scholarly journals ESTIMATION OF RANK POSITION OF TREES IN STAND PHYTOMASS RESEARCH

2021 ◽  
pp. 20-25
Author(s):  
Z.I. Nagimov ◽  
I.N. Artemyeva ◽  
I.V. Shevelina ◽  
V.Z. Nagimov
Keyword(s):  
Rank Position

Benefiting friends or dominants: prosocial choices mainly depend on rank position in long-tailed macaques (Macaca fascicularis)

Primates ◽  
2011 ◽  
Vol 52 (3) ◽  
pp. 237-247 ◽  
Author(s):  
Jorg J. M. Massen ◽  
Inge J. A. F. Luyten ◽  
Berry M. Spruijt ◽  
Elisabeth H. M. Sterck
Keyword(s):  
Macaca Fascicularis ◽  
Rank Position

scholarly journals In Google We Trust: Users’ Decisions on Rank, Position, and Relevance

2007 ◽  
Vol 12 (3) ◽  
pp. 801-823 ◽  
Author(s):  
Bing Pan ◽  
Helene Hembrooke ◽  
Thorsten Joachims ◽  
Lori Lorigo ◽  
Geri Gay ◽  
...  
Keyword(s):  
Rank Position

scholarly journals PREFERENSI MUSIK DI KALANGAN REMAJA

PROMUSIKA ◽  
2013 ◽  
pp. 75
Author(s):  
Ayub Prasetiyo
Keyword(s):  
Popular Music ◽  
Qualitative Method ◽  
Music Preference ◽  
Musical Meaning ◽  
Rank Position ◽  
Data Collecting ◽  
Objective Value ◽  
Musical Styles

The main purpose of this study is to understand the interest trend of music preference among youths as well as to analyse factors that influence the trend. Although qualitative method was used, this study utilized questioner for its data collecting technique. The collected data was analyzed to gather respondents’s point of views and answers concerning music that they like. This study conclude that music interest trend among youth is mostly to popular music. Although the music has taken the highest rank position, its supporters are not only listen to their main preference but also other musical styles. Youths’s tendency to choose popular music have been based on several factors such as, the objective value of the music, the capability of the music to represent conditions that are experienced by them, as well as musical meaning among the youths. Keywords: Music preference, popular music, youths

scholarly journals Aphid migrant activity in refuge habitats of the Wielkopolska agricultural landscape

2015 ◽  
Vol 55 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Barbara Wilkaniec ◽  
Beata Borowiak-Sobkowiak ◽  
Agnieszka Wilkaniec ◽  
Wojciech Kubasik ◽  
Maria Kozłowska ◽  
...  
Keyword(s):  
Rural Areas ◽  
Agricultural Landscape ◽  
Flight Activity ◽  
Seasonal Activity ◽  
Species Numbers ◽  
Rank Position

Abstract Refuge habitats have a stabilising effect on the entomofauna in the agricultural landscape. The objective of this research was to follow the migrant activity of aphids in two types of refuge habitats: shrubs and roadsides of rural areas. Moericke traps method were used for testing the seasonal activity of aphids. The dynamics of species numbers were assessed, and the phenology of the dominating taxa was examined. From 2008 to 2010, more than 5,000 winged aphids from 94 species were caught in shrub habitats, and 83 species were caught in rural roadside habitats. The characteristics of aphid groups were defined on the basis of selected indicators. Annually, in both locations, flight activity was shown by a group of several taxa. The rank position of the species was varied in the particular sites and years of the research. Species participation differed when analysing aphid flights, so the seasons were divided into three separate periods: spring, summer, and autumn

scholarly journals Analysis of agonistic interactions between sows with different rank position during mixing

2009 ◽  
Vol 52 (6) ◽  
pp. 603-612 ◽  
Author(s):  
C. Borberg ◽  
S. Hoy
Keyword(s):  
Low Rank ◽  
Agonistic Interactions ◽  
High Ranking ◽  
Rank Position ◽  
The Individual ◽  
Individual Rank

Abstract. Ninety-six sows in twelve groups of eight sows were continuously video-observed during 48 hours after weaning and mixing. The occurrence and the outcome of all agonistic interactions (AI) were recorded and data of wins and defeats were transformed into a 8 × 8 (winner-loser) matrix. Aggressor and receiver of the attacks and fights in the dyads have been determined and the rank index for each sow was calculated leading to the individual rank place RP (1 to 8) and to the classifying into high ranking (HRS: RP 1 to 4) and low ranking sows (LRS: RP 5 to 8). Based on a total number of 2 414 AI dominant sows with RP=1 initiated 94.3 % of the AI they were involved in and were the receiver in 5.7 % of the AI. In contrast, every fourth AI was orientated towards the subdominant sow with RP=8. The sows with RP 7 or 8 were the receivers in nearly half of all AI (44.9 %). Fights occurred more frequently between sows with a low rank place difference. More than 60 % of the attacks and more than 40 % of the fights were initiated from high ranking sows towards low ranking sows even though 28.6 % was the estimated value for those dyads. In only 2 % of the attacks and 18.8 % of the fights LRS engaged HRS which means that HRS usually react with a counter-attack when being attacked by a LRS.

scholarly journals Rank Position Forecasting in Car Racing

2021 ◽  
Author(s):  
Bo Peng ◽  
Jiayu Li ◽  
Selahattin Akkas ◽  
Takuya Araki ◽  
Ohno Yoshiyuki ◽  
...  
Keyword(s):  
Rank Position ◽  
Car Racing

Rank Improvement Optimization Using PROMETHEE and Trigonometric Differential Evolution

2008 ◽  
pp. 266-283
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Food Product ◽  
Data Sets ◽  
Objective Functions ◽  
Rank Position ◽  
Environmental Projects ◽  
Subsequent Improvement

This chapter investigates the modelling of the ability to improve the rank position of an alternative in relation to those of its competitors. PROMETHEE is one such technique for ranking alternatives based on their criteria values. In conjunction with the evolutionary algorithm Trigonometric Differential Evolution, the minimum changes necessary to the criteria values of an alternative are investigated, for it to achieve an improved rank position. This investigation is compounded with a comparison of the differing effects of two considered objective functions that measure the previously mentioned minimization. Two data sets are considered, the first concerns the ranking of environmental projects, and the second the ranking of brands of a food product. The notion of modelling preference ranks of alternatives and the subsequent improvement of alternative’s rank positions is the realism of a stakeholders’ appreciation of their alternative in relation to their competitors.

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