Results 41 to 50 of about 8,805 (204)

Front Propagation Through a Perforated Wall

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT We consider a bistable reaction– diffusion equation ut=Δu+f(u)$u_t=\Delta u +f(u)$ on RN${\mathbb {R}}^N$ in the presence of an obstacle K$K$, which is a wall of infinite span with many holes. More precisely, K$K$ is a closed subset of RN${\mathbb {R}}^N$ with smooth boundary such that its projection onto the x1$x_1$‐axis is bounded and that ...
Henri Berestycki, François Hamel, Hiroshi Matano   +2 more
wiley   +1 more source

Almost periodic functions on Hausdorff almost periodic time scales

Advances in Difference Equations, 2017
This paper is devoted to generalizing the notion of almost periodic functions on time scales. We introduce a new class of almost periodic time scales called Hausdorff almost periodic time scales by using the Hausdorff distance and propose a more general ...
Desheng Ji, Liu Yang, Jimin Zhang
doaj   +1 more source

A Review of Artificial Intelligence in Ophthalmology: Key Aspects, Challenges, and Future Directions

Eye &ENT Research, EarlyView.
ABSTRACT Artificial intelligence (AI) is increasingly reshaping ophthalmology because the specialty depends heavily on structured imaging, quantitative measurements, and repeatable diagnostic workflows. This review provides a clinically grounded and translationally oriented synthesis of AI in ophthalmology, covering methodological foundations ...
Partha Pratim Ray
wiley   +1 more source

Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study

Epilepsia, EarlyView.
Abstract Objective Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative magnetic resonance imaging (MRI). Current methods for resection cavity masking are time‐consuming and labor‐intensive, and existing automated approaches exhibit variable segmentation ...
Jieun Seo, Mathilde Ripart, Helene Kaas, Cornelius Kronlage, Ben Sinclair, Lucy Vivash, Merran R. Courtney, Terence J. O'Brien, Siby Gopinath, Harilal Parasuram, Sedat Kandemirli, Natally Alarab, Lillian Lai, Marcus Likeman, Kai Zhang, Jiajie Mo, Georgian Ciobotaru, James Galea, Philip Sequeiros‐Peggs, Khalid Hamandi, Hua Xie, Venkata Sita Priyanka Illapani, William D. Gaillard, Nathan T. Cohen, Alexander G. Weil, Florence Henrichon‐Goulet, Kenza S. Lahlou, Aristides Hadjinicolaou, Agustín Ibáñez, Gonzalo M. Rojas‐Costa, Horst Urbach, Lara Bücheler, Marcel Heers, Adrián Valls Carbó, Rafael Toledano, Giulia Nobile, Costanza Parodi, Domenico Tortora, Alessandro Consales, Antonella Riva, Mariasavina Severino, Martin Tisdall, Felice D'Arco, Kshitij Mankad, Aswin Chari, Maria H. Eriksson, Rory J. Piper, J. Helen Cross, Torsten Baldeweg, Sofia González‐Ortiz, Jose Pariente, Nuria Bargalló, Yawu Liu, Reetta Kälviäinen, Carmen Barba, Matteo Lenge, Renzo Guerrini, Masaki Iwasaki, Daichi Sone, Hiroyuki Maki, Tomoki Imokawa, Noriko Sato, Julien Jung, Francisco Sepulveda, Daniel Mansilla, Andres Goycoolea, Ingeborg Lopez, Antonio Napolitano, Alessandro De Benedictis, Luca De Palma, Maria Camilla Rossi‐Espagnet, Nikolaos Kondylidis, Kostakis Gkiatis, Kyriakos Garganis, Joshua Pepper, Stefano Seri, John S. Duncan, Clarissa L. Yasuda, Lucas Scárdua‐Silva, Marina K. M. Alvim, Fernando Cendes, Antonio G. Gennari, Ruth O'Gorman Tuura, Georgia Ramantani, Mariam Josyula, Joel Stein, Nishant Sinha, Kate Davis, R. Edward Hogan, Luigi Maccotta, Sophie Adler, Konrad Wagstyl   +91 more
wiley   +1 more source

Some Properties of Gromov–Hausdorff Distances [PDF]

Discrete & Computational Geometry, 2012
Let \({\mathcal G}\) stand for the class of all compact metric spaces and let \(GH(.,.)\) be the Gromov-Hausdorff distance on it. In this paper, a modified Gromov-Hausdorff distance is introduced as \(\widehat{GH}(X,Y)= (1/2)\max\{\text{infdis}(X\to Y),\text{infdis}(Y\to X)\}\), \(X,Y\in {\mathcal G}\).
openaire   +3 more sources

3D Printing a Clinically Available Solution From Plaster‐Based Positives in Superficial Radiation Therapy

Journal of Medical Radiation Sciences, EarlyView.
This study compares two techniques for creating lead shielding masks to replace the current process for superficial/orthovoltage facial radiotherapy: Plaster Cast Positive Mould (PCPM), CT Scanned 3D Print (CT3DP), and Optical Scan 3D Print (OS3DP). CT3DP was found to be more accurate and preferred over OS3DP.
Rory Hartley, Linda Bell, Toby Lowe, Jeremy Booth, Sarah Bergamin, Dasantha Jayamanne, Ryan Brown   +6 more
wiley   +1 more source

Color-Based Image Retrieval Using Perceptually Modified Hausdorff Distance

EURASIP Journal on Image and Video Processing, 2008
In most content-based image retrieval systems, the color information is extensively used for its simplicity and generality. Due to its compactness in characterizing the global information, a uniform quantization of colors, or a histogram, has been the ...
Park BoGun, Lee KyoungMu, Lee SangUk
doaj   +2 more sources

MRI‐based deep learning models match or surpass physician performance for early detection and staging of avascular necrosis of the femoral head: A systematic review

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose Efficient and reliable magnetic resonance imaging (MRI)‐based diagnosis of early avascular necrosis of the femoral head (AVNFH) is essential for guiding treatment but remains challenging due to variability in clinician experience. Deep learning (DL) models offer a promising solution. This review evaluates and summarizes the performance
Khaled Skaik, Jean Pierre Abdallah, Marc Daniel Bouchard, Olufemi R. Ayeni, Mahmoud Almasri   +4 more
wiley   +1 more source

On the complexity of computing the Hausdorff distance

Journal of Complexity, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

Synchrotron‐Based Deep Learning Network of the Inner Ear: Development and Expert Validation

The Laryngoscope, EarlyView.
A deep learning network to automatically segment the inner ear from preoperative clinical scans was developed using synchrotron‐radiation phase contrast imaging (SR‐PCI). On an unseen test set, the network significantly outperformed seven expert otologists/radiologists, the mean expert segmentation, and a simultaneous truth and performance level ...
Ashley Micuda, Kyle Rioux, Luke Helpard, Munir Demir Bajin, Zahra Kassam, Sachin Pandey, Jaehyun Hwang, Griselda T. Romero‐Sanchez, Manas Sharma, Lorne S. Parnes, Hanif M. Ladak, Sumit K. Agrawal   +11 more
wiley   +1 more source