Previous Issue
Volume 13, October
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.9
Journals
Computation
Volume 13
Issue 11
share announcement

Computation, Volume 13, Issue 11 (November 2025) – 2 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
17 pages, 4949 KB  
Article
Numerical Analysis Applying a Complex Model of the Foot Bone Structure Under Loading Conditions During Race Walking Practice
by Edder Jair Rodríguez-Granados, Guillermo Urriolagoitia-Sosa, Beatriz Romero-Ángeles, Jorge Alberto Gomez-Niebla, Jonathan Rodolfo Guereca-Ibarra, Maria de la Luz Suarez-Hernandez, Yonatan Yael Rojas-Castrejon, Manuel Nazario Rocha-Martinez, Reyner Iván Yparrea-Arreola and Guillermo Manuel Urriolagoitia-Calderón
Computation 2025, 13(11), 249; https://doi.org/10.3390/computation13110249 - 22 Oct 2025
Viewed by 251
Abstract
This study presents a three-dimensional finite element (FE) analysis of the human foot bone structure under mid-stance loading during race walking. A subject-specific biomodel comprising 26 bones and over 40 ligaments was reconstructed from computed tomography (CT) data using Materialise Mimics Research 21.0 [...] Read more.
This study presents a three-dimensional finite element (FE) analysis of the human foot bone structure under mid-stance loading during race walking. A subject-specific biomodel comprising 26 bones and over 40 ligaments was reconstructed from computed tomography (CT) data using Materialise Mimics Research 21.0 and 3-Matic Research 13.0, and subsequently analyzed in ANSYS Workbench 2024 R1. The model included explicit cortical, trabecular, and ligamentous volumes, each assigned linear-elastic, isotropic material properties based on biomechanical literature data. Boundary conditions simulated the mid-stance phase of race walking, applying a distributed plantar pressure of 0.25 MPa over the metatarsal and phalangeal regions. Numerical simulations yielded maximum total displacements of 0.00018 mm, maximum von Mises stresses of 0.171 MPa, and maximum strains of 2.5 × 10−5, all remaining well within the elastic range of bone tissue. The results confirm the model’s numerical stability, geometric fidelity, and capacity to represent physiologically realistic loading responses. The developed framework demonstrates the potential of high-resolution, image-based finite element modelling for investigating stress–strain patterns of the foot during athletic gait, and establishes a reproducible reference for future analyses involving pathological gait, orthotic optimisation, and musculoskeletal load assessment in sports biomechanics. Full article
(This article belongs to the Special Issue Application of Biomechanical Modeling and Simulation)
►▼ Show Figures

Graphical abstract

15 pages, 6252 KB  
Article
EKAResNet: Enhancing ResNet with Kolmogorov–Arnold Network-Based Nonlinear Feature Mapping
by Zhiming Dang, Tonghua Wu, Wulin Zhang, Jianxin Chen, Huanlin Chen, Xuan Liu and Zirui Liu
Computation 2025, 13(11), 248; https://doi.org/10.3390/computation13110248 - 22 Oct 2025
Viewed by 166
Abstract
Residual Networks (ResNet) address the vanishing gradient problem through skip connections and have become a fundamental architecture for computer vision tasks. However, standard convolutional layers exhibit limited capacity in modeling complex nonlinear relationships. We present EKAResNet, a residual backbone enhanced with a spline-based [...] Read more.
Residual Networks (ResNet) address the vanishing gradient problem through skip connections and have become a fundamental architecture for computer vision tasks. However, standard convolutional layers exhibit limited capacity in modeling complex nonlinear relationships. We present EKAResNet, a residual backbone enhanced with a spline-based Kolmogorov–Arnold Network (KAN) head. Specifically, we introduce a KAN-based Feature Classification Module (KAN-FCM) that replaces a portion of the traditional fully connected classifier. This module employs piecewise polynomial (spline) approximation to achieve adaptive nonlinear mapping while maintaining a controlled parameter budget. We evaluate EKAResNet on CIFAR-10 and CIFAR-100, achieving top accuracies of 95.84% and 80.06%, respectively. Importantly, the model maintains a parameter count comparable to strong ResNet and WideResNet baselines. Ablation studies on spline configurations further confirm the contribution of the KAN head. These results demonstrate the effectiveness of integrating KAN structures into ResNet for modeling high-dimensional, complex features. Our work highlights a promising direction for designing deep learning architectures that balance accuracy and computational efficiency. Full article
(This article belongs to the Section Computational Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Previous Issue
Back to TopTop