scholarly journals Statistical determination of diagnostic, constant and dominant species of the higher vegetation units of Poland

2014 ◽  
Vol 103 ◽  
pp. 1-267 ◽  
Author(s):  
Zygmunt Kącki ◽  
Marta Czarniecka ◽  
Grzegorz Swacha
Keyword(s):  
Vascular Plants ◽  
Dominant Species ◽  
Hierarchical Classification ◽  
Lichenized Fungi ◽  
Data Set ◽  
Stellarietea Mediae ◽  
Vegetation Database ◽  
Statistical Determination ◽  
Asplenietea Trichomanis

This paper presents a syntaxonomical revision and statistical determination of diagnostic, constant and dominant species of higher syntaxa of Poland based on releves stored in the Polish Vegetation Database. All the analyses were performed on a data set consisting of 43,686 releves containing 2,853 species of vascular plants, bryophytes, algae and lichenized fungi. The data set was subjected to formalized and hierarchical classification, which revealed 44 classes and 153 alliances. The vegetation of Poland is divided into the classes: <em>Zosteretea marinae</em>, <em>Charetea</em>, <em>Elyno-Seslerietea</em>, <em>Violetea calaminariae</em>, <em>Stellarietea mediae</em>, <em>Cakiletea maritimae</em>, <em>Salicetea herbaceae</em>, <em>Isoëto-Nano-Juncetea</em>, <em>Oxycocco-Sphagnetea</em>, <em>Ammophiletea arenariae</em>, <em>Potametea</em>, <em>Thero-Salicornietea</em>, <em>Carici- Kobresietea</em>, <em>Festuco-Puccinellietea</em>, <em>Erico-Pinetea</em>, <em>Juncetea trifidi</em>, <em>Loiseleurio-Vaccinietea</em>, <em>Lemnetea</em>, <em>Quercetea pubescentis</em>, <em>Littorelletea unifiorae</em>, <em>Koelerio-Corynephoretea</em>, <em>Roso pendulinae-Pinetea mugo</em>, <em>Cymbalario-Parietari- etea</em>, <em>Bidentetea tripartitae</em>, <em>Alnetea glutinosae</em>, <em>Scheuchzerio-Caricetea</em>, <em>Robinietea</em>, <em>Montio-Cardaminetea</em>, <em>Thlaspi- etea rotundifolii</em>, <em>Festuco-Brometea</em>, <em>Salicetea purpureae</em>, <em>Molinio-Arrhenatheretea</em>, <em>Mulgedio-Aconitetea</em>, <em>Carpino- Fagetea</em>, <em>Calluno-Ulicetea</em>, <em>Quercetea robori-petraeae</em>, <em>Vaccinio-Piceetea</em>, <em>Polygono arenastri-Poëtea</em>, <em>Asplenietea trichomanis</em>, <em>Phragmito-Magno-Caricetea</em>, <em>Artemisietea vulgaris</em>, <em>Epilobietea angustifolii</em>, <em>Galio-Urticetea</em>, <em>Rhamno- Prunetea</em>. In order to determine a diagnostic species for allianc sures of fidelity were used. A revised list of vegetation units of Poland is presented.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

Statistical determination of normal modes

1987 ◽  
Vol 64 (5) ◽  
pp. 425 ◽  
Author(s):  
John F. Geldard ◽  
Lawrence R. Pratt
Keyword(s):  
Normal Modes ◽  
Statistical Determination

A NEW TECHNIQUE ESTIMATING LOCATION OF MEAN JOINT CENTERS OF ROTATION WITH ASSOCIATED DISPERSIONS AND ASSESSING TO FUNCTIONAL COORDINATE SYSTEM OF MOVING LIMB SEGMENTS

2006 ◽  
Vol 06 (04) ◽  
pp. 373-384
Author(s):  
ERIC BERTHONNAUD ◽  
JOANNÈS DIMNET
Keyword(s):  
Numerical Technique ◽  
New Technique ◽  
Helical Axis ◽  
Joint Movement ◽  
Time Step ◽  
Data Set ◽  
Minimization Procedure ◽  
A New Technique ◽  
Joint Center

Joint centers are obtained from data treatment of a set of markers placed on the skin of moving limb segments. Finite helical axis (FHA) parameters are calculated between time step increments. Artifacts associated with nonrigid body movements of markers entail ill-determination of FHA parameters. Mean centers of rotation may be calculated over the whole movement, when human articulations are likened to spherical joints. They are obtained using numerical technique, defining point with minimal amplitude, during joint movement. A new technique is presented. Hip, knee, and ankle mean centers of rotation are calculated. Their locations depend on the application of two constraints. The joint center must be located next to the estimated geometric joint center. The geometric joint center may migrate inside a cube of possible location. This cube of error is located with respect to the marker coordinate systems of the two limb segments adjacent to the joint. Its position depends on the joint and the patient height, and is obtained from a stereoradiographic study with specimen. The mean position of joint center and corresponding dispersion are obtained through a minimization procedure. The location of mean joint center is compared with the position of FHA calculated between different sequential steps: time sequential step, and rotation sequential step where a minimal rotation amplitude is imposed between two joint positions. Sticks are drawn connecting adjacent mean centers. The animation of stick diagrams allows clinical users to estimate the displacements of long bones (femur and tibia) from the whole data set.

Moment-tensor solutions for the 24 November 1987 Superstition Hills, California, earthquakes

1989 ◽  
Vol 79 (2) ◽  
pp. 493-499
Author(s):  
Stuart A. Sipkin
Keyword(s):  
Main Shock ◽  
Moment Tensor ◽  
Time Dependent ◽  
Origin Time ◽  
Data Set ◽  
Filter Theory ◽  
Long Period ◽  
Seismograph Network ◽  
Scalar Moment

Abstract The teleseismic long-period waveforms recorded by the Global Digital Seismograph Network from the two largest Superstition Hills earthquakes are inverted using an algorithm based on optimal filter theory. These solutions differ slightly from those published in the Preliminary Determination of Epicenters Monthly Listing because a somewhat different, improved data set was used in the inversions and a time-dependent moment-tensor algorithm was used to investigate the complexity of the main shock. The foreshock (origin time 01:54:14.5, mb 5.7, Ms 6.2) had a scalar moment of 2.3 × 1025 dyne-cm, a depth of 8 km, and a mechanism of strike 217°, dip 79°, rake 4°. The main shock (origin time 13:15:56.4, mb 6.0, Ms 6.6) was a complex event, consisting of at least two subevents, with a combined scalar moment of 1.0 × 1026 dyne-cm, a depth of 10 km, and a mechanism of strike 303°, dip 89°, rake −180°.

P-wave backazimuth anomalies observed by a small-aperture seismic array at Pinyon Flat, southern California: Implications for structure and source location

1996 ◽  
Vol 86 (2) ◽  
pp. 470-476 ◽  
Author(s):  
Cheng-Horng Lin ◽  
S. W. Roecker
Keyword(s):  
Southern California ◽  
Seismic Array ◽  
P Wave ◽  
Small Aperture ◽  
Data Set ◽  
Single Interface ◽  
Seismic Networks ◽  
Dense Seismic Array ◽  
Beamforming Technique

Abstract Seismograms of earthquakes and explosions recorded at local, regional, and teleseismic distances by a small-aperture, dense seismic array located on Pinyon Flat, in southern California, reveal large (±15°) backazimuth anomalies. We investigate the causes and implications of these anomalies by first comparing the effectiveness of estimating backazimuth with an array using three different techniques: the broadband frequency-wavenumber (BBFK) technique, the polarization technique, and the beamforming technique. While each technique provided nearly the same direction as a most likely estimate, the beamforming estimate was associated with the smallest uncertainties. Backazimuth anomalies were then calculated for the entire data set by comparing the results from beamforming with backazimuths derived from earthquake locations reported by the Anza and Caltech seismic networks and the Preliminary Determination of Epicenters (PDE) Bulletin. These backazimuth anomalies have a simple sinelike dependence on azimuth, with the largest anomalies observed from the southeast and northwest directions. Such a trend may be explained as the effect of one or more interfaces dipping to the northeast beneath the array. A best-fit model of a single interface has a dip and strike of 20° and 315°, respectively, and a velocity contrast of 0.82 km/sec. Application of corrections computed from this simple model to ray directions significantly improves locations at all distances and directions, suggesting that this is an upper crustal feature. We confirm that knowledge of local structure can be very important for earthquake location by an array but also show that corrections computed from simple models may not only be adequate but superior to those determined by raytracing through smoothed laterally varying models.

scholarly journals Invasive Scots Pine, Pinus sylvestris, Replacing Corema, Corema conradii, Heathland in the Annapolis Valley, Nova Scotia

2005 ◽  
Vol 119 (2) ◽  
pp. 237 ◽  
Author(s):  
Paul M. Catling ◽  
Susan Carbyn
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Vascular Plants ◽  
Dominant Species ◽  
Vascular Plant ◽  
Agricultural Crops ◽  
Deschampsia Flexuosa ◽  
Natural Habitats ◽  
Annapolis Valley ◽  
The Impact

Examination of air photos from 1930, 1970 and 2002 revealed stands of the European Scots Pine (Pinus sylvestris) invading remnants of natural Corema (Corema conradii) heathland in the Annapolis valley. To document the impact of the introduced pines, four natural habitats were compared with two adjacent habitats already invaded by the pines. All surveyed habitats had been dominated by Corema heath based on air photos taken in 1930. Twenty 1 m2 quadrats were used to record presence and cover of vascular plants at each site. The invasive alien pines reduce the native cover to 12%. Vascular plant biodiversity is reduced to less than 42% and the cover of the heathland dominant, Corema conradii, is reduced from over 100 % to less than 2%. with Deschampsia flexuosa becoming the dominant species. The modified ecosystem and loss of biodiversity has economic impacts through loss of pollinators of agricultural crops and loss of germplasm of native crop relatives.

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