Keyword search (4,163 papers available)

"prediction" Keyword-tagged Publications:

Title Authors PubMed ID
1 Imagining the beat: causal evidence for dorsal premotor cortex (dPMC) role in beat imagery via transcranial magnetic stimulation (TMS) Lazzari G; Ferreri L; Cattaneo L; Penhune V; Lega C; 41248776
PSYCHOLOGY
2 Assessing in silico tools for accurate pathogenicity prediction in CHD nucleosome remodelers Rabouhi N; Guindon S; Coleman EA; van Heesbeen HJ; Greenwood CMT; Lu T; Campeau PM; 40907936
ENCS
3 Application of machine learning for predicting the incubation period of water droplet erosion in metals AlHammad K; Medraj M; Tembely M; 40612685
ENCS
4 Rubber Fatigue Revisited: A State-of-the-Art Review Expanding on Prior Works by Tee, Mars and Fatemi Wang X; Sedaghati R; Rakheja S; Shangguan W; 40219307
ENCS
5 Perceptions of carbon dioxide emission reductions and future warming among climate experts Wynes S; Davis SJ; Dickau M; Ly S; Maibach E; Rogelj J; Zickfeld K; Matthews HD; 39280638
CONCORDIA
6 Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions Sun Y; Wang C; 38493601
ENCS
7 Development and validation of risk of CPS decline (RCD): a new prediction tool for worsening cognitive performance among home care clients in Canada Guthrie DM; Williams N; O' Rourke HM; Orange JB; Phillips N; Pichora-Fuller MK; Savundranayagam MY; Sutradhar R; 38041046
CRDH
8 Context changes judgments of liking and predictability for melodies Albury AW; Bianco R; Gold BP; Penhune VB; 38034280
PSYCHOLOGY
9 NMDA Receptors in the Basolateral Amygdala Complex Are Engaged for Pavlovian Fear Conditioning When an Animal's Predictions about Danger Are in Error Tuval Keidar 37607821
CSBN
10 Deep learning approach to security enforcement in cloud workflow orchestration El-Kassabi HT; Serhani MA; Masud MM; Shuaib K; Khalil K; 36691661
ENCS
11 Calcium activity is a degraded estimate of spikes Hart EE; Gardner MPH; Panayi MC; Kahnt T; Schoenbaum G; 36368324
PSYCHOLOGY
12 Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning Bouhamed O; Amayri M; Bouguila N; 35590880
ENCS
13 Prediction error determines whether NMDA receptors in the basolateral amygdala complex are involved in Pavlovian fear conditioning Williams-Spooner MJ; Delaney AJ; Westbrook RF; Holmes NM; 35410880
PSYCHOLOGY
14 Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern Sun X; Zhang X; Wang L; Li Y; Muir DCG; Zeng EY; 34388923
CHEMBIOCHEM
15 Arcuate fasciculus architecture is associated with individual differences in pre-attentive detection of unpredicted music changes Vaquero L; Ramos-Escobar N; Cucurell D; François C; Putkinen V; Segura E; Huotilainen M; Penhune V; Rodríguez-Fornells A; 33454403
MLNP
16 Integrative approach for detecting membrane proteins. Alballa M, Butler G 33349234
CSFG
17 Inter-protein residue covariation information unravels physically interacting protein dimers Salmanian S; Pezeshk H; Sadeghi M; 33334319
ENCS
18 CCCDTD5 recommendations on early non cognitive markers of dementia: A Canadian consensus Montero-Odasso M; Pieruccini-Faria F; Ismail Z; Li K; Lim A; Phillips N; Kamkar N; Sarquis-Adamson Y; Speechley M; Theou O; Verghese J; Wallace L; Camicioli R; 33094146
CRDH
19 Prediction Errors in Depression: A Quasi-Experimental Analysis. Radomsky AS, Wong SF, Dussault D, Gilchrist PT, Tesolin SB 32746394
PSYCHOLOGY
20 TooT-T: discrimination of transport proteins from non-transport proteins. Alballa M, Butler G 32321420
CSFG
21 Water Droplet Erosion of Wind Turbine Blades: Mechanics, Testing, Modeling and Future Perspectives. Elhadi Ibrahim M, Medraj M 31906204
ENCS
22 Cue-Evoked Dopamine Neuron Activity Helps Maintain but Does Not Encode Expected Value. Mendoza JA, Lafferty CK, Yang AK, Britt JP 31693885
CSBN
23 Genotype scores predict drug efficacy in subtypes of female sexual interest/arousal disorder: A double-blind, randomized, placebo-controlled cross-over trial. Tuiten A, Michiels F, Böcker KB, Höhle D, van Honk J, de Lange RP, van Rooij K, Kessels R, Bloemers J, Gerritsen J, Janssen P, de Leede L, Meyer JJ, Everaerd W, Frijlink HW, Koppeschaar HP, Olivier B, Pfaus JG 30016917
CSBN
24 Evaluating Programs for Predicting Genes and Transcripts with RNA-Seq Support in Fungal Genomes. Reid I 29876820
CSFG

 

Title:Rubber Fatigue Revisited: A State-of-the-Art Review Expanding on Prior Works by Tee, Mars and Fatemi
Authors:Wang XSedaghati RRakheja SShangguan W
Link:https://pubmed.ncbi.nlm.nih.gov/40219307/
DOI:10.3390/polym17070918
Publication:Polymers
Keywords:experimentfatiguelife predictionmagnetorheological elastomersrubber and elastomeric material
PMID:40219307 Category: Date Added:2025-04-13
Dept Affiliation: ENCS
1 School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
2 Department of Mechanical, Industrial and Aerospace, Concordia University, Montreal, QC H3G 1M8, Canada.
3 School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.

Description:

Rubber materials can endure substantial deformation while avoiding permanent damage or rupture, making them highly suitable for applications in the automotive industry and other sectors, particularly for noise and vibration reduction. However, rubber experiences degradation over time as defects or cracks appear and propagate under fluctuating loads. Therefore, it is of critical importance to prevent the failure of rubber components during service. As highlighted in prior literature surveys by Tee et al. in 2018, Mars and Fatemi in 2002 and 2004, significant research has focused on the mechanics and analysis of rubber fatigue. This body of work has grown rapidly and continues to evolve. Therefore, this study aims to compile and analyze the vast body of recent research on rubber fatigue conducted over the last decade, supplementing the reviews by Tee et al. in 2018, Mars and Fatemi in 2002 and 2004. The gathered studies were analyzed to identify current trends and emerging research gaps in the fatigue study of rubber, including advanced composite rubber materials such as magnetorheological elastomers (MREs). This review emphasizes the analysis techniques and fatigue experiments available for fatigue life prediction in rubber materials, while illustrating their practical applications in engineering analyses through specific examples.





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