bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning
Authors: Xu Darui , Lyon Stephen , Bu Chun Hui , Hildebrand Sara , Choi Jin Huk , Zhong Xue , Liu Aijie , Turer Emre E. , Zhang Zhao , Russell Jamie , Ludwig Sara , Mahrt Elena , Nair-Gill Evan , Shi Hexin , Wang Ying , Zhang Duanwu , Yue Tao , Wang Kuan-wen , SoRelle Jeffrey A. , Su Lijing , Misawa Takuma , McAlpine William , Sun Lei , Wang Jianhui , Zhan Xiaoming , Choi Mihwa , Farokhnia Roxana , Sakla Andrew , Schneider Sara , Coco Hannah , Coolbaugh Gabrielle , Hayse Braden , Mazal Sara , Medler Dawson , Nguyen Brandon , Rodriguez Edward , Wadley Andrew , Tang Miao , Li Xiaohong , Anderton Priscilla , Keller Katie , Press Amanda , Scott Lindsay , Quan Jiexia , Cooper Sydney , Collie Tiffany , Qin Baifang , Cardin Jennifer , Simpson Rochelle , Tadesse Meron , Sun Qihua , Wise Carol A. , Rios Jonathan J. , Moresco Eva Marie Y. , Beutler Bruce 
Published: July 2021

Significance
We developed software called Candidate Explorer (CE) that uses a machine-learning algorithm to identify chemically induced mutations that are causative of screened phenotypes. CE determines the probability that a mutation will be verified as causative for a phenotype if the gene is independently targeted for knockout or recreation of the mutation. CE uses 67 parameters from the mapping data—including gene, mutation, genotype, allelism, and phenotype information—to determine the CE Score and verification probability. We used CE to evaluate ∼87,000 mutation/phenotype associations identified by flow cytometry screening of circulating immune cells from mutagenized mice: 1,279 genes representing 2,336 mutations were rated good or excellent candidates for causation of phenotypes. Many of these genes were not previously implicated in immunity.
Tangential cone condition and Lipschitz stability for the full waveform forward operator in the acoustic regime
Authors: Eller Matthias , Rieder Andreas 
Published: July 2021

Abstract
Time-domain full waveform inversion (FWI) in the acoustic regime comprises a parameter identification problem for the acoustic wave equation: pressure waves are initiated by sources, get scattered by the earth’s inner structure, and their reflected parts are picked up by receivers located on the surface. From these reflected wave fields the two parameters, density and sound speed, have to be reconstructed. Mathematically, FWI reduces to the solution of a nonlinear and ill-posed operator equation involving the parameter-to-solution map of the wave equation. Newton-like iterative regularization schemes are well suited and well analyzed to tackle this inverse problem. Their convergence results are often based on an assumption about the nonlinear map known as tangential cone condition. In this paper we verify this assumption for a semi-discrete version of FWI where the searched-for parameters are restricted to a finite dimensional space. As a byproduct we establish that the semi-discrete seismic inverse problem is locally Lipschitz stable, in particular, it is conditionally well-posed.
The Berry phase rectification tensor and the solar rectification vector
Authors: Matsyshyn Oles , Dey Urmimala , Sodemann Inti , Sun Yan 
Published: July 2021

Abstract
We introduce an operational definition of the Berry Phase Rectification Tensor as the second order change of polarization of a material in response to an ideal short pulse of electric field. Under time reversal symmetry this tensor depends exclusively on the Berry phases of the Bloch bands and not on their energy dispersions, making it an intrinsic property to each material which contains contributions from both the inter-band shift currents and the intra-band Berry Curvature Dipole. We also introduce the Solar Rectification Vector as a technologically relevant figure of merit for bulk photo-current generation which counts the number of electrons contributing to the rectified current per incoming photon under ideal black-body radiation in analogy with the classic solar cell model of Shockley and Queisser. We perform first principle calculations of the Berry Phase Rectification Tensor and the Solar Rectification Vector for the Weyl semi-metal TaAs and the insulator LiAsSe2 which features large shift currents close to the peak of solar radiation intensity. We also generalize the formula for the Glass coefficient to include the spectral distribution of the incoming radiation, the directionality dependence of the conductivity of the material and the reflectivity at its surface.
Fatigue life prediction of the additively manufactured specimen
Authors: Paul Surajit Kumar , Tarlochan Faris , Hilditch Timothy 
Published: July 2021

Abstract
Additively manufactured specimens generally exhibit comparable or improved tensile properties, such as yield stress, ultimate tensile strength, and uniform elongation, compared to conventionally manufactured specimens. However, the defects that are typically present in additively manufactured microstructures result in inferior fatigue performance. A representative volume element-based modeling technique incorporating these defects has been used to predict the reduction in endurance limit of an additively manufactured stainless steel compared to the conventionally manufactured material. This physics-based model can clearly demonstrate the poor fatigue performance of additively manufactured specimens based on the micro-plasticity generated by the defects in the microstructure under cyclic loading. A Neuber analytical model has also been applied to predict the fatigue life of additively manufactured materials for a given stress amplitude. Both the prediction from the finite element model and the analytical Neuber model are very close to the experimental endurance limit.
Structural superlubricity in 2D van der Waals heterojunctions
Authors: Yuan Jiahao , Yang Rong , Zhang Guangyu 
Published: July 2021

Abstract
Structural superlubricity is a fundamentally important research topic in the area of tribology. Van der Waals heterojunctions of 2D materials are an ideal system for achieving structural superlubricity and possessing potentially a wide range of applications in the future due to their ultra-flat and incommensurate crystal interfaces. Here we briefly introduce the origin and mechanism of structural superlubricity and summarize the representative experimental results, in which the coefficient of friction has achieved the order of 10−5. Furthermore, we analyze the factors affecting structural superlubricity of 2D materials, including dynamic reconstruction of interfaces, edge effects, interfacial adsorption, etc, and give a perspective on how to realize the macroscopic expansion and where it can be applied in practice.
Use of Gamification in Indonesian for Non-Native Speakers (BIPA)
Authors: Arie Sandy Teguh , Ulfa Saida , Wedi Agus 
Published: July 2021

Abstract
The purpose of this study was to discuss the use of gamification to students of the BIPA program at State University of Malang. The platform used a mobile application that contains elements of gamification which includes challenges, missions, badges, leaderboard and multiplayer. The results showed that the learning motivation and interest of BIPA program students were very high when they were using gamification in the classroom. This study uses a quantitative approach because information will be presented in numerical form and analyzed using statistics. To find a mobile platform that is suitable for use in gamification, a literature study is conducted to choose a mobile platform that has gamification mechanics including challenges, missions, badges, leaderboard and multiplayer. The population in the study were all BIPA program students at State University of Malang. The sampling technique used purposive sampling method with a sample of 18 people. The gamification in the BIPA program uses BIPAJAR application that has adopted gamification mechanics namely challenges, missions, badges, leaderboard and multiplayer. BIPA students were given 3 days to try BIPAJAR application. The Minimum Standards for Completion Criteria (KKM) is 70. The results of the questionnaires distributed to the students concluded that students’ interest and motivation in using gamification were quite high with a score of 78.67% and student learning progress grew by 82.35%.
Augmented Reality (AR) Press Machine as the application of the latest learning media technology in the XXI Century
Authors: Bagus Nur Rahma Putra Andika , Mukhadis Amat , Ulfatin Nurul , Anwar Syafrudie Haris , Mursyidun Nidhom Ahmad , Ahmad Smaragdina Azhar , Asari Andi , Dzakiya Nurul , Hasbi Ramadani Ali , Bin Md Yunos Jailani , Irfano Sembiring Andrew 
Published: July 2021

Abstract
The objectives of this study are: (1) creating an augmented reality (AR) application in the form of a press machine; (2) testing the attractiveness of the technology developed; and (3) testing the effectiveness of AR technology in the form of press machines. The method used is research and development (R&D). The final stage of product development is to carry out quasi-experiments. The phase is focused on all vocational students at Malang State University. The findings in this study include: (1) AR-shaped press machine that was developed proved to have a high level of design attractiveness and material; (2) AR shaped press machine that was developed proved effective in increasing student’s basic understanding of the competence in understanding the function of press machine components; and (3) all results in this study are expected to be the main reference in the development of AR technology for learning.
Depression Detection Using Comparative Analysis of QRS Detection Algorithms and HRV Of ECG Signal Implemented on MATLAB and Verilog
Authors: Mariya L Prasanna , Kumareshan N 
Published: July 2021

Abstract
Nowadays depression is a major health issue with critical psychological wellness, that influencing the world lives. It impacts emotionally as well as physical and physiological individuals Condition. It can also lead to suicide. Ongoing studies say 43% Indians experience depression. Depression can detect using HRV ECG signal, before getting into Severe depression because, HRV has been associated with depression. The QRS detection algorithm is utilized for calculating heart rate. In this project, comparative analysis of QRS detection algorithms is executed in MATLAB and performance compared for analyzing HRV of various MIT-BIH ECG information base acquired from physio.net The QRS detection algorithm technique has been decided for R peak detection and heart rate calculation, proposes a simple method and it doesn’t include complex numerical model. The detection design plans in Verilog to figure heart pulse. Thus, the designed code for analyzing QRS detection algorithms and HRV ECG signal simulated in MATLAB as well as on XILINXS ISE 14.7.
79GHz Substrate Integrated Waveguide antenna for short-range radar applications
Authors: Santhakumar G , Vadivelu R , Revathy Lakshmi M , Saranya G J , Subasini R 
Published: July 2021

Abstract
It is evident that radar sensing is robust technology compared to camera or LiDAR in the case of autonomous vehicles. Since, they are capable of working even under harsh weather conditions like thick fog, glaring sun, etc. The 77-81GHz frequency band for short range radar provides a global framework for automotive radar by preventing collisions, reducing traffic accidents and thus improvingvehicular safety. The proposed antenna resonates at 79GHz. Below-10dB, return loss characteristic greater than -35dB is obtained in the band of 77.5GHz to 80.4GHz (i.e., impedance bandwidth of 2.86GHz) and gain of above 8dB is achieved. Rectangular vias and complementary concentric square loops of the metamaterial structure are introduced to suppress the surface waves and to achieve the desired frequency of resonance. The feed line and the metamaterial structure are positionedaccordinglyto provide a better impedance matching. VSWR obtained is 1.04 and the antenna is linearly polarized and has a low profile. The total dimension of the antenna is 1.96×2.26×0.127mm3. The antenna structure can be used in an array for automotive radar applications.
Sustainable Intelligent Prediction and Price Recommendation System
Authors: Poorani G , Rishwan S Mohammed , Kumar R Pavan , Ragul M 
Published: July 2021

Abstract
Online brief time frame period condominium stages, comprising of Airbnb, had been transforming into mainstream, and a higher evaluating technique is indispensable for hosts of most recent postings. In this paper, the association is broken down among the portrayal of each rundown and its charge, and proposed a content essentially based absolutely charge counsel gadget alluded to as TAPE to suggest a moderate charge for recently brought postings. The deep knowledge utilized makes them more familiar with strategies (for example, feed-forward networks, longer short-term memory, and recommended moves) to design and put in an influential TAPE. By using a data set of 4 indistinguishable urban areas that were deleted sequentially, it was found that basic elements (such as small indoor objects and high-thickness areas) would undoubtedly have an adverse effect on the charges of each property, and evaluated that it is basic and more attractive style of. Our style is completed with 33.73 in Boston, 20.50 in London, 34.68 in Los Angeles and 26.31 in New York. This is related to the use of other current forms of highlighting.
An efficient machine learning approach for activity recognition
Authors: Jashwant K , Jasvant R , Devi S Siamala 
Published: July 2021

Abstract
The population of older people in western nations are growing drastically. Independent lifestyle is their preference and this leads to fall instances often. Falls of this kind results in severe health issues or sometimes causes deadly damages to the elderly people. Considering this problem, it is highly important to come up with fall discovery systems. A machine learning framework has been proposed in this regard which covers both fall discovery and day to day movement recognition. Utilizing acceleration and speed inputs from dual unrestricted repositories are highly helpful in diagnosing to the maximum of seven movements. Acceleration and angular velocity parameters can be used to extract the attributes that are relevant to time and frequency domain. These attributes are then offered to a classification algorithm. An attempt has been made to check the outcomes of four different procedures for categorizing manual movements. The above-mentioned procedures are the Artificial Neural Network (ANN), K-Nearest Neighbours (KNN), Quadratic Support Vector Machine (QSVM), and FP growth. Power spectral density of the acceleration is used to maximize the outcomes of the classifier. Acceleration data alone is considered for activity discovery in the initial step. Experimental results show that the KNN, ANN, QSVM, and FP growth procedures can attain correctness of 81.2%, 87.8%, 93.2%, and 94.1%. The accuracy of fall detection touches 97.2% and 99.1% with no false positive values for the QSVM and FP growth procedures. As a next phase, the attributes can be taken autocorrelation function and the power spectral density of the acceleration and the angular velocity data that obviously maximizes the classification correctness. Projected attributes are adopted to achieve notable improvement in accuracy of 85.8%, 91.8%, 96.1%, and 97.7% for the KNN, ANN, QSVM, and FP growth algorithm. The accurateness of fall discovery touches 100% for both the QSVM and FP growth procedures in the absence of any incorrect alarm, which is the finest attainable performance.
Analysis of air quality using IoT with machine learning prediction
Authors: Shanmugaraja T , Sakthivel M , Shaila Shree R , Sooraj P , Vishnu K 
Published: July 2021

Abstract
The Think speak technology is a dynamic field that seeks to engage ‘matters,’ human beings, and machines as whole and, making them a necessary part of the world wide web. The complete society is shifting toward industrialization and computerization, which may result in substantial environmental pollution. Deciding the air best is a top need of great importance. This paper offers the improvement of contamination global positioning frameworks with the organization of astute sensors. Checking the gas spillage stage from any piece of the globe can be finished by methods for a combination of enormous records to the Google Cloud through the networkers. Examination of the realities is improved accordingly permitting simplicity of following. Alarms might be caused if there should arise an occurrence of uncommon decay of air decent. The proposed approach discovers application in industry and also in the checking of brought about by like a fan, engines. This strategy utilizing the proposed framework is data set transferred to at long last pc and AI.
Brain Tumor Classification Using Convolution Neural Network
Authors: Saranya N , Karthika Renuka D , kanthan J Nikkesh 
Published: July 2021

Abstract
Brain tumour is a very serious brain cancer. It is present or become due to the separation of the brain cells. In the recent field of this study, tells us that deep learning will help in health industry of medical diseases imaging in the Medical Diagnostic of all the diseases. CNN is mostly used in this Machine learning algorithm. Likewise, in this paper also, we bring out the Convolution Neural Network algorithm, image processing and data augmentation to say the brain images are cancerous and which are not cancerous. This project will require less computational power due to the transfer learning compared to the old CNN model. This has good accuracy results than the old pre-trained models.
Prediction of potential intrusion areas of Ambrosia L. plant in Jilin Province
Authors: Lan Xuehan , Fu Cong , Li Chenghong , Wang Meifang , Cheng Chenguang , Sun Hua , Du Fengguo 
Published: July 2021

Abstract

Ambrosia L. plant refers to one of the critical alien invasive species in Jilin Province, which can significantly jeopardize the environment. In this study, the MaxEnt model and the ArcGIS were combined to predict the potential intrusion areas of Ambrosia plant in Jilin Province. Subsequently, the dominant environmental factors affecting the geographical distribution of Ambrosia plant we’re determined. Next, the geographical distributions and ecological niche overlap degrees of two species of Ambrosia plant were analyzed. As revealed from the results, the total areas of Ambrosia artemisiifolia L. and Ambrosia trifida L. in potential high and middle suitable distribution areas of Jilin Province reached 11988.71km2 and 48039.68 km2, respectively. According to results, the environmental factors primarily affecting the distributions of Ambrosia artemisiifolia L. and Ambrosia trifida L. consisted of solar radiation (sard9) and annual mean temperature range (bio7) in September, as well as solar radiation (sard9) and isothermality in September (bio). Ambrosia plant was suggested to exhibit a high degree of ecological niche overlap and a large geographical distribution overlap. For the mentioned reason, this study laid a scientific basis for monitoring and controlling the spread of alien invasive species of Ambrosia plant in Jilin Province.
The Electromagnetic Signal Track Correlation Algorithm based on Attribute Clustering and Space-time Constraints
Authors: Wu Bo , Wang Jianan , Wang Zhaojun , Yu Jiecheng , Gao Yue , Li Peiyun 
Published: July 2021

Abstract
In view of the problem that start-end correlation of ship electromagnetic signal, the electromagnetic signal track correlation algorithm, based on attribute clustering and space-time constraints, is proposed. The algorithm consists of K-means dynamic clustering stage, space-time constraints of the track association stage. By extracting the relevant properties of electromagnetic signals, through the clustering stage, obtaining an indefinite number of clusters, and through the space-time correlation stage, obtaining a number of tracks per cluster. The main contributions of this paper are: 1. the K-means dynamic clustering method is proposed; 2. the track correlation method of space-time constraints is proposed. In this paper, the results of the comparison experiment show that the accuracy of this algorithm is better than that of the comparison algorithm, and the practical feasibility is verified.
Research on Personalized Recommendation System Based on Association Rules
Authors: Song HaiYan , Zhang Huan , Xing ZhiHao 
Published: July 2021

Abstract
With the rapid development of network technology, the amount of information is rapidly expanding, and the amount of various data has become huge and scattered. Using traditional keywords to retrieve search data has become quite time-consuming and difficult to focus on. Traditional search engines have been unable to help people effectively solve this problem, so personalized recommendation systems have emerged. In view of the user’s behavior and preferences, this article makes a detailed comparison of different recommendation technologies. It focuses on analyzing the applicability of the current popular content-based, association rules, and collaborative filtering recommendation technologies to the current needs. The technical scheme and system structure of recommendation system based on association rules are proposed, and aiming at the shortcomings of traditional recommendation system algorithms (the algorithm’s sparsity problem) and the current cold start problem of the recommendation system. Based on the MovieLens data set, a series of evaluation indicators are set up, comparative experiments are carried out, and the effectiveness of the proposed improved algorithm is analyzed.
Comparison of multi-beam bathymetric system and 3D sonar system in underwater detection of beach obstacles
Authors: Wang Shirui , Rong Yingjiao , Jiang Hejun , Li Menglei , Sun Ping 
Published: July 2021

Abstract
The instrument detection methods of underwater part of beach obstacle mainly include multi-beam bathymetric system and three-dimensional panoramic imaging sonar system. SeaBat T50-P multi beam bathymetry and BV5000-1350 sonar system are widely used in the area of underwater detection. The underwater part of beach obstacles is measured and analyzed, and the application scope of both equipment is discussed. The results show that the BV5000-1350 sonar system can be used to detect underwater incomplete obstacles with wider application scope, high accuracy, convenient installation of instruments, and no auxiliary equipment such as navigation, positioning and attitude sensor is needed; SeaBat T50-P multi-beam bathymetric system has high requirements for the structure of the measured object, moderate accuracy, and is more suitable for large-scale underwater obstacle survey.
Trajectory time series classification algorithm based on conv-olutional self-attention mechanism
Authors: Yang Hang , Li Xiaohui , Liu Zhonglin , Wang Licai , Luo Qibin 
Published: July 2021

AbstractAs a kind of time series data in the field of transportation, trajectory has complex multi-dimensional information. How to fully excavate the multi-dimensional information of the trajectory and analyse its complex dynamic pattern is an important problem to be solved in the classification of trajectory time series. In response to the above problems, this paper improves the existing self-attention mechanism, and proposes a trajectory time series classification algorithm based on convolutional self-attention mechanism. According to the characteristics of the trajectory data, the convolutional self-attention mechanism first extracts the characteristics of the trajectory fragments in the trajectory to obtain the flight status characteristics, and then performs the self-attention operation on these characteristics, so as to achieve a better feature extraction effect. The experimental results show that the trajectory time series classification algorithm based on the convolutional self-attention mechanism has better feature extraction ability and classification accuracy than other existing algorithms.