meikandan, Das, Puja, Kalimuthu, Kalishwaralal, Balamurugan, Nivedha, Subramanian, Vijayalakshmi, and Selvan Christyraj, Johnson Retnaraj Samuel

Published
2024
Full Text
View/download PDF

10. QoS-Aware Decentralized Trustworthy Forensic Evidence Management Framework Using the Internet of Vehicle Things (IoVT)

Author

Das, Puja, Singh, Moutushi, Roy, Deepsubhra Guha, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Giri, Debasis, editor, Das, Swagatam, editor, Corchado Rodríguez, Juan Manuel, editor, and De, Debashis, editor

Published
2024
Full Text
View/download PDF

11. Agri-Chain: A Blockchain-Empowered Smart Solution for Agricultural Industry

Author

Das, Puja, Jain, Chitra, Ansul, Singh, Moutushi, Kacprzyk, Janusz, Series Editor, Dorigo, Marco, Editorial Board Member, Engelbrecht, Andries, Editorial Board Member, Kreinovich, Vladik, Editorial Board Member, Morabito, Francesco Carlo, Editorial Board Member, Slowinski, Roman, Editorial Board Member, Wang, Yingxu, Editorial Board Member, Jin, Yaochu, Editorial Board Member, Sumithra, M. G., editor, Sathyamoorthy, Malathy, editor, Manikandan, M., editor, Dhanaraj, Rajesh Kumar, editor, and Ouaissa, Mariya, editor

Published
2024
Full Text
View/download PDF

12. Cartilage Segmentation from MRI Images Towards Prediction of Osteoarthritis

Author

Das, Puja, Bhaumik, Rabin, Dey Roy, Sourav, Nath, Satyabrata, Kanti Bhowmik, Mrinal, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kaur, Harkeerat, editor, Jakhetiya, Vinit, editor, Goyal, Puneet, editor, Khanna, Pritee, editor, Raman, Balasubramanian, editor, and Kumar, Sanjeev, editor

Published
2024
Full Text
View/download PDF

13. Food-Health-Chain: A Food Supply Chain for Internet of Health Things Using Blockchain

Author

Das, Puja, Haldar, Amrita, Singh, Moutushi, Pise, Anil Audumbar, Guha Roy, Deepsubhra, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mandal, Jyotsna Kumar, editor, Jana, Biswapati, editor, Lu, Tzu-Chuen, editor, and De, Debashis, editor

Published
2024
Full Text
View/download PDF

23. Remote Sensing in Public Health Environment: A Review

Author

Das, Puja, Martin Sagayam, K., Rahaman Jamader, Asik, Acharya, Biswaranjan, Kinshuk, Series Editor, Huang, Ronghuai, Series Editor, Dede, Chris, Series Editor, Biswas, Suparna, editor, Chowdhury, Chandreyee, editor, Acharya, Biswaranjan, editor, and Liu, Chuan-Ming, editor

Published
2022
Full Text
View/download PDF

24. Blockchain-Based COVID-19 Detection Framework Using Federated Deep Learning

Author

Das, Puja, Singh, Moutushi, Roy, Deepsubhra Guha, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Giri, Debasis, editor, Mandal, Jyotsna Kumar, editor, Sakurai, Kouichi, editor, and De, Debashis, editor

Published
2022
Full Text
View/download PDF

26. Machine Learning Capability in the Detection of Malicious Agents

Author

Sharma, Anurag, Das, Puja Archana, Ijaz, Muhammad Fazal, Rana, Abu ul Hassan S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Dhar, Sourav, editor, Mukhopadhyay, Subhas Chandra, editor, Sur, Samarendra Nath, editor, and Liu, Chuan-Ming, editor

Published
2022
Full Text
View/download PDF

28. Artificial neural networks (ANN)-genetic algorithm (GA) optimization on thermosonicated achocha juice: kinetic and thermodynamic perspectives of retained phytocompounds.

Author

Das, Puja, Nayak, Prakash Kumar, and Kesavan, Radha krishnan

Subjects
*ARTIFICIAL neural networks, *VEGETABLE juices, *FLAVONOIDS, *GENETIC algorithms, *SONICATION
Abstract

The extraction of phytocompounds from Achocha (Cyclanthera pedata) vegetable juice using traditional methods often results in suboptimal yields and efficiency. This study aimed to enhance the extraction process through the application of thermosonication (TS). To achieve this, an artificial neural network (ANN) and a genetic algorithm (GA) were utilized to simulate and optimize the process parameters. The study investigated the influence of ultrasonic amplitude (30%–50%), temperature (30 °C–50 °C), and sonication duration (15–60 min) on total polyphenolic content (TPC), total flavonoid content (TFC), antioxidant activity (AOA), and ascorbic acid content (AA). Remarkably, the ANN-GA optimization resulted in optimal TS conditions: an ultrasonic amplitude of 40%, a temperature of 40 °C, and a sonication duration of 30 min. Subsequent analysis of extraction kinetics and thermodynamics across various temperatures (30 °C–50 °C) and extraction times (0–30 min) demonstrated R2 (0.98821) and χ2 (1.74773) for TPC with activation energy (Ea) 26.0456, R2 (0.99906) and χ2 (0.07215) for TFC with Ea 26.2336, R2 (0.99867) and χ2 (0.03003) for AOA with Ea 26.0987, R2 (0.99731) and χ2 (0.13719) for AA with Ea 26.0984, validating the pseudo second-order kinetic model. These findings indicate that increased temperature enhances the saturation concentration and rate constant of phytochemical extraction. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

32. An improved protocol for inducing the gut cleaning process in earthworm for various experiments

Author

Kandaswamy, Karthikeyan, primary, Vadivelu, Kayalvizhi, additional, Durairaj Selvan Christyraj, Jackson, additional, Subbiahanadar Chelladurai, Karthikeyan, additional, Rajagopalan, Kamarajan, additional, Das, Puja, additional, Chandrasekar, Meikandan, additional, Balamurugan, Nivedha, additional, Subramanian, Vijayalakshmi, additional, and Selvan Christyraj, Johnson Retnaraj Samuel, additional

Published
2024
Full Text
View/download PDF

34. An Approach to Compress English Posts from Social Media Texts

Author

Rudrapal, Dwijen, Datta, Manaswita, Datta, Ankita, Das, Puja, Das, Subhankar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Behera, Himansu Sekhar, editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, and Pelusi, Danilo, editor

Published
2020
Full Text
View/download PDF

47. Integrated approach of ultrasound with mild heat treatment on the quality characteristics of yacon (Smallanthus sonchifolius) tuber juice.

Author

Das, Puja, Nayak, Prakash Kumar, and Kesavan, Radha krishnan

Subjects
ARTIFICIAL neural networks, YACON, VITAMIN C, TUBERS, GENETIC algorithms, FOOD pasteurization
Abstract

The research delved into the ascendancy of thermosonication (TS) on elevating the alimentary components of yacon (Smallanthus sonchifolius) tuber juice, derived from the yacon plant. This investigation introduces an innovative approach utilizing artificial neural networks (ANNs) coupled with genetic algorithm (GA) for the multifaceted optimization of extraction procedures. It aims to pinpoint the most effective processing parameters for TS, including variations in amplitudes: X1 (40, 50, and 60%) and temperatures: X2 (30, 40, and 50°C) over sonication times: X3 (15, 30, 45, and 60 min). Further, the approach is focused to amplify quality of yacon juice (YJ) and microbial attributes by enhancing certain quality characteristics such as anti‐oxidant activity (AOA), ascorbic acid (AA), total phenolic content (TPC), total carotenoid content (TCC), total flavonoid content (TFC), yeast and mold count (YMC) and total viable count (TVC). The maximum levels of AOA (33.73 ± 2.21%), AA (25.82 ± 2.03 mg/100 ml), TPC (85.08 ± 3.67 mg GAE/ml), TCC (115.82 ± 2.46 βCE μg/ml), TFC (22.52 ± 2.11 mg QE/ml), TVC, and YMC (not detected) were achieved in thermosonicated YJs under optimal conditions using ANN coupled with GA. Practical Applications: Yacon tuber juice (YJ) treated with thermosonication (TS) showed improved nutritional characteristics, making them a valuable source of functional compounds for nutraceuticals and food industries. Research emphasizes the effectiveness of artificial neural network in predicting the extraction efficiency of YJ and proposes TS as a potential replacement for traditional thermal pasteurization to maintain or enhance YJ quality and functionality. TS provides cost‐effective and sustainable solutions to the food industry by reducing processing time and energy consumption when compared to pasteurization. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results