scholarly journals Cluster analysis reveals seasonal variation of sperm subpopulations in extended boar semen

2018 ◽  
Vol 64 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Iulian IBĂNESCU ◽  
Claus LEIDING ◽  
Heinrich BOLLWEIN
Keyword(s):  
Cluster Analysis ◽  
Seasonal Variation ◽  
Boar Semen

Multiparameter cluster analysis of seasonal variation of water masses in the eastern Beibu Gulf

2011 ◽  
Vol 67 (6) ◽  
pp. 709-718 ◽  
Author(s):  
Shengli Chen ◽  
Yan Li ◽  
Jianyu Hu ◽  
Airong Zheng ◽  
Lingfeng Huang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Seasonal Variation ◽  
Water Masses ◽  
Beibu Gulf

Seasonality of cruise ship activityin neighboring regions

2022 ◽  
pp. 147509022110702
Author(s):  
Jeronimo Esteve-Perez ◽  
Antonio Garcia-Sanchez
Keyword(s):  
Cluster Analysis ◽  
Seasonal Variation ◽  
Cruise Ship ◽  
Negative Effects ◽  
The Baltic Sea ◽  
Continuous Growth ◽  
Cruise Industry ◽  
Variation Index ◽  
The Baltic ◽  
The Empirical Analysis

The continuous growth of the cruise industry has brought with it a series of threats. Among them is the management of the seasonality of cruise activity in order to address its negative effects. This study examines the monthly cruise passenger movement distribution among a group of harbors located in the northeast sector of the Atlantic Ocean and the Baltic Sea with the aim of determining the existence of seasonality patterns in cruise traffic and their relationship between different regions. A database of cruise passenger movements during the period from 2007 to 2019 among 24 harbors forms the backbone of the empirical analysis. First, the seasonal variation index of each harbor was calculated to determine the seasonality pattern. Second, a cluster analysis was performed to classify harbors into clusters with analogous seasonality patterns. The results reveal that seasonality of cruise activity in a consolidated region is explained by own factors of the region, but also by an induced seasonality imported from neighboring cruise regions.

scholarly journals Home-range estimation within complex restricted environments: importance of method selection in detecting seasonal change

2009 ◽  
Vol 36 (3) ◽  
pp. 213 ◽  
Author(s):  
Carolyn M. Knight ◽  
Robert E. Kenward ◽  
Rodolphe E. Gozlan ◽  
Kathryn H. Hodder ◽  
Sean S. Walls ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Seasonal Variation ◽  
Home Range ◽  
Reserve Design ◽  
Linear Estimator ◽  
Home Ranges ◽  
Telemetry Data ◽  
Range Estimation ◽  
River Bank ◽  
Wide Range

Estimating the home ranges of animals from telemetry data can provide vital information on their spatial behaviour, which can be applied by managers to a wide range of situations including reserve design, habitat management and interactions between native and non-native species. Methods used to estimate home ranges of animals in spatially restricted environments (e.g. rivers) are liable to overestimate areas and underestimate travel distances by including unusable habitat (e.g. river bank). Currently, few studies that collect telemetry data from species in restricted environments maximise the information that can be gathered by using the most appropriate home-range estimation techniques. Simulated location datasets as well as radio-fix data from 23 northern pike (Esox lucius) were used to examine the efficiency of home-range and travel estimators, with and without correction for unusable habitat, for detecting seasonal changes in movements. Cluster analysis most clearly demonstrated changes in range area between seasons for empirical data, also showing changes in patchiness, and was least affected by unusable-environment error. Kernel analysis showed seasonal variation in range area more clearly than peripheral polygons or ellipses. Range span, a linear estimator of home range, had no significant seasonal variation. Results from all range area estimators were smallest in autumn, when cores were least fragmented and interlocation movements smallest. Cluster analysis showed that core ranges were largest and most fragmented in summer, when interlocation distances were most variable, whereas excursion-sensitive methods (e.g. kernels) recorded the largest outlines in spring, when interlocation distances were largest. Our results provide a rationale for a priori selection of home-range estimators in restricted environments. Contours containing 95% of the location density defined by kernel analyses better reflected excursive activity than ellipses or peripheral polygons, whereas cluster analyses better defined range cores in usable habitat and indicate range fragmentation.

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Measuring “Language Access Profiles” in Deaf and Hard-of-Hearing Children With the DHH Language Exposure Assessment Tool

2020 ◽  
pp. 1-25
Author(s):  
Matthew L. Hall ◽  
Stephanie De Anda
Keyword(s):  
Cluster Analysis ◽  
Exposure Assessment ◽  
Hard Of Hearing ◽  
Assessment Tool ◽  
Communication Mode ◽  
Developmental Potential ◽  
Language Access ◽  
Retest Reliability ◽  
Language Exposure ◽  
Test Retest Reliability

Purpose The purposes of this study were (a) to introduce “language access profiles” as a viable alternative construct to “communication mode” for describing experience with language input during early childhood for deaf and hard-of-hearing (DHH) children; (b) to describe the development of a new tool for measuring DHH children's language access profiles during infancy and toddlerhood; and (c) to evaluate the novelty, reliability, and validity of this tool. Method We adapted an existing retrospective parent report measure of early language experience (the Language Exposure Assessment Tool) to make it suitable for use with DHH populations. We administered the adapted instrument (DHH Language Exposure Assessment Tool [D-LEAT]) to the caregivers of 105 DHH children aged 12 years and younger. To measure convergent validity, we also administered another novel instrument: the Language Access Profile Tool. To measure test–retest reliability, half of the participants were interviewed again after 1 month. We identified groups of children with similar language access profiles by using hierarchical cluster analysis. Results The D-LEAT revealed DHH children's diverse experiences with access to language during infancy and toddlerhood. Cluster analysis groupings were markedly different from those derived from more traditional grouping rules (e.g., communication modes). Test–retest reliability was good, especially for the same-interviewer condition. Content, convergent, and face validity were strong. Conclusions To optimize DHH children's developmental potential, stakeholders who work at the individual and population levels would benefit from replacing communication mode with language access profiles. The D-LEAT is the first tool that aims to measure this novel construct. Despite limitations that future work aims to address, the present results demonstrate that the D-LEAT represents progress over the status quo.

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