NOISY NEIGHBOURS: TOWARDS A HYBRID PREDICTION AND TESTING PROCESS FOR SOUND INSULATING PARTITIONS

2021 ◽  
Author(s):  
G Whittle ◽  
A Elliott ◽  
J Meggitt ◽  
D Wong-McSweeney ◽  
R Parsons
Keyword(s):  
Hybrid Prediction

An online hybrid prediction model for mud pit volume in the complex geological drilling process

2021 ◽  
Vol 111 ◽  
pp. 104793
Author(s):  
Yang Zhou ◽  
Xin Chen ◽  
Edwardo F. Fukushima ◽  
Min Wu ◽  
Weihua Cao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Drilling Process ◽  
Hybrid Prediction

scholarly journals Multi-omics-based prediction of hybrid performance in canola

2021 ◽  
Author(s):  
Dominic Knoch ◽  
Christian R. Werner ◽  
Rhonda C. Meyer ◽  
David Riewe ◽  
Amine Abbadi ◽  
...  
Keyword(s):  
Hilbert Space ◽  
Genetic Markers ◽  
Reproducing Kernel ◽  
Prediction Models ◽  
Agronomic Traits ◽  
Reproducing Kernel Hilbert Space ◽  
Hybrid Performance ◽  
Hybrid Prediction ◽  
Gaussian Kernels ◽  
Breeding Programmes

Abstract Key message Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.

A novel hybrid prediction algorithm to network traffic

2015 ◽  
Vol 70 (9-10) ◽  
pp. 427-439 ◽  
Author(s):  
Dingde Jiang ◽  
Zhengzheng Xu ◽  
Hongwei Xu
Keyword(s):  
Network Traffic ◽  
Prediction Algorithm ◽  
Hybrid Prediction

scholarly journals Failure Prediction in Commercial Banks with a Hybrid Prediction Model

2014 ◽  
Vol 1 (2) ◽  
pp. 209-220
Author(s):  
Yinhua Li ◽  
Yong Shi ◽  
Anqiang Huang ◽  
Haizhen Yang
Keyword(s):  
Prediction Model ◽  
Commercial Banks ◽  
Failure Prediction ◽  
Hybrid Prediction

scholarly journals Handoff Delay Optimization Using Hybrid Prediction Model

2018 ◽  
Vol 6 (2) ◽  
pp. 99
Author(s):  
M R Regitha ◽  
Dr. Paul Varghese ◽  
Shailesh Sivan ◽  
Antony Nijo
Keyword(s):  
Prediction Model ◽  
Hybrid Prediction ◽  
Delay Optimization ◽  
Handoff Delay
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