bluuebunny/crossref_metadata_2025_split huggingface dataset:
NR/precipitated silica/dodecanol composites: Torque, hardness and morphology behaviors
Authors: Philbert  , Hafni K N , Surya I 
Published: April 2021

Abstract
The effects of dodecanol (DDC) incorporations on torque, hardness and morphology behaviors/properties of natural rubber (NR)/precipitated silica (PSi) composites were studied using a typical Semi EV. The NR was loaded with the PSi at a settled dose, thirty phr. The DDC is one type of fatty alcohols that is produced based on the oil of palm kernel. The DDC was incorporated toward the PSi-loaded NR systems/compounds with various doses from one to four 4.0 phr. Because of its roles as a co-curing and internal plasticizer, it was revealed that the DDC affected the torque, hardness and morphology properties of PSi-loaded NR systems. The DDC exhibited the enhancements in torque change and hardness, especially till a three phr of DDC dose. The study on morphology behavior confirmed that the three phr of DDC was the optimum dose in which the cracked surface of PSi-loaded NR system with a three phr of DDC displayed the tremendous line of matrix fracture and surface breaking. Based on the overall results, the DDC incorporations caused the PSi-loaded NR systems in a less hardness but a higher torque change.
Redesign packaging on Aloe Vera bottle product based on Kansei Engineering
Authors: Nasution S , Hidayati J , Nissa N A , Agustiara S M 
Published: April 2021

Abstract
Packaged designers must be able to select packaging elements that characterize products, communicate brand products, and create packaging into attractive media to attract consumers to purchase products among the various available products. The aim of this study is to understand visual design concepts and structural design concepts in the design of aloe vera bottled bottles. The method used for the development of a design product that focuses on consumer perceptions and links it with the attributes of the product is Kansei engineering. Kansei engineering is a technology capable of translating consumer feelings into design concepts. Research came up with a visual design concept of the chosen Kansei word, which is elegant, commercial, fresh, modern, varnish, aesthetic, interesting, simple, subjective, summarized. The visual design concept is barcode, nutrition fact-general, 1ogo-word mark, brand top sign, product name-top sign, produce-bearing name, product present composition, family-size approach, back ground cool colors, font color-white, font type-san serif, Figure-original, material label-film plastic, and position label-wrap around label positions. The structural design concept is tropical as the top structure of the bottle, jugo as the center structure of the bottle, plastic colored as bottled material, and the size of the bottle 500 ml.
Index Point Detection for Text Summarization using Cosine Similarity in Educational Videos
Authors: Sharma Rekha , Mahrishi Mehul , Morwal Sudha , Sharma Girish 
Published: April 2021

Abstract
Explosive growth in digital content creates a trend of video-based learning and knowledge sharing. Several educational videos are shared by various trainers/bloggers/ instructors daily. It is now evident that even during emergencies like the recent ongoing CoVid-19 outbreak, video lectures are the saviors when the whole world comes to rest. This rapid and expansive growth has encouraged the researchers to efficiently and automatically index, browse, and retrieve video data. This research aims to process videos and precisely identify the index points which are discussed within the video. Experimental results show a good performance of the topic detection model in finding keywords and reducing dimensionality.
A Review on Segmentation of Vitiligo image
Authors: Agrawal Neha , Aurelia Sagaya 
Published: April 2021

Abstract
Digital image processing is playing a vital role in medical science. Segmentation is a very important and critical step of image processing to analyse the image. Segmentation has become an eminent objective in image analysis and computer vision algorithms. Vitiligo is a very common skin disease that causes depression for the patient suffering from it. In current work, several segmentation techniques like thresholding, watershed, Region-based RAG, Felzenszwalb Segmentation, SLIC - K-Means based image segmentation, and Quickshift image segmentation were analysed along with their experimental results on vitiligo image. The execution time of different algorithms was also measured and analysed. Some of the advantages and disadvantages of applied techniques were discussed.
Stock trend prediction and analysis using LSTM neural network and dual moving average crossover algorithm
Authors: Pradhan Rahul , Bharadwaj Kanishk , Saxena Aman , Rajpoot Vikram 
Published: April 2021

Abstract
In today’s world where data is the new currency, computer machines are capable enough to find and predict the future by using the previous data and the algorithms which are becoming more precise day by day. The proposed solution by us includes the pre-processing of the 10 years of Bombay Stock Exchange (BSE) stock market data of HDFC Bank. Our solution implemented the engineering techniques of deep learning and machine learning which are combined with the LSTM neural network algorithm and Dual Moving Average Crossover algorithm.
Minimizing Respiratory Diseases using Warning from Hand-Touch-Over-Face Avoidance Based Algorithm on Deep Learning
Authors: Bhatia Jitesh Kumar 
Published: April 2021

Abstract
The most moved parts of a human body are hands. They may get contaminated by touching any surface we are working with. The contaminated hands can intentionally or unintentionally reach the part of our face while eating, sneezing, setting of hair, itching a scratch, biting the nails, soothing down when stressed, etc. The touch of hands in eyes, nose and mouth may result in unhygienic symptoms and may result in various respiratory diseases. We need to minimize our touch to the parts of our face. In the presented work, we propose a system that warns the user each time his/her hand reaches his/her face. We have used CNN to identify the class feature automatically rather than manually. Also, we have used RNN so that the proposed system learns recursively. The use of such system may minimize the subsequent touch on face and will lead to minimizing the respiratory diseases due to unhygienic touch conditions.
Impact of Hidden Dense Layers in Convolutional Neural Network to enhance Performance of Classification Model
Authors: Helen Josephine V L , Nirmala A.P. , Alluri Vijaya Lakshmi 
Published: April 2021

Abstract
Education and Health care sectors are two predominant areas where societal growth is expected through innovation and technology development. Machine Learning and Deep learning classification models have been entertained in predicting, detecting, and diagnosing major diseases in the early stage. In this research paper, we have analyzed the impact of hidden dense layers in the Convolution neural network to improve the performance of the classification model. Three different classification deep network models have been constructed, analyzed and the result was tested with a diabetes dataset. Results concluded that the more layers with deeper the network better was the classification performance. The classification model with six hidden dense layers outperforms all other less number of hidden dense layers.
Comparative analysis of machine learning techniques based on chronic kidney disease dataset
Authors: Gulati Vineeta , Raheja Neeraj 
Published: April 2021

Abstract
Kidney failure is one of the chronic diseases that becoming a very common health issue in the world. It is a state in which kidney is damaged and cannot clean blood as well as they should. Excess fluid and waste may cause more health issue in our body and takes long period to diagnose. Moreover, with no early symptoms, the disease is detected only at a later critical end stage. As this Chronic Disease is becoming threat in today’s world, research is being done on a large scale to predict the presence of this chronic disease using machine learning. Machine learning is playing such a tremendous role in healthcare system like identify diseases and diagnoses, drug discovery and manufacturing, smart health record with the use of various techniques like Support Vector Machine, Decision Tree, Naïve Bayes, Random Forest etc. This paper comparatively analyzes the accuracy of pre-existing techniques for prediction of chronic kidney disease based on data from various research papers. Furthermore, this study also considers the different attributes from either already existing database or from real life database by using multiple techniques of machine learning. It is concluded that working with real life datasets with all possible attributes taken into consideration yields the accurate prediction for the presence of chronic kidney disease using machine learning.
Performance Study of Various Machine Learning Classifiers for Arc Fault Detection in AC Microgrid
Authors: Joga S Ramana Kumar , Sinha Pampa , Maharana Manoj Kumar 
Published: April 2021

Abstract
Fault is the abnormal condition in power system, which must be detected as early as possible. It is very important to detect the fault as quick as possible to reduce the effects of fault like equipment damage, property loss and human loss. Arc faults have high power discharge property between two conductors; this property causes damage to the conductors which leads to electric fire between the conductors. It is very necessary to detect these faults immediately to avoid fire accidents. There are various methods to detect these arc faults in microgrid. In this paper voltage and current signals are measured through instrumental transformers and voltage signal is decomposed by the discrete wavelet transform signal processing technique. The decomposed signals are further processed in various machines learning classifier’s for detecting the arc fault. The Proposed methodology studies the performance of various machine learning classifiers to detect arc fault in microgrid and it is carried out in MATLAB/Simulink Software.
Static Vulnerability Analysis of Docker Images
Authors: Jain Vipin , Singh Baldev , Khenwar Medha , Sharma Milind 
Published: April 2021

Abstract
Many organizations are renovating their businesses by grasping DevOps, microservices, and container technologies. Docker is emerged as a new technology, proving an efficient means to develop and deploy applications. Docker containers are created by images to run an application with all its dependencies so that it could run isolated from other processes. Security is always being a foremost concern as our industries are already persistent to improve the reliability and efficiency of new software applications. Security of local Docker containers from the attacks of malicious containers, perceived threats present in Docker images need to be detected and the risks identified when instances of Docker containers run on the host machine. This paper reviews Docker’s existing security mechanisms, vulnerabilities, threats and the related tools required for static security analysis.
An adaptive approach for word sense disambiguation for Hindi language
Authors: Soni Vimal Kumar , Gopalaniî Dinesh , Govil M C 
Published: April 2021

Abstract
Word sense disambiguation has been a classical problem and it is still considered as a big challenge for NLP researchers. Word vectors have shown remarkable improvements from baseline performances for various NLP challenges. An adaptive approach is developed to resolve the ambiguity at word level using word vectors and presented in this paper. CBOW and Skip-gram models of Word2Vec are used to generate the word embeddings. An input sentence is split into two sets: The first set contains all possible ambiguous words and the second set is constituted from all the unambiguous words. The words from the first set are processed through IndoWordNet to collect all the senses of respective words, and such senses are stored in sensedictionary. An adaptive approach is applied to find out the exact interpretation of the ambiguous word from the sense dictionary for each word from the set of ambiguous words with the context words from the set of unambiguous words. The proposed approach is evaluated on a large scale Hindi corpus and claimed better results than previous attempts.
Dispersive mirror characterization and application for mid-infrared post-compression
Authors: Flender Roland , Kurucz Mate , Grosz Timea , Borzsonyi Adam , Gimzevskis Ugnius , Samalius Arturas , Hoff Dominik , Kiss Balint 
Published: April 2021

Abstract
This paper presents a second harmonic assisted spectrally resolved interferometric technique that can overcome the limited spectral resolution of commercially available spectrometers in the mid-infrared. The discussed scheme was validated by measuring the group delay of several well-known and frequently used materials. Our main motivation was to characterize the spectral phase shift of newly designed and manufactured dispersive mirrors to be used for mid-infrared (MIR) post-compression. These mirrors were successfully implemented in the post-compression stage of our MIR laser system, where pulse duration was shortened below two optical cycles and the pulse peak power increased by 30.3% compared to the original output.
Measurement and control of main spatio-temporal couplings in a CPA laser chain
Authors: Kabacinski Adeline , Oubrerie Kosta , Goddet Jean-Philippe , Gautier Julien , Tissandier Fabien , Kononenko Olena , Tafzi Amar , Leblanc Adrien , Sebban Stéphane , Thaury Cédric 
Published: April 2021

Abstract
We report a straightforward method to control main spatio-temporal couplings in a chirped pulse amplification (CPA) laser chain system using a specially designed chromatic doublet in a divergent beam configuration. The centering of the doublet allows for the control of the spatial chirp of the CPA laser chain, while its longitudinal position in the divergent beam enables the control of the amount of longitudinal chromatism in a wide dynamic range. The performance of this technique is evaluated by measuring main spatio-temporal couplings with a simple method, based on an ultrafast pulse shaper, which allows for a selection of narrow windows of the spectrum.
Numerical investigation of metasurface narrowband perfect absorber and a plasmonic sensor for a near-infrared wavelength range
Authors: Khonina S N , Butt M A , Kazanskiy N L 
Published: April 2021

Abstract
Herein, we have presented a computation study of a metasurface (MS) narrowband perfect absorber performed utilizing the three-dimensional finite element method. In the first part of the paper, the periodic sequence of silicon meta-atoms (MAs) is positioned on a ∼100 nm gold thin-film. The gold thin-film obstructs a broadband light at normal incidence, and silicon MAs are utilized to stimulate the surface plasmon by scattering light through it. The highest absorption of 96% is procured at 930.26 nm in the air medium which can be further enhanced by using a layered structure of MAs deposited on gold. The MAs are composed of Si/SiO2/Si with an optimized layers height. Consequently, the perfect impedance matching of the electric and magnetic dipoles takes place providing a 99% of absorption insensitive to the incidence angle of light and almost negligible reflection at a resonating wavelength of 889.4 nm. This feature allows us to utilize this device as a plasmonic sensor. That is why, in the second part of the paper, the proposed device design is studied for the detection of the refractive index of the surrounding medium. The sensitivity and figure of merits of the MS device are in the range of 460–492 nm RIU−1 and 76.7–82 RIU−1, respectively. We claim that the anticipated MS element can be employed in solar photovoltaic and biomedical sensing purposes.
Study of focusing parameters of wavelength-scale binary phase Fresnel zone plate
Authors: Geints Yu E , Panina E K , Minin I V , Minin O V 
Published: April 2021

Abstract
Binary Fresnel zone plates (FZP) are among the most commonly used focusing elements of 2D-planar optical circuits in micro- and nano-photonics. When the diameter and focal distance of a FZP are reduced to the wavelength dimensions, the parameters of the focal area experience strong influence by FZP constructive design (material, thickness, depth of zone relief). By means of the numerical simulations, the near-field diffraction of monochromatic optical wave on a wavelength-scale binary phase plate is investigated. We found a range of optimal depths of zone plate grooves etching as well as substrate thicknesses providing the best focusing of the incident circularly polarized optical wave in terms of maximum field intensity and minimum size of the focal spot. A certain improvement of these focus parameters can be achieved by filling the zone grooves with a dielectric having a specific refractive index contrast against the FZP substrate. Additionally, the concept of a super-focus binary phase plate with a solid immersion layer (SIL) in the form of a truncated cone made of the ZP substrate material is proposed. Similar to conventional SIL device, this flat SIL-FZP can focus a circularly polarized optical radiation into a subdiffraction spot with a full-width of the order λ/2n (n is FZP refraction index).
Performances on the Montreal Cognitive Assessment Along the Cardiovascular Disease Continuum
Authors: Gagnon Christine , Saillant Kathia , Olmand Miloudza , Gayda Mathieu , Nigam Anil , Bouabdallaoui Nadia , Rouleau Jean-Lucien , Desjardins-Crépeau Laurence , Bherer Louis 
Published: April 2021

Abstract Cardiovascular diseases involve a continuum starting with risk factors, which can progress to coronary heart disease and eventually, to heart failure. Cognitive impairment (CI) is observed as early as cardiovascular risk factors, and in up to 50% of patients with heart failure. Because CI in cardiovascular disease is linked to poorer clinical outcomes, early detection is essential. The Montreal Cognitive Assessment (MoCA) is a screening tool widely used in clinical setting. To date, little is known about MoCA scores along the cardiovascular disease continuum.ObjectiveThis study compared performances of different cardiovascular disease profiles on the MoCA and its subscores.MethodEighty participants (&gt;50 years) from two studies conducted at the Montreal Heart Institute were separated into four groups: low cardiovascular risk factors (&lt;2), high cardiovascular risk factors (&gt;2), coronary heart disease, and stable heart failure. ANCOVAs were performed on the total score and on subscores, with sex, age, and education as covariates.ResultsGroup differences were observed on the MoCA total score (heart failure &lt; low cardiovascular risk), verbal fluency (heart failure &lt; low cardiovascular risk), memory (coronary heart disease &lt; low cardiovascular risk), and orientation (coronary heart disease &lt; low and high cardiovascular risk) subscores.ConclusionResults suggest that the MoCA, particularly verbal fluency and memory subscores, can detect cognitive changes in later stages of the cardiovascular disease continuum, such as heart failure. Detecting cognitive changes earlier on the cardiovascular disease continuum may require more in depth neuropsychological assessments.
Nitrogen deficiency maintains the yield and improves the antioxidant activity of Coreopsis tinctoria Nutt.
Authors: Li Zhiyuan , Jiang Hong , Qin Yanan , Yan Huizhuan , Jiang Xiumei , Qin Yong 
Published: April 2021

ABSTRACT
Nitrogen (N) deficiency levels were investigated for their potential to maintain the yield and improve antioxidant activity of Coreopsis tinctoria. Inflorescences and leaves at 0, 10, 20, 30, 40, and 50 d after flowering were frozen at −80 °C and plant growth, antioxidant activity, bioactive substance, enzyme activity, and gene expression were evaluated. N deficiency maintained the total number of flowers, promoted phenol and flavonoid accumulation, and enhanced antioxidant activity. Moreover, N deficiency stimulated activities of phenylalanine ammonia-lyase (PAL), cinnamate-4-hydroxylase (C4H) and 4-coumarate:coenzyme A ligase (4CL), and induced CtPAL, CtC4H and Ct4CL gene expression. The data also suggest that N-deficiency-induced phenolic and flavonoid accumulation occurs due to the activation of biosynthetic pathways in C. tinctoria. We characterize the unique features of C. tinctoria under N-deficiency conditions and provide valuable information for the cultivation of high-N use efficiency varieties with low input and high output.
Confidence regions in Wasserstein distributionally robust estimation
Authors: Blanchet Jose , Murthy Karthyek , Si Nian 
Published: April 2021

Summary
Estimators based on Wasserstein distributionally robust optimization are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a certain distance from the underlying empirical measure in a Wasserstein sense. While motivated by the need to identify optimal model parameters or decision choices that are robust to model misspecification, these distributionally robust estimators recover a wide range of regularized estimators, including square-root lasso and support vector machines, among others. This paper studies the asymptotic normality of these distributionally robust estimators as well as the properties of an optimal confidence region induced by the Wasserstein distributionally robust optimization formulation. In addition, key properties of min-max distributionally robust optimization problems are also studied; for example, we show that distributionally robust estimators regularize the loss based on its derivative, and we also derive general sufficient conditions which show the equivalence between the min-max distributionally robust optimization problem and the corresponding max-min formulation.