Zobrazeno 1 - 10
of 4 196
pro vyhledávání: '"Vinod Kumar, P. A."'
Autor:
Lobo, Allen, Sayal, Vinod Kumar
In this work, coalescence of phase-space holes of collision-less, one-dimensional plasmas is studied using kinetic simulation techniques. Phase-space holes are well-known Bernstein-Greene-Kruskal waves known for exhibiting coalescence, are numericall
Externí odkaz:
http://arxiv.org/abs/2411.17908
Autor:
Lobo, Allen, Sayal, Vinod Kumar
In this work, the recently introduced fluid-like treatment of the phase-space has been further extended and some interesting outcomes have been presented. A modified form of the Vlasov equation has been presented which describes the diffusion of the
Externí odkaz:
http://arxiv.org/abs/2410.22840
Autor:
Dwivedi, Vinod Kumar
In recent years, solid state magnetic cooling based on magnetocaloric effect (MCE) have drawn attention worldwide as a promising alternative potential candidate to the conventional gas compression-expansion cooling technique. In this chapter, the cur
Externí odkaz:
http://arxiv.org/abs/2410.16777
Autor:
Sihmar, Isha, Pandey, Abhishek, Solet, Vinod Kumar, Chaudhary, Neeru, Goyal, Navdeep, Pandey, Sudhir K.
Lead chalcogenides are the promising thermoelectric (TE) materials having narrow band gap. The present work investigates the TE behaviour of PbSe in the temperature range 300-500 K. The transport properties of the sample have been studied using the A
Externí odkaz:
http://arxiv.org/abs/2408.03786
In this research, an effort is made to address microgrid systems' operational challenges, characterized by power oscillations that eventually contribute to grid instability. An integrated strategy is proposed, leveraging the strengths of convolutiona
Externí odkaz:
http://arxiv.org/abs/2407.14984
Autor:
Lobo, Allen, Sayal, Vinod Kumar
The kinetic analyses of many-particle soft matter often employ many simulation studies of various physical phenomena which supplement the experimental limitations or compliment the theoretical findings of the study. Such simulations are generally con
Externí odkaz:
http://arxiv.org/abs/2405.16916
Autor:
Chauhan, Vinod Kumar, Clifton, Lei, Salaün, Achille, Lu, Huiqi Yvonne, Branson, Kim, Schwab, Patrick, Nigam, Gaurav, Clifton, David A.
While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predictions. In this paper, we focus on sample selection bias (SSB), a spec
Externí odkaz:
http://arxiv.org/abs/2405.07841
We have explored a transitioning cosmic model, depicting late-time accelerated expansion in $f(R,T^{\phi})$ theory of gravity for an isotropic and homogeneous universe, where the trace of energy-momentum tensor $T^{\phi}$ is the function of the self-
Externí odkaz:
http://arxiv.org/abs/2403.19458
Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data. The literature on GNN highlights the potential of this evolving research area and its widespread adoption in real-life applications.
Externí odkaz:
http://arxiv.org/abs/2403.15077
Autor:
Solet, Vinod Kumar, Pandey, Sudhir K.
The present work investigates the performance parameters of low-cost & nontoxic Mg$_{2}$Si and Ca$_{2}$Si compounds for photovoltaic (PV) applications by using density functional theory (DFT) and spectroscopic limited maximum efficiency (SLME) calcul
Externí odkaz:
http://arxiv.org/abs/2401.05296