bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structure, thermal stability and mechanical properties of composite wires made of conducting microalloyed aluminum alloys
Authors: Nokhrin A V , Shadrina I S , Chuvil’deev V N , Bobrov A A , Kopylov V I , Berendeev N N , Piskunov A V 
Published: January 2021

Abstract
Thermal stability of the structure and mechanical properties of composite wires made of microalloyed aluminum alloys were studied. The wire was produced by co-rolling of the copper-coated aluminum alloy. It has been demonstrated that annealing at 200 °C for 200 hours leads to the onset of recrystallization, reduced hardness and strength of the wire as per the Hall-Petch equation. The grain size in the annealed wire was less than 1 -2 μm, manifesting the stabilized effect of precipitates of Al3(Sc,Zr) phases. At that the rate of diffusion of copper into the aluminum was quite low. The formation of a uniform fine-grained structure and zero copper-induced embrittlement resulted in increased by up to ∼10% plasticity of the wire.
High-pressure torsion of Zr-based bulk metallic glasses and amorphous melt-spun ribbons
Authors: Khasanova D A , Gunderov D V , Astanin V V , Gunderova S D , Churakova A A , Bazlov A I , Louzguine-Luzgin D V 
Published: January 2021

Abstract
High-pressure torsion allows one to achieve high strains of bulk metal glasses without their destruction. The article presents the results of studies on the effect of high-pressure torsion on the amorphous alloys of the same composition but obtained in different ways: in a form of a bulk plate and a melt-spun ribbon. It is shown that the degree of nonequilibrium after high-pressure torsion, determined by the diffraction and calorimetry methods, increases for bulk metallic glass, approaching the values characteristic for melt-spun alloy.
Regimes of mass transfer in a 2D crystal in nonequilibrium states
Authors: Korznikova E A , Shepelev I A , Vardanyan E L , Ramazanov K N , Zakharov P V 
Published: January 2021

Abstract
A variety of new technologies associated with the severe impact on materials have been developed recently. The promotion of these technologies is, in turn, associated with the stability of the structure and properties of materials under nonequilibrium conditions. It is well known that under such conditions new mechanisms of structure evolution can occur. The study of these mechanisms is an important task of modern materials science. In our work,a we present the results of an analysis of possible scenarios of impact induced mass transfer in a two-dimensional crystal with a Morse interaction potential including a topological compression soliton, a acrowdion complex and a moving vacancy – a voidion. The influence of the initial impact energy distribution is analysed. The obtained results contribute to the fundamental understanding of mass transfer processes under nonequilibrium conditions.
Nanowhiskers for lithium battery anode
Authors: Podlesnov E , Dorogov M V 
Published: January 2021

Abstract
The paper presents the prospect of using copper oxide nanowhiskers as an anode material in a lithium-ion battery. Technology for the synthesis of copper oxide nanowhiskers by electrochemical deposition and thermal oxidation was developed. The stress state of CuO nanowhiskers under lithiation/delithiation was considered. A test was carried out of lithium batteries with anodes containing copper oxide nanowhiskers with various binders.
Behaviour of carbon nanotube bundle under quasistatic and dynamic transverse compression
Authors: Rysaeva L Kh , Murzaev R T , Kudreyko A A , Korznikova E A , Dmitriev S V 
Published: January 2021

Abstract
Carbon nanotubes (CNTs) have a unique combination of physical and mechanical properties, which makes them attractive for a number of applications. Investigation of the mechanical response of a CNT bundle under conditions of plane strain (uniaxial and biaxial lateral compression), since it can be used as an elastic damper for vibration and shock protection. In this work, using a chain model with a reduced number of degrees of freedom, the behaviour of a CNT bundle under uniaxial lateral compression is investigated. Quasistatic loading is used to calculate the elastic constants of the bundle. Dynamic loading reveals the propagation of shock waves of two types: the faster one propagates with the speed of longitudinal sound and causes insignificant deformation of the CNT cross sections, while the second one propagates approximately three times slower and leads to the collapse of the CNTs.
Influence of machining parameters on taper square areas during slant type taper profiling using wire electric discharge machining
Authors: Manoj I. V. , Narendranath S 
Published: January 2021

AbstractThe oblique/tapered machining is a unique method in wire electric discharge machining to get taper profiles difficult to machine materials. These profiles have many applications like dies, nozzles, inserts, cutting tools and other components. In the present study, a simple square profile is machined using a slant type fixture to achieve a tapered profile made of Hastelloy X. A simple square is machined at different slant angles to get a 0°, 15° and 30° tapered profiles. This paper aims to study the effects of the machining parameters like wire guide distance, wire offset, corner dwell time and cutting speed override on the area of machined profiles. Both scanning electron microscope and coordinate measuring machine were used to measure the areas of 1mm and 5mm. It is observed that as the wire guide distance and cutting speed override increases the area decreases. Whereas in case of wire offset, as offset increases the areas also increase and corner dwell time doesn’t affect on the area. As the angle increased the area also increased leading to bigger taper profiles.
Cumulative post-buckling buckling behavior of smart sandwich structure
Authors: Lal Achchhe , Markad Kanif 
Published: January 2021

Abstract
Cumulative post-buckling buckling behavior of smart sandwich structure has been presented with the combination of shape memory alloy (SMA) and shape memory polymer (SMP) in sandwich composite structure under uniform temperature distribution. The analysis is based on higher order shear deformation theory (HSDT) with von Karman nonlinearity by finite element method (FEM). Evaluation of nondimensional critical buckling temperature (NCBT) has been executed under varying plate thickness ratio. Thermal buckling performance of SMA reinforced SMP laminated composites under dynamic temperature condition with different strain rate and variation in SMA volume fraction is also presented first time to understand the cumulative post-buckling behavior.
Comparative Experimental Investigation of Erosion behaviour of AA2024/B4C and AA6061/B4C Metal Matrix Composites with Particulate coating
Authors: Vijay S , Rao Ch Srinivasa , Laxmi Lalitha J 
Published: January 2021

AbstractDifferent amounts of B4C reinforcement with 5% and 10% are coated with Nickel using Electroless coating technique and are used for reinforcement in Aluminium alloys to produce aluminium metal-matrix composites (AMC). The composites with/without nickel coating thus produced were tested for erosion rate at various pressure conditions and impinging angle. The addition of certain amount of nickel coating provides not only the wettability effect of B4C, it also increases the strength of parent materials (pure Aluminium, AA2024 and AA6061). SEM micrographs confirmed the formation of a Nickel layer on Boron Carbide. Sand erosion tests are designed using L27 orthogonal array with factors as four having three levels. External factors such as, type of matrix and the weight-percentage of particulate are selected. Internal factors viz., the pressure of the air/sand mixture and the angle of impingement. Three levels for every factor used in the experimentation and out of which, Impingement angle shows the predominant significant effect on the erosion rate. It is found that the impingement angle of level 1 (i.e. 30o) has the max influence of 39.75 % contribution on erosion rate. The Regression model is developed for estimating the erosion rate, in terms of the levels of all factor considered. From these models, it is observed that the 5% B4C reinforcement with nickel coating gives minimum erosion rate for aluminium alloys.
Effect of Concentration of NaOH Treatment on Mechanical Properties of Epoxy/Sisal Fiber Composites
Authors: Yadav D , Selokar G R , Agrawal A , Mishra V , Khan I A 
Published: January 2021

Abstract
In recent years, natural fibers incorporation in polymeric resin has received huge attention among the research community. The reasons of demand are multiple that includes its light weight, environmental friendly nature; non-toxic, low cost, easy availability, low processing cost and most importantly they possess characteristics which are comparable to conventional material. With this approach, present work comprises of fabrication of new category of natural fiber reinforced composites with polymer as base matrix. Sisal fiber is selected as reinforcing phase with epoxy matrix. Four different combination of composites are prepared with sisal fiber loading varies from 2.5 wt. % to 10 wt. % using well-known hand lay-up method. Sisal fibers were treated with NaOH at varied concentration to observe the effect of surface modification and its concentration on the developed material. Three different concentration of NaOH is used i.e. 2 mole, 4 mole and 6 mole for preparing three sets of composites. One set is prepared with raw sisal fiber to make total of four sets of composites. Mechanical properties under investigation are tensile behaviour, compressive behaviour and flexural behaviour. The experimental results obtained are compared for optimizing the concentration of NaOH. From the analysis it is seen that composite with surface modified sisal fiber yield better results and further fiber treated with 2 mole NaOH concentration is superior among their counterpart. The maximum tensile strength, compressive strength and flexural strength obtained are 31.5 MPa, 72.5 MPa and 37.8 MPa respectively. All these values are obtained for fiber treated with 2 mole NaOH aqueous solution.
Comparative Structural Analysis of Various Composite Materials based Unmanned Aerial Vehicle’s Propeller by using Advanced Methodologies
Authors: Ramesh M , Vijayanandh R , Raj Kumar G , Mathaiyan Vijayakumar , Jagadeeshwaran P , Senthil Kumar M 
Published: January 2021

Abstract
In the recent technology over the Unmanned Aerial Vehicle (UAV), the implication of propeller contributes a principal part in the production of thrust by pushing the air with the rotating blades. Especially, propeller’s incomparably works perfect in the advanced UAVs, because of its huge contribution to UAV’s construction. Analyzing the structural behavior of propeller and its rotodynamic effects with the help of advanced engineering approaches can provide better performance and lifetime in advanced operating conditions. The composites of CFRP (Carbon Fiber Reinforced Polymer) and GFRP (Glass Fiber Reinforced Polymer) are widely used for the propellers, due to the advantage of load resisting capacity, lightweight, higher production. This work deals with the comparative studies of the propeller with the different composites such as CFRP and GFRP to inculcate the structural characteristics of deformation with respect to the applied load, maximum stress, and normal stress by coupled cum advanced simulations for different rotational velocities. The primary outcome of these comparative studies is providing the optimized performance as well as its ultimate lifetime of the rotor component for an advanced UAV. Finally, material optimizations are executed for UAV’s propeller by using two advanced numerical methodologies that are FSI (Fluid-Structure Interaction) for the evaluation of structural parameters and coupling of CFD (Computational Fluid Dynamics) - MRF (Moving Reference Frame) for the representation of rotating nature of propeller. Through these approaches and grid convergence test, the best composite material is finalized, which is GFRP. Especially, Epoxy-E-Glass UD and Epoxy-S-Glass-UD are performed better than other composite materials, wherein the Epoxy-E-Glass-UD is reacted low deformed value of 3.7829μm and Epoxy-S-Glass-UD is induced low stress value f 55556Pa than other composites at average loading conditions.
Bitcoin Financial Forecasting
Authors: Jain Dhyanendra , Jain Ashu , Pandey Amit Kumar , Kumar Jogender 
Published: January 2021

Abstract
Bitcoin is one of the crypto currencies and is most unpredictable currencies. In the world of crypto currencies, the value of a coin can unpredictably upgrade or degrade. In this study, the model has been trained to predict the value of Bitcoin in USD at any given time stamp. For this prediction, three algorithms of machine learning - Linear Regression (LR), Support Vector Regression (SVR) and Neural Network Regression (NNR) have been used. The model is trained using the collected dataset. After the collection of data set, we first applied SVR algorithm, then we used LR and then NNR to calculate the error compared to the actual value. Root mean squared error (RMSE) is used as the predictive measure. Out of the three algorithms, LR was found out to be more accurate for predicting the value of bitcoin.
Water Feature Extraction, Enhancement and Change Detection of Multi-Temporal Satellite Images using MNDWI2-PCA
Authors: Hemalatha M 
Published: January 2021

Abstract
Water feature extraction is a challenging task in remote sensing. In this research work, a new water index is implemented for easy identification of water pixels. The Area of interest is extracted with desired shape file. Here water bodies from kalahasti region are extracted, which is in Chittoor district. The Water indices are used to identify water pixels from Landsat-8 image, which has high spectral resolution. This image is multi-spectral image comprising of eleven bands. Interactive supervised classification is implemented for segmenting the satellite image. The image is classified into two categories i.e. water bodies and non-water bodies. Then MNDWI2-PC (Normalized Difference Water Index2-Principal Component) is applied to LANDSAT-8 image. Then this image is segmented into water bodies and non-water bodies. Finally accuracy assessment is carried out by confusion or error matrix. Quantitative parameters such as Overall Accuracy (OA), Kappa Coefficient (KC), Overall Kappa Coefficient (OKC) User’s accuracy (UA), Producer’s Accuracy (PA), and F1 score (F-Measure) are calculated for this multi-spectral satellite imagery. The algorithm reduced misclassification of water pixels with urban pixels, vegetation and other land covers. The algorithm outperforms in terms of quantitative performance metrics.
Gazetteer based unsupervised learning approach for location extraction from complaint tweets
Authors: Yenkar Pranali , Sawarkar S D 
Published: January 2021

Abstract
In the era of smart city, social media platform like twitter has become the most powerful medium for sharing urban issues. Extracting the locations from the complaint tweets is very much helpful to local government for monitoring and performing spatial analysis of civic issues at city level. We propose a natural language processing and regular expression based system to identify probable locations of complaints in terms of landmarks (hospital, mall, railway station), street level (e.g. road marg), region (e.g. nagar, colony) and suburb. Our system has used n-gram matching, string similarity and spatial proximity measures to confirm and disambiguate the extracted locations using GeoNames and OSM gazetteer data. Proposed system has achieved the F-score of 93% which is very promising. Locations are also displayed on map for better situational awareness across the city.
Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks
Authors: Shrwan Ram , Gupta Anil 
Published: January 2021

Abstract
Automatic classification of Brain Tumor and brain Lesions has become a very important step in the field of medical image analytics. The machine learning/Deep learning approaches are playing a tremendous role in the field of medical imaging classification, due to the drastic changes in the field of computing power and image analytics techniques. The deep learning, which is the subfield of machine learning, is playing the major role in the automatic classification of Magnetic Resonance Images (MRIs) having various brain abnormalities. Convolutional Neural Networks are widely used for the classification and detection of various brain disorders. In this research paper, Convolutional Neural Networks are designed with considering various learning parameters for the classification of Multiple Sclerosis Brain Lesions and Pituitary Tumor. In the proposed research, T1-weighted Contrast-enhanced Magnetic Resonance images are preprocessed with various image-preprocessing approaches such as to resize the images, to convert the images into suitable image format so that the experimental work can be performed with deep learning in the Matlab environment. The Experiment is conducted with the dataset of Multiple Sclerosis and Pituitary Tumor each of having 718 and 930T1-weighted MRI images respectively. The experimental results we achieved 99.7% classification accuracy of pituitary Tumor, and 99.2% accuracy of Multiple Sclerosis brain Lesions. The average accuracy of both classifications is 99.55%. The precision of the classification of Pituitary Tumor is 99.7, recall value is 99.7 and the f1_score of the classification is 99.7%. Similarly, the Precision of the classification of Multiple Sclerosis Brain Lesions is 99.15%, the recall value is 99.15%, and the f1_score is 99.15%. The purposed approach of the Convolutional Neural Network architecture exhibited outstanding performance as compared to other research outcomes.
Estimating creditworthiness using profit modeling
Authors: Ramesh S 
Published: January 2021

Abstract
The recent advancements in machine learning have enabled retail credit issuers to predict a customer’s net present value using behavioral and profit-based modeling approaches. The objective of this research is to curtail the risk associated with credit card underwriting which typically helps in deciding whether an applicant is creditworthy or not. It attempts to estimate the creditworthiness of a customer instead of evaluating the chances of them going delinquent on a debt. Credit issuers can utilize these models to identify potentially risky as well as profitable customers. Using a machine learning approach, profit-based models are constructed in this research using information acquired at the time of customer-acquisition.
Stacked graphene with nanoscale wrinkles supports osteogenic differentiation of human adipose-derived stromal cells
Authors: Park Jong Bo , Ahn Ji Yeon , Yang Woo Sub , Park Seong Chae , Jung Young Jin , Lee Myungook , Hong Byung Hee , Lim Jeong Mook 
Published: January 2021

Abstract
Based on the concept that microenvironment and physical stimuli regulate the cell behaviors like proliferation, migration, and differentiation, this study was conducted to investigate whether nanoscale spacing by stacked graphene film affect osteogenic differentiation of human adipose-derived mesenchymal stromal cells (hADSCs). The graphene films were synthesized by a chemical vapor deposition method, followed by etching and rinsing process to fabricate single or 3-, 5-, and 7-multilayers. The height and width of wrinkles of the graphene were confirmed by SEM and atomic force microscopy (AFM), ranging from 1.5 to 12.5 nm and from 30 to 100 nm, respectively. Osteogenic differentiation was significantly (p < 0.0001) promoted as the stacking layer increased. Immunofluorescent imaging and osteogenesis-related gene expression showed which increment was saturated from three layers. The calcium deposits and expression of osteogenesis-related genes (Runt-related transcription factor 2 and Osteocalcin) were highest in the three layers. In the hADSCs cultured on the three layers, the intensity of protein expression levels of filamentous actin (F-actin) was significantly increased (p = 0.0319) and focal adhesion kinase/extracellular signal-regulated kinase signal related genes were concomitantly activated. These results demonstrated that multilayer-stacked graphene creating nanoscale spaces promotes calcium deposit and cytoskeletal integrity in hADSC-related, in vitro-osteogenesis.
From scaled-up production of silicon-graphene nanocomposite to the realization of an ultra-stable full-cell Li-ion battery
Authors: Abouali Sara , Garakani Mohammad Akbari , Silvestri Laura , Venezia Eleonora , Marasco Luigi , Brescia Rosaria , Ansaldo Alberto , Serri Michele , Panda Jaya Kumar , Pugliese Giammarino , Mantero Elisa , Bonaccorso Francesco , Pellegrini Vittorio 
Published: January 2021

Abstract
The realization of a high-performance Li-ion full-cell with an anode prominently based on silicon, which can surpass the energy densities of commercial graphite-based Li-ion batteries and cyclability compatible for industrial applications, is still a challenge. Here, we report a Li-ion full-cell that combines a silicon/graphene/carbon (Si/G/C) nanocomposite anodic material, with a commercial LiNi0.33Mn0.33Co0.33O2 (NMC111) cathode. Using a pre-lithiation technique, the proposed Li-ion full-cell exhibits an energy density of ∼400 Wh kg−1 at the electrode material level, with a capacity >1.6 mAh cm−2 and a capacity retention exceeding 90% after 300 cycles at C/2. These performances have been achieved by properly designing the anode material composed by Si nanoparticles wrapped by few-layer graphene flakes. An additional carbon coating is used to further improve the electron conductivity and mechanical integrity of the anodic structure upon charge/discharge cycles. The remarkable performance of the full-cell considering the scalability of the Si-based anode synthesis is a step forward towards the commercialization of high-capacity and high-energy density Li-ion batteries.
Non-monotonic transients to synchrony in Kuramoto networks and electrochemical oscillators
Authors: Ocampo-Espindola Jorge Luis , Omel’chenko Oleh E , Kiss István Z 
Published: January 2021

Abstract
We performed numerical simulations with the Kuramoto model and experiments with oscillatory nickel electrodissolution to explore the dynamical features of the transients from random initial conditions to a fully synchronized (one-cluster) state. The numerical simulations revealed that certain networks (e.g., globally coupled or dense Erdős–Rényi random networks) showed relatively simple behavior with monotonic increase of the Kuramoto order parameter from the random initial condition to the fully synchronized state and that the transient times exhibited a unimodal distribution. However, some modular networks with bridge elements were identified which exhibited non-monotonic variation of the order parameter with local maximum and/or minimum. In these networks, the histogram of the transients times became bimodal and the mean transient time scaled well with inverse of the magnitude of the second largest eigenvalue of the network Laplacian matrix. The non-monotonic transients increase the relative standard deviations from about 0.3 to 0.5, i.e., the transient times became more diverse. The non-monotonic transients are related to generation of phase patterns where the modules are synchronized but approximately anti-phase to each other. The predictions of the numerical simulations were demonstrated in a population of coupled oscillatory electrochemical reactions in global, modular, and irregular tree networks. The findings clarify the role of network structure in generation of complex transients that can, for example, play a role in intermittent desynchronization of the circadian clock due to external cues or in deep brain stimulations where long transients are required after a desynchronization stimulus.