bluuebunny/crossref_metadata_2025_split huggingface dataset:
Usance of industrial 4.0 technique to overcome the pandemic situation of COVID-19
Authors: Bhardwaj Vaibhav , Agarwal Pankaj , Nayak Soumya Ranjan , Sisodiya Mangal Singh , Singh Vijander 
Published: January 2021

ABSTRACT
COVID 19 (Coronavirus) pandemic has created surge demand for essential good and other items like medical equipment and industries which were shut and need to revive back themselves again with new strategies and plan to bring the situation back towards normalcy the study also aimed to propose new technique including advantage of Industry 4.0 ethics to reduce the impact and tackle the situation of COVID-19 and post COVID-19. A detailed review of the literature is done on the technologies along with their Industry 4.0 applications during this COVID-19 pandemic, The review holds some basic and advance technologies application to enhance industrial safety and boosting the work culture to normal and efficient. Using appropriate search words on the databases of IEEE papers, Google Scholar and Research Gate. We found several useful applications which can be inculcated along with modern human-machine interfacing which can help for proper control and management of COVID-19 pandemic and these techniques are been discussed in this paper. Available technologies blend along with Industry 4.0 technique and procedure could also help the society to work and understand smartly and get aware of this methodology which can lead them to stay safe and secure along with maintaining working efficiency. Industry 4.0 can fulfil the requirements of society/community during this crisis’s situation. The new technique which firstly make assure that the induvial and belonging health and safety is first priority and their demand are need to be fulfil on time so that no one suffers. These Industry 4.0 technologies could provide a lot of innovative ideas and solution at from base local areas to global platforms. The supply chain partners and government bodies should work along with new technologies wisely for improving the services during COVID-19 and of any similar situations happening anywhere anytime.
Investigation of rheological and performance characteristics of oxidized polyethylene polymer blended with SBS modified bitumen
Authors: Mandrawalia Ajay Kumar , Gaur Arun , Mittal Abhishek 
Published: January 2021

Abstract
This research article investigates the influence of oxidized polyethylene (PE) polymer on rheological and performance properties of SBS modified bitumen at an optimum dose of 0.5 % by weight. To evaluate the impact of PE polymer on SBS modified bitumen different performance testing such as indirect tensile strength and resilient modulus was conducted on PE polymer blended bituminous concrete mixes and rheology of modified bitumen was determined using dynamic shear rheometer. It was concluded that the intrusion of PE polymer in SBS modified bitumen improved elastic recovery and viscosity. The findings of resilient modulus test, indicated that the addition of PE polymer enhanced the response of modified bituminous mixes under repeated loading (Mr value) by 11%, 20% and 16% at 25°C, 35°C and 45°C respectively as compared to SBS bituminous mix.
Overview on Hardfacing Processes, Materials and Applications
Authors: Garbade R. R. , Dhokey N. B. 
Published: January 2021

Abstract
Hardfacing alloys are mainly obtained through atomization of powders and by determining of alloying elements in pure form or ferro alloys. They are widely used materials as weld overlay to improve wear and corrosion resistance in applications such as agricultural, construction, mining, etc. Wear is an important factor that controls life of any component. Different types of wear such as corrosion, impact, abrasion, metallic, etc are responsible for worn out of components. Many researches have been done to either reduce the wear or improve the wear resistant of the existing materials. Among these, hardfacing technique has made many developments since last few years. As in nuclear reactors, hardfacing of the grid plate through Laser cladding and Plasma Transferred arc welding, to improve the life of the components and resistance from wear and corrosion. Hardfacing involves applying a consumable material with desired wear properties over a soft base metal to enhance resistance to different wear mechanism. Using Calphad technique for development of desired composition and application of Schaffler diagram has been attempted successfully. In this paper a review of hardfacing processes and materials, highlighting the use of Laser cladding and Plasma transfer arc for overlaying hardfacing alloys has been attempted.
Industrial Temperature Measurement of Flame and Flue gases using IOT and Image processing
Authors: Sujatha K. , Ponmagal R.S. , SenthilKumar K. , Karthikeyan V. , Ramkumar K.S. , Bhavani N P G 
Published: January 2021

Abstract
This study effort contracts with measuring the flame temperature in the combustion chamber along with a scheme to monitor the temperature of flue gases from the power plants. Presently, the gases from the exhaust are monitore dusing gas analyzers which are positioned at the exit point of the chimney. These gas analyzers are prone to cold end corrosion which will lead to malfunctioning of the equipment causing erroneous results during measurements. Thiskind of indigenous technique facilitates to develop an Industrial IoT (IIoT) temperature monitoring system at power plants. A combination of Principal Component Analysis (PCA) with Cascaded Neural Network (CNN) is used. The features extracted and reduced serve as inputs to CNN. An efficient technology to scrutinize and measure the temperature inside the combustion chamber thereby reducing the amount of flue gas emissions by the partnership of two methods.
An enhanced intelligent classification approach to improve the encryption of big data
Authors: Gupta Gitanjali , Lakhwani Kamlesh 
Published: January 2021

Abstract
Progression in cloud applications have been hindered due to critical issues such as Data security and privacy. The aggravating concern remains the ability and access of cloud operators in acquiring sensitive data. Consequently, usage and excitement of cloud computing has remained lukewarm, with users remaining skeptical of the level of confidentiality promised. Principally, the financial industry and governmental agencies have shown lackluster and inimical enthusiasm of these indications. The paper serves to analyze the aforementioned issue, and present a creative proposal into cryptography, as a means to virtually restrict cloud service operator’s access to sensitive data. The free-will of cloud operators in handling sensitive data will be undermined. Through this method, file is divided with more accuracy using an intelligent classification technique. Alternatively, a different method can be utilized to find out whether data need splitting to obtain lesser operating time and minimize storage space. The results show that the approach can resolve innumerable risks associated with cloud computing, whilst requiring sufficient computing time using a very good intelligent machine learning classification techniques. As such, novel approach has been proposed which is entitled as a model for Sensitive Encrypted Storage (SES). In this model, three proposed algorithms Convolution Neural Network with Logistic Regression, Elliptic-curve Diffie Hellman-Shifted Adaption Homomorphism Encryption and Decryption have been integrated.
Efficiency and Green House Emission Response of Different Vehicles Operating on Various Driving Cycles
Authors: Puri Deepali , Leena G 
Published: January 2021

Abstract
In today’s scenario, energy security and global warming are considered as vital parameters of concern. The use of large number of conventional vehicles on roads has caused serious environmental pollution and they consume considerable amount of energy resources. Thus solution lies in switching to cleaner and greener energy alternatives, such as electrical energy. In this paper, conventional, electric and hybrid electric vehicles are compared on the basis of efficiency and greenhouse gas emissions. For efficient comparison, three different cycles, namely, Manhattan, Urban Dynamometer Driving Schedule and Arterial, are chosen. The simulation and computer modelling is implemented using the Advanced vehicle simulator (ADVISOR) platform. When Electric vehicle is tested on three different types of driving cycles, it exhibits highest efficiency and no tail pipe emission as compared to other vehicles. Thus the use of Pure electric vehicle eliminates both the problem of depleting energy resources and environmental pollution.
Furnace Flame Tracking for Combustion Quality Estimation in Coal Fired Boilers
Authors: Sujatha K. , Sinthia P. , Babu Jada Satish , Ponmagal R.S. , Veerendrakumar J. , Balaji V. , Srividhya V. 
Published: January 2021

Abstract
This research work focus is on optimization of the combustion quality as the combustion quality plays a vital role in minimizing NOx and CO emissions in the flue gas. Power generated is also another important factor under consideration so as to meet the demand. Flame monitoring in boilers using digital imaging provides replacement for the conventional technique of temperature measurement. The indigenous method can be replaced by incorporating an online PC based system by analyzing the flame images.
An Integrated Approach for Flood Prediction by Using Block Chain Network and Machine Learning
Authors: Murugan Kalpana , Seeta Reddy A. , Sivasainath Reddy M. , Raviteja Reddy P. 
Published: January 2021

Abstract
Now a days heavy rainfall becomes the major problem in India because of the changes that occur in global environment and in climatic conditions this problem rises so, To overcome this problem, this proposed method i.e advanced flood alerting system (AFAS) had been introduced by using machine learning and block chain network. This method helps to predict an early occurrence of flood with an efficient output probability of prediction. The output of this project gives an accurate and approximate probability percentage of flood prediction. After predicting the flood it gives an alert or caution to that particular area that has been effected by flood so, it helps them to take necessary precaution steps to follow, If this happened then the victims that effected by flood can be saved and also death loss and property loss can be reduced in an effective manner.
Side-channel Analysis using Deep Learning on Hardware Trojans
Authors: Kokila J. , Chithra S. , Ramasubramanian N. 
Published: January 2021

Abstract
A power side-channel analysis is proposed to nd the correct key in hardware trojan affected AES by evaluating correlation between every sub-key guesses. A deep learning method for side-channel analysis (SCA) is proposed, which consists of two phases-characterization and attack. Multilayer perceptron (MLP) and convolutional neural networks(CNN) are used as the models to determine the SCA and their performances are evaluated. Two kinds of desynchronization were used, where maximum possible value with which the trace can be shifted was 0 in rst and 100 in the second. SCA is performed in these desynchronized traces also.
Modified direct clustering algorithm for group formation in cellular manufacturing
Authors: Potdar G C , Sapkal S U , Shivade A S 
Published: January 2021

AbstractIn cellular manufacturing system formation of machine-part families is an important task. Cellular manufacturing systems deals with various clustering algorithms which are particularly relevant to the problem of machine component group formation. In this paper, case study from well-known gearbox manufacturing company is considered where there is scope for using cellular manufacturing system. Company is manufacturing the gearbox varying from single stage reduction to four stage reduction. A modified direct clustering algorithm is proposed and is applied after the initial incidence matrix derived from direct clustering algorithm. This algorithm overcomes the problem arises during application of direct clustering algorithm and gives the optimum solution. The algorithm is specifically designed to deal with large amount of data during realistic situations. The results of proposed algorithm are compared with well-known existing algorithms and it is found that the proposed algorithm gives the better solution than the existing algorithms under consideration.
Classification of lung infected Corona Virus X Ray Images using Deep Learning CNN Model
Authors: Gupta Yogesh Kumar , Agrawal Saroj , Mittal Aman 
Published: January 2021

Abstract
All Start In this examination as learning, a dataset of COVID-19 lung contaminated X-beam pictures with common infection pneumonia, insisted Covid-19 sickness, and ordinary occurrences were utilized for the programmed acknowledgment of the Corona virus infirmity. The purpose of the investigation is to the comparable portrayal of Covid-19 utilizing Convolution Neural Network and assesses the precision proposed model convolutional neural framework over the progressing years for clinical picture characterization. Corona Virus lung infected X-Ray Images accessible by Kaggle a complete 590 images, which classified in 2 classes: typical and Covid-19. To survey the hypothesis accuracy of the models. The plan models end up being productive appeared differently in relation to made by (Arpan Mangal et al, 2020), (A. A. Saraiva et al.,2020), (Ioannis D. Apostolopoulos et al, 2020), (Ali Narin et al. 2020), which gained accuracy 90.5,95.30, 96.78, 98 independently and the current work had an ordinary precision of 99.54.
Evaluation of compressive and split tensile strength of slag based aluminosilicate geopolymer reinforced by waste polymeric materials using Taguchi method
Authors: Khezrloo Amirreza , Tayebi Morteza , Shafiee Abbas , Aghaie Alireza 
Published: January 2021

Abstract
In this work, slag based aluminosilicate geopolymer was reinforced with polymeric fibers including, polyester (PES) (waste tire cap plies), polymeric particles including polyethylene terephthalate (PET) (waste water bottle), styrene-butadiene rubber (SBR) (waste tire), and polyvinyl chloride (PVC) (waste water hose). The tensile and compressive strength of the material was evaluated. Taguchi method was employed to assess the influence of the effective parameters on the mechanical characteristics of the geopolymer composite. QUALITEK-4 software was used to create the L32 orthogonal array with 192(96+96) geopolymer specimens and 32+32 experiments. Analysis of variance (ANOVA) was utilized to analyze the results of the experiments. The prepared geopolymers were characterized by SEM analysis. The results revealed that the compressive and split tensile strength of the aluminosilicate geopolymer was substantially improved after reinforced by the PES fibers compared with other particles such as SBR, PVC, and PET.
The role of applied strain and volume percentage of components on mechanical properties and fracture toughness in multilayered Al/Mg composite fabricated by the accumulative roll bonding process
Authors: Delshad Gholami M , Rahmatabadi D , Shojaee T , Hashemi R , Mohammadi B. 
Published: January 2021

Abstract
In this study, for the first time, the effect of applied strains and volume percentage of components of layered composite on the mechanical properties and fracture toughness of Al/Mg were investigated experimentally. The multilayered Al/Mg were produced by the accumulative roll bonding (ARB) process. For the investigation, three Al/Mg composites with different volume percentages (25%, 50%, and 66.6%Al) at different applied strains (0.8–3.2) were produced. The experimental evaluation included microscopic examination by optical microscope imaging, uniaxial tensile test, and plane strain fracture toughness. As the applied strain for all three composites increased, plastic instability in the magnesium reinforcement intensified, but due to the low thickness of the Al layers compared to the Mg layer, uniform structure of Mg distribution in Al for all three composite was not achieved. Also, by adding Al layers to the primary composite, a lower shear strain was applied to the magnesium reinforcement, and instability intensity in the reinforcement layer decreased. For this reason, as Al layers increased, plastic instability diminished. By raising the exerted strain, the values of tensile strength increased, and by adding Al layers, the elongation increased. The maximum amount of tensile strength and elongation for each composite was achieved in the same ARB pass (last pass) and the highest values of UTS and elongation were reached to 384.1 MPa and 1.95% for Al25%Mg, respectively. However, the highest amount of fracture toughness for each composite was obtained in the different exerted strains and the maximum value of 41.4 MPa·m1/2 was achieved for Al33.3% in the third pass. The present phenomena indicated that many factors such as higher Mg volume with higher energy absorption, plastic instability, thickness ratio, plastic instability, and value of applied strain affected the fracture toughness. In summary, the relationship between fracture toughness with applied strain and also with volume percent of Al was not always straightforward. It depends on other factors, such as how the reinforcement was distributed, the thickness of the layers, the workability, and the addition of aluminum. Also, the applied strain has a more significant effect on increasing fracture toughness in multilayered composite if they cause a uniform distribution of reinforcement particles in the field or continuity in the reinforcement layer.
Investigation of the fast magnetosonic wave excited by the Alfvén wave phase mixing by using the Hall–MHD model in inhomogeneous plasma
Authors: LI Zhongzheng , HAN Juanfang , WANG Fangping , CHEN Zhengwu , DUAN Wenshan 
Published: January 2021

Abstract
The inhomogeneity is introduced by a nonzero density gradient which separates the plasma into two different regions where plasma density are constant. The Alfvén waves, the phase mixing and the fast magnetosonic wave are excited by the boundary condition in inhomogeneous magnetized plasma. By using the Hall–magnetohydrodynamics (MHD) model, it is found that there are Alfvén waves in the homogeneous regions, while the phase mixing appears in the inhomogeneous region. The interesting result is that a fast magnetosonic wave is excited in a different direction which has a nonzero angle between the wave propagation direction and the direction of the background magnetic field. The dependence of the propagation direction of the excited fast magnetosonic wave and its strength of the magnetic field on the plasma parameters are given numerically. The results show that increasing both the driving frequency and the ratio of magnetic pressure to thermal pressure will increase the acceleration of the electrons. The electron acceleration also depends on the inhomogeneity parameters.
Adaptive pruning-based optimization of parameterized quantum circuits
Authors: Sim Sukin , Romero Jonathan , Gonthier Jérôme F , Kunitsa Alexander A 
Published: January 2021

Abstract
Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the Dynamic Sparse Reparameterization scheme which was originally proposed for training deep convolutional neural networks. We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k-UpCCGSD, as well as the Low-Depth Circuit Ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).
Preparation and water flow velocity measurement of a large diameter single-wall carbon nanotube
Authors: Fan Aoran , Hu Yudong , Zhang Yufeng , Ma Weigang , Zhang Xing 
Published: January 2021

Abstract
To fill the gap in the measurement of large diameter single-wall carbon nanotube (SWCNT) and further predict the variation rule of mass flux versus diameter, this work measured the water flow velocity and mass flux coefficient in an individual SWCNT with a 3.07 nm diameter. A mechanical method is used to obtain the large diameter SWCNT by removing the internal tube of a double-wall carbon nanotube, and then the water flow velocity through this SWCNT was measured by an electrical method. The water flow velocity of large diameter SWCNT can reach to 146.1 ± 32.5 μm s−1, and the enhancement factor compared with no-slip Hagen–Poiseuille relation is about 14.5. A mass flux coefficient is defined to describe the mass flow ability through SWCNT and calculated by the experiment data. Although the enhancement factor decreased to ∼1/4 of the normal size SWCNT (∼1.5 nm), the mass flux coefficient in the large diameter SWCNT increased efficiently, and which is about 5.7 times to the normal size SWCNT. Based on the above measurement result, a reported simulation result can be revised and then verified to describe the enhancement factor versus diameter, and the mass flux coefficient of the SWCNT can be further predicted. According to the prediction result, in the bulk-like liquid region, the mass flux of an individual SWCNT can reach to maximum when the diameter is around 2.9 nm, which would provide a new idea for the design of the SWCNT-based nanodevices in the future.
New twists in actin–microtubule interactions
Authors: Pimm Morgan L. , Henty-Ridilla Jessica L. 
Published: January 2021

Actin filaments and microtubules are cytoskeletal polymers that participate in many vital cell functions including division, morphogenesis, phagocytosis, and motility. Despite the persistent dogma that actin filament and microtubule networks are distinct in localization, structure, and function, a growing body of evidence shows that these elements are choreographed through intricate mechanisms sensitive to either polymer. Many proteins and cellular signals that mediate actin–microtubule interactions have already been identified. However, the impact of these regulators is typically assessed with actin filament or microtubule polymers alone, independent of the other system. Further, unconventional modes and regulators coordinating actin–microtubule interactions are still being discovered. Here we examine several methods of actin–microtubule crosstalk with an emphasis on the molecular links between both polymer systems and their higher-order interactions.