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8,731 results on '"Ajit K"'

202. Synchronization and Secure Communication of Chaotic Systems

Author

Singh, Ajit K., 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, Iyer, Brijesh, editor, Ghosh, Debashis, editor, and Balas, Valentina Emilia, editor

Published
2022
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204. Soil acidification and the liming potential of biochar

Author

Bolan, Nanthi, Sarmah, Ajit K., Bordoloi, Sanandam, Bolan, Shankar, Padhye, Lokesh P., Van Zwieten, Lukas, Sooriyakumar, Prasanthi, Khan, Basit Ahmed, Ahmad, Mahtab, Solaiman, Zakaria M., Rinklebe, Jörg, Wang, Hailong, Singh, Bhupinder Pal, and Siddique, Kadambot H.M.

Published
2023
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211. A high-density relativistic reflection origin for the soft and hard X-ray excess emission from Mrk 1044

Author

Mallick, Labani, Alston, William N., Parker, Michael L., Fabian, Andrew C., Pinto, Ciro, Dewangan, Gulab C., Markowitz, Alex, Gandhi, Poshak, Kembhavi, Ajit K., and Misra, R.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the first results from a detailed spectral-timing analysis of a long ($\sim$130 ks) XMM-Newton observation and quasi-simultaneous NuSTAR and Swift observations of the highly-accreting narrow-line Seyfert 1 galaxy Mrk 1044. The broadband (0.3$-$50 keV) spectrum reveals the presence of a strong soft X-ray excess emission below $\sim$1.5 keV, iron K$_{\alpha}$ emission complex at $\sim$6$-$7 keV and a `Compton hump' at $\sim$15$-$30 keV. We find that the relativistic reflection from a high-density accretion disc with a broken power-law emissivity profile can simultaneously explain the soft X-ray excess, highly ionized broad iron line and the Compton hump. At low frequencies ($[2-6]\times10^{-5}$ Hz), the power-law continuum dominated 1.5$-$5 keV band lags behind the reflection dominated 0.3$-$1 keV band, which is explained with a combination of propagation fluctuation and Comptonization processes, while at higher frequencies ($[1-2]\times10^{-4}$ Hz), we detect a soft lag which is interpreted as a signature of X-ray reverberation from the accretion disc. The fractional root-mean-squared (rms) variability of the source decreases with energy and is well described by two variable components: a less variable relativistic disc reflection and a more variable direct coronal emission. Our combined spectral-timing analyses suggest that the observed broadband X-ray variability of Mrk~1044 is mainly driven by variations in the location or geometry of the optically thin, hot corona., Comment: 23 pages, 19 figures, 4 tables, Published in MNRAS

Published
2018
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217. Impact of the COVID-19 pandemic on American College of Surgeons–Accredited Education Institutes & American Society of Anesthesiologists–Simulation Education Network: Opportunities for interdisciplinary collaboration

Author

Wisbach, Gordon G., Johnson, Kathleen A., Sormalis, Catherine, Johnson, Amy, Ham, Jennifer, Blair, Patrice G., Houg, Steven, Burden, Amanda R., Sinz, Elizabeth H., Fortner, Sally A., Steadman, Randolph H., Sachdeva, Ajit K., and Rooney, Deborah M.

Published
2022
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219. Resident Readiness for Senior Level Decision Making: Identifying the Domains for Formative Assessment and Feedback

Author

AbdelFattah, Kareem R., Alam, Hasan B., Brown, Carlos V.R., Choi, Jennifer N., Cochran, Amalia, Delman, Keith A., Demetriades, Demetrios, Dort, Jonathan M., Frey, E. Shields, Gauvin, Jeffrey, Hildreth, Amy N., Jarman, Benjamin T., Johnson, Jason M., Jung, Enjae, Katz, Steven G., Kooby, David A., Korndorffer, James R., Jr., LaFemina, Jennifer, Lau, James N., Lazar, Eric L., Lipsett, Pamela A., Maier, Ronald V., Maker, Ajay V., Maker, Vijay K., Mellinger, John D., Meyerson, Shari Lynn, Morrissey, Shawna Lynn, Napolitano, Lena M., Narayan, Mayur, Reilly, Linda M., Sanfey, Hilary, Salcedo, Edgardo S., Schropp, Kurt P., Stuke, Lance E., Tracy, Thomas F., Jr., Vaporciyan, Ara A., Verrier, Edward D., Vetto, John T., Yang, Stephen C., Park, Keon Min, Gesbeck, Melissa M., Alseidi, Adnan A., Park, Yoon Soo, Sullivan, Maura E., Blair, Patrice Gabler, Liscum, Kathy, and Sachdeva, Ajit K.

Published
2022
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229. Detection of Bars in Galaxies using a Deep Convolutional Neural Network

Author

Abraham, Sheelu, Aniyan, Arun, Kembhavi, Ajit K., Philip, N. S., and Vaghmare, Kaustubh

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network which is easy to use and provides good accuracy. In our study we use a sample of 9346 galaxies in the redshift range 0.009-0.2 from the Sloan Digital Sky Survey, which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of ~94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. Since Deep Convolutional Neural Networks can be scaled to handle large volumes of data, the method is expected to have great relevance in an era where astronomy data is rapidly increasing in terms of volume, variety, volatility and velocity along with other V's that characterize big data. With the trained model we have constructed a catalogue of barred galaxies from SDSS and made it available online., Comment: 11 pages, 12 figures, MNRAS 477, 1, 11, 2018

Published
2017
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230. Complex Optical/UV and X-ray Variability of the Seyfert 1 galaxy 1H 0419-577

Author

Pal, Main, Dewangan, Gulab C., Kembhavi, Ajit K., Misra, Ranjeev, and Naik, Sachindra

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present detailed broadband UV/optical to X-ray spectral variability of the Seyfert 1 galaxy 1H 0419-577 using six XMM-Newton observations performed during 2002-2003. These observations covered a large amplitude variability event in which the soft X-ray (0.3-2 keV) count rate increased by a factor of ~4 in six months. The X-ray spectra during the variability are well described by a model consisting of a primary power law, blurred and distant reflection. The 2-10 keV power-law flux varied by a factor ~7 while the 0.3-2 keV soft X-ray excess flux derived from the blurred reflection component varied only by a factor of ~2. The variability event was also observed in the optical and UV bands but the variability amplitudes were only at the 6-10% level. The variations in the optical and UV bands appear to follow the variations in the X-ray band. During the rising phase, the optical bands appear to lag behind the UV band but during the declining phase, the optical bands appear to lead the UV band. Such behavior is not expected in the reprocessing models where the optical/UV emission is the result of reprocessing of X-ray emission in the accretion disc. The delayed contribution of the broad emission lines in the UV band or the changes in the accretion disc/corona geometry combined with X-ray reprocessing may give rise to the observed behavior of the variations., Comment: 15 pages, 10 figures, accepted for publication in MNRAS

Published
2017
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232. Non-thermal magnetic deicing using two-dimensional chromium telluride.

Author

Chowde Gowda, Chinmayee, Kartsev, Alexey, Tiwari, Nishant, Safronov, Alexander A., Pandey, Prafull, Roy, Ajit K., Ajayan, Pulickel M., Galvão, Douglas S., and Tiwary, Chandra Sekhar

Abstract

Two-dimensional (2D) chromium telluride Cr2Te3 exhibits strong ferromagnetic ordering with high coercivity at low temperatures and paramagnetic behavior when approaching room temperature. The spin states of monolayer Cr2Te3 show ferromagnetic ordering in the ground state. In situ Raman analysis reveals a reversible structure transformation and hence proves ferromagnetic to paramagnetic transition during low-temperature heating cycles (0–25 °C). The magnetic phase transition near room temperature in 2D Cr2Te3 prompted an exploratory study of these layered materials for energy application. We demonstrate that the low-temperature ferromagnetic behavior can be used to magnetically deice material surfaces using an external magnetic source, avoiding the use of harsh chemicals and high temperatures. The hydrophobic nature and dipole interactions of H2O molecules with the surface of the 2D Cr2Te3 coating aid in the condensation of ice droplets formed on the surfaces. First-principles calculations also confirm the observed crystal structure, surface interaction, and magnetic properties of 2D Cr2Te3. [ABSTRACT FROM AUTHOR]

Published
2024
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233. Innovations in animal health: artificial intelligence-enhanced hematocrit analysis for rapid anemia detection in small ruminants.

Author

Siddique, Aftab, Panda, Sudhanshu S., Khan, Sophia, Dargan, Seymone T., Lewis, Savana, Carter, India, Van Wyk, Jan A., Mahapatra, Ajit K., Morgan, Eric R., and Terrill, Thomas H.

Subjects
MACHINE learning, BLOOD cell count, IMAGE recognition (Computer vision), ERYTHROCYTES, HEMATOCRIT
Abstract

Due to their value as a food source, fiber, and other products globally, there has been a growing focus on the wellbeing and health of small ruminants, particularly in relation to anemia induced by blood-feeding gastrointestinal parasites like Haemonchus contortus. The objective of this study was to assess the packed cell volume (PCV) levels in blood samples from small ruminants, specifically goats, and create an efficient biosensor for more convenient, yet accurate detection of anemia for on-farm use in agricultural environments for animal production optimization. The study encompassed 75 adult male Spanish goats, which underwent PCV testing to ascertain their PCV ranges and their association with anemic conditions. Using artificial intelligence-powered machine learning algorithms, an advanced, easy-to-use sensor was developed for rapidly alerting farmers as to low red blood cell count of their animals in this way to enable timely medical intervention. The developed sensor utilizes a semi-invasive technique that requires only a small blood sample. More precisely, a volume of 30 μL of blood was placed onto Whatman filter paper No. 1, previously soaked with anhydrous glycerol. The blood dispersion pattern on the glycerol-infused paper was then recorded using a smartphone after 180 s. Subsequently, these images were examined in correlation with established PCV values obtained from conventional PCV analysis. Four separate machine learning models (ML) supported models, namely support vector machine (SVM), K-nearest neighbors (KNN), backpropagation neural network (BPNN), and image classification-based Keras model, were created and assessed using the image dataset. The dataset consisted of 1,054 images that were divided into training, testing, and validation sets in a 70:20:10 ratio. The initial findings indicated a detection accuracy of 76.06% after only 10 epochs for recognizing different levels of PCV in relation to anemia, ranging from healthy to severely anemic. This testing accuracy increased markedly, to 95.8% after 100 epochs and other model parameter optimization. Results for SVM had an overall F1 score of 74–100% in identifying the PCV range for blood pattern images representing healthy to severely anemic animals, and BPNN showed 91–100% accuracy in identifying the PCV range for anemia detection. This work demonstrates that AI-driven biosensors can be used for on-site rapid anemia detection. Optimized machine learning models maximize detection accuracy, proving the sensor's validity and rapidity in assessing anemia levels. This breakthrough will allow farmers, with rapid results, to increase animal wellbeing and agricultural productivity. [ABSTRACT FROM AUTHOR]

Published
2024
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248. Introduction

Author

Finlayson, C. Max, Rastogi, Gurdeep, Mishra, Deepak R., Pattnaik, Ajit K., Finlayson, C. Max, Series Editor, Rastogi, Gurdeep, editor, Mishra, Deepak R., editor, and Pattnaik, Ajit K., editor

Published
2020
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