bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of FGM Composition for Better Environment Material
Authors: Gautam Mrinal , Chaturvedi Manish 
Published: January 2021

Abstract
In functionally graded heat-resisting material, the volume fraction of constituents varies functionally and continuously, and these are examined for high temperature engineering applications. By the spatial distribution of the volume fraction the thermal behaviour of FGMs is strongly influenced. Thus, the determination of volume fraction distribution becomes a critical part in the FGM design for a given loading condition and specification. In present work, averaging estimation approach viz, modified rule of mixture is considered. The present work is concerned with the Optimization of FGM Composition for better environment material. The investigation is carried out for uniform heating condition through the plate thickness. Classical laminate theory is used to determine residual stress. The code is developed on MATLAB for constrained minimization and the MATLAB function ‘fmincon’ is employed. The comparative study of three different thermal loading is presented. The effectiveness of residual stress minimization is explored for various boundary conditions.
Study & Analysis of Tire Wear Characteristics of TATA LPO 1618
Authors: Gohane Gaurav , Indurkar Anmol , Chimurkar Palash , Patle Sandip , Nagina Suraj Kumar , Tirpude Swapnil , Bawane Ujwal 
Published: January 2021

Abstract
The tires are a key sub-system of vehicles that have a big responsibility in comfort, fuel consumption and traffic safety. As we know that current tires are just passive rubber elements which do not contribute actively to improve the driving experience or the vehicle safety. During the driving of vehicle, we get few and unsatisfied information from tires. Therefore, this is the main reason to develop an intelligent tire, which could provide useful information to other systems and become an active safety system. In the Tata (LPO 1618) Vehicle an experimental tire strain-based system is used to measure tire tread deformation by means of various aids. Tests under different working conditions such as vertical load or slip angle considering a certain camber angle have been carried out using an indoor tire test (Slightly). By this project and research work, we are trying to introduce an automated tire monitoring and inflation system, which can ensure adequate tire pressure to better accommodate handling requirements. In this paper the numerical method for introducing the prediction tire where account has been introduced to reduce break down time.
Increase productivity of vertical roller mill using seven QC tools
Authors: Pareek Praveen , Sankhla Dr. Vijayendra Singh 
Published: January 2021

Abstract
Cement sector in india is enjoyign success and capacity enhancement due to the rapidly growing demand in various sectors but at the same time a threat is approaching to cement industry that its input cost is increasing day by day like power cost, fuel cost, raw material cost. Also cement is a highly energy consuming industry. Hence in order to survive and sustain in the market its profitability need to be enhanced by increasing productivity and reducing power consumption. High productivity and low power costs can be achieved by increasing output, lowering breakdowns and optimizing the grinding process which eats almost 60% of power cost. In recent years vertical roller mill (VRM) has proved to be a popular choice for finished cement grinding due to low power consumption but it is also very sensitive to vibrations and can deteriorate productivity if process optimization is varied slightly. Objective of this research paper is process optimization of Vertical roller mill, improvement in productivity and reduction in vibration breakdowns using seven QC tools. The research explains complete process of identifying possible causes and final root causes, finding possible solutions and implementing them in order to increase the output of vertical roller mill. This study can benefit the organizations using VRM and are not able to utilize its full productivity due to some bottlenecks or constraints.
Computational Study of Small Vanadium and Chromium Oxide Clusters: Effect of Hydrogenation
Authors: Chahal Ankur , Abbas Haider 
Published: January 2021

AbstractIn the present work we report the effect of hydrogenation on small vanadium oxide (V2O4, V2O6and V4O10) and chromium oxide (Cr2O4, Cr2O6andCr4O10) clusters, within the framework of density functional theory. Comparison between dehydrogenated and hydrogenated cluster suggests that hydrogenation of these clusters changes the HOMO-LUMO position and their gap. A considerable enhancement in dipole moment is also observed. It is found that hydrogenation enhances the occupancy of the metal in these clusters and therefore, the metallicity increases. The observed mechanism of charge transfer due to hydrogenation is in good agreement with the reported experimental and theoretical results.
Study the reduction of mill scale with lean grade coal through RI-RDI
Authors: Shekhawat Deepika , Kukshal Vikas , Banerjee M K , Patnaik Amar 
Published: January 2021

AbstractIn the present study, efforts have been made to assess and study the reduction of mill scale in the presence of single gaseous reductant (i.e. CO gas) in RI-RDI (reducibility index (RI) and reduction degradation index) equipment. While conducting this study optimal process parameters like reduction temperature, mill scale size, reduction time and mill scale to coal ratio were analysed. An exceptional experimental profile is maintained inside the furnace to attain the optimum metallization. The mill scales are reduced inside the furnace and the extent of reduction is evaluated from the weight loss. Post conducting reduction in the RI-RDI test apparatus, the reduced mill scale is characterized for the microstructural and morphological studies using SEM, EDS, XRD. The most optimum results of the reduction were attained in RI-RDI apparatus giving iron content of (metallization) 89.15 % at the reduction temperature of 1050°C for 240 mins holding time. During the entire study on RI-RDI apparatus, flow rate of CO is maintained at 4.5 LPM (litres per minute) and N2at 10.5 LPM. Finally, the degree of metallisation in the sample as well as the product material is calculated by using the chemical analysis process (dichromate and ferric chloride method). The characterization of produced iron powder was done by chemical analysis, XRD, and FE-SEM. The FE-SEM results confirm the presence of resultant pore characteristics. It is observed that mill scale can be significantly reduced to metallic iron by utilizing lean grade coal.
A Short Review on Polymer, Metal and Ceramic Based Implant Materials
Authors: Shekhawat Deepika , Singh Amit , Bhardwaj Ashray , Patnaik Amar 
Published: January 2021

Abstract
The present work focuses on the evaluation of polymer, metal and ceramic based biomaterials with particular emphasis on success rate of these materials till date along with the merits and demerits of these material system. Currently, the search for the materials with improved performance is on rise for biomedical (mainly total hip replacement, THR) applications owing to increase in the number of orthopedic patients worldwide. The clinicians utilized polymer and metal-based implants in ancient times to heal and treat the bone imperfections as well as fractures. The next generation materials required an update on properties, cell material interactions, repair and regeneration, that can only be acquired from recent knowledge related to materials. The commonly employed polymers (natural based polymers, proteins or polysaccharides; and synthetic polymers, poly (lactic acid), poly (glycolic acid), poly (ε-caprolactone), poly(3-hydroxybutyrate) (PHB)) and metals (SS, Mg-alloy, Ti-alloys, Co-alloy, silver, and nickel-titanium alloys) failed to fulfill the long-term durability and lacked in generating adequate bonding with the bones. It is intended that the facts and details abridged in this review article ought to be a beneficial tool in the development of progressively eminent implant materials like ceramic based materials against premature failure, cyclic stress generation, wear, corrosion fatigue, implant loosening and ultimate fracture.
1-D/2-D/3-D Hopfield Associative Memories
Authors: Garimella Rama Murthy , Singh Aman , Jyothi Prasanna GC , Jagannadan Manasa , Vankam Vidya Sree , Bairaju Maha Lakshmi 
Published: January 2021

Abstract
In this research paper storage as well as retrieval of 1-D/2-D/3-D information using Hopfield type Associative Memories (AMs) is discussed. Various Artificial Neural Networks(ANN) architectures are proposed. Also, implementational issues associated with those Associative Memories are discussed. Cascade connection of AM and Convolutional Neural Network is proposed for noise immunity.
Prediction of Learner’s Performance in Adaptive E-Learning System using Learning Analytics
Authors: Sweta Soni , Mahato Shalini , Pathak Laxmi Kumari 
Published: January 2021

Abstract
The main objective of Learning Analytics is to collect, interpret and investigate the information for setting proper co-relation to improve the students’ learning experiences. As the popularity and demand of learning analytics are on rise, higher education is continuously moving from offline to online E-Learning Educational System. It has been further promoted on account of COVID-19. Learning pedagogies, evaluation measures and feedback measures have also been changed accordingly. The aim of this paper is to discuss the efficacy of learning analytics in online educational system using e-learning platform. R programming language with GGPLOT2 is used as Visualization tool to focus on gain insights of adaptation of learning analytics. The results show the better computational performance in terms of predicting students to improve their learning achievements and mitigating risk of failures.
Bernoulli Vacation Scheduling (BVS) Model for Energy efficient intrusion detection in WSN
Authors: Kalnoor Gauri , GowriShankar S 
Published: January 2021

Abstract
Cutting-edge wireless sensor networks, security is the most important challenge that has to be considered. Most of the applications of WSN are vulnerable to attacks, as they are deployed in any kind of environment. So, the intrusion detection system is the second line of defense for WSN. The intruder should be detected in WSN more efficiently. We introduce the Bernoulli scheduling rule such that several trials are performed to get the detection of an intruder to be successful once. Since the sensor nodes are always active for detecting an intruder, the energy consumption becomes high. To reduce the consumption of energy, sleep/wakeup, also called small interval vacations are used for the sensors which are almost considered to be like the mechanism of fluid queues. In our proposed work, the vacations are introduced so that the energy used is minimized whenever the sensor is not required to be active. Thus, in our proposed work, since WSN is vulnerable to many harmful attacks, the Bernoulli scheduling rule is applied and the sensor nodes have a vacation state whenever the sensor need not be active, to save the energy consumed. The probability of detecting an intruder is calculated and performance is compared. We have applied a fluid queuing model for the vacations of small intervals in the sensor nodes to minimize the energy consumption and the obtained result is compared with the energy consumed when the vacations are not applied. Thus, the results are simulated for both energy consumed and the number of success rates of detection of an intruder, most importantly the comparison is analyzed with the past work of wsn.
Optimizing process of Healthcare Assistance at Road Side using Particle Swarm optimization and Person Accident Behaviour
Authors: Khan Amreen , Raghuwanshi M.M. , Borkar Prashant 
Published: January 2021

Abstract
In the worldwide road accidents are very common and happened throughout the day. It is not restricted to time, density of population, geographical location, tropical geography, etc. Due to rapid increase in population and many other factors like driving and safety rules, driving pattern, etc., the worldwide rate of roadside accidents are also increasing. The African countries is having highest road accident inhabitants along with highest number of fatalities, whereas the Asian countries are the second rank in the world incurred rate of accidents with more fatalities during the road accidents. Hence, In order to handle these road accidents the major concern is to provide quick and fast emergency and health-care services like deployment of quick response team(QrT), first-aid, immediate hospitalization, and various assistance services that helps in reducing the fatalities during the road accidents. Now there is a need to develop effective and less parameterized approach which will helps in reducing the road accidents and provide quick response assistance to the needy. In order to develop the effective and comprehensive solution to the above mentioned problem, optimization is the key ingredient, which results in the evolvement of one of the most popular and effective algorithm called particle swarm optimization. In this paper we are going to evaluate, analyse and simulate particle swarm optimization (PSO) algorithm in order to design and develop novel solution to the above mentioned problem based on accident pattern and the behaviour of a person who met with the road accident.
Sentiment analysis on micro-blog data using machine learning techniques - A Review
Authors: Kalaivani M S , Jayalakshmi S 
Published: January 2021

Abstract
The user generated content in social networks, websites, ratings and reviews about product, people, places have become very beneficial for various fields to improve its growth. Sentiment analysis, which studies user generated content and find the correct sentiment polarity. Data collection, feature extraction, sentiment classification and decision making are the steps involved in this process. Machine Learning and Deep learning Methods are used to predict the sentiments. Large Input dataset from websites or microblog is taken as input and processed. There are some challenges in identifying accurate decisions. This Review Paper focus on recent studies about learning models such as Support vector Machines, Maximum Entropy, deep learning models and various methods which are used in solving sentiment classification, product review analysis, Text Processing and visual analysis etc.
RETRACTED: Medicine Distribution Robot and Human Less Intervention for Covid-19 Affected People (AKM MED ASSISTIVE BOT)
Authors: Murugan Kalpana , Ali Khan Md Arbas , Kylash MR , Muralidharan M 
Published: January 2021

Abstract
Social robots and administration robots with social insights are beginning to coordinate into our regular day to day activities. Managing health care needs of the patients collaborate with person-centered healthcare. Untimed medications to patients can cause serious health issues for the patients in hospitals and in old age nursing homes. The proposed work is intended to assist these patients with providing or recommending the necessary medication in the correct order at the specific times of the day without the actual need of a human nurse. This is incredibly tricky for the old patient who have issues in monitoring their medication at regular intervals. Thus, to avoid this, we have developed this medication robot. Which is a semi-autonomous microcontroller based programmable robot that works on the ‘line following robot’ concept, which can remind the patients, to consume their pills at regular intervals in a day. After successful intake of pills, the status of the medication is consequently sent to the patient’s relative through an SMS.
Rapid microwave-assisted biosynthesis of chitooligosaccharide coated silver nanoparticles: assessments of antimicrobial activity for paediatric pulp therapy
Authors: Rajasekaran Subhathira , Rao Sneha S , Dalavi Pandurang Appana , Prabhu Ashwini , Anil Sukumaran , Venkatesan Jayachandran , Bhat Sham S 
Published: January 2021

Abstract
Silver nanoparticles (AgNPs) have gained much attention in the treatment of dental caries due to their excellent antimicrobial activities. In this study, we have biosynthesised chitooligosaccharide coated silver nanoparticles (COS-AgNPs) using a rapid microwave-assisted method. The physical and chemical characterisations of biosynthesised COS-AgNPs were investigated using UV–visible spectroscopy (UV–vis), dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM) equipped with selected area electron diffraction (SAED). Further, the developed COS-AgNPs were assessed for antimicrobial activity on Streptococcus mutans. From the DLS analysis, the average particle size of the COS-AgNPs was found to be ∼32 nm. The morphologies of the COS-AgNPs were monodisperse and spherical in shape. XRD peaks are coherent with the standard peaks of AgNPs. Antimicrobial activity of COS-AgNPs showed a significant zone of inhibition due to the synergistic effect of COS-AgNPs. Furthermore, the antibacterial potential of developed COS-AgNPs-Ca(OH)2 paste was checked against Streptococcus mutans, Staphylococcus aureus, and Enterococcus faecalis. COS-AgNPs based composite paste showed better inhibition zone against the tested microbes. Hence, COS-AgNPs and COS-AgNPs-Ca(OH)2 based paste materials will be promising biomaterials for pulp therapy in paediatric dentistry.
Fibre-coupled, multiplexed methane detection using range-resolved interferometry
Authors: Bremner J A A , Kissinger T , Hodgkinson J , Tatam R P 
Published: January 2021

Abstract
We describe the first use of range-resolved interferometric signal processing for measurement of spectral transmission. This was applied to gas sensing using tunable diode laser spectroscopy, allowing the simultaneous and independent measurement of methane concentrations in multiple gas cells. The system uses a single injection-current modulated diode laser and a single photodetector. For three gas cells, we show the ability of the system to measure methane at noise equivalent concentrations of less than 200 ppm for a 0.5 s measurement period and a potential noise equivalent concentration (1σ) of <20 ppm with 150 s averaging time. We further show that cross-talk between cells is below the experimental uncertainty for the system.
Defect inspection technologies for additive manufacturing
Authors: Chen Yao , Peng Xing , Kong Lingbao , Dong Guangxi , Remani Afaf , Leach Richard 
Published: January 2021

Abstract
Additive manufacturing (AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity, internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.
Transverse vibration of tapered nanobeam with elastic supports
Authors: Hossain Mainul , Lellep Jaan 
Published: January 2021

Abstract
A tapered nanobeam is modelled by Euler–Bernoulli beam theory and nonlocal theory of elasticity. A solution technique based on homotopy perturbation method is proposed to analyse the dynamic behavior of the tapered nanobeam. Beam becomes tapered along the width exponentially. The effects of flexible and rigid supports on the vibration of tapered nanobeam are analysed. Influences of tapered ratio and nonlocal parameter on the shape mode of deflection are also scrutinized. Homotopy perturbation method shows its effectiveness in this analysis. The results reveal that the effects of nonlocal parameter and tapered ratio are significant for the dynamic behavior of tapered nanobeam.
Kissing number in non-Euclidean spaces of constant sectional curvature
Authors: Dostert Maria , Kolpakov Alexander 
Published: January 2021

This paper provides upper and lower bounds on the kissing number of congruent radius

r
>
0

r > 0

spheres in hyperbolic

H

n

\mathbb {H}^n

and spherical

S

n

\mathbb {S}^n

spaces, for

n
≥
2

n\geq 2

. For that purpose, the kissing number is replaced by the kissing function

κ
H

(
n
,
r
)

\kappa _H(n, r)

, resp.

κ
S

(
n
,
r
)

\kappa _S(n, r)

, which depends on the dimension

n
n

and the radius

r
r

.

After we obtain some theoretical upper and lower bounds for

κ
H

(
n
,
r
)

\kappa _H(n, r)

, we study their asymptotic behaviour and show, in particular, that

κ
H

(
n
,
r
)
∼
(
n
−
1
)
⋅

d

n
−
1

⋅
B
(

n
−
1

2

,

1
2

)
⋅

e

(
n
−
1
)
r

\kappa _H(n,r) \sim (n-1) \cdot d_{n-1} \cdot B(\frac {n-1}{2}, \frac {1}{2}) \cdot e^{(n-1) r}

, where

d
n

d_n

is the sphere packing density in

R

n

\mathbb {R}^n

, and

B
B

is the beta-function. Then we produce numeric upper bounds by solving a suitable semidefinite program, as well as lower bounds coming from concrete spherical codes. A similar approach allows us to locate the values of

κ
S

(
n
,
r
)

\kappa _S(n, r)

, for

n
=
3
,

4

n= 3,\, 4

, over subintervals in

[
0
,
π
]

[0, \pi ]

with relatively high accuracy.
Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach
Authors: Zhao Yi , Naumova Elena N , Bobb Jennifer F , Claus Henn Birgit , Singh Gitanjali M 
Published: January 2021

AbstractThe human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985–2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with −0.09-unit (95% credible interval (CrI): −0.16, −0.02), −0.10-unit (95% CrI: −0.16, −0.03), and −0.11-unit (95% CrI: −0.18, −0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit consumption.