scholarly journals The Elastic Properties of β-Mg2SiO4 Containing 0.73 wt.% of H2O to 10 GPa and 600 K by Ultrasonic Interferometry with Synchrotron X-Radiation

Minerals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 209
Author(s):  
Gabriel D. Gwanmesia ◽  
Matthew L. Whitaker ◽  
Lidong Dai ◽  
Alwin James ◽  
Haiyan Chen ◽  
...  
Keyword(s):  
Finite Strain ◽  
Seismic Velocity ◽  
Strain Analysis ◽  
Data Sets ◽  
Third Order ◽  
Data Set ◽  
Earth’S Mantle ◽  
Ultrasonic Interferometry ◽  
Linear Fit ◽  
Derivatives Of

We measured the elastic velocities of a synthetic polycrystalline β-Mg2SiO4 containing 0.73 wt.% H2O to 10 GPa and 600 K using ultrasonic interferometry combined with synchrotron X-radiation. Third-order Eulerian finite strain analysis of the high P and T data set yielded Kso = 161.5(2) GPa, Go = 101.6(1) GPa, and (∂Ks/∂P)T = 4.84(4), (∂G/∂P)T = 1.68(2) indistinguishable from Kso = 161.1(3) GPa, Go = 101.4(1) GPa, and (∂Ks/∂P)T = 4.93(4), (∂G/∂P)T = 1.73(2) from the linear fit. The hydration of the wadsleyite by 0.73 wt.% decreases Ks and G moduli by 5.3% and 8.6%, respectively, but no measurable effect was noted for (∂Ks/∂P)T and (∂G/∂P)T. The temperature derivatives of the Ks and G moduli from the finite strain analysis (∂KS/∂T)P = −0.013(2) GPaK−1, (∂G/∂T)P = −0.015(0.4) GPaK−1, and the linear fit (∂KS/∂T)P = −0.015(1) GPaK−1, (∂G/∂T)P = −0.016(1) GPaK−1 are in agreement, and both data sets indicating the |(∂G/∂T)P| to be greater than |(∂KS/∂T)P|. Calculations yield ∆Vp(α-β) = 9.88% and ∆VS(α-β) = 8.70% for the hydrous β-Mg2SiO4 and hydrous α-Mg2SiO4, implying 46–52% olivine volume content in the Earth’s mantle to satisfy the seismic velocity contrast ∆Vs = ∆VP = 4.6% at the 410 km depth.

scholarly journals On the Lense-Thirring test with the Mars Global Surveyor in the gravitational field of Mars

Open Physics ◽  
2010 ◽  
Vol 8 (3) ◽  
Author(s):  
Lorenzo Iorio
Keyword(s):  
Gravitational Field ◽  
Data Sets ◽  
Mars Global Surveyor ◽  
Confidence Bounds ◽  
Data Set ◽  
Frame Dragging ◽  
Plane Component ◽  
Out Of Plane ◽  
Systematic Effects ◽  
Linear Fit

AbstractI discuss some aspects of a recent frame-dragging test performed by exploiting the Root-Mean-Square (RMS) orbit-overlap differences of the out-of-plane component (N) of the Mars Global Surveyor (MGS) spacecraft’s orbit in the gravitational field of Mars. A linear fit to the complete time series for the entire MGS data set (4 February 1999–14 January 2005) yields a normalized slope 1.03 ± 0.41 (with 95% confidence bounds). Other linear fits to different data sets confirm agreement with general relativity. Huge systematic effects induced by mismodeling the martian gravitational field which have been claimed by some authors are absent in the MGS out-of-plane record. The same level of effect is seen for both the classical non-gravitational and relativistic gravitomagnetic forces on the in-plane MGS orbital components; this is not the case for the out-of-plane components. Moreover, the non-conservative forces experience high-frequency variations which are not important in the present case where secular effects are relevant.

The integrated stress-strain analysis of calcite twins: Consistent stress and strain determined from natural data

2020 ◽  
Author(s):  
Kei Wakamori ◽  
Atsushi Yamaji
Keyword(s):  
Strain Analysis ◽  
Stress And Strain ◽  
Data Sets ◽  
Analysis Method ◽  
Stress Strain ◽  
Data Set ◽  
Principal Axes ◽  
Calcite Veins ◽  
Natural Data ◽  
Paleostress Analysis

<p>Stress and strain are different physical entities. Do the stress and strain determined from <em>e</em>-twins in a sample of polycrystalline calcite have similar principal orientations and similar shape ratios? Köpping et al. (2019) tackled this question by applying Turner’s (1953) classical method of paleostress analysis to natural data. However, despite the assumption of the method, the orientations of P- and T-axes of an <em>e</em>-twin lamella do not have a one-to-one correspondence with the principal orientations of the stress that formed the lamella. And, the method cannot determine a shape ratio. Another difficulty arises when one tackles the question: Natural calcite has usually been subjected to polyphase tectonics with different stress conditions. One has to separate stresses and to evaluate corresponding strains from a sample. Once lamellae are grouped according to the stresses, the strain achieved by the formation of a group of twin lamellae is easily evaluated by the method of Conel (1962) if the total strain represented by a group is small.</p><p>The present authors tackled the question by combining Conel’s strain analysis method with a novel method of paleostress analysis of mechanical twins, which clusters the directional data of <em>e</em>-twins by means of a statistical mixture model and determines stresses for each group of data. And, the appropriate number of stresses is determined by means of Bayesian information criterion. The method also determines the probabilities of each lamella to be formed by the stresses, which are called the memberships of the lamella. The strain achieved under a stress condition can be computed using the memberships. We applied this integrated stress-strain analysis method to Data Sets I and II from two calcite veins in a Miocene forearc basin deposit in central Japan. Since the sampling area was close to a triple-trench junction, the young formation has experienced polyphase tectonics.</p><p>As a result, we obtained the consistent stress and strains from both of the data sets. Three stresses were obtained from Data Set I, and the corresponding strains were 0.17, 0.25 and 0.13%. Two stresses were obtained from Data Set II, and the strains were 0.39 and 0.42%. The stress and strain determined from the data sets for each deformation phase were consistent with each other. That is, the principal axes had difference as small as < 20 degrees, and the shape ratios of stress and strain had also similar values. It is not straightforward to generalize this result, but both the stress and strain analyses seem to give appropriate results, providing that polyphase deformations are coped with.</p>

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

scholarly journals Detecting Heterogeneity of Substitution Along DNA and Protein Sequences

Genetics ◽  
1996 ◽  
Vol 143 (1) ◽  
pp. 589-602 ◽  
Author(s):  
Peter J E Goss ◽  
R C Lewontin
Keyword(s):  
Null Hypothesis ◽  
Nonuniform Distribution ◽  
Critical Values ◽  
Third Order ◽  
Variance Test ◽  
Data Set ◽  
Significance Level ◽  
Uniformly Most Powerful Test ◽  
Interval Lengths ◽  
Uniformly Most Powerful

Abstract Regions of differing constraint, mutation rate or recombination along a sequence of DNA or amino acids lead to a nonuniform distribution of polymorphism within species or fixed differences between species. The power of five tests to reject the null hypothesis of a uniform distribution is studied for four classes of alternate hypothesis. The tests explored are the variance of interval lengths; a modified variance test, which includes covariance between neighboring intervals; the length of the longest interval; the length of the shortest third-order interval; and a composite test. Although there is no uniformly most powerful test over the range of alternate hypotheses tested, the variance and modified variance tests usually have the highest power. Therefore, we recommend that one of these two tests be used to test departure from uniformity in all circumstances. Tables of critical values for the variance and modified variance tests are given. The critical values depend both on the number of events and the number of positions in the sequence. A computer program is available on request that calculates both the critical values for a specified number of events and number of positions as well as the significance level of a given data set.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

Sign in / Sign up

Export Citation Format

Share Document