bluuebunny/crossref_metadata_2025_split huggingface dataset:
DES map shows a smoother distribution of matter than expected: a possible explanation
Authors: Oks Eugene 
Published: December 2021

Abstract
The largest and most detailed map of the distribution of dark matter in the Universe has been recently created by the Dark Energy Survey (DES) team. The distribution was found to be slightly (by a few percent) smoother and less clumpy than predicted by general relativity. This result was considered as a hint of some new physical laws. In the present paper we offer a relatively simple model that could explain the above result without resorting to any new physical laws. The model deals with the dynamics of a system consisting of a large number of gravitating neutral particles, whose mass is equal to the mass of hydrogen atoms. The central point of the model is a partial inhibition of the gravitation for a relatively small subsystem of the entire system. It would be sufficient for this subsystem to constitute just about a few percent of the total ensemble of particles for explaining the few percent more smooth distribution of dark matter (observed by the DES team) compared to the prediction of general relativity. The most viable candidate for the dark matter particles in this model is the second flavor of hydrogen atoms (SFHA) that has only S-states and therefore does not couple to the electric dipole radiation or even to higher multipole radiation, so that the SFHA is practically dark. The SFHA has experimental confirmation from atomic experiments, it does not go beyond the Standard Model, it is based on standard quantum mechanics and it explains puzzling astrophysical observations of the redshifted line 21 cm from the early Universe. Thus, our model explaining the DES result of a little too smooth distribution of dark matter without resorting to any new physical laws seems to be self-consistent.
Simulation and analysis of co-phasing errors of the segmented primary mirror tiled by hexagonal segments in LOT
Authors: Shen Shi-Dong , Cui Xiang-Qun , Zhang Yong 
Published: December 2021

Abstract
In this paper, co-phasing errors of a segmented primary mirror tiled by hexagonal segments are successfully calculated for the 12-meter Large aperture Optical/infrared Telescope (LOT). Co-phasing errors including out-of-plane errors are simulated separately and comprehensively based on several software simulation platforms. PAOLA simulation results show that the Strehl Ratio (SR) of LOT is larger than 0.8 when the RMS value of tip-tilt obeying a normal distribution is less than 0.018 arcsec, and the SR of LOT is larger than 0.8 when the RMS value of piston obeying a normal distribution is less than 40 nm. Besides, simulation results of Zemax show that the SR of LOT is larger than 0.8 when the RMS value of tip-tilt obeying a normal distribution is less than 0.02 arcsec, and the SR of LOT is larger than 0.8 when the RMS value of piston obeying a normal distribution is less than 40 nm. These simulation results successfully lay a solid foundation for LOT (especially the segmented primary mirror with active optics).
Effects of displacement current on wave dispersion relation and polarization properties in auroral plasmas
Authors: Xiang Liang , Ma Bing , Li Qiu-Huan , Chen Ling , Yu Hong-Wei , Wu De-Jin 
Published: December 2021

Abstract
In-situ observations from the FREJA magnetospheric research satellite and the Fast Auroral SnapshoT satellite have shown that plasma waves are frequently observed in the auroral plasma, which are believed to be fundamentally important in wave energy dissipation and particle energization. However, the effects of a displacement current on these waves have not been examined. Based on the two-fluid theory, we investigate the dispersion relation and polarization properties of fast, Alfvén, and slow modes in the presence of a displacement current, and the effects of the displacement current on these waves are also considered. The results show that the wave frequency, polarization, magnetic helicity and other properties for the fast and Alfvén modes are highly sensitive to the normalized Alfvén velocity vA
/c, plasma beta β, and propagation angle θ, while for the slow mode the dependence is minor. In particular, for both fast and Alfvén modes, the magnetic helicity is obviously different with and without the displacement current, especially for the Alfvén mode with the helicity reversals from right-handed to left-handed when vA
/c increases from 0 to 0.3. The charge-neutral condition of both fast and Alfvén modes with frequencies larger than the proton cyclotron frequency is invalid in the presence of the displacement current. Moreover, the presence of the displacement current leads to relatively large magnetic compressibility for the Alfvén mode and relatively large electron compressibility for the fast mode. These results can be useful for a comprehensive understanding of the wave properties and the physics of particle energization phenomena in auroral plasmas.
Enhanced remote astronomical archive system based on the file-level Unlimited Sliding-Window technique
Authors: Shi Cong-Ming , Deng Hui , Wang Feng , Mei Ying , Guo Shao-Guang , Yang Chen , Wu Chen , Wei Shou-Lin , Wicenec Andreas 
Published: December 2021

Abstract
Data archiving is one of the most critical issues for modern astronomical observations. With the development of a new generation of radio telescopes, the transfer and archiving of massive remote data have become urgent problems to be solved. Herein, we present a practical and robust file-level flow-control approach, called the Unlimited Sliding-Window (USW), by referring to the classic flow-control method in the TCP protocol. Based on the USW and the Next Generation Archive System (NGAS) developed for the Murchison Widefield Array telescope, we further implemented an enhanced archive system (ENGAS) using ZeroMQ middleware. The ENGAS substantially improves the transfer performance and ensures the integrity of transferred files. In the tests, the ENGAS is approximately three to twelve times faster than the NGAS and can fully utilize the bandwidth of network links. Thus, for archiving radio observation data, the ENGAS reduces the communication time, improves the bandwidth utilization, and solves the remote synchronous archiving of data from observatories such as Mingantu spectral radioheliograph. It also provides a better reference for the future construction of the Square Kilometer Array (SKA) Science Regional Center.
The relative calibration of radial velocity for LAMOST medium resolution stellar spectra
Authors: Xiong Jian-Ping , Zhang Bo , Liu Chao , Li Jiao , Zhao Yong-Heng , Hou Yong-Hui 
Published: December 2021

Abstract
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) started a median-resolution spectroscopic (MRS, R ∼7500) survey since October 2018. The main scientific goals of MRS, including binary stars, pulsators and other variable stars, were launched with a time-domain spectroscopic survey. However, the systematic errors, including the bias induced from wavelength calibration and the systematic difference between different spectrographs, have to be carefully considered during radial velocity measurement. In this work, we provide a technique to correct the systematics in the wavelength calibration based on the relative radial velocity measurements from LAMOST MRS spectra. We show that, for the stars with multi-epoch spectra, the systematic bias which is induced from the exposures on different nights can be corrected well for LAMOST MRS in each spectrograph. In addition, the precision of radial velocity zero-point of multi-epoch time-domain observations reaches below 0.5 km s−1. As a by-product, we also give the constant star candidates**, which can be the secondary radial-velocity standard star candidates of LAMOST MRS time-domain surveys.
Simulation study of energetic-particle driven off-axis fishbone instabilities in tokamak plasmas
Authors: Li Hanzheng , Todo Yasushi , Wang Hao , Idouakass Malik , Wang Jialei 
Published: December 2021

Abstract
Kinetic-magnetohydrodynamic hybrid simulations were performed to investigate the linear growth and the nonlinear evolution of off-axis fishbone mode (OFM) destabilized by trapped energetic ions in tokamak plasmas. The spatial profile of OFM is mainly composed of m/n = 2/1 mode inside the q = 2 magnetic flux surface while the m/n = 3/1 mode is predominant outside the q = 2 surface, where m and n are the poloidal and toroidal mode numbers, respectively, and q is the safety factor. The spatial profile of the OFM is a strongly shearing shape on the poloidal plane, suggesting the nonperturbative effect of the interaction with energetic ions. The frequency of the OFM in the linear growth phase is in good agreement with the precession drift frequency of trapped energetic ions, and the frequency chirps down in the nonlinear phase. Two types of resonance conditions between trapped energetic ions and OFM are found. For the first type of resonance, the precession drift frequency matches the OFM frequency, while for the second type, the sum of the precession drift frequency and the bounce frequency matches the OFM frequency. The first type of resonance is the primary resonance for the destabilization of OFM. The resonance frequency which is defined based on precession drift frequency and bounce frequency of the nonlinear orbit for each resonant particle is analyzed to understand the frequency chirping. The resonance frequency of the particles that transfer energy to the OFM chirps down, which may result in the chirping down of the OFM frequency. A detailed analysis of the energetic ion distribution function in phase space shows that the gradient of the distribution function along the E′ = const. line drives or stabilizes the instability, where E′ is a combination of energy and toroidal canonical momentum and conserved during the wave–particle interaction. The distribution function is flattened along the E′ = const. line in the nonlinear phase leading to the saturation of the instability.
Steady state of overdamped particles in the non-conservative force field of a simple non-linear model of optical trap
Authors: Mangeat Matthieu , Guérin Thomas , Dean David S 
Published: December 2021

Abstract
Optically trapped particles are often subject to a non-conservative scattering force arising from radiation pressure. In this paper, we present an exact solution for the steady state statistics of an overdamped Brownian particle subjected to a commonly used force field model for an optical trap. The model is the simplest of its kind that takes into account non-conservative forces. In particular, we present the exact results for certain marginals of the full three-dimensional steady state probability distribution, in addition to results for the toroidal probability currents that are present in the steady state, as well as for the circulation of these currents. Our analytical results are confirmed by numerical solution of the steady state Fokker–Planck equation.
On geometric properties of

⊥BJCϵ

symmetric in some types of Banach spaces
Authors: Jhonny Saied A. , Ahmed Buthainah A. A. 
Published: December 2021

Abstract
In this paper, we

⊥

B
J
C

ϵ

-orthogonality and explore

⊥

B
J
C

ϵ

-symmetricity such as a

⊥

B
J
C

ϵ

-left-symmetric (

⊥

B
J
C

ϵ

-right-symmetric) of a vector x in a real Banach space (𝕏, ‖·‖𝕩) and study the relation between a

⊥

B
J
C

ϵ

-right-symmetric (

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B
J
C

ϵ

-left-symmetric) in ℐ(x). New results and proofs are include the notion of norm attainment set of a continuous linear functionals on a reflexive and strictly convex Banach space and using these results to characterize a smoothness of a vector in a unit sphere.
Performance Analysis of Biomass Energy using Machine and Deep Learning Approaches
Authors: Sharma Shagun , Khanra Partha , Ramkumar K R 
Published: December 2021

Abstract
Nowadays, fossil fuels such as petroleum, diesel and coal are being used as an energy source in every modern machinery but these are non-renewable and available in certain domain of nature only. Additionally, excess use of such fuels can cause environmental pollutions, damage of human inhaling process and increase the dependency on other oil rich countries. These challenges could be avoided by using Biomass Energy, which is clean and renewable. Precisely, Biomass Energy is based on hydrocarbon materials which could come from both animal and plant derivatives. There are three forms of Biomass Energy: (i) Gases-methane, ethane etc.; (ii) Liquid-ethanol, biodiesel etc., and (iii) Solid-biochar and activated carbon. These energies are acknowledged for cost effectiveness, renewable nature and less emerging pollutants as compared to fossil fuels. At the present time, these renewable Biomass Energies are useful to operate large number of advanced machines, along with which, the challenge isto estimate the production of Biomass Energy from the available biomass sources without hampering the biodiversity. Therefore, in this study Machine and Deep Learning algorithms are used to calculate the Biomass Energy. Moreover, this work introduces number of Machine and Deep Learning approaches to compute the Biomass Energy production along with Machine Learning tools to analyze the performance of Biomass Energy.
A System to automate the development of anomaly-based network intrusion detection model
Authors: Padmaja B , Sai Sravan K , Krishna Rao Patro E , Chandra Sekhar G 
Published: December 2021

Abstract
Cyber security is the major concern in today’s world. Over the past couple of decades, the internet has grown to such an extent that almost every individual living on this planet has the access to the internet today. This can be viewed as one of the major achievements in the human race, but on the flip side of the coin, this gave rise to a lot of security issues for every individual or the company that is accessing the web through the internet. Hackers have become active and are always monitoring the networks to grab every possible opportunity to attack a system and make the best fortune out of its vulnerabilities. To safeguard people’s and organization’s privacy in this cyberspace, different network intrusion detection systems have been developed to detect the hacker’s presence in the networks. These systems fall under signature based and anomaly based intrusion detection systems. This paper deals with using anomaly based intrusion detection technique to develop an automation system to both train and test supervised machine learning models, which is developed to classify real time network traffic as to whether it is malicious or not. Currently the best models by considering both detection success rate and the false positives rate are Artificial Neural Networks(ANN) followed by Support Vector Machines(SVM). In this paper, it is verified that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperforms support vector machine (SVM) technique while classifying network traffic as harmful or harmless. Initially to evaluate the performance of the system, NSL-KDD dataset is used to train and test the SVM and ANN models and finally classify real time network traffic using these models. This system can be used to carry out model building automatically on the new datasets and also for classifying the behaviour of the provided dataset without having to code.
An Approach to detect multiple diseases using machine learning algorithm
Authors: Mohit Indukuri , Santhosh Kumar K. , Kumar Reddy Uday Avula , Suresh Kumar Badhagouni 
Published: December 2021

Abstract
There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavour, but it can eventually assist practitioners in making timely decisions regarding patients’ health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occurs due to a lack of medical infrastructure and a low ratio of doctors to the population. The statistics clearly show the same, WHO recommended, the ratio of doctors to patients is 1:1000 whereas India’s doctor-to-population ratio is 1:1456, this indicates the shortage of doctors. The diseases related to heart, cancer, and diabetes can cause a potential threat to mankind, if not found early. Therefore, early recognition and diagnosis of these diseases can save a lot of lives. This work is all about predicting diseases that are harmful using machine learning classification algorithms. In this work, breast cancer, heart, and diabetes are included. To make this work seamless and usable by the mass public, our team made a medical test web application that makes predictions about various diseases using the concept of machine learning. In this work, our aim to develop a disease-predicting web app that uses the concept of machine learning-based predictions about various diseases like Breast cancer, Diabetes, and Heart diseases.
Analysis of barrier distributions data for16O + 64Zn reaction at sub-barrier energies
Authors: Vijay  , SinghGautam Manjeet , PalChahal Rishi , Duhan Sukhvinder , Khatri Hitender 
Published: December 2021

Abstract
The barrier distribution data for the 16O + 64Zn reaction at energies spanning around the no minal barrier are examined by employing symmetric-asymmetric Gaussian barrier distribution (SAGBD) approach. The cumulative role of dominant channel couplings in the SAGBD method are determined in terms of the channel coupling parameter λ and percentage decrease of fusion barrier VCBRED with reference to nominal Coulomb barrier. The non-zero and positive values of these parameters for the studied system quantitively measure the influences of dominant intrinsic channels originated from the structure of the participants. The barrier distribution data of 16O + 64Zn reaction is quantitatively as well as qualitatively explained by SAGBD outcomes.
A Hybrid Multiprocessor Scheduler for Soft Real-Time System with Processor Affinity Concept
Authors: Jayna Donga Ms , Holia M.S. 
Published: December 2021

Abstract
Now a days numerous real-time OS like QNX, VxWorks, LynxOS and real-time extension of Linux practicing the processor affinity concept to schedule the real-time tasks and it offers more flexible approach instead of traditional approaches defined in the literature. Limiting the migration may reduce the context switch overhead and improve the cache performance but it largely influence on other parameters which are most important for the any real-time operating system. It degrades overall performance by decreasing the schedulability of tasks, increase the deadline miss ratio. This paper presents the problem in existing processor affinity based approach which confines the schedulability of the tasks and designed a novel processor affinity based algorithm to enhance the schedulability of the tasks and decreases the average deadline miss ratio by providing the flexible migration policy with the priority reassignment mechanism.
Effects of wing twist on lift and drag characteristics of blended wing body aircraft
Authors: Kale S , Pendharkar E 
Published: December 2021

AbstractBlended Wing Body is a revolutionary concept offering phenomenal performance advantages over conventional aircraft. There are various aerodynamic features that are added to the wing in order to adjust the lift distribution and wing twist is one of them. This paper presents a progressive study to examine the effects of twist angles on the aerodynamic characteristics of a Blended Wing Body (BWB) aircraft. CFD analysis is carried out on seven BWB models having wing twists ranging from +3° to -3°. Each model is analyzed at four different Mach numbers. The coefficient of drag (Cd) and coefficient of lift (Cl) is determined and plotted against the twist angle to obtain a trend. The Cl/Cdratio is also calculated to observe the overall aerodynamic performance of the BWB.
Coronavirus Disease Predictor: An RNA-Seq based pipeline for dimension reduction and prediction of COVID-19
Authors: Iqbal Naiyar , Kumar Pradeep 
Published: December 2021

Abstract
SARS CoV-2, the novel coronavirus behind the COVID-19 infection, has caused destruction around the world with human life, detecting a range of complexity which has knocked medical care specialists to investigate new innovative solutions and diagnosis strategies. The soft computing-based approach has assumed a significant role in resolving complex issues, and numerous societies have been shifted to implement and convert these innovations in response to the encounters created by the COVID-19 pandemic. To perform genome-wide association studies using RNA-Seq of COVID-19 and identify gene biomarkers, classification, and prediction using soft computing techniques of Coronavirus disease studies to fight this emergency pandemic in the epidemiological domain, and disease prognosis. The RNA-Seq profiles of both healthy and COVID-19 positive patients’ samples were considered. We have proposed an integrated pipeline from bioinformatics in-silico phase for-omic profile data processing to dimension reduction using various prominent techniques such as formal concept analysis and principal component analysis followed by machine learning phase for prediction of the disease. In this experimental research, we have applied different eminent machine learning techniques to implement an effective integrated model using Classifier Subset Evaluator (CSE) followed by principal component analysis (PCA) for dimension reduction to select the highly significant features and then to do the classification and prediction of Coronavirus disease, different eminent classifiers have been applied on the selected features. In this analysis, the Hoeffding Tree model found the topmost performance classifier with a classification accuracy of 99.21% as well as sensitivity and specificity of 99% and 100% respectively.
Ensemble learning based classifier to predict depression caused due to pandemic
Authors: Vaishali P , Kumari P L S 
Published: December 2021

Abstract
Pandemic caused due to Corona Virus Disease 2019 (COVID-19) affected each and every person life throughout the world. First wave of COVID-19 followed by second wave made situation more panic. Government declared Lockdown imposed strict prohibition on social gathering, unnecessary outing, travelling, and education. During home quarantine, people shared opinion, expressed views, feelings on social media. Home isolation and quarantine affected mental health of people which may lead to depression. Hence in this research article depression is predicted by implementing Neural Network based model. At first level this model implements text classification of COVID-19 based Tweets. Neural network model accuracy is 86.85%. In next level, using same tweet dataset as input, Ensemble learning based model is constructed. This model uses one of the boosting techniques known as Adaboost. Model is executed by varying Train-test-validation ratio. It is observed that accuracy of the model is improved. The model showed accuracy of 99.33 % successfully in every execution. Obtained results are compared with previous work in same area.