Results 91 to 100 of about 8,275 (212)

Second Hankel determinant for a class of analytic functions defined by q-derivative operator

open access: yesAnalele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2019
In this paper, we obtain the estimates for the second Hankel determinant for a class of analytic functions defined by q-derivative operator and subordinate to an analytic function.
Răducanu Dorina
doaj   +1 more source

Second Hankel determinant for a class of analytic functions defined by Komatu integral operator [PDF]

open access: yesRendiconti di Matematica e delle Sue Applicazioni, 2020
In this paper, the authors obtain an upper bound of second Hankel determinant for a new class of analytic functions defined through the Komatu integral operator. Our result extends the corresponding previously known results.
Ram N. Mohapatra, Trailokya Panigrahi
doaj  

The Goldilocks Zone for 3‐T MRS Studies Using Semi‐LASER: Determining the Optimal Balance Between Repetition Time and Scan Time

open access: yesNMR in Biomedicine, Volume 38, Issue 7, July 2025.
The spectral quality of 3‐T 1H‐MR spectra was compared at various repetition times to determine the optimal balance between SNR, T1‐weighting effects, and scan time. For the same number of acquisitions, a short TR results in shorter scans but yields lower SNR performance and significant T1‐weighting.
Alex G. Ensworth   +4 more
wiley   +1 more source

Spanning Multi‐Asset Payoffs With ReLUs

open access: yesMathematical Finance, Volume 35, Issue 3, Page 682-707, July 2025.
ABSTRACT We propose a distributional formulation of the spanning problem of a multi‐asset payoff by vanilla basket options. This problem is shown to have a unique solution if and only if the payoff function is even and absolutely homogeneous, and we establish a Fourier‐based formula to calculate the solution.
Sébastien Bossu   +2 more
wiley   +1 more source

Epileptiform Activity and Seizure Risk Follow Long‐Term Non‐Linear Attractor Dynamics

open access: yesAdvanced Science, Volume 12, Issue 23, June 20, 2025.
This study leverages the HAVOK framework to model long‐term, nonlinear attractor dynamics underlying epileptiform activity and seizure risk in epilepsy patients. By identifying key forcing mechanisms driving chaotic transitions, the findings improve seizure risk forecasting over multi‐day cycles and provide a pathway for personalized, data‐driven ...
Richard E Rosch   +4 more
wiley   +1 more source

Optimization of microwave hyperthermia system for focused breast cancer treatment: A study using realistic digital breast phantoms

open access: yesMedical Physics, Volume 52, Issue 6, Page 3557-3569, June 2025.
Abstract Background Microwave breast hyperthermia is a noninvasive treatment method for breast cancer that utilizes microwave energy (ME) sources to raise tissue temperatures above 42∘C$^{\circ }{\rm C}$, inducing tumor cell necrosis. The efficiency of ME deposition depends on the electric field magnitude and tissue conductivity, with antenna phase and
Burak Acar, Tuba Yilmaz, Ali Yapar
wiley   +1 more source

3D MERMAID: 3D Multi‐shot enhanced recovery motion artifact insensitive diffusion for submillimeter, multi‐shell, and SNR‐efficient diffusion imaging

open access: yesMagnetic Resonance in Medicine, Volume 93, Issue 6, Page 2311-2330, June 2025.
Abstract Purpose To enhance SNR per unit time of diffusion MRI to enable high spatial resolution and extensive q‐sampling in a feasible scan time on clinical scanners. Methods 3D multi‐shot enhanced recovery motion‐insensitive diffusion (MERMAID) consists of a whole brain nonselective 3D multi‐shot spin‐echo sequence with an inversion pulse immediately
Sajjad Feizollah, Christine L. Tardif
wiley   +1 more source

WAND: Wavelet Analysis‐Based Neural Decomposition of MRS Signals for Artifact Removal

open access: yesNMR in Biomedicine, Volume 38, Issue 6, June 2025.
Wavelet analysis‐based neural decomposition (WAND) is a novel method for decomposing magnetic resonance spectroscopy signals into metabolite‐specific, baseline, and artifact components. WAND employs a U‐Net architecture trained on simulated spectra to predict masks for wavelet coefficients, effectively isolating the desired signal components.
Julian P. Merkofer   +5 more
wiley   +1 more source

Home - About - Disclaimer - Privacy