scholarly journals KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System

2022 ◽  
Vol 163 (2) ◽  
pp. 40
Author(s):  
Anusha Pai Asnodkar ◽  
Ji Wang ◽  
B. Scott Gaudi ◽  
P. Wilson Cauley ◽  
Jason D. Eastman ◽  
...  
Keyword(s):  
Radial Velocities ◽  
Data Sets ◽  
Dynamical Mass ◽  
Orbital Inclination ◽  
Hot Jupiters ◽  
Atmospheric Escape ◽  
Spectroscopic Binary ◽  
Consistent Solution ◽  
Using Data ◽  
In Transit

Abstract Transiting hot Jupiters present a unique opportunity to measure absolute planetary masses due to the magnitude of their radial velocity signals and known orbital inclination. Measuring planet mass is critical to understanding atmospheric dynamics and escape under extreme stellar irradiation. Here we present the ultrahot Jupiter system KELT-9 as a double-lined spectroscopic binary. This allows us to directly and empirically constrain the mass of the star and its planetary companion without reference to any theoretical stellar evolutionary models or empirical stellar scaling relations. Using data from the PEPSI, HARPS-N, and TRES spectrographs across multiple epochs, we apply least-squares deconvolution to measure out-of-transit stellar radial velocities. With the PEPSI and HARPS-N data sets, we measure in-transit planet radial velocities using transmission spectroscopy. By fitting the circular orbital solution that captures these Keplerian motions, we recover a planetary dynamical mass of 2.17 ± 0.56 M J and stellar dynamical mass of 2.11 ± 0.78 M ⊙, both of which agree with the discovery paper. Furthermore, we argue that this system, as well as systems like it, are highly overconstrained, providing multiple independent avenues for empirically cross-validating model-independent solutions to the system parameters. We also discuss the implications of this revised mass for studies of atmospheric escape.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

Big Data Security Challenges and Solution of Distributed Computing in Hadoop Environment: A Security Framework

2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Sets ◽  
Security Framework ◽  
Hadoop Distributed File System ◽  
Current Scenario ◽  
Hadoop Cluster ◽  
Ciphertext Policy ◽  
In Transit ◽  
Hadoop Framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

POSTPARTUM AMENORRHOEA IN RURAL EASTERN UTTAR PRADESH, INDIA

1998 ◽  
Vol 30 (2) ◽  
pp. 227-243
Author(s):  
K. N. S. YADAVA ◽  
S. K. JAIN
Keyword(s):  
Higher Education ◽  
Uttar Pradesh ◽  
Current Status Data ◽  
North India ◽  
Current Status ◽  
Data Sets ◽  
Survival Status ◽  
The Mean ◽  
The Difference ◽  
Using Data

This paper calculates the mean duration of the postpartum amenorrhoea (PPA) and examines its demographic, and socioeconomic correlates in rural north India, using data collected through 'retrospective' (last but one child) as well as 'current status' (last child) reporting of the duration of PPA.The mean duration of PPA was higher in the current status than in the retrospective data;n the difference being statistically significant. However, for the same mothers who gave PPA information in both the data sets, the difference in mean duration of PPA was not statistically significant. The correlates were identical in both the data sets. The current status data were more complete in terms of the coverage, and perhaps less distorted by reporting errors caused by recall lapse.A positive relationship of the mean duration of PPA was found with longer breast-feeding, higher parity and age of mother at the birth of the child, and the survival status of the child. An inverse relationship was found with higher education of a woman, higher education of her husband and higher socioeconomic status of her household, these variables possibly acting as proxies for women's better nutritional status.

scholarly journals Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

2018 ◽  
Vol 7 (2.28) ◽  
pp. 312
Author(s):  
Manu Kohli
Keyword(s):  
Data Extraction ◽  
Indirect Costs ◽  
True Positive Rate ◽  
Spare Parts ◽  
Machine Learning Algorithms ◽  
Machine Language ◽  
Support Vector ◽  
Data Sets ◽  
Equipment Failure ◽  
Using Data

Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations. 

Relationship Between Environmental Temperature and Yields of Subarctic and Temperate Zone Fish Species

1983 ◽  
Vol 40 (10) ◽  
pp. 1829-1837 ◽  
Author(s):  
David A. Schlesinger ◽  
Henry A. Regier
Keyword(s):  
Optimal Growth ◽  
Temperate Zone ◽  
Data Sets ◽  
Coregonus Clupeaformis ◽  
Influence Of Temperature On ◽  
Northern United States ◽  
Using Data ◽  
Curvilinear Relationships ◽  
Fish Yields

Fishes inhabiting subarctic and temperate zone lakes exhibit distinct optimal growth temperatures and temperature preferenda. However, within regional data sets, attempts to correlate fish yields with temperature variables have generally been unsuccessful. In our study, curvilinear relationships between "long-term mean annual air temperature" (TEMP) and sustained yields of three species were fitted using data from 23 intensively fished lakes in Canada and the northern United States. Optimum TEMP values for sustained yield were approximately −1.0, 1.5, and 2 °C, respectively, for lake whitefish (Coregonus clupeaformis), northern pike (Esox lucius), and walleye (Stizostedion vitreum vitreum). These differences suggest that the influence of temperature on sustained fish yields from subarctic and temperate zone lakes may, in the past, have been underestimated.

Three-centre C—H...O hydrogen bonds in the DNA minor groove: analysis of oligonucleotide crystal structures

1999 ◽  
Vol 55 (12) ◽  
pp. 2005-2012 ◽  
Author(s):  
Anirban Ghosh ◽  
Manju Bansal
Keyword(s):  
Hydrogen Bonds ◽  
Crystal Structures ◽  
Base Pair ◽  
Minor Groove ◽  
Local Geometry ◽  
Data Sets ◽  
Base Pairs ◽  
Dna Minor Groove ◽  
Close Proximity ◽  
Using Data

AA·TT and GA·TC dinucleotide steps in B-DNA-type oligomeric crystal structures and in protein-bound DNA fragments (solved using data with resolution <2.6 Å) show very small variations in their local dinucleotide geometries. A detailed analysis of these crystal structures reveals that in AA·TT and GA·TC steps the electropositive C2—H2 group of adenine is in very close proximity to the keto O atoms of both the pyrimidine bases in the antiparallel strand of the duplex structure, suggesting the possibility of intra-base pair as well as cross-strand inter-base pair C—H...O hydrogen bonds in the DNA minor groove. The C2—H2...O2 hydrogen bonds in the A·T base pairs could be a natural consequence of Watson–Crick pairing. However, the cross-strand interactions between the bases at the 3′-end of the AA·TT and GA·TC steps obviously arise owing to specific local geometry of these steps, since a majority of the H2...O2 distances in both data sets are considerably shorter than their values in the uniform fibre model (3.3 Å) and many are even smaller than the sum of the van der Waals radii. The analysis suggests that in addition to already documented features such as the large propeller twist of A·T base pairs and the hydration of the minor groove, these C2—H2...O2 cross-strand interactions may also play a role in the narrowing of the minor groove in A-tract regions of DNA and help explain the high structural rigidity and stability observed for poly(dA)·poly(dT).

scholarly journals ATMOSPHERIC ESCAPE FROM HOT JUPITERS

2009 ◽  
Vol 693 (1) ◽  
pp. 23-42 ◽  
Author(s):  
Ruth A. Murray-Clay ◽  
Eugene I. Chiang ◽  
Norman Murray
Keyword(s):  
Hot Jupiters ◽  
Atmospheric Escape

scholarly journals Atmospheric Escape and the Evolution of Close-In Exoplanets

2019 ◽  
Vol 47 (1) ◽  
pp. 67-90 ◽  
Author(s):  
James E. Owen
Keyword(s):  
Radiative Transfer ◽  
Lower Mass ◽  
Hydrodynamic Simulations ◽  
One Dimensional ◽  
Hot Jupiters ◽  
Atmospheric Escape ◽  
Hydrodynamic Calculations ◽  
Ionization Chemistry ◽  
Exoplanet Systems ◽  
Over Time

Exoplanets with substantial hydrogen/helium atmospheres have been discovered in abundance, many residing extremely close to their parent stars. The extreme irradiation levels that these atmospheres experience cause them to undergo hydrodynamic atmospheric escape. Ongoing atmospheric escape has been observed to be occurring in a few nearby exoplanet systems through transit spectroscopy both for hot Jupiters and for lower-mass super-Earths and mini-Neptunes. Detailed hydrodynamic calculations that incorporate radiative transfer and ionization chemistry are now common in one-dimensional models, and multidimensional calculations that incorporate magnetic fields and interactions with the interstellar environment are cutting edge. However, comparison between simulations and observations remains very limited. While hot Jupiters experience atmospheric escape, the mass-loss rates are not high enough to affect their evolution. However, for lower-mass planets, atmospheric escape drives and controls their evolution, sculpting the exoplanet population that we observe today. ▪ Observations of some exoplanets have detected atmospheric escape driven by hydrodynamic outflows, causing the exoplanets to lose mass over time. ▪ Hydrodynamic simulations of atmospheric escape are approaching the sophistication required to compare them directly to observations. ▪ Atmospheric escape sculpts sharp features into the exoplanet population that we can observe today; these features have recently been detected.

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