Zobrazeno 1 - 10
of 98 635
pro vyhledávání: '"A Bhatia"'
Autor:
K K BANDYOPADHYAY, PRIYA BHATTACHARYA, P KRISHNAN, P P MAITY, T J PURAKAYASTHA, A BHATIA, B CHAKRABARTI, SUJAN ADAK
Publikováno v:
The Indian Journal of Agricultural Sciences, Vol 93, Iss 11 (2023)
A two-year field study was carried out during winter (rabi) seasons of 2020–21 and 2021–22 at the research farm of ICAR-Indian Agricultural Research Institute, New Delhi with the aim of examining the impacts of various methods of tillage, residue
Externí odkaz:
https://doaj.org/article/12f9dc4e832140789adbff74006ff691
Autor:
PARTHA PRATIM MAITY, BIDISHA CHAKRABARTI, A BHATIA, S N KUMAR, TJ PURAKAYASTHA, D CHAKRABORTY, S ADAK, A SHARMA, S KANNOJIYA
Publikováno v:
Journal of Agrometeorology, Vol 25, Iss 3 (2023)
Greenhouse gas (GHG) emissions from anthropogenic activities are the most significant drivers of climate change, which has both direct and indirect effects on crop production. The study was conducted during the kharif season for two years inside the
Externí odkaz:
https://doaj.org/article/53137c67610542cd8bee190628bfa510
Autor:
V V KAJE, D K SHARMA, Y S SHIVAY, S L JAT, A BHATIA, T J PURAKAYASTHA, K K BANDYOPADHYAY, RANJAN BHATTACHARYYA
Publikováno v:
The Indian Journal of Agricultural Sciences, Vol 88, Iss 1 (2023)
Long-term (13 years) impact of organic and conventional farming on soil physical properties was evaluated under a rice (Oryza sativa L.) – wheat (Triticum aestivum L.) cropping system in a sandy clay loam soil at New Delhi. The treatments included
Externí odkaz:
https://doaj.org/article/b0af09792dc6419691281d0bbd95b25b
Autor:
B CHAKRABARTI, A BHATIA, P PRAMANIK, S D SINGH, R S JATAV, NAMITA DAS SAHA, A RAJ, R JOSHI, V KUMAR
Publikováno v:
The Indian Journal of Agricultural Sciences, Vol 91, Iss 3 (2022)
A field experiment was conducted inside a temperature gradient tunnel (TGT) at the ICAR-Indian Agricultural Research Institute, New Delhi during rabi 2014-15 to quantify the impacts of elevated temperature on thermal requirement, growth and yield of
Externí odkaz:
https://doaj.org/article/16f044265b8b45438b80299ae82e7032
Potential of Raman scattering in probing magnetic excitations and their coupling to lattice dynamics
Publikováno v:
Journal of Physics: Condensed Matter 2024
Raman scattering is an excellent method for simultaneously determining the dynamics of lattice, spin, and charge degrees of freedom. Furthermore, polarization selection rules in Raman scattering enable momentum-resolved quasiparticle dynamics. In thi
Externí odkaz:
http://arxiv.org/abs/2409.00332
Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user perspectives and subsequently influence their decisions on particular
Externí odkaz:
http://arxiv.org/abs/2408.15879
Background: Data quality is vital in software analytics, particularly for machine learning (ML) applications like software defect prediction (SDP). Despite the widespread use of ML in software engineering, the effect of data quality antipatterns on t
Externí odkaz:
http://arxiv.org/abs/2408.12560
This research presents a comprehensive framework for analyzing liquidity in financial markets, particularly in the context of high-frequency trading. By leveraging advanced machine learning classification techniques, including Logistic Regression, Su
Externí odkaz:
http://arxiv.org/abs/2408.10016
Autor:
Bhatia, Sid
This paper explores the effectiveness of high-frequency options trading strategies enhanced by advanced portfolio optimization techniques, investigating their ability to consistently generate positive returns compared to traditional long or short pos
Externí odkaz:
http://arxiv.org/abs/2408.08866
Autor:
Hong, Charles, Bhatia, Sahil, Haan, Altan, Dong, Shengjun Kris, Nikiforov, Dima, Cheung, Alvin, Shao, Yakun Sophia
Hardware accelerators, in particular accelerators for tensor processing, have many potential application domains. However, they currently lack the software infrastructure to support the majority of domains outside of deep learning. Furthermore, a com
Externí odkaz:
http://arxiv.org/abs/2408.03408