bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematical modeling of chemotaxis guided amoeboid cell swimming
Authors: Wang Qixuan , Wu Hao 
Published: April 2021

Abstract
Cells and microorganisms adopt various strategies to migrate in response to different environmental stimuli. To date, many modeling research has focused on the crawling-based Dictyostelium discoideum (Dd) cells migration induced by chemotaxis, yet recent experimental results reveal that even without adhesion or contact to a substrate, Dd cells can still swim to follow chemoattractant signals. In this paper, we develop a modeling framework to investigate the chemotaxis induced amoeboid cell swimming dynamics. A minimal swimming system consists of one deformable Dd amoeboid cell and a dilute suspension of bacteria, and the bacteria produce chemoattractant signals that attract the Dd cell. We use the mathematical amoeba model to generate Dd cell deformation and solve the resulting low Reynolds number flows, and use a moving mesh based finite volume method to solve the reaction–diffusion–convection equation. Using the computational model, we show that chemotaxis guides a swimming Dd cell to follow and catch bacteria, while on the other hand, bacterial rheotaxis may help the bacteria to escape from the predator Dd cell.
Automatic classification of idiopathic Parkinson’s disease and atypical Parkinsonian syndromes combining [11C]raclopride PET uptake and MRI grey matter morphometry
Authors: Martins Ricardo , Oliveira Francisco , Moreira Fradique , Moreira Ana Paula , Abrunhosa Antero , Januário Cristina , Castelo-Branco Miguel 
Published: April 2021

AbstractObjective.To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [11C]raclopride positron emission tomography (PET) and T1-weighted magnetic resonance imaging (MRI) data.Approach.The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [11C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants. There were healthy controls (CTRLn= 15), patients with Parkinson’s disease (PDn= 27), multiple system atrophy (MSAn= 8), corticobasal degeneration (CBDn= 6), and dementia with Lewy bodies (DLBn= 5). MSA, CBD, and DLB patients were classified into one category designated as atypical Parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from the PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). The grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI.Results.The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%).Significance.This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.
An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model
Authors: Dimitriadis Stavros I , Salis Christos I , Liparas Dimitris 
Published: April 2021

Abstract

Objective. Sleep disorders are medical disorders of a subject’s sleep architecture and based on their severity, they can interfere with mental, emotional and physical functioning. The most common ones are insomnia, narcolepsy, sleep apnea, bruxism, etc. There is an increased risk of developing sleep disorders in elderly like insomnia, periodic leg movements, rapid eye movement behavior disorders, sleep disorder breathing, etc. Consequently, their accurate diagnosis and classification are important steps towards an early stage treatment that could save the life of a patient. Approach. The electroencephalographic (EEG) signal is the most sensitive and important biosignal, which is able to capture the brain sleep activity that is sensitive to sleep. In this study, we attempt to analyze EEG sleep activity via complementary cross-frequency coupling (CFC) estimates, which further feed a classifier, aiming to discriminate sleep disorders. We adopted an open EEG database with recordings that were grouped into seven sleep disorders and a healthy control. The EEG brain activity from common sensors has been analyzed with two basic types of CFC. Main results. Finally, a random forest (RF) classification model was built on CFC patterns, which were extracted from non-cyclic alternating pattern epochs. Our RFCFC model achieved a 74% multiclass accuracy. Both types of CFC, phase-to-amplitude and amplitude–amplitude coupling patterns contribute to the accuracy of the RF model, thus supporting their complementary information. Significance. CFC patterns, in conjunction with the RF classifier proved a valuable biomarker for the classification of sleep disorders.
Testing and Evaluation of a 20-Gsps High-Resolution Waveform Digitizer Based on TIADC Technique
Authors: Dong Ruoshi , Zhao Lei , Qin Jiajun , Zhong Wentao , Fan Yichun , Liu Shubin , An Qi 
Published: April 2021

Abstract
Time-Interleaved Analog-to-Digital Conversion (TIADC) is one of the significant technique in high speed waveform digitization systems. In our latest work, a 20-Gsps 12-bit TIADC system is designed and a series of tests are conducted on the proposed system and a broadband mismatch error correction is implemented. The test methods and results are specifically presented in this paper. The test results show that the -3dB bandwidth of the proposed system is 6 GHz and the ENOB performance is about 8.7 bits at 647 MHz and 7.6 bits at 6 GHz which is significantly enhanced by the mismatch error correction process.
Research on Quantum Programming Framework Based on NISQ Function
Authors: Liu Xiaonan , Xu Le , Hu Chunchao , Jin Lei 
Published: April 2021

Abstract
In the past two decades, the field of quantum computing has developed rapidly, is gradually able to solve some difficult classical problems. With the development of the facilities for Noisy Intermediate-Scale Quantum(NISQ), the NISQ technology addresses the existing Quantum programming languages or frameworks, suggesting that they cannot fully meet the requirements of Quantum computers. In recent years, some quantum programming languages or frameworks have emerged which claim to support the classical computing algorithms of heterogeneous quantum. However, the code is too cumbersome and redundant, and it is difficult to map the code to heterogeneous quantum classical computation while meeting the requirements of timing. Therefore, a modular programming framework of heterogeneous quantum classical computing algorithm with NISQ function is proposed. The framework optimizes the quantum code and retains the quantum classical interactions that satisfy the temporal constraints.
Discussion on Intelligent Disposal System of High-fat Food Waste
Authors: Lu Duan , Zhou Zhou 
Published: April 2021

Abstract
Food waste contains high organic matter and high fat. Discomfort in handling will cause environmental pollution and ultimately affect people’s lives. For a diet characteristic of high starch and high fat in the northwest region, a system that can effectively dispose of kitchen waste is designed. The kitchen waste first needs to pass through the filter of the pre-treatment device and crush it to obtain proper disposal and become particle size. The control system is transferred to the solid-liquid separation device, and the separated liquid is transported to the oil-water separation device through the liquid delivery pipe. The oil-water liquid can be heated by a clean energy heating box, and the PLC control system is used to complete the final oil-water separation. The food waste disposal system designed in this paper can efficiently separate the oil and water, thereby quickly removing the high starch and high oil components in the food waste. And make full use of a series of green and clean energy such as solar energy to keep running, relatively high degree of automation, simple operation and maintenance, and powerful practicability.
RETRACTED: Research on Optimization Plan of Enterprise Employee Training under the Background of Big Data
Authors: Wang Yafen , Bai Xincheng 
Published: April 2021

Abstract
This article takes SMEs as the research object, and aims to study the problems and optimization schemes of SME employee training under the background of big data. This article uses a combination of literature reading, statistical analysis and questionnaire survey to investigate the employees of a certain company’s specific production base, and conducts research and analysis on the company’s employee training management. This article first introduces some concepts of modern enterprise training, and then analyzes the status quo of enterprise employee training. Through the interviews and questionnaires issued to the employees of the company, it analyzes the problems and causes of the company’s employee training management, and provides optimized solutions for the company’s employee training. The experimental results of this paper show that the optimization of corporate employee training programs under the background of big data plays a positive role in corporate management. It improves employee enthusiasm and work commitment, and increases the number of corporate trainings.
Big data and industrial innovation progress in Jiangxi Province incremental effect highlights enabling digital economy cultivation
Authors: Cheng Caijun , Huang Huazhen 
Published: April 2021

Abstract
With the advent of the Internet information age, most social and economic activities rely on data. As a new resource, big data technology plays an important role in the social digital economy. Therefore, it is not only the objective requirement of economic development, but also the value of big data resources to promote the close integration of big data and industry, and help the upgrading and development of industry with big data. In order to emphasize the strength of digital economy, this paper studies the gradual effect of big data and industrial innovation in Jiangxi Province, and carries out a series of studies. With the development of the times, the most popular Internet also changes with the development of the times. The Internet has changed from the IT era at the beginning to the new digital technology era. The creation of big data technology and the establishment of database have become the storage and source direction of new resources for all industries. At present, the digital economy is facing the opportunities and challenges of the “Internet plus” new market. Digital economy related enterprises should strengthen mutual cooperation among resources supply network organizations such as information and information, dissolve and reorganize production factors, strengthen the competitiveness among industrial economies, promote the optimization and upgrading of industrial economic structure, and expand the market space for which the industry can develop. However, the development of the Internet plus industry depends on the innovative investment and innovation power of the industry. In the era of big data, one of the ways to improve the overall level of the industry is innovation. This paper discusses how much influence big data shows in the cultivation of digital economy.
Evaluation System of College English Teaching Based on Big Data
Authors: Tan Qingyan 
Published: April 2021

Abstract
Teaching quality is the fundamental guarantee for colleges and universities(CAU) to achieve their educational goals, and is the basis for training talents. In recent years, an important research field of big data is the research of Data mining(DM). The use of DM algorithms can find out the key factors affecting teaching quality, provide a strong basis for teaching arrangements and teaching management in universities, and then improve the teaching level of universities. The purpose of this article is to study the evaluation system of college English teaching(ET) based on big data. Based on the idea of DM, this paper designs and implements a college ET quality evaluation system(ETQES), and applies the system to the college ET quality monitoring system, and obtains a large amount of evaluation data. This paper extracts relevant data from the school’s ETQES, teacher management system, comprehensive educational administration and other system databases, and builds a data warehouse through data preprocessing. This paper implements the DM module of the ETQES to mine the data in the data warehouse. Through the analysis of these rules, the factors affecting the evaluation results of ET quality are obtained. This paper establishes a database suitable for mining association rules. Through the analysis of association rules, find out which key factors can affect the quality of ET, so as to provide a strong basis for ET decision-making and management. According to the experimental results, it can be concluded that the 4555-year-old teacher gets a higher student evaluation score, indicating that teachers of this age group are more popular with students.
Construction Project Cost Management Model Based on Big Data
Authors: Xu Jing 
Published: April 2021

Abstract
The advent of the era of big data and the development of machine learning have brought new opportunities to the field of engineering cost in China. This paper mainly studies the construction project cost management model based on big data. On the analysis of the listing of large amount of data that exist in the proper nouns, the characteristics of dispersion characteristics of distribution, based on these features to implement and compare several listing classification method, the effect of and analyzes the reason of the result, refer to the classification results of traditional method at the same time, puts forward a project cost listing standardization based on polynomial bayesian classification method. In this paper, we propose a method to detect abnormal data by using the comprehensive unit price as the classification label, and study its association with the description of list according to the list classification method, and verify the effectiveness of this method through experiments.
RETRACTED: Sports Teaching Practice Innovation Mode Based on Big Data Technology
Authors: Zheng Li 
Published: April 2021

Abstract
The arrival of the era of big data provides a new vision and broad thinking for college sports education. this article mainly studies the innovation mode of sports education practice based on big data technology. Through questionnaire survey, interview, comparative analysis, field survey, mathematical statistics, this article aims to explore the purpose, means, measures and ways of application of big data in public sports education, test the effectiveness and provide suggestions develop public sports education course, enrich the theoretical research system of public sports, broaden the theoretical research scope of big data application and promote big data The development of interdisciplinary theory with public sports education.
International Competitiveness of Jilin Province Automobile Industry Based on Big Data and Low-Carbon Economy
Authors: Zhao Xianmin 
Published: April 2021

Abstract
With the rapid development of economic globalization at this stage, the ecological environment of the earth has become one of the hot issues of global concern. As the largest developing country in the world, our country faces the highest carbon emissions and increase in the automotive industry. From the perspective of the integration of big data and low-carbon economy, the international competitiveness of the automobile industry in Jilin Province has also been continuously improved. Based on the situation in Jilin Province, this paper constructs a comprehensive carbon emission research framework suitable for the low-carbon economic automobile industry in Jilin Province. Through this framework, the automobile industry of our country’s Jilin Province can be improved internationally from the perspective of low-carbon economy. This article first analyzes and elaborates the theory of low-carbon concept, and through the theoretical research foundation of low-carbon economy, studies the theoretical foundation of Jilin Province’s automobile industry’s transition to low-carbon economy from the perspective of big data. Secondly, it analyzes the status quo of Jilin Province. Through the use of the extended model of kaya identity and the LMDI logarithmic decomposition method, based on the carbon emissions data of the automobile industry in Jilin Province from 2015 to 2020, the quantitative analysis of energy intensity and economic growth. The impact of five factors, including population, automobile industry structure and energy structure, on carbon emissions in Jilin Province from the perspective of low-carbon economy, will be studied in depth. Finally, the peak value of Tai emission in Jilin Province is calculated, the IPAT model is constructed using Eviews software, and the prediction formula of carbon emission is obtained. The value of carbon emission in Jilin Province is simulated by Matlab software and compared with foreign countries. The experimental research results show that the use of big data analysis technology for technological development in the automotive industry to achieve low-carbon economic development has greatly promoted the competitiveness of Jilin Province’s automotive industry in the international industry.
Big Data Security Storage Based on Hybrid Large-scale Database
Authors: Peng Xuemei , Hu Jie 
Published: April 2021

Abstract
With the development and progress of network technology, hidden dangers of network security have also become a threat to network storage systems. This research mainly discusses the secure storage of big data based on hybrid large-scale databases. The user sends an authentication request to the SSP, and the SSP verifies the user’s time stamp ts to ensure that it is not a replayed message, and checks whether the user ID is legal. The virtualization layer logically divides the hardware devices at the facility layer through virtualization technology, provides unified services to the outside world, and supports the operation of the platform layer; the platform layer builds a file system and database to provide an operating environment for the operation layer; the operation layer provides users with a big data environment resource access behavior and platform traffic are reasonably allocated and managed, platform business deployment, etc.; the application layer provides users with big data computing services. During the file download process, the big data storage module is relatively fast (300-600k/s), while the speed of Seccloud and invisible cloud is relatively slow (200-400k/s). This research helps to improve the data access efficiency of the big data secure storage module.
Information Teaching Platform of College Physical Education Based on Artificial Intelligence Technology
Authors: Li Feifei 
Published: April 2021

Abstract
These years, with the continuous in-depth reform of domestic education, the educational model of high school physical education is changing. The teaching system of physical education curriculum based on AI technology can not only improve the working efficiency of management departments, but also promote the construction of digital, information and intelligent campus. The main purpose of this study is to analyze the basic characteristics of artificial intelligence and the significance and impact of teaching reform.This paper discusses the new idea of artificial intelligence in the elements of teaching activities by using interview method and case analysis method, which enlightens the application of artificial intelligence teaching and promotes the intelligent development of education and teaching.Based on the universities and AI technology as the research direction, this paper puts forward the introduction of AI into physical education curriculum education, and realizes individualized targeted teaching by constructing Agent layer and data service layer. By comparing the results of the control group and the experimental group, and through the investigation of the students in the experimental group, the system has improved the students’ physical education achievement to a certain extent. Compared with the control group, the experimental group improved the students’ interest in sports.
RETRACTED: Design of College English Teaching Information Platform Based on Artificial Intelligence Technology
Authors: Yuan Xinhua 
Published: April 2021

Abstract
With the development of Artificial Intelligence(AI) technology, especially in the construction of College English teaching(ET) IP(IP), more and more applications have promoted the current college ET reform and development. We first analyzes the connotation of AI technology and the related technical problems that need to be solved in the design of ET IP, then analyzes its technology in the development of English learning through the discussion of AI technology, and finally analyzes the application of AI technology from many different aspects. Listening, writing, translation and oral ET.Secondly, under the guidance of modern education theory and learning theory, combined with the information environment, combined with teaching practice, using AI technology and information technology to design college students’ English IP. Through the theoretical construction of the traditional ET model, through interviews and questionnaire surveys, some problems that exist in the learning of the English IP are found. AI technology and new media in information technology are applied to the actual teaching practice of college English, and the whole teaching practice process is tracked and analyzed. In the process of tracking and analysis, the teaching model is continuously improved. The analysis of the teaching practice results shows that “under the mode of College English education informatization, the construction of College English based on AI technology” improves students’ learning ability and greatly improves their English learning achievements. The experimental results show that in the current network connected information society, the construction of College English education IP should abide by the traditional education concept, completely innovate the education mode, and provide the greatest advantages of AI technology and information technology.