We investigate the elastic buckling of a triangular prism made of a soft elastomer. A face of the prism is bonded to a stiff slab that imposes an average axial compression. We observe two possible buckling modes which are localized along the free ridge. For ridge angles ϕ below a critical value ϕ^{⋆}≈90°, experiments reveal an extended sinusoidal mode, while for ϕ above ϕ^{⋆}, we observe a series of creases progressively invading the lateral faces starting from the ridge. A numerical linear stability analysis is set up using the finite-element method and correctly predicts the sinusoidal mode for ϕ≤ϕ^{⋆}, as well as the associated critical strain ε_{c}(ϕ). The experimental transition at ϕ^{⋆} is found to occur when this critical strain ε_{c}(ϕ) attains the value ε_{c}(ϕ^{⋆})=0.44 corresponding to the threshold of the subcritical surface creasing instability. Previous analyses have focused on elastic crease patterns appearing on planar surfaces, where the role of scale invariance has been emphasized; our analysis of the elastic ridge provides a different perspective, and reveals that scale invariance is not a sufficient condition for localization.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevLett.118.165501 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Proximity ferroelectricity in wurtzite heterostructures.
Nature
January 2025
Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA.
Proximity ferroelectricity is an interface-associated phenomenon in electric-field-driven polarization reversal in a non-ferroelectric polar material induced by one or more adjacent ferroelectric materials. Here we report proximity ferroelectricity in wurtzite ferroelectric heterostructures. In the present case, the non-ferroelectric layers are AlN and ZnO, whereas the ferroelectric layers are AlBN, AlScN and ZnMgO.
View Article and Find Full Text PDFSurvival machine learning methods for mortality prediction after heart transplantation in the contemporary era.
PLoS One
January 2025
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms-Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr.
View Article and Find Full Text PDFA deformable SIS/HA composite hydrogel coaxial scaffold promotes alveolar bone regeneration after tooth extraction.
Bioact Mater
April 2025
School and Hospital of Stomatology, Tianjin Medical University, Tianjin, 300070, China.
After tooth extraction, alveolar bone absorbs unevenly, leading to soft tissue collapse, which hinders full regeneration. Bone loss makes it harder to do dental implants and repairs. Inspired by the biological architecture of bone, a deformable SIS/HA (Small intestinal submucosa/Hydroxyapatite) composite hydrogel coaxial scaffold was designed to maintain bone volume in the socket.
View Article and Find Full Text PDFMachine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response.
Front Immunol
January 2025
Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Introduction: Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.
View Article and Find Full Text PDFSTLBRF: an improved random forest algorithm based on standardized-threshold for feature screening of gene expression data.
Brief Funct Genomics
January 2025
School of Mathematics and Statistics, Southwest University, Chongqing, China.
When the traditional random forest (RF) algorithm is used to select feature elements in biostatistical data, a large amount of noise data and parameters can affect the importance of the selected feature elements, making the control of feature selection difficult. Therefore, it is a challenge for the traditional RF algorithm to preserve the accuracy of algorithm results in the presence of noise data. Generally, directly removing noise data can result in significant bias in the results.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!