bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparative Analysis of Association Rule Mining Algorithms
Authors: Saxena Akash , Rajpoot Vikram 
Published: March 2021

Abstract
The field of data mining (DM) has grown rapidly in recent years. One of the most important data mining techniques is association rule mining (ARM). It is a strategy used to identify trends in the database that are normal. There has been a lot of work in the area of ARM. The paper provides a short description of the principles and algorithms of interaction, several of the implementations. To several researchers, ARM has long been and still is of concern. Data mining is one of the essential activities. This helps to identify associations between various elements in the database. The goal of this paper is to provide an outline of the fundamental concepts of the ARM methodology and the recent relevant research in this area. The paper further explains the different algorithms, methods, strategies, and benefits of the ARM areas, drawbacks. The paper also provides a minor distinction focused on the results of various algorithms related to association rules mining. The paper provides a short description of the principles and algorithms of interaction, several of the implementations. Algorithms are present and evaluate base parameters such as precision, algorithm pace, and help for data. To solve the question of apriori algorithms AprioriTid and the AprioriHybrid have been suggested. From the contrast, we infer that, since it has decreased overall pace and increased precision, AprioriHybrid is superior to Apriori and AprioriTid. We may infer that the LogElcat algorithm performs more than every other algorithm based on these parameters.
Automatic Detection of COVID 19 Infection Using Deep Learning Models from X-Ray Images
Authors: Yadav Anju , Verma Vivek Kumar , Pal Vipin , Singh Saumya 
Published: March 2021

Abstract
In worldwide millions of people are infected due to COVID-19 virus and even that is increasing exponentially. The medical researchers are using RT-PCR test to distinguish between COVID (+) and COVID (-). The growth of virus is exponential that’s lead to an outbreak, and to break this we have to conduct test as many as possible. RT PCR test is costly and not available in remote areas this is big challenge that developing countries is facing. The second big challenge whole world facing is the availability of doctors and radiologists. So, we need an automated system based on AI and Machine learning (ML) for detection of COVID (+) cases. In literature AI, ML and Deep Learning (DL) played an important role in designing automated system in medical field by achieving good accuracy. In this paper, we have applied DL models ResNet 34 and ResNet 50 on X-ray images, achieved accuracy 96.4% with ResNet 34. And compared their result on the basis of various quantitative analysis parameter MCC, F1 Score, Precision, Recall and others to solve both challenges.
Study of Baseline Cyber Security for Various Application Domains
Authors: Chaturvedi Mudit , Sharma Shilpa , Ahmed Gulrej 
Published: March 2021

Abstract
In today’s technical life, the means of production and creation, process, storage and exchange of information and data, rather than in paper-based methods, as an alternative to electronic data and electronic means of communication. That means all information / data is done on the paper as not a written document, but in the form of digital and electronic data. A special type of computer-based system is used in preparing, receiving, processing, transmitting or storing this electronic data / information and the main feature of this computer-based system is that there is a rapid data processing by logical, arithmetic, membrane resources by the systems of electronic, magnetic or optical waves by instruction or communication, work of input, product, investment, processing or storage is done. It is very important for the common man to know about the basic security of information / data in every domain of life. This paper analyses the technical changes and cyber risks arising in various fields, as well as a detailed study of technological security efforts and measures to mitigate cyber risks in various technical areas.
Analysis of Design for One-Way Reinforced Concrete Slabs using Machine Learning Models
Authors: Sancheti G , Patil H , Sharma S , Goswami S 
Published: March 2021

Abstract
Preliminary design parameters of one-way Reinforced Concrete (RC) slabs are one of the significant factors and needs to be precisely determined. Though, these parameters can be suitably determined by using the guidelines laid down by various Indian Standards, a high level of expertise is required to perform this operation. At the same time analysing and fixing design parameters considering different configurations of slab make the prediction somewhat more time consuming. In such a condition, one of the supports for a design engineer is the Machine learning (ML) techniques. This paper investigates the performance of various ML techniques in predicting and analysing the design inputs and outputs for a one war RC slabs. The comparison results show the appropriate machine learning model learning to predict the compressive strength of one-way slab.
Analyzing Evolution of the Olympics by Exploratory Data Analysis using R
Authors: Pradhan Rahul , Agrawal Kartik , Nag Anubhav 
Published: March 2021

Abstract
Olympic Games are one of the main international event and also a matter of prestige for countries and therefore each country tries to give their best performance during the event. Despite a lot of hard work, many countries/player are unable to perform well during the events and grab medals whereas there are many countries which performs very well in the event and secures many medals. An Analysis need to be done by each country to evaluate the previous statistics which will detect the mistakes which they have done previously and will also help them in future development. An analysis can also be done by the host country to find out the mistakes in the arrangements of the Event which will help them in overcoming these mistakes and host the event accurately. The primary objective of this Research paper is to analyze the large Olympic dataset using Exploratory Data Analysis to evaluate the evolution of Olympic Games over the years. This analysis will provide detailed and accurate information regarding various factors which leads to the evolution of Olympic Games and improvement of Countries/Players over the time in visual format. Visualization of the data over various factors will provide us with the statistical view of the various factors which leads to the evolution of the Olympic Games and Improvement in performance of various Countries/Players over the time.
Efficient Predictive Modelling for Classification of Coronary Artery Diseases Using Machine Learning Approach
Authors: Savita  , Sharma Ganga , Rani Geeta , Dhaka Vijaypal Singh 
Published: March 2021

Abstract
Cardiovascular is one of the most critical diseases that affect persons very abominably. Coronary artery diseases (CAD) are one of the categories of cardiovascular diseases that cause a high death rate. So it is imperative to control these death rates by developing an advanced model of machine learning through which diseases can be detected at the premature stage. Due to the lack of enough facilities for tools like angiography it has become a substantial challenge for the health care organization to detect such diseases. If tools exist, then these are known for being expensive and also have numerous side effects. The main goal of this research is to enhance the accuracy of existing models using optimization techniques with machine learning techniques. Alizadeh-Sani CAD dataset has been used which consists of a total of 303 records with 56 attributes. The proposed model reported following values of precision (0.92), recall (0.92), and accuracy (0.93). This proves the efficacy of employing the optimization techniques with machine learning algorithms.
Using Internet of Things in Hypervisor Monitoring -Challenges and Opportunities
Authors: Mathur Sandeep , Kaushik Anurag , Rana Ajay 
Published: March 2021

Abstract
Security worries in IoT are solved by embedded virtualization, as the applications are kept running in various detached situations. In the present research work the proposed framework makes utilization of an ease virtual condition that empowers resource sharing among numerous applications in the system. Adding a hypervisor to an embedded framework includes adaptability and larger amount capabilities, transforming the inserted gadget into another classification of the framework. In the proposed virtualization structure both home computerization and condition checking framework are facilitated in the virtual machine and tested. Further information gathered from home will be utilized by the government for condition observing, in this manner decreasing the excess WSN organization. At long last, engineering of various virtual conditions is considered, and points of interest are talked about with the consequences of specific Phoronix tests on various situations. Therefore, this system gives financially effective arrangements; promotes variety, ensures security and adaptability.
Model of Using New Media Technology in Higher Education Learning
Authors: Bhavana S , Vijayalakshmi V 
Published: March 2021

Abstract
Media is an incredible correspondence and information sharing medium. It permits us to refresh innovation and make it around the world connected. New Media covers computerized media, online media, programming, all web-based assets. New Media has often been defined as the medium by which information is transferred between societies and students. Academies used digital media to promote cooperative learning and social interaction. The research examines the effect on higher education of 116 respondents participating in this study of new media innovation in teaching and learning. Modeling of the Structural Equation (SEM) has been used to evaluate the obtained data. The study reports the effect of these variables on cooperative teaching and learning using new media that could result in educators and learners having an impact on higher education.
Improved wavefront estimation accuracy in a programmable grating array based wavefront sensor
Authors: Pathak Biswajit 
Published: March 2021

Abstract
Shack–Hartmann wavefront sensor (SHWS) is one of the popular zonal wavefront sensor that typically consists of a two-dimensional (2D) array of lenslets and a camera. Another variant of a zonal wavefront sensor is the programmable grating array based wavefront sensor (PGAWS) that replaces the lenslets array of the SHWS with a 2D array of programmable diffraction gratings and a single focusing lens to result in a 2D array of +1 order spots. The ability of a PGAWS to correctly measure the incident wavefront depends on the accuracy with which each +1 order spot centroid position in the camera plane is calculated. However, in practical applications the intensities of the +1 order spots are usually non-uniform and there is presence of higher order spots that lead to erroneous calculation of the centroid position. In the present work, an improved PGAWS is proposed and by exploiting its programmable facility the transmittance function of the grating array is defined to generate an array of uniform intensity +1 order spots with negligible contribution from unwanted higher order spots by breaking them into noise. We present proof-of-concept simulation results that illustrate the generation of uniform intensity +1 order spots and also quantify its uniformity numerically by defining a quality metric. Furthermore, the working of the proposed PGAWS is substantiated through simulation results which demonstrate its accurate wavefront estimation capability in comparison to the conventional PGAWS, in the presence of non-uniform intensity +1 order spots.
Fully-structured counter-propagating optical trap sculpted by spherical aberration
Authors: Otte Eileen , Denz Cornelia 
Published: March 2021

Abstract
Aberrations of light are commonly known as undesired effects in different applications, including optical trapping. However, here we demonstrate how to take advantage of controlled spherical aberration in order to shape extended optical trapping landscapes, fully-structured in three-dimensional (3D) space and embedding transverse as well as longitudinal electric field contributions. We numerically analyze the light field customization by the simple means of including glass plates in a counter-propagating trapping configuration, presenting sculpted intensity as well as 3D polarization ellipse structures. Experimentally, we prove the realized counter-propagating optical structure by particle velocimetry as well as the analysis of scattered light of optically guided micro-particles. Finally, we demonstrate the potential of our approach by creating extended 3D particle assemblies.
Complete mapping of magnetic anisotropy for prototype Ising van der Waals FePS3
Authors: Nauman Muhammad , Kiem Do Hoon , Lee Sungmin , Son Suhan , Park Je-Geun , Kang Woun , Han Myung Joon , Jo Younjung 
Published: March 2021

Abstract
Several Ising-type magnetic van der Waals (vdW) materials exhibit stable magnetic ground states. Despite these clear experimental demonstrations, a complete theoretical and microscopic understanding of their magnetic anisotropy is still lacking. In particular, the validity limit of identifying their one-dimensional Ising nature has remained uninvestigated in a quantitative way. Here we performed the complete mapping of magnetic anisotropy for a prototypical Ising vdW magnet FePS3 for the first time. Combining torque magnetometry measurements with their magnetostatic model analysis and the relativistic density functional total energy calculations, we successfully constructed the three-dimensional mappings of the magnetic anisotropy in terms of magnetic torque and energy. The results not only quantitatively confirm that the easy axis is perpendicular to the ab plane, but also reveal the anisotropies within the ab, ac, and bc planes. Our approach can be applied to the detailed quantitative study of magnetism in vdW materials.
Effect of explosive compaction on microstructure of ODS FeCrAl alloy fabricated by oxidation method
Authors: Yan Fuzhao , Li Jing , Xiong Liangyin , Liu Shi 
Published: March 2021

Abstract
A fabrication process of 14Cr ODS FeCrAl alloy including direct oxidation treatment, explosive compaction and vacuum post-sintering was proposed herein. A thin oxide layer formed on the powder surface during a low-temperature oxidation treatment was crushed in the explosive compaction, resulting in the redistribution of oxides at prior powder boundary (PPB) and the subsequent formation of an exclusive iron oxide layer between the grains of compacted materials. A large number of defects (dislocations and grain boundaries) were also produced in the compacted particles. Through characterizing the evolution of oxide dispersoids during the course of fabrication, the contribution of explosive compaction to dislocations, nanoscale precipitates and mechanical properties was clarified. The results show that the iron oxide layer became the container for transporting oxygen and was dissolved during the post-sintering, which affected the composition of nanoscale precipitates. The high-density dislocations not only became the rapid diffusion paths of oxygen, aluminum and yttrium atom, but also became the preferential sites of Y-Al-O precipitation. With the manufacturing method, the more uniform distribution of oxide nanoparticles and the higher microhardness of ODS alloy were obtained.
Warp deformation model of polyetheretherketone composites reinforced with carbon fibers in additive manufacturing
Authors: Sun Qili , Shan Zhongde , Zhan Li , Wang Shaozong , Liu Xiaohang , Li Zheng , Wu Siyuan 
Published: March 2021

Abstract
Fused deposition modeling (FDM) provides a promising technique for the small-batch fabrication of highly customized objects. The required performance of FDM far exceeds the performances of conventional manufacturing methods. However, the temperature difference that occurs during FDM generates internal stress, which causes warp deformation and affects formed sample quality. Hence, identifying the influencing factors of warp deformation is the key to improving the forming quality of FDM samples. In this work, PEEK/short carbon fiber (CF) composites were prepared by using the FDM method, the warp deformation formula was derived, and the warp deformation mechanism was obtained. Results showed that the material linear expansion coefficient, forming chamber temperature, and forming size had considerable effects on warp deformation. Moreover, CF could improve the warp deformation of the PEEK/CF composites because of their low Poisson’s ratio and high thermal conductivity. The addition of CF could reduce the residual stress of the composites and improve warp deformation. In addition, annealing could improve the tensile and bending mechanical properties of the PEEK/CF composites. Specifically, after 3 h of heat treatment at 190 °C, the tensile and bending mechanical properties of the composites reached 10.7% and 11.6%, respectively. Crystallinity analysis revealed that the mechanical properties of the PEEK/CF composites strongly depended on their crystallinity. High cooling temperature rates were associated with low crystallinity, tensile strengths, and elastic moduli. DMA analysis showed that the addition of CF could improve the high-temperature resistance of the PEEK/CF composites as reflected by the higher glass transition temperature of the composites than that of the pure PEEK resin. Porosity analysis showed that the CF content could tailor the pore size and distribution. The study reported here provides a reference for improving the forming quality and mechanical properties of PEEK/CF composites fabricated through FDM.
Analysis and verification of a biomimetic design model based on fish skin
Authors: Zheng Yu , Guo Ce , Li Xin 
Published: March 2021

Abstract
Many biological architectures are Bouligand structures, which comprise uniaxial fiber layers stacked in a periodic helical arrangement and are characterized by high damage resistance. As an effective flexible protective structure, fish skin is a Bouligand structure that protects the body while ensure flexibility during swimming and predation. In this paper, an analytical model inspired by fish skin is established based on previous studies, and the parameters for describing crack growth are determined. Then, mathematical expressions for the local stress intensity factors and plastic zone are used to predict how the helical stacking angle α influences the crack propagation. The results show that crack deflection and twisting improve the fracture toughness of the composite structure greatly, with the optimal fracture toughness being that for α = 60° – 70°. Moreover, biomimetic flexible composite structures inspired by fish skin are produced using silicone and Kevlar fibers. Scanning electron microscopy is used to observe the cross-sectional morphology of the composite structures, showing that the interfaces between the silicone and Kevlar fibers are highly compact. Results from experimental impact tests agree well with the predicted results.
Adsorption and photocatalytic activity of biosynthesised ZnO nanoparticles using Aloe Vera leaf extract
Authors: Jose Lolly Maria , Raj R S Arun , Sajan D , Aravind Arun 
Published: March 2021

AbstractIn this article, we demonstrates the growth of phase pure ZnO nanostructures fromAloe-Veraleaf extract and degradation of an organic dye-Malachite Green (MG)- from aqueous medium using the same as catalyst. Adsorption mechanisms were evaluated using Lagergren’s pseudo-first-order, pseudo-second-order and intraparticle diffusion kinetic models. X-Ray diffraction data showed that the synthesised ZnO is crystalline with hexagonal wurtzite phase. Average crystallite size and lattice strain was estimated from Scherrer equation and Williamson-Hall analysis with the help of Rietveld refinement data. Crystallite size obtained from Scherrer method is 12.62 nm while that from Williamson-Hall analysis is 19.27 nm. Uniform growth of ZnO nano-sheets were confirmed by FE-SEM analysis. Optical characterisation was carried by UV-Visible spectroscopy and the band gap ZnO nanoparticles was found to be 3.19 eV. Zn-O stretching vibrations were recorded at 550 cm−1using FTIR spectrophotometer. Results showed that biosynthesised ZnO nanosheets are particularly effective for the degradation of MG dye.
Food Fortification With Folic Acid for Prevention of Spina Bifida and Anencephaly: The Need for a Paradigm Shift in Evidence Evaluation for Policy-Making
Authors: Martinez Homero , Pachón Helena , Kancherla Vijaya , Oakley Godfrey P 
Published: March 2021

Abstract
Context-specific evidence evaluation is advocated in modern epidemiology to support public health policy decisions, avoiding excessive reliance on experimental study designs. Here we present the rationale for a paradigm shift in evaluation of the evidence derived from independent studies, as well as systematic reviews and meta-analyses of observational studies, applying Hill’s criteria (including coherence, plausibility, temporality, consistency, magnitude of effect, and dose-response) to evaluate food fortification as an effective public health intervention against folic acid–preventable (FAP) spina bifida and anencephaly (SBA). A critical appraisal of evidence published between 1983 and 2020 supports the conclusion that food fortification with folic acid prevents FAP SBA. Policy-makers should be confident that with mandatory legislation, effective implementation, and periodic evaluation, food fortification assures that women of reproductive age will safely receive daily folic acid to significantly reduce the risk of FAP SBA. Current evidence should suffice to generate the political will to implement programs that will save thousands of lives each year in over 100 countries.
Exploring the Seasonal Drivers of Varicella Zoster Virus Transmission and Reactivation
Authors: Bakker Kevin M , Eisenberg Marisa C , Woods Robert , Martinez Micaela E 
Published: March 2021

Abstract
Varicella zoster virus (VZV) is a herpesvirus that causes chickenpox and shingles. The biological mechanisms underpinning the multidecadal latency of VZV in the body and subsequent viral reactivation—which occurs in approximately 30% of individuals—are largely unknown. Because chickenpox and shingles are endemic worldwide, understanding the relationship between VZV transmission and reactivation is important for informing disease treatment and control. While chickenpox is a vaccine-preventable childhood disease with a rich legacy of research, shingles is not a notifiable disease in most countries. To date, population-level studies of shingles have had to rely on small-scale hospital or community-level data sets. Here, we examined chickenpox and shingles notifications from Thailand and found strong seasonal incidence in both diseases, with a 3-month lag between peak chickenpox transmission season and peak shingles reactivation. We tested and fitted 14 mathematical models examining the biological drivers of chickenpox and shingles over an 8-year period to estimate rates of VZV transmission, reactivation, and immunity-boosting, wherein reexposure to VZV boosts VZV-specific immunity to reinforce protection against shingles. The models suggested that the seasonal cycles of chickenpox and shingles have different underlying mechanisms, with ambient levels of ultraviolet radiation being correlated with shingles reactivation.