bluuebunny/crossref_metadata_2025_split huggingface dataset:
Existence, uniqueness and regularity for the stochastic Ericksen–Leslie equation
Authors: De Bouard Anne , Hocquet Antoine , Prohl Andreas 
Published: June 2021

Abstract
We investigate existence and uniqueness for the liquid crystal flow driven by colored noise on the two-dimensional torus. After giving a natural uniqueness criterion, we prove local solvability in L

p
-based spaces, for every p > 2. Thanks to a bootstrap principle together with a Gyöngy–Krylov-type compactness argument, this will ultimately lead us to prove the existence of a particular class of global solutions which are partially regular, strong in the probabilistic sense, and taking values in the ‘critical space’ L
2 × H
1.
Smoothing of nonsmooth differential systems near regular-tangential singularities and boundary limit cycles
Authors: Novaes Douglas D , Rondón Gabriel 
Published: June 2021

Abstract
Understanding how tangential singularities evolve under smoothing processes was one of the first problem concerning regularization of Filippov systems. In this paper, we are interested in C

n
-regularizations of Filippov systems around visible regular-tangential singularities of even multiplicity. More specifically, using Fenichel theory and blow-up methods, we aim to understand how the trajectories of the regularized system transits through the region of regularization. We apply our results to investigate C

n
-regularizations of boundary limit cycles with even multiplicity contact with the switching manifold.
Equilibrium states for maps isotopic to Anosov
Authors: Álvarez Carlos F , Sánchez Adriana , Varão Régis 
Published: June 2021

Abstract
In this work we address the problem of existence and uniqueness (finiteness) of ergodic equilibrium states for partially hyperbolic diffeomorphisms isotopic to Anosov on

T

4

, with two-dimensional center foliation. To do so we propose to study the disintegration of measures along one-dimensional subfoliations of the center bundle. Moreover, we obtain a more general result characterizing the disintegration of ergodic measures in our context.
Spin and polarization effects on the nonlinear Breit–Wheeler pair production in laser-plasma interaction
Authors: Song Huai-Hang , Wang Wei-Min , Li Yan-Fei , Li Bing-Jun , Li Yu-Tong , Sheng Zheng-Ming , Chen Li-Ming , Zhang Jie 
Published: June 2021

Abstract
The spin effect of electrons/positrons (e
−/e
+) and polarization effect of γ photons are investigated in the interaction of two counter-propagating linearly polarized laser pulses of peak intensity 8.9 × 1023 W cm−2 with a thin foil target. The processes of nonlinear Compton scattering and nonlinear Breit–Wheeler pair production based on the spin- and polarization-resolved probabilities are implemented into the particle-in-cell (PIC) algorithm by Monte Carlo methods. It is found from PIC simulations that the average degree of linear polarization of emitted γ photons can exceed 50%. This polarization effect leads to a reduced positron yield by about 10%. At some medium positron energies, the reduction can reach 20%. Furthermore, we also observe that the local spin polarization of e
−/e
+ leads to a slight decrease of the positron yield about 2% and some anomalous phenomena about the positron spectrum and photon polarization at the high-energy range, due to spin-dependent photon emissions. Our results indicate that spin and polarization effects should be considered in calculating the pair production and laser-plasma interaction with the laser power of 10 PW to 100 PW classes.
Hearing Higgs with gravitational wave detectors
Authors: Salvio Alberto 
Published: June 2021

AbstractThe relic gravitational wave background due to tensor linear perturbations generated during Higgs inflation is computed. Both the Standard Model and a well-motivated phenomenological completion (that accounts for all the experimentally confirmed evidence of new physics) are considered. We focus on critical Higgs inflation, which improves on the non-critical version and features an amplification of the tensor fluctuations. The latter property allows us to establish that future space-borne interferometers, such as DECIGO, BBO and ALIA, may detect the corresponding primordial gravitational waves.
Effects of anisotropic energetic particles on zonal flow residual level
Authors: Lu Z.X. , Weiland M. , Lauber Ph. , Wang X. , Meng G. , Zonca F. 
Published: June 2021

Abstract
In tokamak plasmas, the interaction among the micro-turbulence, zonal flows (ZFs) and energetic particles (EPs) can affect the turbulence saturation level and the consequent confinement quality and thus, is important for future burning plasmas. In this work, the EP anisotropy effects on the ZF residual level are studied by using anisotropic EP distributions with dependence on pitch. Significant effects on the long wavelength ZFs have been found when small to moderate width around the dominant pitch in the EP distribution function is assumed. In addition, it is found that ZF residual level is enhanced by barely passing/trapped and/or deeply trapped EPs, but it is suppressed by well passing and/or intermediate trapped EPs. Numerical calculation shows that for ASDEX Upgrade plasmas, typical EP distribution functions can bring in −3% to +5.5% mitigation/enhancement in ZF residual level, depending on the EP distribution functions.
Recognition of Hand Movement for Training Motor Skill of Children With Autism Spectrum Disorder (ASD) Using Myo Gestures Control Armband and Artificial Neural Network
Authors: Syahputra M F , Hardi S M , Prasetyo A 
Published: June 2021

Abstract
Autism disorder causes sufferers to tend to lag behind other children in understanding and accepting the stimuli given, due to the patient’s inability to focus attention on the stimulation given. One of the therapies used to train autistic sufferers is the help of technology. One of the technologies used is the Myo Gestures Control Armband which is equipped with several sensors that can recognize hand and arm movements using the Artificial Neural Network method which adopts a thought through a mechanism that affects the human brain, both for processing various signal elements received, tolerance of errors, and also parallel processing. In this study, there are five movements classified as Ok, Fist, Like, Rock, and Spock movements to train motor movements of children with autism. With this system in the future it can become a method of therapy that is carried out at home with the supervision and control of a therapist so that it can improve the development of hand motor skills in children with autism which can be seen through a graph of the development of movements per day displayed on the system.
Bahasa Indonesia pre-trained word vector generation using word2vec for computer and information technology field
Authors: Putri Syarifah K , Amalia A , Nababan E B , Sitompul O S 
Published: June 2021

Abstract
Words embedding or distributed representations is a popular method for representing words. In this method, the resulting vector value is a set of real values with specific dimensions that are more effective than the Bag of Word (BoW) method. Also, the advantages of distributed representations can produce word vectors that contain semantic and syntactic information, so that word vectors with close meanings will have close word vectors. However, distributed representation requires a huge corpus with a long training time. For this reason, many researchers have created trained word vectors that can be used repeatedly. The problem is that the available trained word vectors are usually general domain word vectors. This study aims to form pre-trained word vectors for specific domains, namely computers and information technology. Researchers used a dataset of student scientific papers from the Universitas Sumatera Utara (USU) repository. Researchers used the word2vec model, where the model has two architectures, namely the Continuous Bag-of-Words (CBOW) and Skip-gram. This research’s result is word2vec model with the CBOW method is more effective than the Skip-gram method.
Web-based E-Commerce Products Grouping
Authors: Elveny M , Hardi S M , Jaya I , Gundari P 
Published: June 2021

Abstract
E-Commerce is a group of technologies, applications, and businesses that connect companies or individuals as consumers to conduct electronic transactions, exchange of goods or information through the internet or television, www, or other computer networks. The rapid growth of E-Commerce is caused by the many conveniences for buyers and sellers. This can be seen by the increasing number of E-Commerce sites such as Bukalapak.com, Tokopedia.com, and so on. The buyers want to get products with the lowest prices, the best quality, and from trusted sellers. This encourages them to search for and compare the same products from one store to another, even between different sites. Variables that they compare vary. Some are based on price, number of reviews, and so on. But it is troublesome when they have to look for the same product from another site. This study used the product name as a reference in grouping the same product from 5 different sites. The algorithm used to group online shop products was Jaro-Winkler Distance. The study was to create a system that can group products from 5 different sites with the highest level of similarity product as the output. From the 20 times of the search tests, the accuracy of the online shop product grouping with Jaro-Winkler Distance was 81.27%.
Face Recognition for a Student Violation Point System in Schools Using the Fisherface Algorithm
Authors: Arisandi D , Syahputra M F , Purnamawati S , Hizriadi A , Azra M H 
Published: June 2021

Abstract
The violation point system is one of the policies to increase discipline in schools. If a student commits a violation, then the student will be processed by the teacher and record the violation into the violation note held by each teacher. If a student’s violation record is contained in the notebooks of several teachers, it will be difficult to recapitulate the violations committed by students. This study makes a system approach and development that can centrally record student violations that run on the Android platform. To facilitate student recognition, the system is made using face recognition. Face recognition is implemented into this research because data retrieval can be done in real time with an Android smartphone camera so there is no need for additional devices. Face recognition uses the Fisherface method, which is a facial recognition method that uses Linear Discriminant Analytics for its feature extraction process. The accuracy obtained from the research is 90%.
Experimental study of seawater evaporation using natural convection principles
Authors: Sibarani Divan Apryanto , Siagian Horas , Ambarita H. 
Published: June 2021

Abstract
Evaporation of seawater into the air is a phenomenon that involves heat and mass transfer. The process of evaporation of water can be divided into different categories based on the flow regime, such as natural convection. The rate of evaporation that occurs in seawater without treatment or is called natural convection method only takes into account the density difference between the existing fluids. The method has been tested in natural convection with the result that the highest rate of evaporation of water in the tub is directly proportional to the radiation received. It was found that experimental result have a good agreement.
Design a Potential Analysis using Fingerprint PC Base
Authors: Hermanus Davy Ronald , Sasmoko  , Indrianti Yasinta 
Published: June 2021

Abstract
Fingerprint analysis is a concept that continues to develop to be implemented in various fields. Starting from the forensic needs, nowadays fingerprint analysis can be applied in various fields including education in terms of exploring one’s potential. This study is a series of preliminary research that aims to develop fingerprint analysis in relation to the concept of multiple intelligence. The research method used is the Neuro Research method with the main stage of exploratory research. The result of the research is the successful use of a fingerprint detection tool via PC so that it can produce patterns that can be analysed further. This pattern will be developed in the next stage to identify various important aspects and components in multiple intelligence.
Implementation of hybrid cryptosystem using Rabin-p algorithm and One Time Pad to secure images
Authors: Budiman M A , Zarlis M , Hafrizah  
Published: June 2021

Abstract
Cryptography is a study of mathematical techniques to secure messages by encoding the messages so it cannot be read by a third party. There are two types of algorithms in cryptography, symmetric algorithm and asymmetric algorithm. Asymmetric algorithms, such as Rabin-p cryptosystem, are known to be secure if the key sizes are long enough. However, asymmetric algorithms are ineffective to encrypt large data since the calculations consume CPU time and the resulting ciphertext is multiple times bigger than the original data. In this research, we combine Rabin-p cryptosystem and One Time Pad (OTP) in a hybrid scheme to solve this problem. The OTP is used to encrypt the data (images) and Rabin-p algorithm is used to encrypt the OTP key. In this research, we demonstrate how to secure a *.bmp file with this hybrid scheme.
A neural cryptography approach for digital image security using Vigenère cipher and tree parity machine
Authors: Budiman M A , Handrizal  , William  
Published: June 2021

Abstract
As with other form of digital media, image can also contain private information that should be kept secret. Commonly, symmetric and asymmetric cryptosystem can be used to secure an image. Symmetric-key cryptography is not a best solution as the secret key has to be transmitted through private channel. Asymmetric-key cryptosystem, however, allows transmission of key through public channel, but requires high computational power and long computational time. This paper proposes a neural cryptography approach to protect image by using tree parity machine and Vigenère cipher. By using this approach, the secret key will never be transmitted. Moreover, the whole process does not require high computational power and long computational time as in public-key cryptosystem. To overcome the weakness of Vigenère cipher, change is made to the algorithm by multiplying the key value with y-axis of image. The effectiveness is validated by the value of pearson correlation that is close to 0. Lastly, the result of experiments show neural synchronization time depends on the size of network, initial weights and randomly generated input vector.
An Automatic Fluid Filling Mechanism Using Delta PLC
Authors: Muralidharan J , Saran S , Tamilkavi G , Thivakar S , Vivin M 
Published: June 2021

Abstract
This project is carried out using Programmable Logic Controller, which is the main component of the proposed system. It is an industrial oriented machine system used to fill water in the empty bottles automatically, which reduce the usage of man power, low power consumption, accuracy. The system is implemented in various soft drink industries, water packaging industries and food industries. The filling process gets efficient, when our filling machine is combined with an automatic conveyor to increase the speed of filling process. The conveyor belt roll-up the empty bottles and an analog timer is used to adjust the timing of the water flow. The empty bottles are sensed by photo electric sensors and it detects the empty bottles present in the conveyor, stops the conveyor, then the valve is opened up to fill the bottles. This sequential process will continue for each and every water bottle filling process. A Programmable Logic Controller machine is preferred due to its and cost efficient high ratings. The proposed method will reduce the cost for small scale industries and help them in setting up and an automated decision for filling water bottles.
Chronological overview and algorithmic analysis of EEG Signal Processing for Brain Response to Stimuli
Authors: Bhat Ravindra , Deshmukh Saurabh 
Published: June 2021

Abstract
The brain is one of the most complicated organs in the human body that controls the entire actions/reactions of the body by getting diverse stimuli via the nervous system. The stimulus that is stronger than the threshold stimulus is decoded by the sensory neurons counts creating information on the frequency and the stimulus of the action potentials. This work intends to plan a detailed survey on brain response to stimuli in EEG signal processing by reviewing about 35 papers selectively to determine the shortcoming of contributed works. The analysis is subjugated in terms of chronological review, and algorithmic analysis. This analysis determines the utilization of diverse models/approaches in the contributed papers. Moreover, the performance parameter analysis along with the best performance values is also stated clearly. Finally, the research gaps and challenges that rely on this topic are clearly described that paves the way for future research contributions.
Digital Thermometers: Its Types, Utilities, and Global Trade Prospects for India
Authors: Anthony Raj S. , Senith S. , Kalpana Sai B. 
Published: June 2021

AbstractDigital thermometer, a developed product from the mercury based aged Clinical thermometer is an apparent device used to sensitize the body temperature with six different latest types of innovation. These are used not only to detect body temperatures of human but also used in animal medicines and industries to check the boiling points. As the world is inching towards alien era, the infusing these thermometers with mobile apps and clouding the data is not a wonder. Understanding the raise of influenzas across the globe triggered biomedical companies to win early global demands and record their presence in global markets. India is also a great player in international demand and supply of biomedical instruments and devices. Government of India, assigned Indian Trade Clarification Harmonized System for digital thermometer. The 8-digit Code allotted is 90251910. This article focuses on the different types of digital thermometer, its utilities and the Indian trade analysis with select countries data.
IoT Based Smart Bus System using wireless sensor networks
Authors: Jeyakkannan N. , Karthik C. , Lukose Vivek 
Published: June 2021

Abstract
Millions of kids want to travel from their houses to their institution daily. The Safety in the transport of the kid’s plays an important role and it is said that, kids were kept locked in the bus during their boarding till they reach the destination. Another challenge is that they may miss the bus and board the wrong bus in certain cases. This paper provides the solution to overcome these issues and to develop a system that monitors the entry and exit of students in their buses with an excellent IoT based technology. The proposed system will record the data of student’s entry and exit in the bus using RFID (Radio Frequency Identification) and GSM technologies when the travel is successful from their house to their institutions, the system itself generates the SMS to all the parents concerned. Meanwhile it detects the children who are entering the wrong bus and absence and it detects whether the driver is drunken also. If any stuck in the traffic the driver can give the information to the management without any delay.
Optimal design of quadratic support vector machine based end stage renal disease predictor model
Authors: Sathyadeepa S , Sivakumar T. 
Published: June 2021

Abstract
At present kidney failure has become one such important paradigm for higher mortality rate. Chronic kidney disease (CKD) is a severe kidney infection, and it has different stage of infection with final condition is being called as End Stage Renal Disease (ESRD). At this juncture patient are supported with dialysis or kidney transplant. The process requires time to analyze, diagnose the different parameters in determining CKD. The study involves using machine learning techniques for predicting the stages involved in infection and prevent the disease progression. There are four different stages of kidney disease, patient above 12 years of age are analyzed with 24 different input parameters. Around 160 subject’s parameters were taken into the database. Important feature includes serum creatinine, blood pressure, bacterial infection, pedal edema, urine sugar and sodium level in the blood are included. Machine learning algorithm was developed using MATLAB 2018 to predict the early end stage of CKD. Algorithms like Fine Decision Tree (FDT), Quadratic Support Vector Machine (Quad-SVM) and Linear Discriminant Analysis (LDA) are the three main algorithms used for designing the predictor model. Classification Learner Toolkit helps is determining the accurate model and Curve Fitting Toolbox helps in curve fitting over the given data set. After analyzing the system with different predictor model, the model with high accuracy and less training time is chosen. The proposed work also incorporates an e-message alert system linking the nephrologists in case of any emergency wirelessly. It enables high speed prediction and diagnosis process for treatment of subject under high risk factor. The threshold value is continuously monitored and in case if the value exceeds the safer limit, a notification is sent to the physician in proper time.
Prognostication of Acute Lymphocytic Leukemia (ALL) using Capsule Network Algorithm
Authors: Melfi Rose S. , Merlin Sheeba J. , Vijay Prabaharan R. , Bhuvaneshwari M. , Subha Hency Jose P. 
Published: June 2021

Abstract
A type of cancer that affects the blood-forming tissues in the body including lymphatic system and bone marrow is Leukemia. The second most commonly occurring acute leukemia is the acute lymphoblastic leukemia or acute lymphocytic leukemia (ALL). Around 25% of the cases are observed to be due to malignant T-cell precursors while the remaining 75% of cases is due to precursors of B-cell lineage. In general, response to chemotherapy, white blood cell count and age are the clinical factors that contribute towards risk stratification. However, in recent years it has been identified that genetic alterations have enabled between individual prognosis and recovery. Despite advancement in technology, chemotherapy using anthracycline, corticosteroids and vincristine serves to be the backbone therapy to treat this disease. In this proposed work, we have used a deep convolutional neural network to detect the presence of ALL accurately and based on the image screened, it is further categorized into one of the 4 subclasses. Using Capsule network algorithm (CapsNet), we have established 100% average sensitivity for ALL detection with a highest specificity of 99.56%, precision of 99.82% and accuracy of 99.36%. When compared with other similar methodologies, we have been able to accomplish higher accuracy without microscopic image segmentation using capsule network algorithm.
Detection of Acute Lymphoblastic Leukemia in Microscopic images using Image Processing Techniques
Authors: Philip Arun Tom , Shifaana S. , Sunny Stelin , Manimegalai P. 
Published: June 2021

Abstract
Leukemia is a form of blood cancer that affects the body’s ability to fight against infection. Every year, about 1 million cases of leukemia are reported with an increased mortality rate that can cause a delay in diagnosis and treatment of leukemia. Conventionally, Acute Lymphoblastic Leukemia is identified by manual counting of cells from a peripheral blood smear or by bone marrow aspiration. However, this method is time consuming and prone to human errors. To prevail over this, various automated techniques were introduced which are faster, reliable and cheaper than the manual methods. This paper focuses on how image processing techniques can be used to identify Acute Lymphoblastic Leukemia (ALL) from the peripheral blood smear images.