bluuebunny/crossref_metadata_2025 huggingface dataset:
Le mésusage des citations et ses conséquences en médecine
Authors: Dumas-Mallet Estelle , Boraud Thomas , Gonon François
Published: December 2021

Les observations biomédicales ne deviennent une source de connaissance qu’après un débat entre chercheurs. Au cours de ce débat, la citation des études antérieures tient un rôle majeur, mais les travaux académiques qui en évaluent l’usage sont rares. Ils ont cependant pu révéler deux types de problèmes : les biais de citation et les écarts de sens entre l’étude antérieure citée et ce qu’en dit l’article citant. Dans cette revue, nous synthétisons ces travaux et en dégageons les principales caractéristiques : les études favorables à la conclusion des auteurs citants sont plus souvent citées que celles qui les questionnent ; des écarts de sens majeurs affectent environ 10 % des citations. Nous illustrons par deux exemples les conséquences de ce mésusage des citations.
Histoire des stéroïdes reproductifs
Authors: Bouchard Philippe
Published: December 2021

Les stéroïdes sexuels, piliers de la vie reproductive de la femme, jouent un rôle essentiel dans la contraception et, bien sûr, dans le traitement hormonal de la ménopause. Ils sont aujourd’hui néanmoins décriés en raison du risque thromboembolique auquel leur utilisation (essentiellement la contraception) expose, et en raison également du risque de cancer du sein chez les femmes de plus de 50 ans (essentiellement suite à un traitement de la ménopause), notamment s’il existe des antécédents familiaux de cancer du sein. Il n’en reste pas moins qu’ils ont transformé la vie de la femme. L’accès des femmes à la contraception a en effet transformé leur vie et leur a permis des progrès sociétaux considérables. Malgré ces progrès, le très grand nombre de grossesses non désirées et d’interruptions volontaires de grossesse, rend indispensable le renforcement de l’offre contraceptive.
Pea and rapeseed acreage and land use for plant-based meat alternatives in the EU
Authors: Pilorgé Etienne , Kezeya Bruno , Stauss Wolfgang , Muel Frédéric , Mergenthaler Marcus
Published: December 2021

Plant-based meat alternatives from grain legumes and oil crops are expected to play an increasing role in human nutrition. Several commercially available products use pea protein isolate as protein basis and rapeseed oil as lipid basis. The aim of the present study is to estimate the prospective area of peas and rapeseed for plant-based meat alternatives in the EU. A simple calculation model is employed to assess the impacts on land use and imported deforestation, in case plant-based meat alternatives substitute meat consumption in different shares. Various data sources and scenarios were used to estimate the cultivation potential. While pea acreage would increase considerably compared to current production, additional rapeseed acreage would be more limited. Even in an extreme scenario of 100% substitution only 12% of EU’s arable land would be used for pea and rapeseed as main ingredients for plant-based meat alternative. If pea protein isolate and rapeseed oil as main ingredients of plan-based meat alternatives increase, the land currently used for animal feed production would become partly available and imported deforestation could be decreased: a substitution of 25% of meat consumption would allow to provide the equivalent of food proteins without extending the cultivated areas in Europe, while avoiding soybean and maize imports for feed.
Targeted Epidural Blood Patch Treatment for Refractory Spontaneous Intracranial Hypotension: A Case Report
Authors: Liu Nan , Fei Yue , He Fei-fang
Published: December 2021

Purpose of Review Spontaneous intracranial hypotension (SIH) is recognized far more commonly than before, and it is well known that SIH is sometimes complicated by chronic subdural hematoma (SDH). We reported a patient who was treated with epidural blood patch (EBP) five times for refractory SIH and SDH surgery.

Recent Findings We experienced that targeted EBP was useful in refractory SIH, and also can be performed safely prior to drainage of the hematoma.

Summary We report the case of SIH patient with a bilateral SDH who came to our hospital and was discharged 2 weeks later with no neurologic deficit after trephination and five times EBP treatment. It shows that targeted EBP was useful in refractory SIH, and also can be performed safely prior to drainage of the hematoma.
Luneburg lens design enhances broadband and omnidirectional acoustic collimation and focusing
Authors: Ikenson Ben
Published: December 2021

Experimental and theoretical study improves acoustic lens functionality in ultrasonic frequencies
On the modeling of some triodes-based nonlinear oscillators with complex dynamics: case of the Van der Pol oscillator
Authors: Ngamsa Tegnitsap Joakim Vianney , Saatsa Tsefack Merlin Brice , Megam Ngouonkadi Elie Bertrand , Fotsin Hilaire Bertrand
Published: December 2021

In this work, the dynamic of the triode-based Van der Pol oscillator coupled to a linear circuit is investigated (Triode-based VDPCL oscillator). Towards this end, we present a mathematical model of the triode, chosen from among the many different ones present in literature. The dynamical behavior of the system is investigated using classical tools such as two-parameter Lyapunov exponent, one-parameter bifurcation diagram associated with the graph of largest Lyapunov exponent, phase portraits, and time series. Numerical simulations reveal rather rich and complex phenomena including chaos, transient chaos, the coexistence of solutions, crisis, period-doubling followed by reverse period-doubling sequences (bubbles), and bursting oscillation. The coexistence of attractors is illustrated by the phase portraits and the cross-section of the basin of attraction. Such triode-based nonlinear oscillators can find their applications in many areas where ultra-high frequencies and high powers are demanded (radio, radar detection, satellites communication, etc) since triode can work with these performances and are often used in the aforementioned areas. In contrast to some recent work on triode-based oscillators, LTSPICE simulations, based on real physical consideration of the triode, are carried out in order to validate the theoretical results obtained in this paper as well as the mathematical model adopted for the triode.
Multi-wavelength spectroscopic probes: biases from neglecting light-cone effects
Authors: Viljoen Jan-Albert , Fonseca José , Maartens Roy
Published: December 2021

Next-generation cosmological surveys will observe larger cosmic volumes than ever before, enabling us to access information on the primordial Universe, as well as on relativistic effects. In a companion paper, we applied a Fisher analysis to forecast the expected precision on f
NL and the detectability of the lensing magnification and Doppler contributions to the power spectrum. Here we assess the bias on the best-fit values of f
NL and other parameters, from neglecting these light-cone effects. We consider forthcoming 21cm intensity mapping surveys (SKAO) and optical galaxy surveys (DESI and Euclid), both individually and combined together. We conclude that lensing magnification at higher redshifts must be included in the modelling of spectroscopic surveys. If lensing is neglected in the analysis, this produces a bias of more than 1σ — not only on f
NL, but also on the standard cosmological parameters.
Proper time reparametrization in cosmology: Möbius symmetry and Kodama charges
Authors: Achour Jibril Ben
Published: December 2021

It has been noticed that for a large class of cosmological models, the gauge fixing of the time-reparametrization invariance does not completely fix the clock. Instead, the system enjoys a surprising residual Noether symmetry under a Möbius reparametrization of the proper time, which maps gauge-inequivalent solutions to the Friedmann equations onto each other. In this work, we provide a unified treatment of this hidden conformal symmetry and its realization in the homogeneous and isotropic sector of the Einstein-Scalar-Λ system. We consider the flat Friedmann-Robertson-Walker (FRW) model, the (A)dS cosmology and provide a first treatment of the model with spatial constant curvature.
We derive the general condition relating the choice of proper time and the conformal weight of the scale factor, and give a detailed analysis of the conserved Noether charges generating this physical symmetry. Our approach allows us to identify new realizations of this symmetry while recovering previous results in a unified manner. We also present the general mapping onto the conformal particle and discuss the solution-generating nature of the transformations beyond the Möbius symmetry. Finally, we show that, at least in a restricted context, this hidden conformal symmetry is intimately related to the Kodama charges of spherically symmetric gravity. This new connection suggests that the Möbius invariance of cosmology is only the corner of a larger symmetry structure which could be relevant beyond cosmological models.
Electronic structure and spin–orbit coupling in ternary transition metal chalcogenides Cu2TlX
2 (X = Se, Te)
Authors: Qin Na , Du Xian , Lv Yangyang , Kang Lu , Yin Zhongxu , Zhou Jingsong , Gu Xu , Zhang Qinqin , Xu Runzhe , Zhao Wenxuan , Li Yidian , Yao Shuhua , Chen Yanfeng , Liu Zhongkai , Yang Lexian , Chen Yulin
Published: December 2021

Ternary transition metal chalcogenides provide a rich platform to search and study intriguing electronic properties. Using angle-resolved photoemission spectroscopy and ab initio calculation, we investigate the electronic structure of Cu2TlX
2 (X = Se, Te), ternary transition metal chalcogenides with quasi-two-dimensional crystal structure. The band dispersions near the Fermi level are mainly contributed by the Te/Se p orbitals. According to our ab-initio calculation, the electronic structure changes from a semiconductor with indirect band gap in Cu2TlSe2 to a semimetal in Cu2TlTe2, suggesting a band-gap tunability with the composition of Se and Te. By comparing ARPES experimental data with the calculated results, we identify strong modulation of the band structure by spin–orbit coupling in the compounds. Our results provide a ternary platform to study and engineer the electronic properties of transition metal chalcogenides related to large spin–orbit coupling.
DES map shows a smoother distribution of matter than expected: a possible explanation
Authors: Oks Eugene
Published: December 2021

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

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

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

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

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

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

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

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

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-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

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

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

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

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

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

Blended 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

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

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.
Binary Black Hole Automated Identification by Agglomerative Clustering based on Gravitational Waves
Authors: Adhikary Subhrangshu , Banerjee Saikat
Published: December 2021

The General Theory of Relativity, proposed by Albert Einstein theoretically predicted that very large accelerating mass creates ripples in spacetime which is the strongest for merging binary black hole system and the ripples can travel billions of light-years and these ripples are called Gravitational Waves. By the time these waves reach Earth, they become very faint and can’t be detected with regular methods. For this, LIGO has created specialized detectors based on the laser interference principle to detect strains caused by gravitational waves in e-19 scale. GW190521 is a gravitational wave event recorded on 21 May 2019 at 03:02:29 UTC and caused by the merger of two black holes of 85M© and 66 M© whose progenitor was the largest ever recorded. Throughout literature, very few amounts of autonomous black hole identification models have been made because of limited data availability. This experiment proposes methods for autonomous identification of black holes by using an unsupervised machine learning algorithm called Agglomerative Clustering with very little data to train which can adapt quickly to gravitational wave events. The model could be easily deployed near laser interferometric observatories for autonomous black hole identification with minimal effort.
Development of Smart Earth Leakage Circuit Breaker Using IoT and Power Electronics
Authors: Mehta Rashesh , Saiyad Junaid , Smart Niyatee , Mali Gordhan , Kamat Vithal
Published: December 2021

The Smart Earth Leakage Circuit Breaker research is based on IoT& power electronics-based protective switchgear for domestic consumers for the protection against earth leakage faults. In this paper, we have shown how to create a custom web server using HTML and CSS language and hosting this webserver to ESP module using Serial Peripheral Flash File System (SPIFFS). Along with protection against earth fault it provides, controlling of tripping current or fault current, monitoring voltage, power, current, power factor parameters of the connected load, monitoring fault parameters, Indicating the device status whether the ELCB is on or off, and the condition of ELCB it is in faulty condition or healthy condition and to let the user set the value of the tripping current on the webserver at which the ELCB must trip.
Review of enhanced power quality using unified power flow control system in electrical network
Authors: Shende Divya , Jagtap Prashant , Hiware Rutuja
Published: December 2021

Flexible FACTS system of AC transmission. FACTS Devices can regulate electricity flow, develop the transmission capacity for power management. UPFC is a multipurpose fact controller carried on design of the constant voltage source. As an electrical device UPFC for rapid reactive power adjustment on high voltage electricity transport grid. Unified power flow control (UPFC). The latest FACTS gadget is UPFC. This combines series and shunting compensator characteristics and enables Power reactive and response to be controlled. UPFC utilization reduces difficulties in power quality including voltage sink and voltage surge. This article addresses UPFC and also several novel topologies for FACTS controllers.
Enhanced power quality using unified power flow controller systems
Authors: Shende Divya , Jagtap Prashant , Hiware Rutuja
Published: December 2021

Electrical system frequently finds and issue due to unstable nature and power quality because of, in relation to great number of nonlinear charges. So, there is need to limit inside of these difficulties and produce fine voltage quality concerns. The Flexible Alternate Flow Transmission Systems (FACTS) are the framework made out of static gear works for the AC transmission of electrical energy. Unified_Power_Flow Controls (UPFC) are the excellent FACTS tools to attach series and shunt together and it could use for framing Power transmission sensitive and active power. Here in the paper, Unified Power Flow Control (UPFC) used to clear the voltage sink and Surge. Unified Control was developed and engineered using amplifiers and rectifiers. The real and reactive modifications in congruous control orientations at the receiver side. Use of Simulink of MATLAB checks quality of energy in the use of Unified Control.
A new design of control & power management strategies of hybrid ac-dc microgrids toward high power quality
Authors: Chaurase Payal , Ramtekkar Pankaj
Published: December 2021

The micro grid idea provides for the lack of several reversing switches to unitary AC-DC grid that enables connection and charges (loads) to the electrical systems with changeable regenerative AC and CC sources. Safe operation and gadget safety involve digital integration with utilities/grid through power converters. Enhanced client reliability, decreased input losses, local voltages are supported, and waste heat efficiency increased, voltage drop or interruptible supply of electricity can be customized to satisfy their unique customer demands. Work at present Analyses the performance in grid tie mode of hybrid AC/DC systems. Here are PV systems, PV systems, For the construction of microgrids wind turbine generators and batteries are employed. Convert procedures for the correct coordination of AC sub-grids to DC subs-grids have also been established for converters. MATLAB/SIMULINK environment results are generated.
IoT Enabled Smart Lighting System for Smart Cities
Authors: Dankan Gowda V , Annepu Arudra , Ramesha M , Prashantha Kumar K , Singh Pallavi
Published: December 2021

The pace of urbanisation has risen tremendously during the last few decades. To provide a higher quality of life, urban dwellers will require a greater variety of improved services and apps. The term “smart city” refers to integrating contemporary digital technology in the setting of a city to improve urban services. There are possibilities to create new services and connect disparate application areas with each other as a result of the use of information and communication technologies in the smart city. However, to make sure the services in an IoT-enabled smart city environment remain running without depleting valuable energy resources, all of the apps have to be maintained using energy resources that are kept at a minimum. IoT can enhance a city’s lighting system since it uses more energy than other municipal systems. An intelligent city integrates lighting system sensors and communication channels with enhanced intelligence features for a Smart Lighting System (SLS). To control lighting more efficiently, SLS systems are built to be autonomous and efficient. We cover the SLS and evaluate several IoT-enabled communication protocols in this article. Furthermore, we evaluated several use scenarios for IoT enabled indoor and outdoor SLS and generated a report detailing the energy consumption in different use cases. By using IoT-enabled smart lighting systems, our research has shown that energy savings are possible in both indoor and outdoor settings, which is equivalent to a forty percent reduction in energy usage. Finally, we went through the SLS in the smart city research plans.
Smart Agriculture and Smart Farming using IoT Technology
Authors: Dankan Gowda V , Sandeep Prabhu M , Ramesha M , Kudari Jayashree M , Samal Ansuman
Published: December 2021

It has become easier to access agriculture data in recent years as a result of a decline in digital breaches between agricultural producers and IoT technologies. These future technologies can be used to boost productivity by cultivating food more sustainably while also preserving the environment, thanks to improved water use and input and treatment optimization. The Internet of Things (IoT) enables the production of agricultural process-supporting systems. Referred to as remote monitoring systems, decision support tools, automated irrigation systems, frost protection systems, and fertilisation systems, respectively. Farmers and researchers must be provided with a detailed understanding of IoT applications in agriculture as a result of the knowledge described above. This study is about using Internet of Things (IoT) technologies and techniques to enhance agriculture. This article is meant to serve as an introduction to IoT-based applications in agriculture by identifying need for such tools and explaining how they support agriculture.
Stress Recognition Using Facial Landmarks and Cnn (Alexnet)
Authors: Ramesh Naidu P , Pruthvi Sagar S , Praveen K , Kiran K , Khalandar K
Published: December 2021

Stress is a psychological disorder that affects every aspect of life and diminishes the quality of sleep. The strategy presented in this paper for detecting cognitive stress levels using facial landmarks is successful. The major goal of this system was to employ visual technology to detect stress using a machine learning methodology. The novelty of this work lies in the fact that a stress detection system should be as non-invasive as possible for the user. The user tension and these evidences are modelled using machine learning. The computer vision techniques we utilized to extract visual evidences, the machine learning model we used to forecast stress and related parameters, and the active sensing strategy we used to collect the most valuable evidences for efficient stress inference are all discussed. Our findings show that the stress level identified by our method is accurate is consistent with what psychological theories predict. This presents a stress recognition approach based on facial photos and landmarks utilizing AlexNet architecture in this research. It is vital to have a gadget that can collect the appropriate data. The use of a biological signal or a thermal image to identify stress is currently being investigated. To address this limitation, we devised an algorithm that can detect stress in photos taken with a standard camera. We have created DNN that uses facial positions points as input to take advantage of the fact that when a person is worried their eye, mouth, and head movements differ from what they are used to. The suggested algorithm senses stress more efficiently, according to experimental data.
Convergence and Stability Results of Modified CUIA Iterative Scheme for Hyperbolic Convex Metric space
Authors: Gonder Surjeet Singh Chauhan , Basra Khushboo
Published: December 2021

The iterative fixed points have numerous applications in locating the solution of some real-life problems which can be modelled into linear as well as nonlinear differential and integral equations. In this manuscript, first of all, a new iterative scheme namely Modified CUIA iterative scheme is introduced. We first prove a theorem to check the convergence of this iteration for Hyperbolic Convex metric space. The result is then supported with one example. Further, another theorem is proved establishing the weak T stability of modified CUIA iterative scheme on the above space.
A Text Mining using Web Scraping for Meaningful Insights
Authors: Reddy C Kishor Kumar , Anisha P R , Gia Nguyen Nhu , Sreelatha G
Published: December 2021

This research involves the usage of Machine Learning technology and Natural Language Processing (NLP) along with the Natural Language Tool-Kit (NLTK). This helps develop a logical Text Summarization tool, which uses the Extractive approach to generate an accurate and a fluent summary. The aim of this tool is to efficiently extract a concise and a coherent version, having only the main needed outline points from the long text or the input document avoiding any type of repetitions of the same text or information that has already been mentioned earlier in the text. The text to be summarized can be inherited from the web using the process of web scraping or entering the textual data manually on the platform i.e., the tool. The summarization process can be quite beneficial for the users as these long texts, needs to be shortened to help them to refer to the input quickly and understand points that might be out of their scope to understand.
False News Recognition Using Machine Learning
Authors: Thirupathi Lingala , gangula Rekha , Ravikanti Sandeep , Sowmya Jujuroo , Shruthi S K
Published: December 2021

In these modern times where internet has become widely popular and used by almost everyone, anyone can share or upload articles without any credibility. False news refers to articles that are published with the intent of deliberately misleading readers. In the recent times false news on internet has become more and it has become a major problem as it is difficult to differentiate between the real and the false news. False news and false posts have become more prevalent on social media sites such as Face book and Twitter. From these platforms the news will be spread like wild fire without any authenticity. It can be used to sway election outcomes against certain candidates, can be used for click baiting purposes, and can be used to earn revenue by misleading the users. In this paper we will use natural language processing techniques like bag of words and TD-IDF and machine learning concepts of classification algorithms like SVM and passive aggressive classifier to train our machine to differentiate false news from real news and we will compare the accuracy of methods used to find accurate model.
Factors influencing the use of Deep Learning for Medicinal Plants Recognition
Authors: Anchitaalagammai J V , Shantha Lakshmi Revathy J S , Kavitha S , Murali S
Published: December 2021

Medicinal plants are very essential in maintaining the physical and mental health of human beings. For providing better treatment, Identification and classification of medicinal plants is essential. In this research paper, main objective is to create a medicinal plant identification system using Deep Learning concept. This system identifies and classifies the medicinal plant species with high accuracy. In this system, five different Indian medicinal plant species namely Pungai, Jamun (Naval), Jatropha curcas, kuppaimeni and Basil are used for identification and classification. The dataset contains 58,280 images, includes approximately 10,000 images for each species. The leaf texture, shape, color, physiological or morphological as the features set for leaf identification. The CNN architecture is used to train the collected dataset and develop the system with high accuracy. As result of this model, 96.67% success rate in finding the corresponding medicinal plant. This model is advisable to use as early detection tool for finding the medicinal plant because of its best success rate