Pollen carryover: experimental comparisons between morphs of Palicourea lasiorrachis (Rubiaceae), a distylous, bird-pollinated, tropical treelet

Oecologia ◽  
1987 ◽  
Vol 73 (2) ◽  
pp. 231-235 ◽  
Author(s):  
P. Feinsinger ◽  
W. H. Busby

Author(s):  
Alessandro Fortunato ◽  
Leonardo Orazi ◽  
Giovanni Tani

The bottleneck in laser hardening principally occurs when large surfaces have to be treated because this process situation leads to multi-tracks laser scanning in order to treat all the component surface. Unfortunately, multi-tracks laser trajectories generate an unwanted tempering effect that depends on the overlapping of two close trajectories. To reduce the softening effects, a simulator capable to optimize the process parameters such as laser power and speed, number and types of trajectories, could sensibly increase the applicability of the process. In this paper an original model for the tempering is presented. By introducing a tempering time factor for the martensitic transformation, the hardness reduction can be predicted according to any laser process parameters, material and geometry. Experimental comparisons will be presented to prove the accuracy of the model.



1988 ◽  
Vol 135 (3) ◽  
pp. 419-429 ◽  
Author(s):  
Candace Galen ◽  
John T. Rotenberry
Keyword(s):  


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ying Li ◽  
Hang Sun ◽  
Shiyao Feng ◽  
Qi Zhang ◽  
Siyu Han ◽  
...  

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.



1929 ◽  
Vol 20 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Arthur I. Gates ◽  
Helen Brown




1997 ◽  
Vol 52 (16) ◽  
pp. 2769-2783 ◽  
Author(s):  
D. Dey ◽  
J.M. Boulton-Stone ◽  
A.N. Emery ◽  
J.R. Blake


Author(s):  
D P Stoten ◽  
M G Dye ◽  
M Webb

The minimal control synthesis (MCS) algorithm is an adaptive control strategy that requires no prior knowledge of plant dynamic parameters, and yet is guaranteed to provide global asymptotic stability of the closed-loop system. The purpose of this paper is to present MCS as applied to web tension und transport control a class of plant that has highly non-linear dynamics and time-varying parameters. The plant is difficult to control by conventional methods over its full operating range. A typical example and model of such a plant is presented along with the implementation of MCS. Experimental comparisons of MCS with conventional control benchmarks are provided. It will be seen that MCS significantly outperforms the conventional controller.



1992 ◽  
Vol 137 (1) ◽  
pp. 35-47 ◽  
Author(s):  
N. Rouge ◽  
J. Dreier ◽  
S. Yanar ◽  
R. Chawla


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