scholarly journals Persistent homology for the quantitative prediction of fullerene stability

2014 ◽  
Vol 36 (6) ◽  
pp. 408-422 ◽  
Author(s):  
Kelin Xia ◽  
Xin Feng ◽  
Yiying Tong ◽  
Guo Wei Wei
Keyword(s):  
Persistent Homology ◽  
Quantitative Prediction ◽  
Fullerene Stability

Calculating Energy Release Rate as a Function of Crack Length Using a Multiple-Step Crack Closure Technique in Tire Finite Element Models

2018 ◽  
Vol 46 (3) ◽  
pp. 130-152
Author(s):  
Dennis S. Kelliher
Keyword(s):  
Finite Element ◽  
Energy Release Rate ◽  
Crack Length ◽  
Energy Release ◽  
Crack Closure ◽  
Release Rate ◽  
Fatigue Behavior ◽  
Three Dimensions ◽  
Quantitative Prediction ◽  
Closure Technique

ABSTRACT When performing predictive durability analyses on tires using finite element methods, it is generally recognized that energy release rate (ERR) is the best measure by which to characterize the fatigue behavior of rubber. By addressing actual cracks in a simulation geometry, ERR provides a more appropriate durability criterion than the strain energy density (SED) of geometries without cracks. If determined as a function of crack length and loading history, and augmented with material crack growth properties, ERR allows for a quantitative prediction of fatigue life. Complications arise, however, from extra steps required to implement the calculation of ERR within the analysis process. This article presents an overview and some details of a method to perform such analyses. The method involves a preprocessing step that automates the creation of a ribbon crack within an axisymmetric-geometry finite element model at a predetermined location. After inflating and expanding to three dimensions to fully load the tire against a surface, full ribbon sections of the crack are then incrementally closed through multiple solution steps, finally achieving complete closure. A postprocessing step is developed to determine ERR as a function of crack length from this enforced crack closure technique. This includes an innovative approach to calculating ERR as the crack length approaches zero.

scholarly journals Exact Topological Inference for Paired Brain Networks via Persistent Homology

2017 ◽  
pp. 299-310 ◽  
Author(s):  
Moo K. Chung ◽  
Victoria Villalta-Gil ◽  
Hyekyoung Lee ◽  
Paul J. Rathouz ◽  
Benjamin B. Lahey ◽  
...  
Keyword(s):  
Persistent Homology ◽  
Brain Networks

scholarly journals Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia from 18F-FDG PET brain persistent homology network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tun-Wei Hsu ◽  
Jong-Ling Fuh ◽  
Da-Wei Wang ◽  
Li-Fen Chen ◽  
Chia-Jung Chang ◽  
...  
Keyword(s):  
Algebraic Topology ◽  
Fdg Pet ◽  
Persistent Homology ◽  
Network Connectivity ◽  
Brain Regions ◽  
Imaging Data ◽  
Cholinergic Pathways ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
Neurochemical Changes

AbstractDementia is related to the cellular accumulation of β-amyloid plaques, tau aggregates, or α-synuclein aggregates, or to neurotransmitter deficiencies in the dopaminergic and cholinergic pathways. Cellular and neurochemical changes are both involved in dementia pathology. However, the role of dopaminergic and cholinergic networks in metabolic connectivity at different stages of dementia remains unclear. The altered network organisation of the human brain characteristic of many neuropsychiatric and neurodegenerative disorders can be detected using persistent homology network (PHN) analysis and algebraic topology. We used 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging data to construct dopaminergic and cholinergic metabolism networks, and used PHN analysis to track the evolution of these networks in patients with different stages of dementia. The sums of the network distances revealed significant differences between the network connectivity evident in the Alzheimer’s disease and mild cognitive impairment cohorts. A larger distance between brain regions can indicate poorer efficiency in the integration of information. PHN analysis revealed the structural properties of and changes in the dopaminergic and cholinergic metabolism networks in patients with different stages of dementia at a range of thresholds. This method was thus able to identify dysregulation of dopaminergic and cholinergic networks in the pathology of dementia.

scholarly journals Carbon clusters near the crossover to fullerene stability

2000 ◽  
Vol 62 (23) ◽  
pp. 15394-15397 ◽  
Author(s):  
P. R. C. Kent ◽  
M. D. Towler ◽  
R. J. Needs ◽  
G. Rajagopal
Keyword(s):  
Carbon Clusters ◽  
Fullerene Stability

Quantitative prediction of flow dynamics and mechanical retention of surface-altered red blood cells through a splenic slit

2021 ◽  
Vol 33 (5) ◽  
pp. 051902
Author(s):  
Xiaojing Qi ◽  
Shuo Wang ◽  
Shuhao Ma ◽  
Keqin Han ◽  
Xuejin Li
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Flow Dynamics ◽  
Quantitative Prediction

scholarly journals A predictable smoothing evolution model for computer-controlled polishing

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Jing Hou ◽  
Pengli Lei ◽  
Shiwei Liu ◽  
Xianhua Chen ◽  
Jian Wang ◽  
...  
Keyword(s):  
Evolution Model ◽  
Quantitative Prediction ◽  
Theoretical Support ◽  
Model Combining ◽  
Process Guidance ◽  
Dependent Model ◽  
Computer Controlled Optical Surfacing ◽  
Computer Controlled ◽  
Time Dependent Model ◽  
Selection Of

AbstractQuantitative prediction of the smoothing of mid-spatial frequency errors (MSFE) is urgently needed to realize process guidance for computer controlled optical surfacing (CCOS) rather than a qualitative analysis of the processing results. Consequently, a predictable time-dependent model combining process parameters and an error decreasing factor (EDF) were presented in this paper. The basic smoothing theory, solution method and modification of this model were expounded separately and verified by experiments. The experimental results show that the theoretical predicted curve agrees well with the actual smoothing effect. The smoothing evolution model provides certain theoretical support and guidance for the quantitative prediction and parameter selection of the smoothing of MSFE.

scholarly journals MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yanping Zhang ◽  
Jing Peng ◽  
Xiaohui Yuan ◽  
Lisi Zhang ◽  
Dongzi Zhu ◽  
...  
Keyword(s):  
Prunus Avium ◽  
Cultivar Identification ◽  
Leaf Shape ◽  
Persistent Homology ◽  
Growth Period ◽  
Image Features ◽  
Crop Species ◽  
Score Level Fusion ◽  
Level Fusion ◽  
Leaf Image

AbstractRecognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online.

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