bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Investigation of wavy channel using several Nanofluids
Authors: Naik Bharat , Hosmani Anand K. , Kerur Shirish M. , Pattanashetti Jagadeesh S. , Ratan Vinayak , Bhushan Chandra 
Published: February 2021

Abstract
This study investigates the effect of nano-fluids in enhancing heat transfer. These nano-fluids contain nanoparticles (Ag, CuO, Fe, Al2O3 and Tin) in volume fraction ranging between 2%-5%. Two-dimensional numerical simulation has been conducted on wavy channel using ANSYS Fluent. Two dimensional governing equations such as mass, momentum and energy equations are solved using SIMPLE algorithm and discretized using Finite Volume Method. The results show that there will be enhancement of heat transfer rate as the nanoparticle volume concentration increases and from the present study it is clear that silver-water nanofluid gives better results as compared to other nanoparticles.
Machining and forecasting of square profile areas using artificial neural modelling at different slant angles by WEDM.
Authors: Manoj I.V. , Narendranath S 
Published: February 2021

Abstract
Wire electric discharge machining non-conventional process that removes materials by thermal erosion. Tapering in WEDM has many applications in machining accurate geometric profiles. In the present investigation, a slant type taper machining was performed to produce taper profiles with the help of the slant fixture. The machining parameters such as wire guide distance, corner dwell time, wire offset and cutting speed override was employed to find the variation in the taper profile area. A simple square profile of 1mm, 3mm and 5mm was machined at different slant angles namely 0°, 15° and 30°. It was observed that each parameter yielded different profile areas. The artificial neural network was used for the forecasting the areas of 1mm, 3mm and 5mm square for different parameters. The optimum artificial neural network model was experimentally validated and the errors were ranging from 0-10%.
Productivity enhancement of Evacuated Tubes solar still of different water depth: Thermal modelling and an Experimental analysis.
Authors: More Sunil , Daniel Joseph 
Published: February 2021

Abstract
In this research article, the author has conducted the comparative thermal experimental analysis and investigation on Conventional still and Modified Evacuated Tubes solar still with different water volume of 30 and 50 Litre in Basin, in respect of Energy Balance Equation. The various variable elements have been taken into account and duly calculated for both the stills i.e. active still and passive still, based on thermal modelling. The variables calculated comprise inner glass surface temperature, distillate yield, basin water temperature etc. The entire study has been carried out at Akison’s Solar Equipments Pvt. Ltd., Shirwal, Maharashtra, India (latitude18.14° N: longitude 73.97° E), with adequate instrumental infrastructure setup appropriate for the purpose. The investigation concluded that there is substantial escalation of. 242% and 246% at 0.03 and 0.05m of water depth respectively in the yields obtained from Modified Evacuated Tubes solar still in comparison of Conventional still. It has been observed that theoretical outcomes from thermal modelling and the outcomes attained from experiments done for simple and modified stills are in respectable consensus.
Design And Fabrication Of Extrusion Machine For Recycling Plastics
Authors: Kumar Sagar , Sooraj R , Vinod Kumar M V 
Published: February 2021

Abstract
The extruder is designed for producing filament that can be used for 3-D printers. The 3-D printer requires a filament having dimension ranging from 2-2.5mm. The quality expected is to obtain continues filament having the specified dimensional range which can be fed in to the 3D printer. Screw extrusion process is adopted to produce filament to fixes cross section. PET pellets were heated between 230° to 250° and pushed through a nozzle. The material experiences compressive and shear force. A continues filament of diameter ranging from 2 to 2.5mm is obtained without any air bubbles and roughness.
Fabrication and Characterization of Aluminum 8011 Alloy and nano Zro2 Metal Matrix Composite
Authors: Thippeswamy J C , Dr.Sathisha N 
Published: February 2021

Abstract
Composites of nano metal matrix produced by stir casting technology have major attractive properties like high ductility, highly conductivity, light weight, and high strength to weight ratio compared to some other approaches. Nano Zro2 particulates with varying weight percentages (2, 4 and 6%) are reinforced with aluminium 8011 using the process of stir casting. Mechanical properties such as tensile, compression and hardness behavior have been investigated. In the current analysis, scanning electron microscopy (SEM) is carried out to show Nano Zro2 particulates are successfully incorporated into the matrix of aluminium 8011 with homogeneous distribution. The findings showed that improved mechanical properties such as tensile strength, compression strength and hardness, were enhanced by increasing weight percentage of Nano Zro2 particulates in the matrix of aluminium 8011.
Comparative Study of Carbide Tools in Turning of High-Chrome White Cast Iron using Hard Turning Methods
Authors: Ravi A.M , Murigendrappa S.M. 
Published: February 2021

Abstract
Thermally enhanced machining (TEM) is a new practice in hard turning of hard materials like White cast iron, Inconel 718 steels etc. Addition of preheating before shear zone improves the elasticity of the material. Before this, Industries invested more capital on toolings and robust machineries/equipments, also it demamed high cost diamond, Cubic boran nitride (CBN) and polyCBN (pCBN) tools. This research paper mainly focusses on the comparative study of machinability characteristics like machining forces and surface roughness of a high hard High chrome white cast iron (HCWCI) when turning with and without the addition preheat using multilayer hard coated carbide (TiC/TiCN/Al2O3). Taguchi technique were used to carryout the experimentational work. The experimental and theoretical investigations are done in different phases in order to derive some of the useful process responses like machining forces, surface roughness etc, and to prove Oxy-LPG aided TEM most appropriate methodology to cut HCWCI. Furthermore derived the suitable conditions at which the coated carbide tools perform better. Hence comparative studies of each process responses are required to arrive to the reasonable conclusions.
Effect of addition of bismuth on the vibration analysis of Mg-Al/Mg2Si composites processed using stir casting technique
Authors: Umesh D E , Ramesh Rao D , Shivalingappa D 
Published: February 2021

Abstract
In this work, the experimental and finite element simulation solutions of vibration characteristics of the Mg-Al/Mg2Si composite has been carried out. The influence of the geometrical structures, percentage of bismuth and material properties on the natural frequency and the material damping of the Mg-Al/Mg2Si composites has been studied. The fast fourier transform (FFT) analyzer has been utilized to find the first three natural frequencies and the modal damping experimentally of the said composite. Finite element simulation method also has been adopted to find the natural frequencies and their mode shapes for the Mg-Al/Mg2Si composites specimens. From the results of the experimental and analytical method it is identified that the composite material exhibits the set of natural frequencies and the modal damping. It is also observed that the modal damping increases with increase in the addition of bismuth.
Enhancement of Fracture toughness for Nano SiC reinforced AA7075 MMC’s By Tensile tests and FEM Analysis
Authors: Kallimani Anilkumar S. , Chikkanna N , Suresha P , Hareesha G 
Published: February 2021

Abstract
After the second world war, The Design and development activities in the field of Aerospace took place with a great acceleration and that resulted in to Evolution of new Aluminum alloy materials for Aircraft Structure. Recent research work reveals that particle reinforced Metal matrix composites (PAMC) of aluminum alloy reinforced with silicon carbide reinforcement have shown better mechanical and wear properties. In this work, AA7075 is chosen matrix and Nano silicon carbide(500nm) in the range of (0-8%) were added in terms of weight percentage as reinforcement. Cast MMC samples were fabricated stir casting techniques. Further the specimens were made for XRD study, and for Tensile Test. The 500nm silicon carbide content varies with the 0%, 2%, 4%, 6% &8%wt. fractions. Theoretical Stress intensity values of Compact tension specimen were compared with FEM analysis. And it was observed that higher values of toughness were obtained as the weight percentage increased. Results were compared with pure matrix material in this paper.
Novel Dataset Generation for Indian Brinjal Plant Using Image Data Augmentation
Authors: Gorad Balwant , Kotrappa Dr. Sirbi 
Published: February 2021

Abstract
Machine learning and deep learning have performed outstandingly in many computer tasks to tackle various real-world problems like disease prediction on plants etc. But unfortunately, there are many research areas like disease prediction in the agricultural crops where there is a lack of large-good quality real-world datasets. One way to solve such a problem is by using an available dataset from the internet. The problem of using an available dataset from the internet creates lots of issues. Major issues are using the dataset from different geographical locations which are deployed at other location, model overfitting due to small-sized dataset etc. The main purpose and experimentation done in this research paper are presenting different techniques to increase the size of the Indian Brinjal dataset so that deep learning models can be improved. Here data augmentation techniques to enhance the small-sized image dataset using rotation, channel shift, width shift, height shift, shear transform, brightness, scaling, uniform aspect ratio, zoom, horizontal flipping, and vertical flipping methods are used. At last, a huge high-quality training dataset of size 39,010 is generated from 350 sample images taken from the real field and 1356 high-quality images are generated to validate the model using above mentioned data augmentation techniques.
Passenger data analysis of Titanic using machine learning approach in the context of chances of surviving the disaster
Authors: Haque Md Arfinul , Shivaprasad G , Guruprasad G 
Published: February 2021

Abstract
Titanic disaster occurred about 100 years back but still it attracts the researchers to understand and study that how some passengers survived and others perished. In this work, the characteristics of the passengers will be identified and the relationship of survival chance from the disaster is found. Feature engineering techniques will be performed, where the alphabetic values will be changed to numeric values, the family size will be calculated. Also, we will extract the title from the name, and deck label from ticket number. Classification is done using Decision tree machine learning classification algorithm using two classes which are survived and not survived. R programming has been used for its implementation. Clustering is performed using KMeans machine learning algorithm. Its implementation has been done using Python programming.
Microstructure evolution and stress rupture properties of A286 superalloy in the 600 to 750 °C temperature range
Authors: Wei Li , Zhao Hongjin , Sun Yongqing , Zhang Yan , Zhao Bo , Liu Zhenbao , Liang Jianxiong , Yang Zhiyong 
Published: February 2021

Abstract
To study the stress rupture properties of the A286 alloy in high temperature service environment, the stress rupture tests of A286 alloy at different temperature ranges and stress levels were carried out. Moreover, the Larson-Miller Parameter (LMP) for predicting the rupture life was established as LMP = (T + 273)(log t

r
+ C) × 10−3, C = 22.3. The fracture morphology and the microstructure evolution of the stress rupture specimens were observed by scanning electron microscopy. The results show that for the stress rupture tests at 700 °C–750 °C/150–400 MPa, the precipitation of the closely arranged cellular η phase and the coarse MC at the grain boundary acted as the crack source. For the stress rupture test at 600 °C–700 °C/400–700 MPa, the coarse MC at the grain boundary led to stress concentration to give rise to crack initiation and propagation during deformation. With decreasing of temperature and increasing of stress, the fracture mode of the A286 alloy changed from the ductile fracture mode of microvoids to the brittle fracture mode of intergranular. The deformation mechanisms of A286 alloy at different temperatures and stress levels also have been systematically analyzed by transmission electron microscopy. The deformation mechanisms of A286 at different temperatures and stress levels were Orowan looping, microtwins and dislocation pairs shearing with Orowan looping and microtwins as the dominant deformation mechanisms. The critical transition radius of γ′ phase during the transition from dislocation shearing mechanism to Orowan looping mechanism was calculated.
Effect of acrylonitrile styrene acrylate on mechanical, thermal and three-body abrasion behaviors of eucalyptus fiber reinforced polyvinyl chloride composite
Authors: Zhang Keping , Hamza Bichi Abdulmalik , Yang Junqian 
Published: February 2021

Abstract
The effect of different content (0–20 wt%) Acrylonitrile-styrene-acrylic (ASA) on mechanical, thermal and three-body wear behaviors of eucalyptus fiber/polyvinyl chloride (EF/PVC) composites were studied in this research. The results show that the mechanical properties of the composites such as impact strength, tensile strength, flexural strength and flexural modulus increased with increasing ASA content lower than 15 wt%. The addition of 15 wt% and 20 wt% ASA can improve thermal stability of the EF/PVC composites in the early period of the heating, and the effect weakened on later stage due to the thermal degradation of ASA, which occurred in the temperature range 416°C–442°C. The three-body wear resistance of the EF/PVC composites can be improved by ASA, and the abrasive wear mechanism of the composites was predominated by linear travel, sand particle plough and cut action, ASA and PVC plastic deformation, and fibers debonding.
Structural and magnetic phase diagram of epitaxial La0.7Sr0.3MnO3 from first principles
Authors: Pilo J , Pruneda M , Bristowe N C 
Published: February 2021

Abstract
ABO3 perovskites host a huge range of symmetry lowering structural distortions, each of which can tune, or even switch on or off, different functional properties due to the strong coupling between the lattice, spin and charge degrees of freedom in these materials. The sheer number of different meta-stable structures present in perovskites creates a challenge for materials design via theory and simulation. Here, we tackle this issue using a first principles structure searching method on a prototypical half-metallic perovskite, La0.7Sr0.3MnO3, to predict how epitaxial strain can engineer structural and magnetic properties. We reveal a rich structural phase diagram through strain engineering in which the octahedral tilt pattern, and hence the crystal symmetry, is altered from the bulk. We show how the low-symmetry of the various phases in turn induces new structural modes, an increase in the magnetic anisotropy energy, and weak antiferromagnetic spin-canting.
Optical sensors for continuous glucose monitoring
Authors: Yu Ziyi , Jiang Nan , Kazarian Sergei G , Tasoglu Savas , Yetisen Ali K 
Published: February 2021

Abstract
For decades, diabetes mellitus has been of wide concern with its high global prevalence, resulting in increasing social and financial burdens for individuals, clinical systems and governments. Continuous glucose monitoring (CGM) has become a popular alternative to the portable finger-prick glucometers available in the market for the convenience of diabetic patients. Hence, it has attracted much interest in various glucose sensing technologies to develop novel glucose sensors with better performance and longer lifetime, especially non-invasive or minimally invasive glucose sensing. Effort has also been put into finding biocompatible materials for implantable applications to achieve effective in vivo CGM. Here, we review the state-of-the-art researches in the field of CGM. The currently commercially available CGM technologies have been analyzed and a summary is provided of the potential types of recently researched non-invasive glucose monitors. Furthermore, the challenges and advances towards implantable applications have also been introduced and discussed, especially the novel biocompatible hydrogel aimed at minimizing the adverse impact from foreign-body response. In addition, a large variety of promising glucose-sensing technologies under research have been reviewed, from traditional electrochemical-based glucose sensors to novel optical and other electrical glucose sensors. The recent development and achievement of the reviewed glucose sensing technologies are discussed, together with the market analysis in terms of the statistical data for the newly published patents in the related field. Thus, the promising direction for future work in this field could be concluded.
Toward a theory of machine learning
Authors: Vanchurin Vitaly 
Published: February 2021

Abstract
We define a neural network as a septuple consisting of (1) a state vector, (2) an input projection, (3) an output projection, (4) a weight matrix, (5) a bias vector, (6) an activation map and (7) a loss function. We argue that the loss function can be imposed either on the boundary (i.e. input and/or output neurons) or in the bulk (i.e. hidden neurons) for both supervised and unsupervised systems. We apply the principle of maximum entropy to derive a canonical ensemble of the state vectors subject to a constraint imposed on the bulk loss function by a Lagrange multiplier (or an inverse temperature parameter). We show that in an equilibrium the canonical partition function must be a product of two factors: a function of the temperature, and a function of the bias vector and weight matrix. Consequently, the total Shannon entropy consists of two terms which represent, respectively, a thermodynamic entropy and a complexity of the neural network. We derive the first and second laws of learning: during learning the total entropy must decrease until the system reaches an equilibrium (i.e. the second law), and the increment in the loss function must be proportional to the increment in the thermodynamic entropy plus the increment in the complexity (i.e. the first law). We calculate the entropy destruction to show that the efficiency of learning is given by the Laplacian of the total free energy, which is to be maximized in an optimal neural architecture, and explain why the optimization condition is better satisfied in a deep network with a large number of hidden layers. The key properties of the model are verified numerically by training a supervised feedforward neural network using the stochastic gradient descent method. We also discuss a possibility that the entire Universe at its most fundamental level is a neural network.
Going Viral: A Systematic Review of Google Trends in Plastic Surgery and a Recommended Framework for Its Use
Authors: Bellaire Christopher P , Rutland John W , Sayegh Farah , Pesce Rico R , Tijerina Jonathan D , Taub Peter J 
Published: February 2021

Abstract
As the leading global search engine with billions of daily queries, Google and its open-source Google Trends (Google, Mountain View, CA) represent an emerging and powerful tool for epidemiological and medical research. Within the field of plastic surgery, Google Trends has yielded insights into online interest for facial feminization surgery, gender-affirmation surgery, cosmetic body procedures, and breast reconstruction, among other common procedures. The existing literature of Google Trends in plastic surgery was systematically reviewed following established Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Google Trends’ 4 input variables—keyword, region, period, and category—were assessed. Seventeen plastic surgery studies employing Google Trends were reviewed. There was strong inter-rater reliability (Cohen’s kappa = 0.68). Analyzing keyword syntax, only 3 of 17 studies (17.6%) used the “+” function to combine terms, which can significantly improve sensitivity. For the region variable, 12 of 16 studies (75%) conducted worldwide searches; yet, none of the studies used any non-English keywords, introducing significant bias. For the period, 88.2% of studies utilized a timespan of greater than 5 years, resulting in monthly intervals between data points in Google Trends. For the “category” variable, none of the studies appear to have employed the “surgery,” “cosmetic surgery” or “health” categories to improve specificity. Google Trends is presented as an emerging methodology in plastic surgery research. The strengths and limitations of Google Trends as a resource for plastic surgeons and medical professionals are discussed, and a recommended step-by-step guide for conducting and interpreting Google Trends research is outlined.
CuBlock: a cross-platform normalization method for gene-expression microarrays
Authors: Junet Valentin , Farrés Judith , Mas José M , Daura Xavier 
Published: February 2021

Abstract

Motivation
Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.

Results
We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.

Availability and implementation
CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock.

Supplementary information
Supplementary data are available at Bioinformatics online.