Research on Renewal Rate of Vehicle Insurance Based on Relevance Analysis

2019 ◽  
Vol 08 (03) ◽  
pp. 513-519
Author(s):  
碧璇 李
Keyword(s):  
Renewal Rate ◽  
Relevance Analysis

Positioning changes of mayfly nymphs due to behavioral regulation of oxygen consumption

1980 ◽  
Vol 58 (4) ◽  
pp. 618-622 ◽  
Author(s):  
Michael J. Wiley ◽  
Steven L. Kohler
Keyword(s):  
Oxygen Consumption ◽  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Field Conditions ◽  
Experimental Investigations ◽  
Behavioral Patterns ◽  
Behavioral Regulation ◽  
Dissolved Oxygen Concentration ◽  
Habitat Distribution ◽  
Renewal Rate

Experimental investigations in a small artificial stream showed that the positioning of mayfly nymphs (Ephemeroptera) on stones varied with dissolved oxygen concentration (DO). At low DO levels nymphs moved to current-exposed positions, presumably to increase the renewal rate of oxygen at respiratory exchange surfaces. The expected magnitude of positioning changes under field conditions was determined and suggests that behavioral regulation of oxygen consumption may commonly influence both habitat distribution and diel behavioral patterns. The implications of these results to drift studies are also discussed.

scholarly journals On the Completeness of Pruning Techniques for Planning with Conditional Effects

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Dunbo Cai ◽  
Sheng Xu ◽  
Tongzhou Zhao ◽  
Yanduo Zhang
Keyword(s):  
Heuristic Search ◽  
State Of The Art ◽  
The State ◽  
Search Procedure ◽  
Conditional Effects ◽  
Relevance Analysis ◽  
Pruning Strategy ◽  
Pruning Techniques

Pruning techniques and heuristics are two keys to the heuristic search-based planning. Thehelpful actionspruning (HAP) strategy andrelaxed-plan-based heuristicsare two representatives among those methods and are still popular in the state-of-the-art planners. Here, we present new analyses on the properties of HAP. Specifically, we show new reasons for which HAP can cause incompleteness of a search procedure. We prove that, in general, HAP is incomplete for planning with conditional effects if factored expansions of actions are used. To preserve completeness, we propose a pruning strategy that is based onrelevance analysisandconfrontation. We will show that bothrelevance analysisandconfrontationare necessary. We call it theconfrontation and goal relevant actionspruning (CGRAP) strategy. However, CGRAP is computationally hard to be exactly computed. Therefore, we suggest practical approximations from the literature.

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