bluuebunny/crossref_metadata_2025_split huggingface dataset:
The binary system of the spinning-top Be star Achernar
Authors: Kervella Pierre , Borgniet Simon , Domiciano de Souza Armando , Mérand Antoine , Gallenne Alexandre , Rivinius Thomas , Lacour Sylvestre , Carciofi Alex , Faes Daniel Moser , Le Bouquin Jean-Baptiste , Taormina Monica , Pilecki Bogumił , Berger Jean-Philippe , Bendjoya Philippe , Klement Robert , Millour Florentin , Janot-Pacheco Eduardo , Spang Alain , Vakili Farrokh 
Published: September 2022

Context.Achernar, the closest and brightest classical Be star, presents rotational flattening, gravity darkening, occasional emission lines due to a gaseous disk, and an extended polar wind. It is also a member of a close binary system with an early A-type dwarf companion.Aims.We aim to determine the orbital parameters of the Achernar system and to estimate the physical properties of the components.Methods.We monitored the relative position of Achernar B using a broad range of high angular resolution instruments of the VLT/VLTI (VISIR, NACO, SPHERE, AMBER, PIONIER, GRAVITY, and MATISSE) over a period of 13 years (2006−2019). These astrometric observations are complemented with a series of ≈750 optical spectra for the period from 2003 to 2016.Results.We determine that Achernar B orbits the primary Be star on a seven-year period, eccentric orbit (e = 0.7258 ± 0.0015) which brings the two stars within 2 au at periastron. The mass of the Be star is found to bemA = 6.0 ± 0.6 M⊙for a secondary mass ofmB = 2.0 ± 0.1 M⊙(the latter was estimated from modeling). We find a good agreement of the parameters of Achernar A with the evolutionary model of a critically rotating star of 6.4 M⊙at an age of 63 Ma. The equatorial plane of the Be star and the orbital plane of the companion exhibit a relative inclination of 30°. We also identify a resolved comoving low-mass star, which leads us to propose that Achernar is a member of the Tucana-Horologium moving group.Conclusions.The proximity of Achernar makes this star a precious benchmark for stellar evolution models of fast rotators and intermediate mass binaries. Achernar A is presently in a short-lived phase of its evolution following the turn-off, during which its geometrical flattening ratio is the most extreme. Considering the orbital parameters, no significant interaction occurred between the two components, demonstrating that Be stars may form through a direct, single-star evolution path without mass transfer. Since component A will enter the instability strip in a few hundred thousand years, Achernar appears to be a promising progenitor of the Cepheid binary systems.
Analysis of convective heat transfer coefficient using shear stress-based velocity scale
Authors: Yamamoto T. 
Published: August 2022

When modeling the thermal performance of a building, it is very important to consider the heat transport from the wall of the building to the interior environment. In energy simulations (ES), the convective heat transfer coefficient (hc) determines this heat transfer. Therefore, hc is a significant factor in the calculation of room temperature. In the field of architecture, hc utilizes the temperature of the turbulent zone as a reference temperature. However, the definition of this reference temperature is ambiguous. The preferred definition of the reference temperature is y+, and this is based on Prandtl's wall law. The reference temperature has a large impact on the calculation of hc, but it is difficult to evaluate the convective component. Nevertheless, it is possible to extract the velocity component near the wall using a shear stress-based velocity scale, but previous studies have not clarified how the latter and the reference temperature contribute to hc. Until now, the method used to calculate hc has been based on a combination of experimental and analytical methods, such as the use of inflow air temperature as the reference temperature in wind tunnel experiments. In this study, we analyze the method used for calculating heat flow and introduce a new method for calculating hc. In an actual environment, the turbulent flow field is expected to be strongly affected by the nearby flow velocity and reference temperature. To model such a turbulent flow field, we conducted a basic study in which a unidirectional flow was blown across a floor mat. Specifically, the influences of the shear stress-based velocity scale and the reference temperature were investigated by varying the inflow velocity temperature from 10 °C to 40 °C. The analysis first showed that hc and the shear stress-based velocity scale were highly correlated (R2 = 0.8073). However, an analysis of the outliers revealed a significant contribution from the reference temperature.
In conclusion, the value of hc cannot be determined solely by the shear stress-based velocity scale, even in cases involving forced convection. Our findings provide very important information for ES settings in the field of architecture and will significantly contribute to the field of thermal environment simulation.
Pericardial Drain Placement in Interventional Radiology: An Overview
Authors: Herren Josi L. , Chan HingKui , Ray Charles E. 
Published: August 2022

AbstractPericardial effusions can occur from multiple different etiologies. Although often incidentally noted or clinically silent, pericardial effusions may cause significant hemodynamic compromise. In some of these patients, pericardiocentesis may be temporizing only and either repeat procedures or placement of pericardial drains may be preferable. While cardiologists typically perform pericardial drain placement, it is a procedure that may also be performed by interventional radiologists. This article describes for the interventional radiologist the indications and placement technique, as well as potential complications occurring from pericardial drain placement.
Light emitting diodes improved the metabolism of rosmarinic acid and amino acids at the transcriptional level in two genotypes of
Authors: Ahmadi Tayebeh , Shabani Leila , Sabzalian Mohammad R. 
Published: August 2022

In the present study, we used different LEDs to evaluate their effect on metabolic and transcriptional reprogramming of two genotypes (Ilam and Isfahan) of lemon balm grown under narrow-band LED lighting. Lemon balm plants were grown in four incubators equipped with artificial lighting and subjected to four LED lamps [White, Blue, Red, and mixed RB (Red + Blue) (70%:30%)] and in greenhouse conditions for 7 weeks. The results showed significant increases in leaf number, pigment and soluble sugar contents, secondary metabolites, and calcium, magnesium, potassium and amino acid contents achieved in growth under mixed RB LEDs. As observed for the content of total phenolics, rosmarinic acid, and amino acids, the expression of genes involved in their production, including TAT, RAS, and DAHPS were also enhanced due to the mixed RB LED lighting. The best condition for both the plant growth and expression of genes was under the mixture of Red + Blue LED lamps. These observations indicate that the increase in secondary metabolites under mixed Red + Blue lights may be due to the increase in primary metabolites synthesis and the increased expression of genes that play an essential role in the production of secondary metabolites.
Smart Home Automation using Augmented Reality and Internet of Things
Authors: Jaivignesh R. , Janarthanan R. D. , Gnanalakshmi V. 
Published: September 2022

Abstract
This paper demonstrates the concept of Augmented Reality and the Internet of Things controlling home appliances through the internet. Nowadays, Home Automation using IoT became an integral part but it can be extended to Augmented Reality. Augmented Reality is used to superimpose interactive content into real-world objects. It helps in placing the 3D objects that help in controlling different IoT devices. The main objective of this paper is to demonstrate and analyse the control of home appliances using augmented reality with the internet of things by a Wi-Fi-based microcontroller. This paper also demonstrates its approach toward multiple devices and proves that augmented reality and IoT are complementary to each other that visual interactive objects can control IoT devices. Performance measures like transmission time, RSSI were taken which plays a major role in terms of Internet of Things. The Transmission time determines the time taken by the Wi-Fi of Esp32 to connect to the blynk server. The Comparative analysis for the Transmission ON time and Transmission OFF time at different ranges is measured. The Transmission ON time is of maximum of 2.36 seconds at a distance of 5m and of 2.65 seconds at a distance of 10m without any hindrance. The Transmission time with a wall hindrance is increases than without the wall hindrance. The Transmission ON time is of 2.9 seconds and the Transmission OFF time is of 2.8 seconds. The Received Signal Strength Indicator indicates the strength of the wifi network is also measured and it indicates the network is good. The Measured value is between -23 dbm to -25 dbm which indicates a better network performance.
Battery Management System to Estimate Battery Aging using Deep Learning and Machine Learning Algorithms
Authors: Harippriya S. , Esakki Vigneswaran E. , Jayanthy S. 
Published: September 2022

Abstract
One of the greatest challenges faced by Electric Vehicle (EV) manufactures is insufficient charging stations. Estimating the aging of the battery in the electric vehicle helps the driver to predict the driving range of the vehicle. This paper proposes a battery management system that is developed to predict remaining battery charge of the Electric Vehicle. The aging of the lithium-ion (Li-Ion) battery present in the electric vehicle is predicted using different machine learning and deep learning algorithms. The parameters such as voltage, current and temperature are taken from the sensors connected to the LPC2148 ARM board and the values are given as dataset to the Long Short-Term Memory (LSTM), Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) Algorithms. The experimental results indicate that for real-time data Naïve Bayes algorithm gave the best results in terms of metrics such as Accuracy, Precision, Recall and F1-score. Naïve Bayes produced results with the accuracy rate of 88% and used to calculate the Remaining Battery Capacity which helps predicting the aging of the lithium- ion battery.
Cost Efficient Virtual Trial Rooms
Authors: Malathi M , Induja R , Mubarak S , Lalitha T 
Published: September 2022

Abstract
The recent covid-19 restrictions like social distancing has limited physical human interaction. There were days when people wish to visit the physical shopping outlets and try the best-suited outfits in the trial rooms. In the post-pandemic era, people are now preferring the E-shopping zone to select the outfits. This type of virtual shopping has its own disadvantages like the suitability of outfits to the customer’s preferences. Regardless of some applications that offer outfit suitability, there are some entrust issues with online stores. This research study analyzes many general customer related concerns like fit of the outfit; appearance; color suitability with the customer skin tone and proposes the possibility of implementing virtual trial rooms. The proposed model empowers the clients to evaluate their outfits based on their stature, shape, size, and wellness estimations by standing in front of the gadget.
Power Quality Enhancement by minimizing the effect of Voltage Sag in Non-linear Load Using D-STATCOM
Authors: Sharma Vivek , Chandrakar Vinod Kumar 
Published: September 2022

Abstract
This research article discusses and examines the voltage sag and power quality issues in distribution networks with D-STATCOM. Power quality issues are examined under various system conditions. Load voltage v/s current characteristics of a nonlinear load is widely used in metal processing industries i.e. ARC Furnace, which is having Non-Linearity in the Load characteristics, is developed in MATLAB environment & recorded using Scopes. The reactive power control with D-STATCOM is applied to enhance the voltage profile and thus the power quality at the load point bus. The significant improvement in voltage and THD calculated by FFT analysis are improved as per IEEE standard.
Computational Analysis of stochastic arithmetic computing and stochastic activation function
Authors: Ashok P , Bala Tripura Sundari B. 
Published: September 2022

Abstract
Stochastic Number Generators (SNG) plays a significant role in designing a stochastic computing system. SNGs make the stochastic system comfortable for computing in the stochastic domain. The challenges in developing the stochastic computing system are correlation and hardware area occupancy. By considering these phenomena, we have considered Linear Feedback Shift Register (LFSR) based SNG and S-box based SNG in this work. Our contributions to this paper are stochastic computation for activation functions using the SNGs mentioned above and stochastic computation for arithmetic components in the stochastic domain. By considering the two SNG methods, the difference in the computation for accuracy has been analyzed for stochastic activation function and stochastic arithmetic computation. The better SNG method will be used as SNG for stochastic convolutional neural network design using this analysis.
Solving Wave Equation and Heat Equation by Rohit Transform (RT)
Authors: Gupta Rohit , Verma Rakesh Kumar , Verma Sanjay Kumar 
Published: September 2022

Abstract
The wave equation and the heat equation are widely known differential equations coming to light in engineering, basic and material sciences. The differential equations which represent the wave equation and the heat equation are usually solved by the exact technique or by the approximate technique or by the purely numerical technique. Since the implementation of these techniques is very complex, computationally vigorous, and requires elaborate computations, therefore, for finding the solutions of differential equations depicting the wave equation and the heat equation, there is a need to ask for integral transform techniques. Integral transform techniques render productive means for finding the solutions of problems coming to light in engineering, basic and material sciences. The Rohit transform (RT) is a new integral transformation put forward by the author Rohit Gupta recently in the year 2020 and has been utilized for finding the solutions of problems coming to light in engineering, basic and material sciences like other transform techniques. In this study, the RT is brought in for finding the solutions of the heat equation and the wave equation expressed in terms of differential equations which are generally partial in nature.
Artificial Intelligence & Deep Learning: Pet and Spect Imaging and Classification on Brain MRI Images List
Authors: Goel Amit Kumar , Yadav Sagar , Zubair  , Jain Naman 
Published: September 2022

Abstract
Cognitive abilities commonly linked with human brains include learning and problem solving. Another option is just a preprogrammed rule that instructs the computer to perform a given action under specified circumstances. Voice recognition and image recognition, for instance, are both widely used in daily life and may be found everywhere. “Deep learning” (DL) approaches, in particular,due to their ability to come up with creative solutions to complex situations throughout the past decade. Both cancerous and non-cancerous tumors can be seen in the human brain (benign). Google developed the open-source deep learning library Tensor Flow. It can be combined with traditional machine learning. It was created on top of PIL that the Python Pillow module exists (Python Image Library). This collection of modules is critical to Python’s ability to perform image processing. “A deep learning API developed in Python” is powered by “the TensorFlow-based machine learning platform TensorFlow.” NumPy is a Python library for manipulating arrays, as the name suggests. Also included are linear algebra, the Fourier transform, and matrix functions. As far as the ResNet50 model goes, it has 48 convolutional layers, a maxpool and an averagepool layer. Up to a scale of 3.8 x 109, floating point operations can be performed. ResNet-50 is a type of neural network with 50 layers. You can import a pre-trained version of the network from the ImageNet database.
Copy Move Forgery Detection: The Current Implications and Contemporary Practices
Authors: Gajjar Pranshav , Saxena Aayush , Shah Het , Kikani Nandish , Lakhani Karan , Shah Pooja , Sharma Ankit , Limbachiya Krishn 
Published: September 2022

Abstract
Copy move forgeries are a type of digital retouching that takes place in the field of image modification. This type of retouching occurs when one element of a picture is replicated over to a different segment of the photograph. This is typically done in an effort to mask or cover up undesirable qualities of the photograph thus copy move forgeries are a form of image modification. When it comes to digital picture modalities, copy move forging is by far the most prevalent form of picture manipulation that may take place. This is mainly observed when certain elements or segments of the recorded file are duplicated in the recorded file. The identification and localization of forgeries is a prominent problem that has piqued the attention of scholars working in the field of digital forensics and continues to do so. The identification of photo forgery has previously been the subject of several methodologies and uncountable publications, all of which were created and published. An abridged description of the present methodology and approaches is provided in this document, along with a survey and condensed summaries of current research and successes, among other things.
Early heart disease prediction using ensemble learning techniques
Authors: Rohit Chowdary K , Bhargav P , Nikhil N , Varun K , Jayanthi D 
Published: September 2022

Abstract
Cardiovascular illnesses claim the lives of 18 million individuals each year (heart-related diseases). According to the WHO, heart disease is to blame for 31% of all deaths worldwide. In this study, a new machine learning model for predicting heart disease is provided. The proposed method was evaluated on Kaggle and the University of California, Irvine datasets. We used sample approaches and feature selection methods to identify the most useful characteristics in the dataset that was unbalanced. Eventually, classifier models were employed, and an ensemble classifier generated great accuracy. In two datasets, the proposed approach showed to be accurate in predicting heart disease. In all cases, Python was used.
Real-Time Quality Assurance of Fruits and Vegetables with Artificial Intelligence
Authors: Tata Jagannadha Swamy , Kalidindi Naga Karthik Varma , Katherapaka Hitesh , Julakal Sharath Kumar , Banothu Mohan 
Published: September 2022

Abstract
Vegetables and fruits make up a major part of the human diet and finding a good grade of its quality is now a major issue in the market. To find a grade of the vegetable or fruit will be based on some parameters like size, shape, appearance, etc. The appearance is now a deciding factor for the market and affects the consumer’s choice. So, we have designed an application that will classify fruits and grade them according to their quality with appearance as a parameter. This paper will describe the process which is involved in the application. This proposed system will use image processing to classify and grade the quality of fruits and vegetables by extracting features such as color, shape, and HOG (Histogram of Gradient) to classify the given fruit or vegetable. Image pre-processing techniques like data-augmentation and normalization along with Principle- Component Analysis (PCA), and also Deep learning (CNN) are used for getting good accuracy and for dimensional reduction. To speed up the identification and increase the usability, compared to current manual systems like a person checking every fruit and vegetable to grade which takes more time and energy, or by using embedded systems (sensors), we opted for a high-performance android application for quicker deployment.
Avoided population exposure to extreme heat under two scenarios of global carbon neutrality by 2050 and 2060
Authors: Lei Yadong , Wang Zhili , Zhang Xiaoye , Che Huizheng , Yue Xu , Tian Chenguang , Zhong Junting , Guo Lifeng , Li Lei , Zhou Hao 
Published: August 2022

Abstract
To mitigate global warming and the resulting climate risk, many countries have accelerated the optimization of industrial structures and mixture of energy type in an attempt to achieve carbon neutrality by the second half of the 21st century. Here, we present the first assessment of the quantitative benefits of population exposure to extreme heat (defined by the heat index) during 2040–2049 under two scenarios of global carbon neutrality by 2060 and 2050, i.e. moderate green (MODGREEN) and strong green (STRGREEN) recovery scenarios, relative to the baseline scenario of Shared Socioeconomic Pathway (SSP) 2–4.5. Global mean extreme heat days increase by 12.1 d yr−1 (108%) during 2040–2049 under the SSP2-4.5 scenario relative to the historical period (1995–2014). The aggravating extreme heat events could be mitigated by as much as 12% and 18% during 2040–2049 under the MODGREEN and STRGREEN scenarios, respectively. Following the changes in extreme heat days, global population exposure to extreme heat is mitigated by 27.3 billion person-days (7%) in the MODGREEN scenario and 39.9 billion person-days (11%) in the STRGREEN scenario during 2040–2049 relative to the SSP2-4.5 scenario. Such benefits from these low-carbon policies are larger in regional hotspots, including India and Northern Africa, which have experienced high population growth and have extremely limited medical infrastructure. Moreover, an early carbon neutrality (2050 vs 2060) could avoid 12.6 billion person-days exposure to extreme heat during 2040–2049. Our results provide an important scientific support for governments to drive early policymaking for climate change mitigation.