ライフサイエンスコーパス: exponent

1 RNA raised to the power of an 'amplification exponent'.

2 percolation threshold, and mu is the dynamic exponent.

3 1 + nu, respectively, where nu is the Flory exponent.

4 ased more steeply, corresponding to a higher exponent.

5 the mRNA level and defines the amplification exponent.

6 surements of the volume-area fractal scaling exponent.

7 engths, but each type has a distinct scaling exponent.

8 ximate entropy, fractal dimension, and Hurst exponent.

9 ates a negative value in the LLK scaling-law exponent.

10 Golden Mean [Formula: see text] as dynamical exponent.

11 ly 2 regardless of the underlying population exponent.

12 ludes optimality models that predict the 0.2 exponent.

13 can be classified according to few critical exponents.

14 is able to produce power laws with arbitrary exponents.

15 each characterized by its specific critical exponents.

16 lations, with only three independent scaling exponents.

17 s diffusion models with adjustable anomalous exponents.

18 sition and compatible values of the critical exponents.

19 be characterized by their power-law scaling exponents.

20 t support the hypothesis of fixed allometric exponents.

21 ic crossover between two different power-law exponents.

22 patterns follow a power law with non-integer exponents.

23 that follow power laws with well-established exponents.

24 rcation analysis depending on the fractional exponent (0 < alpha <= 1).

25 ed by a surprisingly small power-law scaling exponent (0.22) between the radius-of-gyration and Q-len

26 ~1/3) and bacterial chains (increasing with exponent ~0.5-0.8) are substantially lower than for well

27 ltiscale correlation of SERCA group (scaling exponent: 0.77 +/- 0.07), on the other hand, is weaker t

28 than that of the control Drosophila (scaling exponent: 0.85 +/- 0.03) (p = 0.016).

29 han for well-mixed bacteria (increasing with exponent 1).

30 g-dominated and characterized by a power-law exponent 1/2.

31 We validate the hypothesis of fractional exponents (1) by numerical simulation for disease propag

32 ion rates for microcolonies (increasing with exponent ~1/3) and bacterial chains (increasing with exp

33 r sizes and correlation lengths diverge with exponents ~1.6 and 0.8, respectively, consistent with pe

34 smaller and follows a different sequence of exponents, 1 and (1/2).

35 spectrum of cosmic rays, with the universal exponent -2, which is independent of the multiplication

36 Its exponent, -2, is determined by the multiplicative decrea

37 t rate and wingbeat frequency (raised to the exponent 3.5) and estimated metabolic power and wingbeat

38 be recognized by a power-law dependence with exponent 3/2 of the shear modulus on stress, whereas the

39 masses exhibit power-law distributions with exponents -3/2 and -5/2 before and after percolation, as

40 ated metabolic power and wingbeat frequency (exponent 7) of migratory bar-headed geese.

41 on only the crown area-to-diameter allometry exponent: a well-conserved value across tropical forests

42 s is because the aerosol absorption Angstrom exponent (AAE) largely controls the color and larger par

43 t lambda = 550 nm and an absorption Angstrom exponent (AAE) of 1.03 +/- 0.09 (2sigma).

44 organic aerosol with an absorption Angstrom exponent (AAE) of 2.5-2.7 and estimated Brown Carbon con

45 at lambda = 550 nm with absorption Angstrom exponents (AAE) between 3.5 and 6.2.

46 on [Formula: see text], where the 1/3 excess exponent above 2 (smooth surfaces) follows from Kolmogor

47 We find that the critical exponents add up to unity when using a special volumetri

48 al depth, AOD), dominant size mode (Angstrom exponent, AE), and relative magnitude of radiation scatt

49 face exhibit transient subdiffusion, with an exponent alpha approximately 0.5 for times of less than

50 Our analysis reveals that the exponent alpha in the above relationship is not a consta

51 - D)) is valid, however for 1.5 < D < 2, the exponent alpha is different and equal to 2(D - 1)/D.

52 function of the normal contact load with an exponent alpha within the whole range of fractal dimensi

53 of variation of PEF (CVpef) and the scaling exponent alpha, reflecting self-similarity of PEF, in re

54 n coefficient D(t) in terms of the anomalous exponent alpha, the crossover time t(cross), and the lim

55 detrended fluctuation analysis (DFA) scaling exponent alpha.

56 Kardar-Parisi-Zhang (KPZ) model with scaling exponents alpha = 0.71 +/- 0.12, beta = 0.36 +/- 0.03, a

57 ched exponential modeling (ADCSE), anomalous exponent (alpha) obtained at stretched exponential DWI,

58 exponential and segmented scaling laws with exponents (alpha) typically between 0.85 (Horwitz) and 1

59 Detrended Fluctuation Analysis (DFA) scaling exponent, alpha.

60 Time series of size-based spatial TPL exponents also differ between hydrographically distinct

61 scaling theory (MST) posits that the scaling exponents among plant height H, diameter D, and biomass

62 ive diagnosed from health, by characteristic exponent analysis of pulse signals accessed from volunte

63 ue to changes in both the allometric scaling exponent and intercept.

64 a discrepancy between the observed power-law exponent and that predicted from the noise parameters.

65 ous measures of complexity, through Lyapunov exponents and entropy.

66 However, the relationships between scaling exponents and normalization constants remain unclear.

67 Different metals will have different scaling exponents and shapes in their energy spreading, but the

68 ion d + zLambda(T), where z is the dynamical exponent, and temperature-depending parameter Lambda(T)

69 lley transitions, effective mass, scattering exponent, and the Fermi energy may deteriorate or amelio

70 but it also predicts EEG features, the Hurst exponent, and the power spectrum.

71 lay a scale-free power-law distribution with exponent approximately 2.

72 We tested whether allometric scaling exponents are generally constant across plant sizes as p

73 ngth [Formula: see text] Mean-field critical exponents are predicted, since the upper critical dimens

74 In conclusion, scaling exponents are remarkably similar across species, develop

75 Further, size-based temporal TPL exponents are systematically higher (implying more tempo

76 The fitted scaling exponents are typically less than 1, implying that the v

77 This suggests that low Raman exponents arise from the unique spin-phonon coupling of

78 However, the usefulness of TPL exponents as an ecological metric has been questioned, l

79 ymptotes to zero with no need to fit unknown exponents as previously proposed in critical power law b

80 is subsequently used to measure the critical exponents associated with chiral clock models(10,11), pr

81 -structure factor, with continuously varying exponents, at any fixed separation in the late-time limi

82 Thus, the sample exponent b approximately 2 may indeed be a statistical a

83 Is the widely reported sample exponent b approximately 2 the result of ecological proc

84 of measurements is distributed in space, the exponent b of this power law is conjectured to reflect a

85 Greater synchrony typically decreases the exponent b of TL.

86 t a broad range of values for the population exponent b pertaining to the mean and variance of popula

87 The sample exponent b(jk) depends predictably on the number of samp

88 Sample exponents b measured empirically via the scaling of samp

89 ralized TL in terms of sample and population exponents b(jk) for the scaling of the kth vs. the jth c

90 where Tc is the critical temperature and the exponent beta was close to (1/4), as predicted for a tri

91 ed ferromagnetic region yields 3D Heisenberg exponents beta = 0.3460 +/- 0.040, gamma = 1.344 +/- 0.0

92 apparent elastic modulus, Ea, and power-law exponent, beta.

93 , N = 10) = 25.44, p = 1.2492e-05), spectral exponent between 1 and 20 Hz (chi-square (3, N = 16) = 4

94 N = 10) = 20, p = 4.5400e-05), and spectral exponent between 30-50 Hz (chi-square (2, N = 16) = 13.8

95 We then compared TPL exponents between regions of contrasting environmental c

96 f a difference in the values of the critical exponents between the bond and site percolation models i

97 dom few-body circuits with infinite Lyapunov exponent but logarithmic scrambling time.

98 rom 20 human subjects, we calculated scaling exponents by four methods-two derived from local propert

99 When approaching a detection limit, the exponents change and approach an apparently Gaussian (al

100 flux distribution, which links the critical exponents characterizing the spatial dependencies in hum

101 The slope of this decay, the noise exponent (chi), is often <-1 for electrophysiological da

102 e anisotropy exponent zeta and the roughness exponents chix,y that characterize these correlations.

103 rameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a

104 nt durations can be collapsed with a scaling exponent close to 2 supporting critical generational mod

105 reptiles and plants, the relationship has an exponent close to a half.

106 at percent body fat scales to height with an exponent closer to 3, we therefore focused on the tri-po

107 wer, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations.

108 abundance of populations, with the power law exponent considered a measure of aggregation.

109 munities are characterized by an average ISD exponent consistent with three-quarter-power scaling of

110 These findings support the notion that TPL exponents contain ecological information, capturing comm

111 lator-metal transition and calculate scaling exponents corresponding to the transition.

112 A metric called the Hurst exponent could be a useful biomarker for studies explori

113 ve density (rho(eff)), mass-mobility scaling exponent ( D(m)), dynamic shape factor (chi), and mass a

114 found that on populated islands size spectra exponents decreased (analogous to size spectra steepenin

115 bined, leaf vs stem and leaf vs root scaling exponents decreased from c. 1.00 for small plants to c.

116 ut that simply inserting asymptotic critical exponents deduced from the immediate vicinity of the cri

117 s that can be described using single scaling exponents (denoted by beta, [Formula: see text], and lam

118 iently anomalous diffusion and the anomalous exponent depend on the size of model glomeruli and the d

119 follows a power law time dependence, with an exponent determined by the 1/f-type resonator frequency

120 clear relationship was observed between the exponents determined for the power law linking quadratic

121 However, the scaling exponents differed from those predicted by recent simula

122 act power laws, p(x) ~ x(-lambda), where the exponent directly corresponds to the mixing ratio of the

123 ithout spatial correlations but with scaling exponents distinct from those of original data.

124 adapted to quantify the anomalous diffusion exponent dw from the IOI records.

125 's law, a Pareto distribution with power law exponent equal to one.

126 ics near the transition and obtain universal exponents establishing connection between thermal soften

127 ees closely with the mortality rate doubling exponent estimated at the cross-over age near the averag

128 Finite-time fluctuations in these exponents exhibit sharply peaked dynamical timescales an

129 theories predict that combinations of these exponents explain how metabolic, growth, and other biolo

130 any known relationships between the critical exponents explored by them, despite the fact that they o

131 critical behavior with the maximum Lyapunov exponent fluctuating around zero.

132 l gamma = (d + z - 2)/z, where the dynamical exponent for a ferroelectric z = 1 and the dimension is

133 ly lower offset and a possible change in the exponent for Alzheimer's disease subjects compared with

134 etwork architecture for estimating the Hurst exponent for fractional Brownian motion and the diffusio

135 We measure virtually the same scaling exponent for manufacturing for the 1993 to 2015 period a

136 o 1993 period and virtually the same scaling exponent for other sectors as for manufacturing.

137 The exponent for this Cu was close to 0.5, indicating low-di

138 the expectation that the dynamical critical exponent for this universality class is z = 3/2.

139 tinct from the larger and more regular Raman exponents for 2-Dy, 2-Er, and 2-Yb.

140 these allow for different body-size scaling exponents for anabolism (biosynthesis potential), beside

141 ely correlated with their associated scaling exponents for D vs. V and H vs. V, whereas normalization

142 The exponents for dislocation movement are greater (epsilon

143 The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, whic

144 The critical exponents for the perforated membrane are compatible wit

145 btaining the critical noise and the critical exponents for the two and three-state majority-vote mode

146 odel, we extract previously unknown critical exponents for this fixed point.

147 This enables robust estimation of Hurst exponents for very short time series data, making possib

148 near features (approximate entropy and Hurst exponent) for the first time to explore post-concussive

149 particular, more ballistic Levy flights with exponent [Formula: see text] are generally believed to o

150 , whose density is described by a nontrivial exponent [Formula: see text] We build a microscopic theo

151 diffusive universality class with dynamical exponent [Formula: see text], another prominent example

152 caling analysis, we obtain that the critical exponents [Formula: see text] and [Formula: see text] as

153 Remarkably, their dynamical exponents [Formula: see text] are given by ratios of nei

154 osed measure of local dimension explains the exponents found in the network recovery.

155 comitant increase in the absorption Angstrom exponent from 1.2 +/- 0.4 (5% RH) to 1.6 +/- 0.3 (70% RH

156 ntly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within

157 We derived community TPL exponents from a long-term, standardised and spatially d

158 We extract the values of these exponents from already known numerical or theoretical re

159 e is needed in interpreting lag-time scaling exponents from protein assembly data.

160 ional, variant T(-) (gamma) , with the power exponent gamma = 1.4 +/- 0.1 in the cubic phase, indicat

161 lity varying as 1/T(3), i.e. with a critical exponent gamma = 3.

162 n of the parameter space given by the degree exponent gamma and average degree <k>.

163 an interactions with population size with an exponent gamma ranging between 1.11 and 1.21, as observe

164 natural basins and OCNs with varying energy exponent gamma to understand vulnerability and resilienc

165 s a crucial role in determining the critical exponents governing universal nonequilibrium dynamics in

166 s respond exponentially to temperature (with exponents >1).

167 and extruded flours exhibited higher fractal exponent h in agreement with the extended crystalline st

168 Decreases in the Hurst exponent (H), which quantifies scale-free signal, was re

169 The scaling exponent has a drastic effect on the optimal design of s

170 protein concentration as a power law, whose exponent has been used to infer the presence or absence

171 me-averaged optical coefficients, scattering exponent, hemoglobin concentration, oxygen saturation, a

172 us to give a precise meaning to the scaling exponent in terms of the degree to which a given process

173 e for epidemiological models with fractional exponent in the contribution of sub-populations to the i

174 ss implications of the changes in offset and exponent in the data and relate it to existing literatur

175 We suggest that calibration of the repulsive exponent in the LJ potential widens the range of applica

176 ation between quantum and classical Lyapunov exponents in a chaotic system without finite-size effect

177 roughout the neocortex with distinct scaling exponents in different functional brain systems and freq

178 allow us to explain a wide range of scaling exponents in frequency distributions ranging from alpha

179 how signs of kinks: clear changes in scaling exponent, indicating changes in the dominant molecular m

180 e observed variability of ecological scaling exponents into a coherent statistical framework where pa

181 in a broad class of growth models the sample exponent is b approximately 2 regardless of the underlyi

182 This exponent is different from [Formula: see text] of dilute

183 en if its typical form with constant scaling exponent is not obeyed.

184 Thus, the absorption angstrom exponent is not representative of the fuel used and, the

185 ltifractal spectrum based on the local Hurst exponent is used to quantify the complexity of fractal n

186 and temporal domains with the same algebraic exponent, is reproduced with numerical solutions of stoc

187 We extract a critical exponent kappa ~ 0.38 +/- 0.02 in agreement with recent

188 agonists (E(0)) and the affinity-correlation exponent (M)--allows an entire CRC to be calculated from

189 ercolation with a power law distribution and exponent matching the theoretical value in 2 dimensions.

190 Angstrom exponent measurements of equivalent black carbon (BCeq)

191 uter simulations on a model system, that the exponent mu is not universal, but depends on the microsc

192 that can be quantitatively evaluated by the exponent n (ca. 3) of the temperature dependence of the

193 The exponent N of the current-voltage characteristics (inver

194 r rates (>500 s(-1)), at which the power law exponent (n) of zebrafish blood was nearly 1 behaving as

195 The diffusional exponent (n) values of Peppas equation explains a non Fi

196 chanism, with changes observed in the Avrami exponent (n).

197 e results, however, suggest that the release exponents, n, correspond mostly to anomalous case II and

198 The release exponents, n, which correspond to the drug release mecha

199 as expected from metabolic theory, but with exponents near +/-1 across all groups.

200 ize in a remarkably reciprocal fashion, with exponents near +/-3/4 within groups, as expected from me

201 are often well described by power laws with exponents near 3/4 or related to that value, a commonali

202 Also, the MC estimate of the critical exponent nu in the NMF region is about twice as large as

203 decreases with increasing m and the critical exponents nu, alpha, beta and gamma for m > 1 are found

204 Besides, the critical exponents obey the Rushbrooke inequality alpha + 2beta +

205 s transformed into a power law with the same exponents observed in the tree cluster data.

206 The analysis of the stretching exponents obtained from the measurements suggest 1-D cha

207 The result: creep exponents obtained from two kinds of tests agree well wi

208 illar size with an inverse power-law scaling exponent of -0.63 independent of orientation.

209 ometer length scales, with the strengthening exponent of -0.68 at room temperature and of -1.00 at 90

210 scaling of NHP clearance with an allometric exponent of 0.50 allowed for good prediction of human cl

211 in translation and directs an amplification exponent of 1.20 with a 95% confidence interval [1.14, 1

212 ted population distribution with a universal exponent of 1.7.

213 The inverse power law exponent of 1/f-type noise is shown to decrease from 3.0

214 7), which tallies with a dipolar interaction exponent of 2/3 in EC materials and the well-proven frac

215 emory behavior characterized by a relaxation exponent of [Formula: see text].

216 sorting dynamics follow a power law with an exponent of approximately 0.5.

217 further identify these cells as a functional exponent of ARC(AgRP) neuron-driven hunger.

218 ther observe an unprecedented sixfold-higher exponent of growth rate, faster onset, higher steady-sta

219 ith studying E-I ratio based on the spectral exponent of local field potentials and bioenergetics bas

220 olve this problem by considering the scaling exponent of shell thickness as a morphological parameter

221 c modulus E and fluidity beta (the power-law exponent of the cell deformation in response to a step c

222 numerical results on the correlation length exponent of the Chalker-Coddington model at nu ~ 2.6, ra

223 madogram estimator to calculate the scaling exponent of the corresponding MARS residuals.

224 the decay in autocorrelation and the scaling exponent of the detrended fluctuation analysis from EEG

225 d nullity in terms of the energy dissipation exponent of the drainage networks.

226 anding the sorting dynamics and explains the exponent of the power law behavior.

227 produce realistic conditions in terms of the exponent of the power-law distribution, of the number of

228 The power-law exponent of the step length distributions and fractal di

229 ffusion coefficient and subdiffusive scaling exponent of the stochastic motion.

230 t seems likely that synchrony influences the exponent of TL widely in ecologically and economically i

231 imilarly both within and across groups, with exponents of +/-1/4.

232 m pre-existing nuclei as confirmed by Avrami exponents of 0.25 +/- 0.01 and 0.39 +/- 0.01 at the afor

233 By contrast, scaling exponents of A-P and R-P relationships were altered by P

234 Strikingly, power-law exponents of brain/SOG/sensory-blocked larvae averaged 1

235 ly, the lower the individual subject scaling exponents of delta/theta oscillations, the greater the c

236 s are negatively correlated with the scaling exponents of H vs. D.

237 ey fail to predict the correct values of the exponents of power-law degree distributions observed in

238 The exponents of power-law regimen neuronal avalanches and L

239 The averages of scaling exponents of ST and SL MARS residuals are slightly small

240 sis (DFA) has been used to calculate scaling exponents of stride time (ST), stride length (SL), and s

241 detrended fluctuation analysis (DFA) scaling exponents of the abiotic microcosms were lower (ca. 1.20

242 The scaling exponents of the relationships describing root nutrients

243 dent process, in the sense that the critical exponents of the transition are determined by the geomet

244 power-law distributions and that the average exponents of these individual size distributions (ISD) d

245 We demonstrate that the stationary critical exponents of this transition to meso-scale turbulence in

246 The scaling exponents of whole-plant metabolic rate vs body size num

247 We find that the repulsion exponent p approximately 6.5 provides an excellent fit f

248 The last metric is exponent parameter, which is used to capture the 2D stru

249 atin states apparently correspond to smaller exponent parameters and larger radius of gyrations.

250 g the resting state indexed by the Power Law Exponent (PLE) in PostCG and AI.

251 also predicted a spectrum of power laws with exponents ranging between 0 and -2/3 for simple movement

252 zed by quasi-power-law release profiles with exponents ranging from 0.5 to 1, respectively.

253 ifferentiation operation on their input with exponents ranging from zero (no differentiation) to 0.4

254 tic attractor, along with a maximal Lyapunov exponent rate of about 2.94 times the fundamental optome

255 a fat-tailed distribution, with a universal exponent related to the recently observed universal [For

256 s of persistent criticality when testing for exponent relations and universal shape-collapse.

257 records exhibit scaling behavior with large exponents, resulting in larger fluctuations at longer ti

258 These exponents rule an universal scaling behaviour that witne

259 n intensity is described by a power law with exponents sequentially taking values 1, 1/3 and (1/4).

260 1-Ho also exhibits an anomalously low Raman exponent similar to 1-Dy, both being distinct from the l

261 ld reduction in the scale-dependent Lyapunov exponent slope.

262 Low P supply increased scaling exponents (slopes) of area-based log-log A-N or R-N rela

263 periodicity of about 2 y, a global Lyapunov exponent statistically indistinguishable from zero, and

264 d 200 degrees C exhibits a positive Lyapunov exponent, suggesting that the underlying dynamics is cha

265 km(2) are power-law distributed with a tail exponent (tau = 1.97) and fractal dimension (d = 1.38),

266 -law distribution for the cell size, with an exponent that depends inversely on the noise in the time

267 la: see text], where theta is a nonuniversal exponent that depends on the statistics of the disorder.

268 The JMAK model hinges on an exponent that expresses the growth mechanism of a materi

269 istribution of energy over frequency with an exponent that, even in the ultrarelativistic limit, stro

270 istinguishable from zero, and local Lyapunov exponents that alternated systematically between negativ

271 However, we find nonuniversal exponents that cannot be captured by this mechanism or a

272 distributions of states are power laws with exponents that coincide with the multiplication paramete

273 ied electric current and determined critical exponents that coincided with those for thermodynamic li

274 ear stress featuring power law rheology with exponents that match those of reconstituted collagen and

275 a marked suppression in their characteristic exponents that reflects a weakened sensitivity to initia

276 lerated the bacterial and fungal STR and PTR exponents (that is, the w values), yielding significantl

277 s is set by the triple isotope fractionation exponent theta that can be determined precisely for, e.g

278 We propose the inference of the repulsion exponent through Hierarchical Bayesian uncertainty quant

279 pectral shape causes the absorption Angstrom exponent to decrease by 0.18 per unit increase in pH.

280 ratigraphy; converting the temporal Lyapunov exponent to vertical distance using the mean sedimentati

281 om the aorta to capillaries and uses scaling exponents to quantify these changes.

282 caling relations and the connection of their exponents to the local dimension.

283 e found a large variability of the anomalous exponent, used to interpret live cell imaging trajectori

284 For a same scaling phenomenon, however, the exponents vary in cities of similar population sizes.

285 We find that, in general, TPL exponents vary more than expected under random condition

286 he aim of this study was to test whether TPL exponents vary systematically with potential drivers of

287 appears to be ubiquitous, empirical scaling exponents vary with ecosystem type and resource supply r

288 teps drawn from a Levy distribution with the exponent varying from [Formula: see text] to [Formula: s

289 escribed by a continuously varying power law exponent versus energy and temperature (hence named a Po

290 The average absorption Angstrom exponent was 1.2 +/- 0.8, suggesting that most of the li

291 onal Brownian motion with a stochastic Hurst exponent was used to interpret, for the first time, anom

292 mple entropy, trapping time and the Lyapunov exponents) was found in the centrotemporal region of the

293 etabolism scales to body size with a smaller exponent whenever temperatures or activity levels are hi

294 to the kinetic model, we determine the power exponent, which represents the dimensionality of surface

295 Rather, continuous shifts in allometric exponents with plant size during ontogeny and evolution

296 to be a power law function of P with scaling exponent X [demographic conflict investment (DCI)].

297 to be a power law function of W with scaling exponent Y [conflict lethality (CL)].

298 ntum phase transition has dynamical critical exponent z 2, typical of a Lifshitz transition, but is d

299 to be a power law function of P with scaling exponent Z [group conflict mortality (GCM)].

300 determine the exact values of the anisotropy exponent zeta and the roughness exponents chix,y that ch