bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust surface-to-mass coupling and turgor-dependent cell width determine bacterial dry-mass density
Authors: Oldewurtel Enno R. , Kitahara Yuki , van Teeffelen Sven
Published: August 2021

Significance

Intracellular biomass density is an important variable for cellular physiology. It defines the crowded state of the cytoplasm and thus influences macromolecular interactions and transport. To control density during growth, bacteria must expand their cell volumes in synchrony with biomass. The regulation of volume growth and biomass density remain fundamentally not understood—in bacteria or any other organism. Using advanced microscopy, we demonstrate that cells control dry-mass density indirectly through two independent processes. First, cells expand surface area, rather than volume, in proportion with biomass growth. Second, cell width is controlled independently, with an important influence of turgor pressure. Our findings overturn a long-standing paradigm of mass-density constancy in bacteria and reveal fundamental determinants of dry-mass density and shape.
Discourse-referential patterns as a network of grammatical constructions
Authors: Fried Mirjam
Published: August 2021

Grammatical organization of conversational language presents us with the challenge of incorporating recurrent contextual and discourse-relevant properties in grammatical descriptions, as part of speakers’ conventional knowledge. Using data from conversational Czech extracted from the Czech National Corpus, I address this issue by tracing the relationships among a set of dative-marked expressions of interpersonal relations (traditionally labeled ‘ethical datives’) and their connection to argument-expressing dative NPs. The discourse-referential expressions form a family of distinct patterns, the differences having to do with person (1st, 2nd) and number (sg. vs.pl.); functionally, they range from marking subjectively assessed newsworthiness to signaling evidentiality and solidarity to expressing the speaker’s emotional state. The attendant reorganization of formal, semantic, and discourse features that define these dative-marked items amounts to several patterns – ‘interactional datives’ – and I show that they have the status of grammatical constructions, which are conventionally tied to certain types of discourse settings and speaker-hearer expectations. In order to represent these constructions and their relationship to other, partially related, patterns, I propose a network representation in the form of contiguous functional spaces that overlap at the boundary between argument-expression and interactional markers.
Funny you should say that
Authors: Kaltenböck Gunther
Published: August 2021

This paper investigates the formal and functional properties of so-called semi-insubordination (SIS), i.e. complex sentences with an ‘incomplete’ matrix clause (e.g.Funny that you should say that), on the basis of corpus data. It is shown that SIS differs in its function from the structurally related constructionsit-extraposition and exclamatives, exhibiting its own functional profile: viz. expressing a subjectivizing speaker evaluation which is non-exclamative, deictically anchored, and relates to a non-presupposed proposition. Given these functional idiosyncrasies it is argued that SIS is best analysed as a construction in its own right (in terms of Construction Grammar) rather than simply an incomplete elliptical structure.
Scholarly publication of Brazilian researchers across disciplinary communities
Authors: Baumvol Laura , Sarmento Simone , da Luz Fontes Ana Beatriz Arêas
Published: August 2021

This paper examines the context of scholarly knowledge production and dissemination in Brazil by comparing the publishing practices in both Portuguese and in English of Brazilian scholars who hold a research grant, across eight fields of knowledge. Data consists of 1,874 Curricula Vitae and the analysis focused on the language, number, and genres of publications over a three-year period (2014 to 2016). The study revealed a clear contrast regarding the more frequent use of English by researchers in the ‘harder’ sciences and the preference for Portuguese by those in the ‘softer’ sciences. The results also suggested an interconnection in which scholars who published the most tended to adopt English. Multiple factors involved in the genre and language choices made by academics were analysed, such as characteristics of the work produced by each disciplinary community, the audience of the research, the type of language used, and the need to obtain research funding. This investigation can potentially inform policies and investments in Brazilian higher education and research to provide continued support specific to the needs of different disciplinary communities, as well as foster the inclusion of multilingual scholars who do not have English as their first language in the global arena of knowledge production and dissemination.
Lexical selection in bilingual language production
Authors: Hald Lea A. , Nicol Janet L.
Published: August 2021

The goal of this study is to examine whether bilingual speakers can inhibit one language while naming pictures in
the other. In two picture-word interference task experiments, Spanish-English and English-Spanish bilinguals named pictures in
Spanish. We used language-neutral (nonword) interfering stimuli to probe the phonological activation of the nontarget language
(English). Three different interfering stimulus conditions were presented: nonwords phonologically related to the Spanish picture
name (Phono-Spanish), nonwords phonologically related to the English picture name
(Phono-English) and phonologically unrelated nonwords (Unrelated). When participants named
pictures in Spanish (Experiment 1), facilitation was found for both groups in the Phono-Spanish condition. No interference was
found in the Phono-English condition for either group. From this result and the results of a control experiment in which
participants named pictures in English (Experiment 2), we argue that under some circumstances, bilinguals are able to effectively
inhibit the nontarget language during language production.
Precursor skills for learning morphology in 12-month-old children (Habilidades precursoras para el aprendizaje de la morfología en infantes de 12 meses de edad)
Authors: Jasso Tania , Alva Elda A.
Published: August 2021

The purpose of this study was to investigate sub-lexical segmentation in Spanish-speaking children based on the perception of regular syllables (pseudomorphemes), as well as their association with a visual referent. Both of these skills are the precursors to learning morphology for language acquisition. Twenty-three 12-month-old children participated; they were evaluated with three experimental tasks which tested their skill at segmenting pseudomorphemes from a set of pseudowords when they did and did not appear in the final position, as well as the association between them and a visual referent. The results showed that the children were able to segment the pseudomorphemes at the end of the pseudowords and tended to associate them with a visual referent. We also found that when the morpheme did not necessarily appear in the final position, the children who preferred that position looked at the referent for a longer time. These results reveal the skills that the children are developing on their way to learning morphology.
Rebuilding hegemony: passive revolution, state transformation and South Africa’s steel sector
Authors: Reboredo Ricardo
Published: August 2021

This paper draws on Gramscian concepts to analyse the ongoing transformation of the South African state. In particular, it conceptualises the Jacob Zuma administration’s attempt to establish a developmental state as an ongoing passive revolution. The paper argues that the implementation of the developmental state framework has transformed the role and character of the state apparatus. Three major transformations are detailed: a shift towards what ostensibly appears to be state capitalism; the increased leveraging/privileging of inbound global South-based capital; and the construction of novel intra-state alliances. The theoretical insights are then grounded through an examination of South Africa’s steel sector.
Debt, distress, dispossession: towards a critical political economy of Africa’s financial dependency
Authors: Zajontz Tim
Published: August 2021

With China's rise to become Africa's largest bilateral creditor, much research has focused on an evidence-based critique of the politicised narrative about China's supposed ‘debt trap diplomacy'. At a more fundamental level, this debate problematises the function of debt and related power differentials in late capitalism and calls into question development paradigms, notably the hegemonic infrastructure-led development regime, that have sustained Africa's financial dependency into the 2020s. As the International Monetary Fund is yet again shuttling between Addis Ababa, Lusaka, and Nairobi to resurrect fiscal discipline and to ensure debtor compliance for the post-pandemic ‘payback period', it is argued that (i) periodic cycles of debt financing, debt distress and structural adjustment are a systemic feature of the malintegration of Africa into the global capitalist economy, and (ii) critical research on the social costs and economic beneficiaries of renewed rounds of austerity and privatisation in Africa’s current debt cycle is needed.
Unidimensionality of the Strengths and Vulnerabilities Scales in the Short-Term Assessment of Risk and Treatability (START)
Authors: Whittington Richard , Pollak Charlotte , Keski-Valkama Alice , Brown Andrew , Haines-Delmont Alina , Bak Jesper , Hvidhjelm Jacob , Almvik Roger , Palmstierna Tom
Published: August 2021

The Short-Term Assessment of Risk and Treatability (START) is a 20-item structured professional judgment instrument for assessing dynamic risk in mental health services. Much of the START research literature examines the relationship between Strengths and Vulnerabilities sub-scale total scores and various adverse outcomes including violence. This assumes that the two sub-scales have the psychometric property of unidimensionality i.e. all the items cluster together as a measure of a single construct. Such assumed unidimensionality is a necessary condition for any analyses based on scale “total score” and the widespread use of scores summated in this way in research studies may obscure more specific clusters of items within each sub-scale. This multinational study examined START assessments ( n = 685) conducted in four forensic services in Scandinavia and the UK using principal component analysis. It was found that all but three Strengths items (Substance Use, Social Support and Material Resources) and all but four Vulnerabilities items (Substance Use, Social Support, Material Resources and Self care) loaded >0.5 on the expected component. This indicates a unidimensional structure underlying the START and provides empirical support from a large multinational sample for the widespread use of summated Strengths and Vulnerabilities scores in forensic psychiatric risk research.
Integration of ZnO nanorods with MOS capacitor for self-powered force sensors and nanogenerators
Authors: Geng Yulin , Bin Che Mahzan Muhammad Ammar , Jeronimo Karina , Saleem Muhammad Mubasher , Lomax Peter , Mastropaolo Enrico , Cheung Rebecca
Published: August 2021

In this work, we present a novel force-sensing device with zinc oxide nanorods (ZnO NRs) integrated with a metal-oxide-semiconductor (MOS) capacitor and encapsulated with Kapton tape. The details of the fabrication process and working principle of the integrated ZnO NRs-MOS capacitor as a force sensor and nanogenerator have been discussed. The fabricated ZnO-MOS device is tested for both the open-circuit and resistor-connected mode. For an input force in the range of 1–32 N, the open-circuit output voltage of the device is measured to be in the range of 60–100 mV for different device configurations. In the resistor-connected mode, the maximum output power of 0.6 pW is obtained with a 1 MΩ external resistor and input force of 8 N. In addition, the influence of different seed layers (Ag and ZnO) and the patterning geometry of the ZnO nanorods on the output voltage of ZnO-MOS device have been investigated by experiments. An equivalent circuit model of the device has been developed to study the influence of the geometry of ZnO NRs and Kapton tape on the ZnO-MOS device voltage output. This study could be an example of integrating piezoelectric nanomaterials on traditional electronic devices and could inspire novel designs and fabrication methods for nanoscale self-powered force sensors and nanogenerators.
Modified Grover operator for quantum amplitude estimation
Authors: Uno Shumpei , Suzuki Yohichi , Hisanaga Keigo , Raymond Rudy , Tanaka Tomoki , Onodera Tamiya , Yamamoto Naoki
Published: August 2021

In this paper, we propose a quantum amplitude estimation method that uses a modified Grover operator and quadratically improves the estimation accuracy in the ideal case, as in the conventional one using the standard Grover operator. Under the depolarizing noise, the proposed method can outperform the conventional one in the sense that it can in principle achieve the ultimate estimation accuracy characterized by the quantum Fisher information in the limit of a large number of qubits, while the conventional one cannot achieve the same value of ultimate accuracy. In general this superiority requires a sophisticated adaptive measurement, but we numerically demonstrate that the proposed method can outperform the conventional one and approach to the ultimate accuracy, even with a simple non-adaptive measurement strategy.
The unequal-time matter power spectrum: impact on weak lensing observables
Authors: de la Bella Lucia F. , Tessore Nicolas , Bridle Sarah
Published: August 2021

We investigate the impact of a common approximation of weak lensing power spectra: the use of single-epoch matter power spectra in integrals over redshift.
We disentangle this from the closely connected Limber's approximation.
We derive the unequal-time matter power spectrum at one-loop in standard perturbation theory and effective field theory to deal with non-linear physics. We compare these formalisms and conclude that the unequal-time power spectrum using effective field theory breaks for larger scales. As an alternative we introduce the midpoint approximation.
We also provide, for the first time, a fitting function for the time evolution of the effective field theory counterterms based on the Quijote simulations.
Then we compute the angular power spectrum using a range of approaches: the Limber approximation, and the geometric and midpoint approximations. We compare our results with the exact calculation at all angular scales using the unequal-time power spectrum.
We use DES Y1 and LSST-like redshift distributions for our analysis. We find that the use of the Limber's approximation in weak lensing diverges from the exact calculation of the angular power spectrum on large-angle separations, ℓ < 10. Even though this deviation is of order 2% maximum for cosmic lensing, we find the biggest effect for galaxy clustering and galaxy-galaxy lensing. We show that not only is this true for upcoming galaxy surveys, but also for current data such as DES Y1.
Finally, we make our pipeline and analysis publicly available as a Python package called unequalpy.
Research on Vehicle Control System of Construction Machinery Based on Machine Vision
Authors: Huang Mian , Liu Dafeng
Published: August 2021

With the continuous development of construction machinery technology, great breakthroughs have been made in the application of electronic information technology. The control and operation system of construction machinery vehicles refers to the system that applies and receives certain instructions and makes the construction machinery produce specified actions according to the requirements of operators through its power transmission device. Compared with the previous construction machinery, the developing construction machinery has made good achievements in high efficiency, intensification, cost saving and environmental protection. The traditional speed measuring method can not meet the requirement of measuring accuracy of actual walking speed of construction machinery, and can not accurately reflect the actual walking speed and instantaneous motion state of construction machinery. This paper analyzes a kind of construction machinery vehicle using machine vision. It obtains important information through image processing, adopts intelligent control technology based on machine vision as an important control means of power drive, and can judge and control driving according to road conditions, thus improving the accuracy and stability of industrial autonomous transportation..
Youth physical training robot based on visual recognition and training method
Authors: Dong Hanbin , Cui Zhihe , Tian Liao
Published: August 2021

With the continuous improvement of people’s living standards, the continuous development of training methods and concepts, resulting in the continuous improvement of sports level, which puts higher demands on teenagers’ physical function. How to use high-efficiency and low-risk training methods to train has become a headache for coaches. In this paper, an accurate target detection and recognition method based on stereo matching algorithm is proposed. Based on stereo matching algorithm, after data training, the target can be accurately identified, and the position information of the target can be given. Then, the target can be accurately positioned by binocular vision and its three-dimensional coordinates can be obtained. Finally, it can be transmitted to the robot and control the robot to move and grab the target object. Experimental results show that this method can identify and locate the target accurately and quickly, and successfully guide the robot to grasp the target object. This physical training robot can provide theoretical basis and technical guidance for improving the physical training level and sports performance of young athletes.
Research on Key Technologies of Basketball Object Segmentation and Tracking Based on Image Analysis
Authors: Ma Liangying
Published: August 2021

Since the 21st century, with the continuous development of society, people’s living standards have been improved, and their physical qualities have been constantly improved. These factors have promoted the development rhythm of basketball, and at the same time put forward higher requirements for players’ abilities. In order to improve players’ basketball accomplishment quickly, many high-tech equipments are also used in basketball, which provides a lot of technical support for coaches to make training programs for players. With the rapid development of image processing technology, moving target detection technology in video has been more and more widely used. Tracking the flight trajectory in basketball shooting and analyzing the trajectory with image processing technology can improve the accuracy of basketball shooting. In this paper, a basketball tracking method based on image analysis is proposed, and an adaptive object model is further established. Then, the object model is used to improve the block matching method to track basketball. Finally, the tracking deviation is corrected, and the basketball tracking effectiveness detection mechanism is established.
Research on artificial intelligence machine learning character recognition method based on Feature Fusion
Authors: Qiao Xiaolin
Published: August 2021

In this paper, the artificial intelligence machine learning based on feature fusion is briefly introduced, and on this basis, the character recognition method based on artificial intelligence machine learning is studied from the perspective of character recognition system classifier and character recognition performance. The continuous development of artificial intelligence technology has made people more in-depth research on it. Machine learning has also become a new core research issue in artificial intelligence. It has been widely used in network search, language recognition, and machine vision. The emergence of artificial intelligence has brought great changes to people’s life and production mode, coupled with the development of science and technology and network technology, people have successfully entered the information age, and information technology is indispensable in all aspects of people’s life. With the progress of science and technology in the current era, artificial intelligence has developed very rapidly, and people’s research on machine learning has also made a breakthrough. Learning ability is an important intelligent behavior of human beings. Human beings can adjust their own actions through learning, so as to obtain strong environmental adaptability.
Research on Character Behavior Recognition Based on Local Spatio-temporal Relationship in Surveillance Video
Authors: Dong Weisong
Published: August 2021

Because of its wide application value, behavior recognition has long been one of the research hots pots in the field of computer vision and pattern recognition. At present, the method based on local features and word packet model has been widely used in the field of behavior recognition. However, this method does not consider the temporal and spatial relationship between features, and the local temporal and spatial relationship between features is very important for behavior representation and behavior recognition. In view of the above problems, this paper proposes a modeling method of character behavior recognition based on local spatio-temporal relationship in surveillance video. Firstly, each part of the proposed network model is introduced in detail, and then the proposed model is compared with the advanced skeleton action recognition methods in recent years on several skeleton data sets. Finally, the effectiveness of the proposed method is verified. The experimental results show that, compared with the recognition results of related literatures, the features extracted by choosing the starting point of trajectory have better recognition performance under the fusion framework.
Research on Face Recognition Technology Based on Improved YOLO Deep Convolution Neural Network
Authors: Fan Yu , Luo Yiyue , Chen Xianjun
Published: August 2021

With the development of information technology, deep learning has become a research hots pot in the field of computer vision, among which face recognition technology, as one of its applications, has received extensive attention in recent years. A face recognition method based on improved YOLOv3 deep convolution neural network is proposed. The feature pyramid network is used to obtain the four scale features of the target to fuse the shallow features and the deep features, and the influence weight of the loss function is adjusted according to the size of the detected target, so as to enhance the detection effect of small targets and mutually occluded objects, and the Softmax function is used for classification and recognition. Experimental results on mixed data sets show that this method can improve the real-time performance of face detection.
Research on Image Recognition Based on Neural Network
Authors: Zhang Jinhai
Published: August 2021

With the rapid development of Internet and mobile Internet, the image data in the network shows explosive growth. Image data is simple and intuitive, and contains rich information, which is widely used as a carrier of information exchange. Content-based image recognition can start from the image itself, first extract significant features from the image, and then recognize the image according to the differences of features, with good recognition effect. Neural network plays a very important role in artificial intelligence. Neural network is the key to realize artificial intelligence by learning data through algorithms and then making predictions and decisions. The image recognition of neural network is based on the image content. Neural network starts from the image data itself, extracts the image features of the bottom layer from the data, and then establishes the relationship between the image semantics of the bottom layer and the top layer through the algorithm of neural network, thus realizing the image recognition.
Research on Ship Collision Avoidance Expert System in big data Era
Authors: Tong Jinqiang
Published: August 2021

In the era of big data, in order to effectively reduce or avoid the occurrence of ship collision accidents caused by human errors, adapt to the development trend of ship Manning reduction, promote the research of ship automation, and ensure the safety of ship navigation at sea, based on the in-depth study of the principle of ship collision avoidance, this paper uses expert system technology and a large number of quantitative research results of ship collision avoidance, A more reasonable and complete knowledge base of collision avoidance is established, and a collision avoidance expert system is designed and partially implemented, which can help the ship’s pilot to make decision of collision avoidance in real time and realize automatic collision avoidance under certain conditions.
Research and Application of Computer Big Data System in Early-Warning Methods and Online Monitoring Technologies
Authors: Su Hongyu , Wang Juan
Published: August 2021

Corporate financial index is a collection of corporate financial information, which can truly and reliably reflect the financial status of a company. In order to monitor the size of corporate financial risks and prevent and reduce risks from bringing crises to enterprises, this paper proposes a financial early-warning computer big data model for listed real estate companies based on extension theory based on traditional financial early-warning methods, and combining them with each company The specific actual data is verified to provide suggestions for the development of listed real estate companies quickly and dynamically.
Application of Wireless Sensor Networks in the Sensitive Data Security of Intelligent Data Center under the Big Data Environment
Authors: Lin Han , Weng Binxin , Pan Jianli , Lin Chaodeng , Yang Qifan
Published: August 2021

In the big data environment, wireless sensor networks (WSN) are constantly improving themselves and are widely used in all walks of life. The WSN collects, processes and transmits the information of the sensed object in the monitoring area. In recent years, WSN have also made good progress in environmental change detection, traffic control, and sensitive data security management. The purpose of this article is to study the application of WSN in the security management of sensitive data in the data center under the big data environment. This article first describes the characteristics of WSN, and discusses the security management requirements of sensitive data in WSN based on big data technology. Secondly, it analyzes the problems in the security management of sensitive data and conducts research from different directions. Finally, in response to these problems, the implementation of the WSN in the security management of sensitive data provides some help to solve the problem. Thereby effectively reducing the energy consumption of network nodes, reducing the amount of data transmitted by the network, and extending the life cycle of the WSN. Experimental research results show that the most demanded level in security management is data confidentiality, which is 47.15%, followed by data freshness and data integrity, which are 28.49% and 24.36%, respectively. In summary, it is necessary to increase efforts to improve the security management of sensitive data.
Application of Clustering Algorithm by Data Mining in the Analysis of Smart Grid from the Perspective of Electric Power
Authors: Qi Yaru , Ren Junda , Sun Ni , Yu Yangqi
Published: August 2021

In recent years, the research on the relationship between economic development and power consumption is also a focus of government departments at all levels. The development of electric power (EP) not only has an impact on power supply enterprises and EP industry, but also is closely related to the social and economic development of the whole region and residents’ life, that is, there must be some internal relationship between economic development and EP consumption. In many areas of our country, the situation of power consumption and economic development is not coordinated. Insufficient and untimely power supply will hinder economic growth, and excessive power supply will bring unnecessary waste of resources. This also reflects that the research on the relationship between economic development and power consumption is not in-depth, so the research on the relationship between power consumption and economic development has a certain theoretical significance. This paper studies the application of clustering algorithm based on data mining in the analysis of economic development characteristics(EDC) from the perspective of EP, uses K-means clustering algorithm to understand the relationship between EP development and economic development, understands its characteristic development application, studies the relationship between EP consumption and economy from the EP elastic coefficient method and output value unit consumption method, and uses K-means clustering algorithm to calculate, this paper uses the chart analysis method to analyze the correlation degree of power consumption structure and the relationship between different industrial GDP and power consumption. The results show that the total output of each industrial structure is in direct proportion to the power consumption. The total value of each industrial structure is increasing, and the power consumption is on the rise. For example, in 2016, the total value of the first industry was 186.81 trillion yuan, the second industry was 960.54 trillion yuan, the third industry was 515.96 trillion yuan, and the power consumption was 41.246 billion kwh, by 2020, the total value of the primary industry will be 220.66 trillion yuan, the secondary industry 1916.51 trillion yuan, the tertiary industry 866.22 trillion yuan, and its electricity consumption (EC) will be 57.697 billion kwh.
Data Mining and Intelligent Matching Algorithm in Electric Power Terminology Database
Authors: Chen Yanan , Lin Yuxi , Fang Luebin , Cheng Xiuyuan , Wang Weilan
Published: August 2021

The paper designs a method and system for automatic extraction of professional vocabulary in the electric power field based on data mining and matching algorithms. The system extracts professional vocabulary related to power marketing from the corpus in the field of power marketing, converts it into corresponding English and adds it to the professional vocabulary of power marketing. Then we will collect the relevant vocabulary of the power system and the corresponding translation and add it to the professional vocabulary of power marketing. The thesis establishes a two-level index structure for the professional vocabulary of electric power marketing. We search the professional vocabulary database of electric power marketing, the order of retrieval is electric power system vocabulary, general dictionary, electric power marketing field extraction vocabulary, and the search result is returned to the user after the retrieval. The experiment verified that the system facilitates the efficient retrieval of user vocabulary, and then realizes the automatic conversion of professional vocabulary in the marketing standardization design results, so as to improve the quality and efficiency of professional vocabulary conversion.
Research on Intelligent Parking Space Design in Limited Space through Big Data
Authors: Zou Han , Zhu Tianjian
Published: August 2021

With the rapid development of urbanization and motorization in China, the problem of parking in the city becomes more and more serious. Due to a series of reasons such as spatial layout, insufficient parking facilities and lagging property management, the parking problem in old urban communities is difficult to be solved. Based on the perspective of big data, this paper analyzes the reasons for the parking difficulties in the old community, and designs a set of intelligent parking system to provide an effective solution to the problem of parking difficulties in the old community. At the same time, it proposes design strategies for the renewal of parking space in the old community.
The impact of AR-HUD intelligent driving on the allocation of cognitive resources under the breakthrough of 5G technology
Authors: Ma Xiangdong , Hong Zhicong , Huang Junhong , He Jingjing , Song Jianpeng , Jia Mengting
Published: August 2021

This paper focuses on the establishment of an ARHUD assisted driving test system based on a VR platform, which has the advantages of high security and immersion, repeatable experiments and the ability to perform eye-movement analysis. This paper first defines and designs the vehicle driving safety icons based on human-computer interaction principles and engineering psychology, supplemented by PS to define and design the AR-HUD interface while combining mental load and other factors, then uses 3Dsmax software to build the 3D model material required for driving, then builds the driving environment and designs various driving emergencies in Unity based on featured technologies such as multi-channel rendering and global illumination, and then combines HTC VIVE Pro eye display. Thirty drivers were then tested on a distraction task. Analysis of the subjects’ eye-movement data revealed that the AR-HUD system improved the cognitive efficiency of the drivers compared to the traditional driving method while allocating cognitive resources to the central driving area, speed module, navigation information, and hazard warnings in a balanced manner, thus improving the ability to react to unexpected driving events.
Research on Modelling of Innovation Information System in Smart Libraries Using Computer Big Data
Authors: Zhou Yufeng
Published: August 2021

To change the traditional ideological and political teaching in higher vocational colleges, students passively accept knowledge, and the teaching efficiency is low. The article developed an ideological and political network teaching system based on computer big data NET. The teaching system uses NET computer platform technology and castle framework and adopts the MVC system architecture model based on the B/s three-tier structure to develop the teaching system. It was found that the ideological and political network teaching system is running stably during several tests and use by the developers. The system page is displayed normally, and all functions of the system can be correctly responded. The system meets the design requirements.
LSTM Framework Design and Volatility Research on Intelligent Forecasting Model for Solving the Parallel Dislocation Problem
Authors: Weng Yiran , Wang Zhiyi , Zhou Longzhen
Published: August 2021

The yield of treasury bonds is the benchmark interest rate in the financial market which is worth predicting and judging. Based on the Long Short-Term Memory (LSTM) neural network model in deep learning, combined with the vector autoregression method (VAR), this paper creatively constructs the VAR-LSTM framework and uses the predicted values of macroeconomic variables and lagged value of the time sequence as input factors to solve the problem of “parallel dislocation” of the fitting results of the traditional LSTM model which significantly improves the prediction accuracy. In order to meet the requirements of active quantitative investment for high precision prediction of stock market index, adaptive noise complete ensemble empirical mode decomposition (EMD) is introduced into the modeling of stock market index prediction. Combined with the efficient modeling ability of long-term and short-term memory network for medium- and long-term dependence of complex series, using the idea of “decomposition-reorganization-prediction-integration”, an integrated prediction method of stock market index CEEMDAN-LSTM is proposed. CEEMDAN is used to decompose and reconstruct the index to obtain its high and low frequency components and trend items. The LSTM prediction models of each component are constructed respectively and the IMF reorganization mode of high frequency subseries is optimized. Then the overall predicted value of the index is obtained by adding and integrating the predicted values of each component. Taking five representative stock market indexes as test data, the prediction results of CEEMDAN-LSTM and mainstream financial time series machine learning modeling methods are compared systematically. The results show that for treasury bond yield series, the prediction accuracy of ARIMA model is higher than that of general LSTM method, while VAR-LSTM model is better than ARIMA model. The prediction error in the training set and the test set is reduced by about 55% and 50% respectively, and the prediction accuracy of the change direction is improved by about 5% and 8% respectively, which has higher application value. The prediction performance of CEEMDAN-LSTM is consistently better than that of existing modeling methods, and has less prediction error and lower lag.
Research on Topology and Security Behaviour Intelligent Analysis by Hybrid P2P Traffic
Authors: Wang Yike , Feng Yuhang , Shang Longshan , Zhang Haonan , Zhang Boshen
Published: August 2021

At present, most of the popular P2P topology sharing systems are based on unstructured P2P topology. This topology uses flooding method to spread query information, which has good stability, but the efficiency is very low. According to the changing characteristics of the communication attribute state of the network behavior which occupies a large network bandwidth and communicates more frequently, an attribute migration state-oriented network communication behavior analysis method is proposed. The research on the identification of P2P traffic is of great significance for the management of P2P network. In view of the shortcomings of the current P2P traffic identification methods, such as large error and unstable identification results, in order to improve the identification effect of P2P traffic, a P2P traffic identification method based on neural network is proposed. This paper proposes a network file sharing system Kapa, which is based on a hybrid hierarchical P2P topology, which combines the advantages of unstructured and structured P2P topology and has strong practicability. The recognition effect and reliability of this method for P2P applications are verified.
Research on Intelligent Evaluation Model of Global Food System Using Analytic Hierarchy Process with Statistics and Calculations
Authors: Jiang Xiaoxuan , Li XinNing , Zuo YiFan
Published: August 2021

In order to cope with the instability of the current global food system, this paper established a global food system evaluation model and a global food system optimization model. We reimagine and determine the priority of the food system, conduct a comprehensive and reasonable assessment of the current food system, optimize it according to the current situation and predict the time required for the implementation of the system and the changes it will bring. We have established a global food system evaluation model. We begin with constructing a strict agreement-based global food system indicator system, consisting of 4 measurement dimensions and 21 indicators and their characteristics. (The four dimensions of the metrics are environment, society, food security and nutrition, and economy. The 21 indicators include 7 environmental indicators, 3 economic indicators, 3 social indicators, and 14 food and nutrition indicators) Next, depend on a large number of statistics and calculations, the Spearman correlation coefficient between the indicators is obtained. The Spearman correlation matrix is made to measure the correlation and correlation between the indicators. Then, we use the correlation as a reference to determine the weight of each indicator through the analytic hierarchy process and sort them.
A study on the development of computer-aided design in the context of artificial intelligence
Authors: Wu Xin , Yang Fengqi
Published: August 2021

With the rapid development of social economy, Internet technology has been widely used, and in this social environment, artificial intelligence has been highly valued by all walks of life, which has brought a great impact on the development of all walks of life, and in this regard, the art and design industry is also the same. This paper does an in-depth study, firstly, it gives a brief introduction to artificial intelligence, then analyzes the artistic expression of artificial intelligence, and the limitations of artificial intelligence design, and finally discusses the application and development of art design in the context of artificial intelligence.
Application of PREEvision in Functional Intelligent Safety Design for Integrating Automotive Electronic and Electrical Architecture
Authors: Zhou Xuan , Hou Zhiwen , Meng Xianghu , Zhou Xin , Fang Jia , He Zhifa
Published: August 2021

With the increase of people’s attention to car safety and the introduction of functional safety, designers have changed their thinking from fault-based to functional-based safety architecture design. PREEvision, an architecture design tool, can integrate automotive electronic and electrical architecture design and integrate functional safety design into it, which greatly simplifies the design process and design time, and at the same time can also reduce the probability of product failure to Improve security.
Application of Minimum Spanning Tree in the Solution of Forest Fire Intelligent Drone Deployment
Authors: Liu Dongxiao , Zhao Tianyi
Published: August 2021

In recent years, devastating wildfires have occurred in all states of Australia, and the wildfires in eastern Victoria have been extremely affected. In order to prevent the disaster from happening again, we built a model to warn the wildfires in Victoria. First, we extracted the Victorian fire data from NASA’s official website. After sorting by credibility and flame temperature, we performed grid division and labeling based on latitude and longitude and the number of fires per unit time. Then, we used the k-means method to cluster the locations of wildfires, and obtained three clusters. Second, we divide wildfires into two categories, wildfires near the cluster center and others not. Among them, 3 regions are near the cluster center, and 4 region is away from the cluster center. For wildfires near the cluster center, remote sensing detection drones are used to fully cover the fire, and use the minimum spanning tree algorithm to solve the position of the repeater drone. For other wildfires, we establish a nonlinear programming equation to find the combination of drones with the lowest rate for the alarm missed by the fire, and get the number of telemetering drones is 65, the number of repeater drones is 49. Third, we map the data of different distances in the three-dimensional topographic map to the two-dimensional plane according to the geographic data of Victoria, and use the Dijkstra algorithm to obtain the approximate distance between the two points in the given point set. Then use the minimum spanning tree to solve, get the number of telemetering drones is 65, the number of repeater drones is 52, it is still close to the solution of model 1 after adding the terrain, indicating that the optimization result is successful.
Research on Intelligent Detection and Improvement Technology of Urban Rail Transit Electromagnetic Compatibility
Authors: Wan Jia
Published: August 2021

The design and installation of the rail transit power supply system should be completed under the premise of careful comparison with relevant national standards. The problems of electromagnetic compatibility and electromagnetic interference in urban rail transit engineering involve a wide range of subjects, and have the problems of being neglected, forgotten, and mixed. In order to solve this problem, this paper proposes an electromagnetic compatibility design for urban rail transit in a complex electromagnetic environment. In this design, electromagnetic interference sources, coupling paths and sensitive equipment are analyzed in detail, and electromagnetic coupling path cutting technologies such as grounding, shielding, filtering and wiring are strictly designed to meet the electromagnetic compatibility requirements of urban rail transit.
Research on the Practical Application of Visual Knowledge Graph in Technology Service Model and Intelligent Supervision
Authors: Yu Jun , Zhang Yintie , Wu Yu , Mao Linhui
Published: August 2021

The paper analyses the relevant elements of enterprise technical services in combination with the relevant knowledge service requirements of enterprise technology management. On this basis, based on enterprise information extraction technology, enterprise knowledge reasoning technology, machine learning technology, etc., it constructs an enterprise technology service model of knowledge graph. The model is divided into five levels: data acquisition layer, data processing layer, technology fusion layer, technology discovery layer, and technology application layer. The knowledge graph is integrated into the enterprise technology service platform to visualize the organizational form of technology management, which makes the enterprise technology service platform more relevant, and the enterprise business solutions are more intelligent and personalized. The research results obtained in the thesis promote the in-depth and universal development of the enterprise technical service model theory, and provide practical reference for improving the enterprise technical service level.
Application of Machine Learning and CoVaR Model on Intelligent Decision Method
Authors: Yang Zheyu , Dai Anna
Published: August 2021

The traditional asset allocation model does not take into account the spread of systemic risk in the process of portfolio optimization, which will lead to great losses in the portfolio when facing financial risks, especially extreme risks. In order to solve this problem, this paper improves the efficiency frontier of Markowitz, takes the factors that cause changes in the rate of return on individual underlying assets into the consideration of systemic risk, applies CoVaR model to measure the spread of systemic risk, and constructs a new asset allocation model based on Mean-CoVaR. The results show that when considering systemic risk shocks, the impact of systemic risk diffusion on Mean-CoVaR portfolio is significantly lower than that of traditional Mean-Variance portfolio. And Mean-CoVaR model is more efficient for portfolio allocation.
A Fast Correction Algorithm for Inverse Longitude and Latitude Mapping of Fish Eye Image
Authors: Li Xingxing , Duan Chao , Yin Panpan , Wang Xingning
Published: August 2021

In order to solve the problem of large barrel distortion in fisheye lens, an image distortion correction algorithm based on double longitude is proposed to improve the visual effect. Firstly, in the case of unknown lens angle and non-circular fisheye image, the spherical center and its radius are obtained by using fisheye image features, and the radius is optimized to reduce pole distortion; on this basis, the fisheye image is mapped to the spherical surface by orthogonal projection strategy using double longitude model method, which is transformed into the transverse longitude and longitudinal longitude coordinates of the sphere The projection is a square plane image based on the horizontal and vertical double longitude coordinates to realize the fast and accurate image transformation.