Results 1 to 10 of about 26,365 (167)

Local clustering via approximate heat kernel PageRank with subgraph sampling [PDF]

open access: yesScientific Reports, 2021
Graph clustering, a fundamental technique in network science for understanding structures in complex systems, presents inherent problems. Though studied extensively in the literature, graph clustering in large systems remains particularly challenging ...
Zhenqi Lu   +2 more
doaj   +2 more sources

Diameter Estimate in Geometric Flows

open access: yesMathematics, 2023
We prove the upper and lower bounds of the diameter of a compact manifold (M,g(t)) with dimRM=n(n≥3) and a family of Riemannian metrics g(t) satisfying some geometric flows. Except for Ricci flow, these flows include List–Ricci flow, harmonic-Ricci flow,
Shouwen Fang, Tao Zheng
doaj   +1 more source

Responses of Physiological Traits and Grain Yield to Short Heat Stress during Different Grain-Filling Stages in Summer Maize

open access: yesAgronomy, 2023
Maize kernel growth is sensitive to heat stress, which is predicted to result in yield loss. However, the response of maize to short-term heat stress during different kernel-growth stages is still not clear.
Wanlu Zhang   +10 more
doaj   +1 more source

Pointwise monotonicity of heat kernels [PDF]

open access: yesRevista Matemática Complutense, 2021
AbstractIn this paper we present an elementary proof of a pointwise radial monotonicity property of heat kernels that is shared by the Euclidean spaces, spheres and hyperbolic spaces. The main result was discovered by Cheeger and Yau in 1981 and rediscovered in special cases during the last few years.
Alonso Orán, Diego   +3 more
openaire   +7 more sources

Physicochemical Properties and Tissue Structure of High Kernel Elongation Rice (Oryza sativa L.) Varieties as Affected by Heat Treatment

open access: yesFoods, 2023
Heat treatment could affect the structure and properties of rice varieties. The present study was conducted in order to determine the effects of heat treatment on the physicochemical properties and tissue structure of Mahsuri Mutan, Basmati 370 and MR219
Anna Arina Bt Ab. Halim   +4 more
doaj   +1 more source

Convergence of approximate solutions by heat kernel for transport-diffusion equation in a half-plane [PDF]

open access: yesVestnik Samarskogo Gosudarstvennogo Tehničeskogo Universiteta. Seriâ: Fiziko-Matematičeskie Nauki, 2022
In this paper, by using the heat kernel and the transport operator on each step of time discretization, approximate solutions for the transport-diffusion equation on the half-plane+2are constructed, and their convergence to a function which satisfies the
Meryem Aouaouda   +2 more
doaj   +1 more source

Capacity and the Corresponding Heat Semigroup Characterization from Dunkl-Bounded Variation

open access: yesFractal and Fractional, 2021
In this paper, we study some important basic properties of Dunkl-bounded variation functions. In particular, we derive a way of approximating Dunkl-bounded variation functions by smooth functions and establish a version of the Gauss–Green Theorem.
Xiangling Meng, Yu Liu, Xiangyun Xie
doaj   +1 more source

Connecting quasinormal modes and heat kernels in 1-loop determinants

open access: yesSciPost Physics, 2020
We connect two different approaches for calculating functional determinants on quotients of hyperbolic spacetime: the heat kernel method and the quasinormal mode method.
Cynthia Keeler, Victoria L. Martin, Andrew Svesko
doaj   +1 more source

Computational modeling of spatial variation in moisture content and temperature distribution in corn at different superheated steam temperatures

open access: yesCogent Engineering, 2023
The Superheated steam has been shown to be more effective than hot air for drying corn. Modeling studies have been carried out in fluidized bed dryers to determine the moisture and heat transfer characteristics of corn, but there are no modeling studies ...
Mercy Jepchirchir Kimwa   +2 more
doaj   +1 more source

Geometric Deep Learning for Protein–Protein Interaction Predictions

open access: yesIEEE Access, 2022
This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux   +3 more
doaj   +1 more source

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