scholarly journals The Effects of Hydrogen Distribution on the Elastic Properties and Hydrogen-Induced Hardening and Softening of α-Fe

2020 ◽  
Vol 10 (24) ◽  
pp. 8958
Author(s):  
Zheng Wang ◽  
Xiaoming Shi ◽  
Xu-Sheng Yang ◽  
Zhuhong Liu ◽  
San-Qiang Shi ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Shear Modulus ◽  
Atomistic Simulation ◽  
Elastic Moduli ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Hydrogen Distribution ◽  
Hydrogen Atoms ◽  
Microscopic Distribution ◽  
Hardening And Softening

In this work, we conducted a high-throughput atomistic simulation of the interstitial solid solutions of hydrogen in α-Fe. The elastic constants and moduli were calculated. Through statistical analysis of structures and results, the influences of the microscopic distribution of hydrogen on the elastic moduli, as well as hydrogen-induced hardening and softening, are discussed. We found that even though the uniformly distributed hydrogen caused slight softening in α-Fe, the distribution of hydrogen at different adjacent positions significantly affected the elastic moduli. For example, hydrogen increased the Young’s modulus and shear modulus at the 5th and 10th nearest neighbors, resulting in hardening, but decreased the bulk modulus at the 7th nearest neighbor, making the material easier to compress. These phenomena are related to the distribution densities of the positions that hydrogen atoms can occupy on the two major slip families, {110} and {112}, at different nearest neighbors distinguished by distances.

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 Quantitative Evaluations of Hydrogen Diffusivity in V-X (X = Cr, Al, Pd) Alloy Membranes Based on Hydrogen Chemical Potential

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 67
Author(s):  
Asuka Suzuki ◽  
Hiroshi Yukawa
Keyword(s):  
Nearest Neighbor ◽  
Chemical Potential ◽  
Hydrogen Permeability ◽  
First Principle ◽  
Hydrogen Diffusivity ◽  
Hydrogen Atoms ◽  
First Principle Calculations ◽  
Blocking Effects ◽  
Pd Alloy ◽  
Quantitative Evaluations

Vanadium (V) has higher hydrogen permeability than Pd-based alloy membranes but exhibits poor resistance to hydrogen-induced embrittlement. The alloy elements are added to reduce hydrogen solubility and prevent hydrogen-induced embrittlement. To enhance hydrogen permeability, the alloy elements which improve hydrogen diffusivity in V are more suitable. In the present study, hydrogen diffusivity in V-Cr, V-Al, and V-Pd alloy membranes was investigated in view of the hydrogen chemical potential and compared with the previously reported results of V-Fe alloy membranes. The additions of Cr and Fe to V improved the mobility of hydrogen atoms. In contrast, those of Al and Pd decreased hydrogen diffusivity. The first principle calculations revealed that the hydrogen atoms cannot occupy the first-nearest neighbor T sites (T1 sites) of Al and Pd in the V crystal lattice. These blocking effects will be a dominant contributor to decreasing hydrogen diffusivity by the additions of Al and Pd. For V-based alloy membranes, Fe and Cr are more suitable alloy elements compared with Al and Pd in view of hydrogen diffusivity.

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.

A systematic neutron reflectometry study on hydrogen absorption in thin Mg1-xAlx alloy films Special issue on Neutron Scattering in Canada.

2010 ◽  
Vol 88 (10) ◽  
pp. 723-728 ◽  
Author(s):  
H. Fritzsche ◽  
E. Poirier ◽  
J. Haagsma ◽  
C. Ophus ◽  
E. Luber ◽  
...  
Keyword(s):  
Hydrogen Absorption ◽  
Scattering Length ◽  
Catalyst Layer ◽  
Neutron Reflectometry ◽  
Hydrogen Distribution ◽  
Hydrogen Atoms ◽  
Alloy Films ◽  
Negative Scattering Length ◽  
Insight Into ◽  
Optimum Alloy

In this article, we show how neutron reflectometry (NR) can provide deep insight into the absorption and desorption properties of commercially promising hydrogen storage materials. NR benefits from the large negative scattering length of hydrogen atoms, which changes the reflectivity curve substantially, so that NR can determine not only the total amount of stored hydrogen but also the hydrogen distribution along the film normal, with nanometer resolution. To use NR, the samples must have smooth surfaces, and the film thickness should range between 10 and 200 nm. We performed a systematic study on thin Mg1–xAlx alloy films (x = 0.2, 0.3, 0.4, 0.67) capped with a Pd catalyst layer. Our NR experiments showed that Mg0.7Al0.3 is the optimum alloy composition with the highest amount of stored hydrogen and the lowest desorption temperature. All the thin films expand by about 20% because of hydrogen absorption, and the hydrogen is stored only in the MgAl layer with no hydrogen content in the Pd layer.

scholarly journals Statistical analysis of k-nearest neighbor collaborative recommendation

2010 ◽  
Vol 38 (3) ◽  
pp. 1568-1592 ◽  
Author(s):  
Gérard Biau ◽  
Benoît Cadre ◽  
Laurent Rouvière
Keyword(s):  
Statistical Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Collaborative Recommendation

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.

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