bluuebunny/crossref_metadata_2025_split huggingface dataset:
Surface roughness prediction and parametric optimization of shot blasting of Al7068 using RSM
Authors: Pritima D , Dhinakaran V , Stalin B , Ravichandran M , Balasubramanian M , Anand Chairman C 
Published: December 2020

AbstractThe mechanical based shot blasting was used to remove the oxides and scales from the surface of the materials. It was used to enhance the surface properties. In this work, aluminium 7068 alloy was involved in the shot blasting process. The particle velocity, standoff distance (SOD) and impact angles were included as input factors. These factors were mainly affects the Surface Roughness (SR). The round steel balls with diameter with 2 mm diameter were used for the shot blasting process. In addition to that, the elliptic and square shape of glass beads particles were used to enhance the surface finish. The parametric effect and surface roughness optimization was achieved through Response Surface Methodology (RSM). The role of factors has been analyzed through the variance test.
Recent Progress of Hybrid Architectures Based on Nanomaterials
Authors: Dhinakaran V , Stalin B , Ravichandran M , Balasubramanian M , Anand Chairma C , Marichamy S 
Published: December 2020

Abstract
The recent development in environment and energy of hybrid structures with excellent and multifunctional properties aims to promote single-component nanomaterials. In this phase, Noble Metals are rationally integrated in carbon dots (CD), which is one of the most common nano-hybrids that combine their material strength with mechanical properties including electrical characteristics, enhancing and putting surface resonance plasma (LSPR). In this research work, synthetic approaches, physicochemical properties and recent developments are investigated in the applications of noble nanohybrid metal/carbon dots (NMs/CDs. The CD/NMs will help coordinate the synthesis of nanohybrids to accomplish the goals in the manufacturing and architectural industries accordingly. In these implementations, special focus is put on processes and synergistic activity between the two components. Lastly, for further improving these nanohybrids, limitation and opportunities are suggested for NMs/CDs.
Reverse Engineering & Stable Analysis of Corrosion Protective Fibre Reinforced Plastic Fan Blade
Authors: Vijayaganapathy D , Dhinakaran V , Dhivakar Raviram R P 
Published: December 2020

Abstract
The radiator fan blades is manufactured using various materials and we have chosen FRP has the material because it provides excellent corrosion resistance. The heat from the radiator is reduced by circulating cooling water and the fan supplies the air required for cooling. The design of entire blade is taken into consideration for analysing the failure of the blade as the radiator fan is analysed under static condition as the fan blade is in corrosive environment. The Fibre reinforced plastic radiator fan blade analysis is conducted to test the Fibre reinforced plastic object that is able to withstand dynamic as well as structural loads. For this paper the blade design is achieved by reverse engineering, static analysis is achieved using ANSYS where the FRP radiator fan’s 3D rigid model is used for structure stability (analysis). The weight acting and external properties were implied to the radiator fan throughout its entire length. The FRP radiator fan is brushed with a coat of enamel based epoxy of grey in colour and validates the Fibre reinforced plastic radiator fan which can withstand the property of corroding by environment.
Dielectric Fluid Parameter Optimization in Machining of Composite Material using WEDM Process
Authors: Senthil Kumar K L , Dhinesh S K 
Published: December 2020

Abstract
Wire Electric Discharge Machining (WEDM) has become most popular among the non-conventional machining processes because of precise machining and accuracy of parts. The process is preferable for accurate machining of complex geometries in hard materials such as Aluminium Silicon Carbide metal matrix composite. In this work, an attempt is made to analyze and optimize the dielectric fluid parameters in machining of Al/SiCp 10% work material. The input variables such as volume flow rate, flow velocity and chemical composition of dielectric medium used in the WEDM are chosen for this. Taguchi’s L9 orthogonal array is used to conduct experiments in WEDM machine and ANOVA results have confirmed the influence of chosen parameters on the output responses. Computer simulation model of the WEDM tank with dielectric fluid, work piece and nozzle is presented using Fluent software. The Fluent model is analyzed based on the maximum removal of debris in the work material. Optimization is carried out to maximize the Material Removal Rate (MRR) and minimize surface roughness. The results obtained from Taguchi’s optimization and Fluent analysis have been validated by carrying out tests on WEDM. The study shows that volume flow rate and flow pressure can adequately influence MRR and surface roughness.
Experimental Investigation of Automobile radiator using Tungsten trioxide Nano-fluid
Authors: Hanumanth Ramji K S , Vinoth kumar J , Amar Karthik A 
Published: December 2020

Abstract
Conventional fluids (ethylene glycol and H2O) heat transfer performance results to be poor when incorporated with an automobile radiator. An experimental study was performed to explore the enactment of automobile radiator using H2O and WO3 nanofluid as operational fluids. Analysis and comparison were carried out for the heat transfer rate and outlet temperature of fluid for various flow rate ranging from 0.2m3/hr to 0.5 m3/hr. Preperation of nanofluid was done for water with 0.0167%, 0.034% and 0.0667% volume concentration of WO3 nanoparticle. The inlet condition of fluid was set at 50°C as the impact for the variation of temperature conditions on system performance were minimal. On experimentation, the maximum increase in the heat transfer rate was by 4.18% on comparison with base fluid.
Design and Fabrication of Step Feeder automation Machine for the Automobile Steering Ball joints
Authors: Oorappan G M , Jeevanandham A , Sivabalakrishnan R , Sudhahar S. 
Published: December 2020

Abstract
Automation feeding machine helps to replace the manual feeding of the components to achieve the production rate. Even so, all industrialists have been trying to get 100% efficiency from every machine and to give more production. The production rate of the machine is depended on certain factors like feeding, accuracy, Manpower, etc. However, feeding is the most important factor, because the production will be fast only while the component is federal quick. This paper is providing an automation solution of feed ball joints for an automotive industry when the vehicle gathers the steering assembly. There were many feeding mechanisms available. Still, the ball joint is such a large component and should need a certain interval to feed it correctly as required. In this machine, the hoppers to be a trapezoidal tanks stores the sorted of long components. The two-step feeders consist of cylinders, which make the movement of steps. The steps that are used the EN-8 materials are used to make the feeder assembly, and those are connected with the cylinders that are used to construct the up and down movements, when the cylinder makes the forward and retracts motion. The steps scoop up the components and feed them to the additive chute. The linear chute transfers the properly oriented components to the singling unit feeds the components to the machine for making further operations.
Classification of Benign or Malignant Tumor Using Machine Learning
Authors: Kalaiyarasi M , Dhanasekar R , Sakthiya Ram S , Vaishnavi P. 
Published: December 2020

Abstract
The abnormal growth of the cell in the human body may leads to tumor. There are almost 100 types of cancer will affect the different parts of the human. The affected human may have symptoms like lump, abnormal bleeding, prolonged cough, weight loss etc. And depends on the part where the tumor formed. Breast cancer is one of the types among the 100 types of cancer and it is most commonly found on the female than the male. Breast cancer is a disease that caused due to the over growth of the cell in the breast area and they started to form lump over the breast. The people who are suffering from the breast cancer will have many emotional side effects and they are supped to undergo lot of pain in their day to day life. The most important problem for the health care people is earlier prediction. So, it can be rectified by employing machine learning algorithms in the prediction stage. In this paper the classification algorithms are used to classify whether the tumor is benign or malignant. The supervised learning algorithms of machine learning such as logistic regression, Support vector machine and K Nearest neighbour algorithm are usually used to analyse the tumour detection. Stacking ensemble method used in order to combine the entire three algorithms is proposed and the performance of algorithm is compared with the logistic regression, Support vector machine and K Nearest neighbour algorithm in order to get an efficient model for the classification.
Design and implementation of low-cost electric bicycle with battery level indicator
Authors: Rajalashmi K , Vignesh S , Chandru M , Arulshri K P 
Published: December 2020

Abstract
In the recent days, carbon emission from the vehicles are found to increase the level of air pollution in a huge manner. In order to reduce the pollution due to transportation, electric vehicles are seen as an alternative. This approach paved way for developing an electric bicycle by incorporating 250 W BLDC hub motor, 295.6 WH lithium-ion battery and pedal assist system (PAS). The maximum speed of the bicycle is 25 km/h. The distance covered using PAS is 75 km. The maximum carrying capacity is 120 kg with a charging time of 2.5 to 3 hours. The pedal assisting system helps to share the torque between manual pedalling & electric motoring. This ensures that the battery is used in an efficient manner. The cost of this bicycle includes cycle assembly, battery, hub motor, controller circuit and wireless odometer. The total expenses involved in developing this prototype is nearly 38.57% lesser without profit when compared to other products available in the market. The pulling capacity of the cycle can be enhanced by increasing the battery capacity which would add additional cost based on capacity.
Autonomous Vehicle for Drug Delivery
Authors: Nandakumar K , Surya S R , Karthick K , Raviraj N , Cheran B , Poovizhi V , Gokul S , Nimisha C 
Published: December 2020

Abstract
Autonomous vehicles are intended to move in an environment without any human intervention. In order to determine the structure of the environment, we have to rely on sensors data to build the map and to localize the vehicle within its environment. This paper presents the autonomous vehicle built using 2D LIDAR and Robot Operating System(ROS). If the environment is to be explored is outdoor we can rely on GPS to track the location the autonomous vehicle, whereas in an indoor environment we cant do so. For building the map of the environment and for the vehicle’s localisation simultaneous localisation and mapping (SLAM) is used. The LIDAR data is used to find out the corresponding distance of the obstacle from the robot. Based on this point cloud data map is generated denoting the obstacle, explored area, unexplored area and the location of the robot with respect to the map. Data from the encoder is used to navigate the robot. Path planning is done using A* algorithm to find out the shortest distance between the starting and the destination point. The experimental result helped us in comparing the performance of the autonomous mobile robot built using ultrasonic sensor with the autonomous mobile robot built using LIDAR.
Thermomechanical and morphological properties of sustainable mortars employing blast furnace slag and fly ash reinforced cement
Authors: Farina I , Singh R , Singh M , Preet P , Kumar R , Fraternali F , Colangelo F 
Published: December 2020

Abstract
The blast furnace slag (BFS) is non-metallic co-product (such as silicates and alumina silicates etc.) which absorbs sulphur from the charge and comprises of around 20% (by weight) of Fe production and its use as reinforcement in cement mortar has been widely explored to improve thermal and compressive properties along with addressing the sustainability issues related to Fe production. But hitherto little has been reported on effect of BFS and fly ash (FA) reinforced cement mortar from surface topography, thermal stability and morphological properties view point. This paper reports the comparison of compressive, thermal and morphological properties of cement mortar with air cooled BFS and FA reinforced cement mortar as a case study. The results of study suggests that 80% of BFS and 20% FA reinforced cement mortar (cement: (BFS+FA): 1: (0.8+0.2) by weight%) possess lowest thermal conductivity (0.65W/mK), greatest porosity (29.65%) and acceptable compressive strength (6.6MPa) in comparison to cement mortar comprising of cement: sand as 1:1. The results are supported with compressive strength data, optical photo micrographs, thermal analysis based upon differential scanning calorimetry (DSC), surface topography (based upon 3D rendered images).
Mechanical characterization of FDM filaments with PVDF matrix reinforced with Graphene and Barium Titanate
Authors: Farina I , Sharma R , Singh R , Batish A , Singh N , Fraternali F , Colangelo F 
Published: December 2020

Abstract
In past one decade number of studies has been reported on optimization of process parameters of fused deposition modelling (FDM) for in-house developed thermoplastic composite based feed stock filaments. This paper investigates smart polymer-based composites prepared with hybrid feed stock filament (comprising of polyvinylidene fluoride (PVDF) reinforced with graphene (Gr) and barium titanate (BTO) powder). This work started with the Functional prototypes were 3D printed for tensile and flexural characterization using inhouse developed filament (PVDF (78%)+Gr (2%) with BTO (20%)) at optimized settings of FDM. The printed specimens were subjected to destructive testing for mechanical properties (to analyze the process capability indices, Cp and Cpk). For morphological properties, scanning electron microscopy (SEM) images and 3D rendered images of the fractured surfaces of tensile and flexural specimen were used. It has been revealed from the SEM and 3D rendered images that the optimized settings of 3D printing process parameters resulted into uniform morphological features (based upon surface roughness (Ra) and amplitude distribution function (ADF), peak count (PC) and bearing ratio (BR) curve).
Waste Reduction with Lean Manufacturing Model in an Alpaca Wool Workshop
Authors: Cristobal J , Quispe G , Dominguez F , Zapata G , Raymundo C 
Published: December 2020

Abstract
This paper proposes the design of a quasi-experimental time-series model comprising lean manufacturing tools—such as the value stream mapping and 5S’s—with the aim of reducing the level of waste and losses in a micro-company dedicated to the production of Alpaca wool clothing in Junín, Perú. The model implementation managed to reduce the level of waste by 7%, increase the efficiency in material consumption, and reduce the cycle time and total supply time. The implementation of the model allowed reducing the level of waste in the production process and creating an organizational culture based on order, cleanliness, discipline, and teamwork.
NinimHMDA: neural integration of neighborhood information on a multiplex heterogeneous network for multiple types of human Microbe–Disease association
Authors: Ma Yuanjing , Jiang Hongmei 
Published: December 2020

Abstract

Motivation
Many computational methods have been recently proposed to identify differentially abundant microbes related to a single disease; however, few studies have focused on large-scale microbe-disease association prediction using existing experimentally verified associations. This area has critical meanings. For example, it can help to rank and select potential candidate microbes for different diseases at-scale for downstream lab validation experiments and it utilizes existing evidence instead of the microbiome abundance data which usually costs money and time to generate.

Results
We construct a multiplex heterogeneous network (MHEN) using human microbe-disease association database, Disbiome and other prior biological databases, and define the large-scale human microbe-disease association prediction as link prediction problems on MHEN. We develop an end-to-end graph convolutional neural network-based mining model NinimHMDA which can not only integrate different prior biological knowledge but also predict different types of microbe-disease associations (e.g. a microbe may be reduced or elevated under the impact of a disease) using one-time model training. To the best of our knowledge, this is the first method that targets on predicting different association types between microbes and diseases. Results from large-scale cross validation and case studies show that our model is highly competitive compared to other commonly used approaches.

Availabilityand implementation
The codes are available at Github https://github.com/yuanjing-ma/NinimHMDA.

Supplementary information
Supplementary data are available at Bioinformatics online.
At the Root of T2R Gene Evolution: Recognition Profiles of Coelacanth and Zebrafish Bitter Receptors
Authors: Behrens Maik , Di Pizio Antonella , Redel Ulrike , Meyerhof Wolfgang , Korsching Sigrun I 
Published: December 2020

Abstract
The careful evaluation of food is important for survival throughout the animal kingdom, and specialized chemoreceptors have evolved to recognize nutrients, minerals, acids, and many toxins. Vertebrate bitter taste, mediated by the taste receptor type 2 (T2R) family, warns against potentially toxic compounds. During evolution T2R receptors appear first in bony fish, but the functional properties of bony fish T2R receptors are mostly unknown. We performed a phylogenetic analysis showing the “living fossil” coelacanth (Latimeria chalumnae) and zebrafish (Danio rerio) to possess T2R repertoires typical for early-diverged species in the lobe-finned and the ray-finned clade, respectively. Receptors from these two species were selected for heterologous expression assays using a diverse panel of bitter substances. Remarkably, the ligand profile of the most basal coelacanth receptor, T2R01, is identical to that of its ortholog in zebrafish, consistent with functional conservation across &gt;400 Myr of separate evolution. The second coelacanth receptor deorphaned, T2R02, is activated by steroid hormones and bile acids, evolutionary old molecules that are potentially endogenously synthesized agonists for extraoral T2Rs. For zebrafish, we report the presence of both specialized and promiscuous T2R receptors. Moreover, we identified an antagonist for one of the zebrafish receptors suggesting that bitter antagonism contributed to shape this receptor family throughout evolution.
CO Multi-line Imaging of Nearby Galaxies (COMING). IX.12CO(J = 2–1)/12CO(J = 1–0) line ratio on kiloparsec scales
Authors: Yajima Yoshiyuki , Sorai Kazuo , Miyamoto Yusuke , Muraoka Kazuyuki , Kuno Nario , Kaneko Hiroyuki , Takeuchi Tsutomu T , Yasuda Atsushi , Tanaka Takahiro , Morokuma-Matsui Kana , Kobayashi Masato I N 
Published: December 2020

AbstractWhile molecular gas mass is usually derived from 12CO(J = 1–0)—the most fundamental line for exploring molecular gas—it is often derived from 12CO(J = 2–1) assuming a constant 12CO(J = 2–1)$/$12CO(J = 1–0) line ratio (R2/1). We present variations of R2/1 and effects of the assumption that R2/1 is a constant in 24 nearby galaxies using 12CO data obtained with the Nobeyama 45 m radio telescope and IRAM 30 m telescope. The median of R2/1 for all galaxies is 0.61, and the weighted mean of R2/1 by 12CO(J = 1–0) integrated intensity is 0.66 with a standard deviation of 0.19. The radial variation of R2/1 shows that it is high (∼0.8) in the inner ∼1 kpc while its median in disks is nearly constant at 0.60 when all galaxies are compiled. In the case that the constant R2/1 of 0.7 is adopted, we found that the total molecular gas mass derived from 12CO(J = 2–1) is underestimated/overestimated by ∼20%, and at most by 35%. The scatter of molecular gas surface density within each galaxy becomes larger by ∼30%, and at most by 120%. Indices of the spatially resolved Kennicutt–Schmidt relation by 12CO(J = 2–1) are underestimated by 10%–20%, at most 39%, in 17 out of 24 galaxies. R2/1 has good positive correlations with star-formation rate and infrared color, and a negative correlation with molecular gas depletion time. There is a clear tendency of increasing R2/1 with increasing kinetic temperature (Tkin). Further, we found that not only Tkin but also pressure of molecular gas is important in understanding variations of R2/1. Special considerations should be made when discussing molecular gas mass and molecular gas properties inferred from 12CO(J = 2–1) instead of 12CO(J = 1–0).
The impact of disability on employment and financial security following the outbreak of the 2020 COVID-19 pandemic in the UK
Authors: Emerson Eric , Stancliffe Roger , Hatton Chris , Llewellyn Gwynnyth , King Tania , Totsika Vaso , Aitken Zoe , Kavanagh Anne 
Published: December 2020

AbstractBackgroundThe coronavirus disease 2019 (COVID-19) pandemic may have a greater impact on people with disabilities than non-disabled people. Our aim was to compare the short-term impact of the 2020 COVID-19 pandemic and first lockdown on the employment and financial security of working age adults with and without disabilities in the UK.MethodsSecondary analysis of data collected in Wave 9 and the special April, May and June COVID-19 monthly surveys of ‘Understanding Society’, the UK’s main annual household panel study.ResultsDuring the first 3 months of the introduction of the COVID-19 lockdown in the UK, respondents with disability were more likely than their peers to be working reduced hours and experience higher levels of financial stress. These differences were attenuated, but not eliminated, when estimates were adjusted to take account of pre-lockdown financial status.ConclusionsWorking age adults with disability were particularly disadvantaged by the financial impact of the COVID-19 lockdown in the UK. The UN Secretary-General António Guterres has stated the need for a disability-inclusive COVID-19 government response. The results of our analysis suggest that these pleas have either not been heeded, or if measures have been implemented, they have so far been ineffectual in the UK.
AIDS in the Heartland–Hemophilia Was the Harbinger of Things to Come
Authors: Lederman Michael M. 
Published: December 2020

Abstract:
The history of the AIDS epidemic in the United States has focused largely on the experience in coastal cities where the syndrome was first recognized among gay men. In Cleveland and in many other heartland cities, early recognition of this syndrome was primarily among men with hemophilia who were at risk because of exposure to HIV during treatment with lyophilized antihemophilic factor concentrates that were pooled from plasmas of thousands of donors. Disease and subclinical immune deficiency in these men and in other populations drove recognition that AIDS was due to a blood-borne and sexually transmissible agent. As the AIDS epidemic expanded, heartland cities mobilized their staff and resources to meet the needs of a growing epidemic that ultimately affected the entire nation.