Interactions dans une chaîne de sphères ou de cavités alignées. Plasmons de surface et énergie de Van der Waals

1977 ◽  
Vol 55 (16) ◽  
pp. 1407-1422 ◽  
Author(s):  
A. Ronveaux ◽  
A. Moussiaux ◽  
A. A. Lucas
Keyword(s):  
Power Series ◽  
Surface Plasmon ◽  
Nearest Neighbor ◽  
Van Der Waals ◽  
Nearest Neighbors ◽  
Distance Ratio ◽  
First Approximation ◽  
Spherical Cavities ◽  
Plasmon Oscillation ◽  
Neighbor Approximation

The eigenmodes of surface plasmon oscillation for an arbitrary number of aligned spheres or spherical cavities are given by a power series of the radius–distance ratio. The Van der Waals energy is calculated in first approximation for an arbitrary chain, and in second approximation for an array of two holes and two spheres.The error due to the neglect of all interactions except that between nearest neighbors is analysed in detail together with the bands of the infinite chain in the nearest neighbor approximation. [Journal translation]

Raman Spectra of Ion Intercalated Vermiculites

1987 ◽  
Vol 111 ◽  
Author(s):  
H. X. Jiang ◽  
S. A. Solin ◽  
H. Kim ◽  
T. J. Pinnavaia
Keyword(s):  
Raman Spectra ◽  
Nearest Neighbor ◽  
Van Der Waals ◽  
Mode Frequency ◽  
Basal Spacing ◽  
Physical Parameters ◽  
Force Model ◽  
Torsional Mode ◽  
Calculation Results ◽  
Neighbor Approximation

AbstractThe Raman spectra of ion intercalated Vermiculites have been studied. The torsional mode frequencies exhibit a shift to higher energy with increasing basal spacing d. Using a van der Waals type force model, and the nearest neighbor approximation, we have successfully derived the formula for the torsional mode frequency as functions of basal spacing d and other physical parameters. The calculation results are quite consistent with the experimental observations.

Nearest-neighbor coordination polyhedral clusters in metallic phases defined using Friedel oscillation and atomic dense packing

2015 ◽  
Vol 48 (6) ◽  
pp. 2002-2005 ◽  
Author(s):  
Dandan Dong ◽  
Shuang Zhang ◽  
Zengrui Wang ◽  
Chuang Dong
Keyword(s):  
Nearest Neighbor ◽  
Pair Potential ◽  
Radial Distance ◽  
Nearest Neighbors ◽  
Atomic Radius ◽  
Metallic Phase ◽  
Dense Packing ◽  
Distance Ratio ◽  
Friedel Oscillation ◽  
Cutoff Distance

It is generally difficult to define coordination polyhedral clusters with multi-shelled nearest neighbors. The present work defines their cutoff distances in typical metallic phase structure typesviaFriedel oscillation and atomic dense packing. The nearest neighbors of a cluster are considered to be confined within the first effective Friedel minimum in the pair potential, with the radial distance ratio of the outermost shell to the innermost shell beingrL/rS= 1.5. This ratio is further correlated to atomic radius ratio, facet-capping configuration and cluster coordination number after considering hard-sphere atomic dense packing. The cluster cutoff distance is obtained by multiplying therL/rSratio with the measured innermost shell distance, as is well validated in common cluster types.

Observation of Superlattice Dislocations on Cube Planes in Ni3Al

1973 ◽  
Vol 31 ◽  
pp. 174-175 ◽  
Author(s):  
J. M. Oblak ◽  
W. H. Rand
Keyword(s):  
Nearest Neighbor ◽  
Antiphase Boundary ◽  
Nearest Neighbors ◽  
High Energy ◽  
Cross Slip ◽  
Octahedral Plane ◽  
Trapping Mechanism ◽  
Slip Traces

The energy of an a/2 <110> shear antiphase. boundary in the Ll2 expected to be at a minimum on {100} cube planes because here strue ture is there is no violation of nearest-neighbor order. The latter however does involve the disruption of second nearest neighbors. It has been suggested that cross slip of paired a/2 <110> dislocations from octahedral onto cube planes is an important dislocation trapping mechanism in Ni3Al; furthermore, slip traces consistent with cube slip are observed above 920°K.Due to the high energy of the {111} antiphase boundary (> 200 mJ/m2), paired a/2 <110> dislocations are tightly constricted on the octahedral plane and cannot be individually resolved.

Temporal Changes in Habitat Selection and Nest Spacing in a Colony of Ross' and Lesser Snow Geese

The Auk ◽  
1983 ◽  
Vol 100 (2) ◽  
pp. 335-343 ◽  
Author(s):  
M. Robert McLandress
Keyword(s):  
Nearest Neighbor ◽  
Average Distance ◽  
Habitat Preferences ◽  
Nearest Neighbors ◽  
Nest Density ◽  
Snow Goose ◽  
Snow Geese ◽  
Lesser Snow Geese ◽  
Average Size ◽  
Island Habitats

Abstract I studied the nesting colony of Ross' Geese (Chen rossii) and Lesser Snow Geese (C. caerulescens caerulescens) at Karrak Lake in the central Arctic of Canada in the summer of 1976. Related studies indicated that this colony had grown from 18,000 birds in 1966-1968 to 54,500 birds in 1976. In 1976, geese nested on islands that were used in the late 1960's and on an island and mainland sites that were previously unoccupied. Average nest density in 1976 was three-fold greater than in the late 1960's. Consequently, the average distance to nearest neighbors of Ross' Geese in 1976 was half the average distance determined 10 yr earlier. The mean clutch size of Ross' Geese was greater in island habitats where nest densities were high than in less populated island or mainland habitats. The average size of Snow Goose clutches did not differ significantly among island habitats but was larger at island than at mainland sites. Large clutches were most likely attributable to older and/or earlier nesting females. Habitat preferences apparently differed between species. Small clutches presumably indicated that young geese nested in areas where nest densities were low. The establishment of mainland nesting at Karrak Lake probably began with young Snow Geese using peripheral areas of the colony. Young Ross' Geese nested in sparsely populated habitats on islands to a greater extent than did Snow Geese. Ross' Geese also nested on the mainland but in lower densities than Ross' Geese nesting in similar island habitats. Successful nests with the larger clutches had closer conspecific neighbors than did successful nests with smaller clutches. The species composition of nearest neighbors changed significantly with distance from Snow Goose nests but not Ross' Goose nests. Nesting success was not affected by the species of nearest neighbor, however. Because they have complementary antipredator adaptations, Ross' and Snow geese may benefit by nesting together.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

scholarly journals Chi-Squared Distance Metric Learning for Histogram Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Yang ◽  
Luhui Xu ◽  
Xiaopan Chen ◽  
Fengbin Zheng ◽  
Yang Liu
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Metric Learning ◽  
Nearest Neighbors ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Projected Gradient Method ◽  
Proper Distance ◽  
Chi Squared ◽  
Real World Datasets

Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. The chi-squared distance is a nonlinear metric and is widely used to compare histograms. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. With the iterative projected gradient method for optimization, we naturally introduce thel2,1norm regularization into the proposed method for sparse metric learning. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method.

scholarly journals The K Nearest Neighbor Algorithm for Imputation of Missing Longitudinal Prenatal Alcohol Data

2021 ◽  
Author(s):  
Ayesha Sania ◽  
Nicolo Pini ◽  
Morgan Nelson ◽  
Michael Myers ◽  
Lauren Shuffrey ◽  
...  
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Drinking Behavior ◽  
Nearest Neighbors ◽  
First Trimester ◽  
Epidemiologic Studies ◽  
K Nearest Neighbor ◽  
Timeline Followback

Abstract Background — Missing data are a source of bias in epidemiologic studies. This is problematic in alcohol research where data missingness is linked to drinking behavior. Methods — The Safe Passage study was a prospective investigation of prenatal drinking and fetal/infant outcomes (n=11,083). Daily alcohol consumption for last reported drinking day and 30 days prior was recorded using Timeline Followback method. Of 3.2 million person-days, data were missing for 0.36 million. We imputed missing data using a machine learning algorithm; “K Nearest Neighbor” (K-NN). K-NN imputes missing values for a participant using data of participants closest to it. Imputed values were weighted for the distances from nearest neighbors and matched for day of week. Validation was done on randomly deleted data for 5-15 consecutive days. Results — Data from 5 nearest neighbors and segments of 55 days provided imputed values with least imputation error. After deleting data segments from with no missing days first trimester, there was no difference between actual and predicted values for 64% of deleted segments. For 31% of the segments, imputed data were within +/-1 drink/day of the actual. Conclusions — K-NN can be used to impute missing data in longitudinal studies of alcohol use during pregnancy with high accuracy.

Monte Carlo simulations of long-range order kinetics in B2 phases

1995 ◽  
Vol 10 (3) ◽  
pp. 591-595 ◽  
Author(s):  
K. Yaldram ◽  
V. Pierron-Bohnes ◽  
M.C. Cadeville ◽  
M.A. Khan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Nearest Neighbor ◽  
Vacancy Concentration ◽  
Relaxation Times ◽  
Nearest Neighbors ◽  
Migration Energy ◽  
Ordering Kinetics ◽  
Migration Barriers ◽  
Temperature Dependences

The thermodynamic parameters that drive the atomic migration in B2 alloys are studied using Monte-Carlo simulations. The model is based on a vacancy jump mechanism between nearest neighbor sites, with a constant vacancy concentration. The ordering energy is described through an Ising Hamiltonian with interaction potentials between first and second nearest neighbors. Different migration barriers are introduced fur A and B atoms. The results of the simulations compare very well with those of experiments. The ordering kinetics are well described by exponential-like behaviors with two relaxation times whose temperature dependences are Arrhenius laws yielding effective migration energies. The ordering energy contributes significantly to the total migration energy.

scholarly journals Spatial and Temporal Distribution of Ecballium elaterium in Almond Orchards

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 751 ◽  
Author(s):  
Lior Blank ◽  
Nitzan Birger ◽  
Hanan Eizenberg
Keyword(s):  
Spatial Pattern ◽  
Weed Management ◽  
Nearest Neighbor ◽  
Temporal Distribution ◽  
Nearest Neighbors ◽  
Chemical Application ◽  
Site Specific ◽  
Herbicide Treatments ◽  
Suitable Target ◽  
Almond Orchards

The concept of site-specific weed management is based on the assumption that weeds are aggregated in patches. In this study, we surveyed four plots in four commercial almond orchards for three years and mapped the locations of Ecballium elaterium, a troublesome weed in Israeli agriculture, specifically in almond orchards. We analyzed the spatial pattern of the plants’ locations using nearest neighbor analysis and Ripley’s L function. The number of E. elaterium plants increased by more than 70% in the four plots from 2015 to 2016. In addition, the observed mean distance between nearest neighbors increased by more than 10% from 2016 and 2017. We found in all four plots that the spatial pattern of E. elaterium was clustered and that these weed patch locations were consistent over the years although the density within the patches increased. The extent of these clusters ranged between 40 to 70 m and remained similar in size throughout the study. These features make E. elaterium a suitable target for site-specific weed management and for pre-emergence patch spraying. Knowledge of the spatial and temporal pattern of weeds could aid in understanding their ecology and could help target herbicide treatments to specific locations of the field and, thus, reducing the chemical application.

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