Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “diffusion parameters”

Electron diffusion parameters /

Beeler, J. R., Jr. (Joe R.), Smith, M. R., General Electric Company. Aircraft Nuclear Propulsion Department. et al. · The Journal of the Abraham Lincoln Association

Cited by 0Open access

Diffusion and degradation parameters.

Juan Camilo Arboleda-Rivera (12085918), Gloria Machado-Rodríguez (12085921), Boris A. Rodríguez (12085924) et al. · Figshare · 2022

(A) Ternary plot showing the combination of diffusion parameters for genes A, B and C. (B) Ternary plot showing the combination of degradation parameters for genes A, B and C. The color gradient that goes from topology 1 in white to topology 714 in black shows no distinguishable pattern. (PDF)

Cited by 0Open access

DIFFUSION PARAMETERS OF WATER

R.L. Hobson, G. Calame, P. B. Daitch · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1963

Comparison of experiment and theory in the determination of diffusion cooling coefficients is investigated. The asymptotic eigenvalue for the pulsed neutron problem was computed for 11 bucklings and least square fitted to a curve. The form of the curve to provide the best fit with experimental data is discussed. (D.C.W.)

Cited by 1

Intra-particle diffusion parameters.

Jia Feng (430626), Shengli Shi (3221094), Liangyu Pei (3221091) et al. · Figshare · 2016

Cited by 0Open access

Diffusion parameters in biofilms.

Bénédicte Martin (2441965), Zohreh Tamanai-Shacoori (637430), Julie Bronsard (3808732) et al. · Figshare · 2017

Cited by 0Open access

Obtained diffusion model parameters.

Florian Borse (7879085), Dovydas Kičiatovas (15228839), Teemu Kuosmanen (11473623) et al. · Figshare · 2024

The diffusion model introduced in this article uses 7 global parameters—r0 and m are involved in the αi(t) term, D, ν1 and ν2 are involved in the calculation of ∂s/∂t, and K and κ are involved in the f(s) function—which are inferred by fitting the model on two different data sets: first the Scan-o-matic data, labelled here as experimental, and then the same synthetic data set as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1011585#pcbi.1011585.s003" target="_blank">S2 Fig</a> which was simulated with the real data inferred parameters. The inferred synthetic parameters are within a reasonable range from the inferred experimental parameters thus demonstrating veracity and self-consistency of the inference approach. This is true in particular for the parameters r0, m, D and ν1, while the other three, ν2, K and κ show a degree of interplay. (PDF)

Cited by 0Open access

Effective ion diffusion parameters.

Steven Niederer (400117) · Figshare · 2015

Cited by 0Open access

Diffusion Parameters of the

Extracellular Space In, Human Gliomas, Ly Dia Vargová et al. · 2003

Tumor cell migration through the extracellular space (ECS) might be affected by its pore size and extracellular matrix molecule content. ECS volume fraction # (##ECS volume/total tissue volume), tortuosity # (# and nonspecific uptake k# were studied by the real-time tetramethylammonium method in acute slices of human tissue. The diffusion parameters in temporal cortical tissue resected during surgical treatment of temporal lobe epilepsy (control) were compared with those in brain tumors. Subsequently, tumor slices were histopathologically classified according to the grading system of the World Health Organization (WHO), and proliferative activity was assessed. The average values of #, #, and k# in control cortex were 0.24, 1.55, and 3.66 # , respectively. Values of #, #, and k# in oligodendrogliomas did not significantly differ from controls. In pilocytic astrogliomas (WHO grade I) as well as in ependymomas (WHO grade II), # was significantly higher, while # and k# were unchanged. Higher values of # as well as # were found in low-grade diffuse astrocytomas (WHO grade II). In cellular regions of high-grade astrocytomas (WHO grade III and IV), # and # were further increased, and k# was significantly larger than in controls. Classic medulloblastomas (WHO grade IV) had an increased #, but not # or k#, while in the desmoplastic type # and k# remained unchanged, but # was greatly increased. Tumor malignancy grade strongly corresponds to an increase in ECS volume, which is accompanied by a change in ECS structure manifested by an increase in diffusion barriers for small molecules. GLIA 42:77--88, 2003.

Cited by 0

Diffusion-weighted imaging parameters.

Massoud S. Sharif (14014271), Emily B. Goldberg (11501452), Alexandra Walker (10778825) et al. · Figshare · 2022

FOV = field of view; TR = repetition time; TE = echo time. (DOCX)

Cited by 0Open access

Diffusion with time dependent parameters

Giammoli, A., Pilan, L., Vincenzi, S. et al. · OpenGrey (Institut de l'Information Scientifique et Technique) · 1998

Consiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome / CNR - Consiglio Nazionale delle Richerche

Cited by 0

Varying Diffusion and Degradation Parameters

Mark Isalan (26971), Caroline Lemerle (26972), Luis Serrano (5871) · Figshare · 2015

<div>Computer model of gene network, scaled to Drosophila length (0.5 mm). Diffusion parameters are varied for mRNA (Dm), protein (Dp), and T7 activator (DX). Data are plotted as percentage of total output protein (y-axes) against chamber position (x-axes), for 10-min simulations.\n (A) Outputs for protein A.\n (B) Output for protein B. Graphs with “target behaviour” are shaded grey, and the four asterisks mark the parameter sets used to generate outputs for proteins A and C.\n (C) Outputs for protein C.\n (D) Effect of adding protease degradation to B-output, shown at 15-min intervals, over a 2.5-h time course (parameters: DX = 0.43 μm2s−1; Dm = Dp = 0.02 μm2s−1; t1/2 = 770 s).</div>

Cited by 0Open access

Drift-Diffusion Model parameterS Optimization

T. T. Trung, A. M. Borovik, V. R. Stempitsky · SHILAP Revista de lepidopterología · 2019

A new approach of nanoscale MOSFETs electrical characteristics calculating, the essence of which is the use of correction factors, as well as such values of classic drift-diffusion models, which would effectively take into account the quantum-mechanical transport mechanisms is proposed. Modified direct search method of drift-diffusion model optimization for MOSFET with a 90 nm channel length is developed and used.

Cited by 0Open access

Diffusion and growth parameters optimization.

W. P. Petersen (3639907), S. Callegari (3639892), G. R. Lake (3639901) et al. · Figshare · 2017

Left: the RMS value of simulation compared to earliest known populations of Meadowcroft and Fell’s Cave. The approximate optimal value of D is 190km2/yr. The minimum RMS value is shallow in D but sensitive to r = λ, showing we are probing both the traveling wave and diffusion tail. Right: a detailed minr(RMS) optimization in D and in r at D = 190: λmin = rmin = 2.04 × 10−3 yr−1.

Cited by 0Open access

Estimation of protein diffusion parameters

Zara Ghodsi, Hossein Hassani, Mahdi Kalantari et al. · Stat · 2018

Protein diffusion offers an essential and elegant mechanism for morphogen gradient formation. Morphogens are signalling molecules that emanate from a particular region of cells and create a gradient, which has an impact on most biological processes, including cell signalling and embryonic development. Using a method that is based on singular spectrum analysis, we estimate parameters introduced in the synthesis diffusion degradation model, which is a commonly applied model for a transcription factor known as bicoid. Our findings, consistent with simulation results, indicate that the proposed method can be practically applied as an enhanced parameter estimation technique with reduced sensitivity to various levels of noise. © 2018 John Wiley & Sons, Ltd.

Cited by 1

Drift-diffusion model parameters optimization

Чан Туан Чунг, Боровик, А. М., Стемпицкий, В. Р. · Репозиторий БГУИР (BSUIR Repository) · 2014

Предложен новый подход расчета электрических характеристик наноразмерных МОП-транзисторов, сущность которого заключается в использовании корректирующих коэффициентов, а также таких значений параметров классических диффузионно-дрейфовых моделей, которые бы эффективно учитывали квантово-механические механизмы транспорта. Разработан и использован при оптимизации диффузионно-дрейфовой модели модифицированный метод прямого поиска для МОП-транзистора с длиной канала 90 нм.

Cited by 0

Estimation of parameters from diffusion processes

Weiss, E.A. Jr., Bonn Univ. (Germany). Sonderforschungsbereich 303 - Information und die Koordination Wirtschaftlicher Aktivitaeten · OpenGrey (Institut de l'Information Scientifique et Technique) · 1987

Bibliothek Weltwirtschaft Kiel C 151011 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische Informationsbibliothek

Cited by 0

Arrhenius Parameters for Grain Boundary Diffusion

Defect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forum · 1986

Cited by 0

Correlation of relaxometry and diffusion parameters.

Michael Syka (703387), Jiří Keller (449301), Jiří Klempíř (419812) et al. · Figshare · 2015

The graphs describe the correlation between the relaxometry and diffusion data obtained from the pallidum of HD patients with corresponding standard deviations (SD). Top: correlation between RR (1/T2) and FA, middle: correlation between RR (1/T2) and MD, bottom: correlation between FA and MD.

Cited by 0Open access

Diffusion by means of random parameters

L. V. Sergeeva, N. I. Teterina · Ukrainian Mathematical Journal · 1971

Cited by 0

Trait Characteristics of Diffusion Model Parameters

Anna‐Lena Schubert, Gidon T. Frischkorn, Dirk Hagemann et al. · Journal of Intelligence · 2016

Cognitive modeling of response time distributions has seen a huge rise in popularity in individual differences research. In particular, several studies have shown that individual differences in the drift rate parameter of the diffusion model, which reflects the speed of information uptake, are substantially related to individual differences in intelligence. However, if diffusion model parameters are to reflect trait-like properties of cognitive processes, they have to qualify as trait-like variables themselves, i.e., they have to be stable across time and consistent over different situations. To assess their trait characteristics, we conducted a latent state-trait analysis of diffusion model parameters estimated from three response time tasks that 114 participants completed at two laboratory sessions eight months apart. Drift rate, boundary separation, and non-decision time parameters showed a great temporal stability over a period of eight months. However, the coefficients of consistency and reliability were only low to moderate and highest for drift rate parameters. These results show that the consistent variance of diffusion model parameters across tasks can be regarded as temporally stable ability parameters. Moreover, they illustrate the need for using broader batteries of response time tasks in future studies on the relationship between diffusion model parameters and intelligence.

Cited by 80Open access