bluuebunny/crossref_metadata_2025 huggingface dataset:
Synergistic engineering of bromine and cetyltrimethylammonium chloride molecules enabling efficient and stable flexible perovskite solar cells
Authors: Mei Hao , Wu Yuliang , Wang Changlei , Ren Shengqiang , Zhang Mingdi , Dai Haitao , Zhao Dewei , Li Zhengying , Du Qingguo , Li Xiaofeng
Published: August 2020

We report synergetic strategies of combining bromine and cetyltrimethylammonium chloride additives to passivate both bulk and surface defects, leading to improved performance and stability of flexible PVSCs.
Variable speed reference control of a high‐speed BLDC motor for a blender machine
Authors: Bae Jong‐Nam , Lukman Grace Firsta , Ahn Jin‐Woo , Lee Dong‐Hee
Published: August 2020

This study presents the design of a 2‐pole high‐speed BLDC motor for kitchen blenders and its control method with the aim to reduce the speed ripple that decreases the grinding performance in the high‐speed region. In blender machines, the high pulsating load caused by the blending materials such as ice cubes acting on the grinding blade, create significant speed ripple in the constant power region. Because current and torque are limited to protect the drive, the actual motor torque cannot overcome the pulsated load if the speed reference is maintained. Therefore, the speed fluctuates, and the grinding performance is lowered. In the proposed variable speed reference control scheme, the speed command is continuously modified to overcome the maximum pulsating torque requirement which results in lower speed ripple. To achieve this, if the load torque suddenly increases, the current value is then set based on the maximum load and speed reference value is temporarily lowered. If then the load torque becomes lower than the actual torque, the previously set speed reference value is reused. The design result and grinding performance of the proposed control method are verified by simulations and experiments. Hard white beans are used for actual performance testing.
Modified complementary sliding mode control with disturbance compensation for permanent magnet linear synchronous motor servo system
Authors: Jin Hongyan , Zhao Ximei , Wang Tianhe
Published: August 2020

In this study, a modified complementary sliding mode control (MCSMC) method based on a disturbance force observer with mass identification (DFOB‐MI) applicable to the permanent magnet linear synchronous motor is proposed to achieve high‐performance servo control fields. MCSMC is an improvement on the complementary sliding mode control (CSMC) method, which incorporates an approach angle into the saturation function. MCSMC allows for asymptotic convergence of the position tracking errors and guarantees the global robustness of the system. In addition, compared to hybrid control strategies combining neural networks with CSMC, the MCSMC method has a simpler structure and faster response. However, in practical applications, the mass variation of the mover has a significant impact on system performance. To achieve better dynamic and static characteristics, a disturbance force observer capable of identifying the mass variation based on model reference adaptive identification theory is proposed. DFOB‐MI can identify the mass of the mover and provide information on the disturbance caused by the change of the load. Thus, the compensation current is calculated to reduce the disturbance and realise compensation. The more accurate tracking performance and stronger robustness of the proposed control scheme compared to conventional approaches have been confirmed through comparative experimental studies.
Evidence for localized onset of episodic mass loss in Mira
Authors: Perrin G. , Ridgway S. T. , Lacour S. , Haubois X. , Thiébaut É. , Berger J. P. , Lacasse M. G. , Millan-Gabet R. , Monnier J. D. , Pedretti E. , Ragland S. , Traub W.
Published: August 2020

Context. Mass loss from long-period variable stars (LPV) is an important contributor to the evolution of galactic abundances. Dust formation is understood to play an essential role in mass loss. It has, however, proven difficult to develop measurements that strongly constrain the location and timing of dust nucleation and acceleration.

Aims. Interferometric imaging has the potential to constrain the geometry and dynamics of mass loss. High angular resolution studies of various types have shown that LPVs have a distinct core-halo structure. These have also shown that LPV images commonly exhibit a non-circular shape. The nature of this shape and its implications are yet to be understood.

Methods. Multi-telescope interferometric measurements taken with the Interferometric Optical Telescope Array (IOTA) provide imagery of the LPV Mira in the H-band. This wavelength region is well suited to studying mass loss given the low continuum opacity, which allows for emission to be observed over a very long path in the stellar atmosphere and envelope.

Results. The observed visibilities are consistent with a simple core-halo model to represent the central object and the extended molecular layers but, in addition, they demonstrate a substantial asymmetry. An analysis with image reconstruction software shows that the asymmetry is consistent with a localized absorbing patch. The observed opacity is tentatively associated with small dust grains, which will grow substantially during a multi-year ejection process. Spatial information along with a deduced dust content of the cloud, known mass loss rates, and ejection velocities provide evidence for the pulsational pumping of the extended molecular layers. The cloud may be understood as a spatially local zone of enhanced dust formation, very near to the pulsating halo. The observed mass loss could be provided by several such active regions around the star.

Conclusions. This result provides an additional clue for better understanding the clumpiness of dust production in the atmosphere of AGB stars. It is compatible with scenarios where the combination of pulsation and convection play a key role in the process of mass loss.
Search and analysis of giant radio galaxies with associated nuclei (SAGAN)
Authors: Dabhade P. , Mahato M. , Bagchi J. , Saikia D. J. , Combes F. , Sankhyayan S. , Röttgering H. J. A. , Ho L. C. , Gaikwad M. , Raychaudhury S. , Vaidya B. , Guiderdoni B.
Published: August 2020

We present the first results of a project called SAGAN, which is dedicated solely to the studies of relatively rare megaparsec-scale radio galaxies in the Universe, called giant radio galaxies (GRGs). We have identified 162 new GRGs primarily from the NRAO VLA Sky Survey with sizes ranging from ∼0.71 Mpc to ∼2.82 Mpc in the redshift range of ∼0.03−0.95, of which 23 are hosted by quasars (giant radio quasars). As part of the project SAGAN, we have created a database of all known GRGs, the GRG catalogue, from the literature (including our new sample); it includes 820 sources. For the first time, we present the multi-wavelength properties of the largest sample of GRGs. This provides new insights into their nature. Our results establish that the distributions of the radio spectral index and the black hole mass of GRGs do not differ from the corresponding distributions of normal-sized radio galaxies (RGs). However, GRGs have a lower Eddington ratio than RGs. Using the mid-infrared data, we classified GRGs in terms of their accretion mode: either a high-power radiatively efficient high-excitation state, or a radiatively inefficient low-excitation state. This enabled us to compare key physical properties of their active galactic nuclei, such as the black hole mass, spin, Eddington ratio, jet kinetic power, total radio power, magnetic field, and size. We find that GRGs in high-excitation state statistically have larger sizes, stronger radio power, jet kinetic power, and higher Eddington ratio than those in low-excitation state. Our analysis reveals a strong correlation between the black hole Eddington ratio and the scaled jet kinetic power, which suggests a disc-jet coupling. Our environmental study reveals that ∼10% of all GRGs may reside at the centres of galaxy clusters, in a denser galactic environment, while the majority appears to reside in a sparse environment. The probability of finding the brightest cluster galaxy as a GRG is quite low and even lower for high-mass clusters. We present new results for GRGs that range from black hole mass to large-scale environment properties. We discuss their formation and growth scenarios, highlighting the key physical factors that cause them to reach their gigantic size.
Calf Vein Thrombosis Comparison of Outcomes for Axial and Muscular Venous Thrombosis
Authors: Kuczmik Wiktoria , Wysokinski Waldemar E. , Hesley Gina K. , Vlazny Danielle T. , Houghton Damon E. , Swanson Keith E. , Casanegra Ana I. , Hodge David , White Launia , McBane Robert D.
Published: August 2020

Background The objective of this study was to characterize clinical features and outcomes among patients with calf deep vein thrombosis (DVT) limited to the muscular veins compared with axial veins.

Methods Consecutive patients with ultrasound confirmed acute DVT involving the calf veins (January 1, 2016–August 1, 2018) were identified from the Gonda Vascular Center ultrasound database. Patients were divided into axial or muscular groups based on thrombus location. Demographics, management, and outcomes were compared.

Results Over the study period, there were 647 patients with calf DVT equally distributed between axial (n = 321) and muscular (n = 326) locations. Within these groups, peroneal and soleal veins were most commonly involved. Nearly all cases were provoked (97%). Synchronous pulmonary embolism (PE) were more common for axial (30.8%) compared to muscular groups (20.2%; p = 0.001); nearly one-third had no pulmonary symptoms. Anticoagulation for a median of 3 months was initiated for 85.5% of both groups. Venous thromboembolism (VTE) recurrence was more common in the axial group (15.9% vs. 7.1%, p = 0.0015) including more frequent DVT propagation (9.4% vs. 3.1%; p = 0.0017) and PE (3.4% vs. 0.6%; p = 0.0168). Major bleeding, clinically relevant nonmajor bleeding, and mortality rates did not differ between groups. Withholding anticoagulation led to more frequent thrombus propagation in the axial group (3.4% vs. 0.9%; p = 0.029).

Conclusion Several important features distinguish muscular from axial DVT. Axial DVT are more likely to have an associated PE and are more likely to experience recurrent VTE, particularly if anticoagulation is withheld.
A Novel Assessment of Factor VIII Activity by Template Matching Utilizing Weighted Average Parameters from Comprehensive Clot Waveform Analysis
Authors: Shimonishi Naruto , Ogiwara Kenichi , Oda Yukio , Kawabe Toshiki , Okazaki Shinji , Shima Midori , Nogami Keiji
Published: August 2020

Background Activated partial thromboplastin time (aPTT)-based clot waveform analysis is used to evaluate the comprehensive dynamics of fibrin clot formation. In addition, the technique can be usefully utilized for the rapid assessment of factor (F)VIII procoagulant activity in various clinical settings in patients with hemophilia A (HA). We defined a novel algorithm based on the weighted average parameters from aPTT-based waveforms to devise a template-matching procedure for assessing FVIII activity (FVIII:C).

Methods The first derivatives of original clot waveforms triggered by the aPTT reagent (Coagpia APTT-N) were used to determine weighted averages of areas surrounded by the waveform at different percentages of maximum height in various clotting factor-deficient plasmas. Prepared templates based on 50 weighted average-related parameters were compared with 78 aPTT-prolonged plasmas.

Results Original nonsmoothed waveforms of the various clotting factor-deficient plasmas with prolonged aPTTs demonstrated a variety of shapes. The weighted averages were calculated after adjustments for different baselines, and the patterns seemed to be governed by the specific clotting factor deficiency. The weighted average-related parameters including baseline wedge (r 2 = 0.998) and aspect ratio (r 2 = 0.998) were highly correlated with FVIII:C levels. Template-matching analyses based on weighted average-related waveform parameters obtained from 158 samples demonstrated that the sensitivity was 97.2% and specificity was 83.3% in aPTT-prolonged plasmas (n = 78).

Conclusion This novel algorithm based on weighted averages of aPTT-based waveforms together with template-matching may support clinical usefulness for judging of HA and may aid clinical management in the patients in the absence of specific clotting factor assays.
How interpreter-translators are assessed and hired in the market
Authors: Kim Hoonmil
Published: August 2020

In South Korea where there is no national accreditation system for translators and interpreters, it is left to the
respective employers to devise and administer an assessment instrument for hiring translators and interpreters. While studies
calling for reliable and valid testing and assessment in the field of Translation and Interpretation Studies have increased during
the past decade, empirical research on how tests and assessments are carried out in the marketplace, especially outside of Europe
and North America, remain scarce. This study closely examines how tests and assessments are carried out at hiring by tapping into
questions of how tests are developed, by whom, and who rates the tests and on which criteria. Then, the soundness of the overall
hiring process is evaluated based on the six qualities of Bachman and Palmer’s Test Usefulness Model; construct validity,
reliability, authenticity, interactiveness, impact, and practicality.

The study found that (1) the hiring process involves three stages of assessment: document screening, interpreting
and translation tests, and one-on-one interview; (2) the interpreting and translation tests are developed, administered and rated
by a combined group of experts: professors of interpreting and translation studies; professional interpreters/translators;
subject-matter experts at the institutions; (3) the overall usefulness of the tests based on Bachman and Palmer’s Test Usefulness
Model is medium to high; and (4) the employers of interpreter-translators look for qualities beyond interpreting/translation
skills upon hiring. The implications of the findings on professional translators and interpreters and educators are discussed,
followed by suggestions for future research.
An empirical study on impact of suggestopedia on student interpreters’ anxiety
Authors: Zhu Yuanyuan , Ruan Hongmei
Published: August 2020

This paper aims to investigate if suggestopedia, a teaching method that advocates “learning with both brains and
learning while relaxing”, could help reduce interpreting anxiety and improve interpreting results when applied to interpreting
classes. By using this teaching method in an experimental class and comparing the interpreting quality with that of the control
class, the paper draws conclusions on the influence of suggestopedia in reducing interpreting anxiety. Two interpreting results
and two questionnaires on interpreting anxiety, one before using the method and the other after, are collected to analyze the link
between suggestopedia and interpreting anxiety. The experiment took place over eight weeks and involved 51 students. The
experiment focused on English-Chinese consecutive interpretation.
The impact of source text presence on simultaneous interpreting performance in fast speeches
Authors: Yang Shanshan , Li Defeng , Lei Victoria Lai Cheng
Published: August 2020

The issue addressed in this study is the impact of source text presence on simultaneous interpreting performance in fast speeches. Fast speech rate is assumed to frustrate even professional interpreters in simultaneous interpreting (SI) without source text (ST) scenario, yet little is known about what happens when the ST is available to interpreters, an interpreting practice of increasing popularity. Previous studies present mixed results concerning the effect of fast speech rate and ST presence on SI quality, which further adds to the complexity of this issue. This study adopted the experimental approach with a qualitative assessment of SI quality, quantisation of output parameters and retrospective interview, to unravel some myths surrounding this issue. The 54 trainees were randomly divided into two groups to interpret two fast Chinese speeches into English under with and without ST conditions. The results found a significant effect of ST presence on the qualitative assessment of interpreting performance, on the interpreting delivery fluency, and on the information completeness. The facilitative effect of ST presence in fast speeches was confirmed while new issues also arise concerning trainee interpreters’ ability in eye-ear coordination.
Deconstructing discourses of diversity
Authors: Song Heejin
Published: August 2020

Despite the growing number of cross-cultural adolescents from immigrants and families formed through international/interracial marriages in South Korea, empirical studies investigating newcomer adolescents’ identity positioning in secondary education have been scarce. Drawing upon Ruíz’s (1984) framework for language policy and planning and Cummins’ (1986, 2001) empowerment framework for minority education, this article investigates how diversity is conceptualised in South Korea through a case study of multicultural education. Specifically, the article examines how newcomer adolescents’ linguistic and cultural identities are perceived by teachers and peers in two high schools. The findings revealed that diversity is dominantly viewed as an impediment to academic success for newcomer youth and is only appreciated once students are fully assimilated into Korean society. The concept of diversity as a resource and right and the notion of multicultural and multilingual identities in the Korean educational context are absent, or hidden, at best. Although there are examples where newcomer learners see diversity as empowerment and resources for their identity construction and positionality, this orientation has not been acknowledged in educational practices. The study calls for conceptualizing diversity as empowerment and resources as ethical lenses to move away from ethnocentric and deficit orientations.
Bilateral difference between lower limbs in children practicing laterally dominant vs. non‐laterally dominant sports
Authors: Guan Yanfei , Bredin Shannon , Taunton Jack , Jiang Qinxian , Wu Lina , Kaufman Kai , Wu Nana , Warburton Darren
Published: August 2020

Bilateral asymmetry in lower‐limb power and dynamic balance has been associated with increased risk of sport injury, whereas there is a lack of research examining this asymmetry for child athletes. Twenty‐eight fencers (19 boys and 9 girls, aged 9.71 ± 1.08 years) and 28 Taekwondo athletes (19 boys and 9 girls, aged 9.71 ± 1.08 years) were examined on the single‐leg jump and Star Excursion Balance Test (SEBT) performance. A mixed model design ANOVA (2 [Sex: Boys, Girls] × 2 [Sport group: Fencing, Taekwondo] × 2 [Limb: Dominant, Non‐dominant Leg]) was conducted to examine for difference for each test. There was a significant main effect of limb on hop and triple hop distance (p < 0.05). A significant limb by sex interaction (p = 0.000) was shown for the single‐leg countermovement jump (CMJ) performance, wherein a bilateral difference was only shown in boys. In SEBT, a main effect (p = 0.007) of limb was identified for posterolateral (PL) reach distance. A limb by sex interaction (p = 0.009) was also shown for posteromedial (PM) reach distance, wherein a bilateral difference was only shown in girls. These findings suggest that child athletes in both laterally dominant and non‐laterally dominant sports showed inter‐limb asymmetry of leg power and dynamic balance. Sex should be an important consideration when evaluating bilateral difference of leg power and dynamic balance for child athletes.
RETRACTED: Discussion on Optimization and Integration of Network Teaching Resources of College Ideological and Political Courses in the Era of Big Data
Authors: Ting Jiang
Published: August 2020

At the present, the information technology develops with high-speed, the advent of the period of big data allows people to analyse and handle various data generated in production and life, and discover the internal laws of event development based on these data. The operation of big data is helpful for us to grasp the development law of the event better, forecast and assess the future process current of the event. At the same time, the big data thinking mode helps us to get information and learn knowledge more deeply and quickly. If we apply the big data model at the area of college teaching, it will bring about tremendous changes to college education. Nowadays, the ideological and political education in Chinese colleges and universities still maintains traditional classroom education model. Recently, due to the epidemic situation, online education has gradually come into view. In the information age, online education will be the general trend, therefore, the usage of big data in high-school education will receive unprecedented attention. In the era of big data, university ideological and political education should up to date and fully reflect the features of the new age. In addition, we must integrate information technology into all aspects of the educational mechanism, organically combine data thinking with ideological and political education, and contribute to the development of my country’s university education. In this article, the author will discuss the optimization of network teaching resources of high-school ideological and political courses at the period of big data.
Application of Big Data Technology in Construction Organization and Management of Engineering Projects
Authors: Pei Lijian
Published: August 2020

Under the background of modernization construction, the construction engineering industry has also ushered in a new situation of new development. There are new changes in the organizational form and management connotations of its projects, and new challenges in the construction organization and management model. Based on this situation, big data technology is applied to the process of construction organization and management and is supported by relevant industries and advocated by the state. On the one hand, big data technology, which can fully integrate resources and analyze data, can provide the most accurate and comprehensive organization scheme in the construction organization process of the project. It is an indispensab le key link in the application of construction organization of engineering projects at this stage. On the other hand, under the application of big data technology, the management system of the project can be optimized and the refined management system can be improved. Therefore, this article analyzes its basic role and difficulties based on the application status of big data technology in engineering projects at this stage and carries out the following analysis and research on its specific application measures.
Automatic Analysis And Intelligent Information Extraction Of Remote Sensing Big Data
Authors: Li Ge , Jiajun Li
Published: August 2020

Aiming at the problems of low analysis accuracy, incomplete information extraction and low quality of traditional remote sensing data automatic analysis and extraction methods, a new remote sensing big data automatic analysis and intelligent information extraction method is proposed. Automatic analysis of remote sensing big data is realized through the expression, retrieval and understanding of remote sensing big data, and intelligent information extraction is carried out on the basis of automatic analysis of remote sensing big data. According to the procedures of remote sensing data mining, data preprocessing, feature acquisition, target recognition and evaluation, intelligent information extraction can be realized by utilizing the good learnability of convolutional neural network. In order to verify that the proposed method is superior to the traditional method, six different experimental areas are used as experimental objects, and a comparative experiment is designed. Experimental results: The proposed method automatically analyzes and extracts the remote sensing data, which is superior to the traditional method in terms of integrity, correctness and quality.
Research on Grid Data Model Fusion Based on Big Data Extraction Technology
Authors: Ruifeng Zhao , Shiming Li , Yang Liu , Bo Li , Jiangang Lu
Published: August 2020

The task of the electric power Internet of Things is to break through the various obstacles that form an open and shared energy ecology, which needs to be broken through by building an integrated energy system. Besides, the theoretical and practical application of cloud computing-based power big data task scheduling and extraction methods can help power companies to form the effect of resource sharing. Therefore, based on dynamic task priority, a scheduling algorithm is proposed in the paper to dynamically calculate the priority of all tasks, and effectively use idle periods to make tasks execute as quickly as possible to shorten the execution time, which is used for low-latency tasks such as power systems. After experimental analysis and comparison tests with other methods, it proves that the data extraction technology studied in the paper has the best effect and can be used to obtain better data sharing and access results in power companies.
The Application of Big Data Thinking in Ideological and Political Education
Authors: Jin Xiaomeng
Published: August 2020

The arrival of the era of big data has brought new vitality to the teaching of ideological and political education in universities. At the same time, the sparse, redundant and fuzzy features of massive data pose challenges to the application of big data in university ideological and political education. This article describes the establishment of the ideological and political education database, the application of various stages of data analysis in university ideological and political education and how to use the results of big data analysis for educational reflection and teaching reform. Only by using these data reasonably and effectively can the advantages of big data really benefit college teachers and students. At the same time, the problems of data leakage and privacy leakage that may exist in the process of big data analysis should also attract the teachers’ full attention and take appropriate measures to avoid these problems.
Research on construction of Railway Financial Sharing System based on Business-financial Integration
Authors: Dan Ying , Guo Zhida
Published: August 2020

In order to adapt to the need of railway financial reform and innovation and to strengthen the railway financial management advantages and risk control ability, this paper studies the construction of railway financial sharing service system based on the theory of business-financial integration. Combining the business-financial integration with the construction of railway financial sharing service system, this paper puts forward the construction idea, designs the organizational structure and analyses control mode of railway financial sharing service system. The key point is the overall structural design of railway financial sharing service system. And then specific implementation methods are given. Finally this paper explains the development choice path of railway financial staff transformation. The purpose of this paper is to realize the integration of railway operation and finance and to improve the overall operation quality and efficiency of railway system.
Comprehensive Analysis and Mining Big Data on Smart E-commerce User Behavior
Authors: Wang Yinggui , Wang Ben , Huang Yuxin
Published: August 2020

With the gradual development of big data technology and the rapid growth of e-commerce industry, big data comprehensive analysis technology is particularly important. Therefore, the user behavior and historical shopping data have been focused in recent progress of e-commerce industry. Based on e-commerce user data sets, this paper analyzes the user behavior based on Pareto principle (80-20 rule) and RFM data models. By statistically analyzing the behavior characteristics of users, user groups, user preferences, and purchase behavior characteristics, several models and research algorithms have been proposed to service targeted products and functions for e-commerce users, and figure out further research topics of e-commerce.
Application of RBF Neural Network in Navigation Filtering Algorithm
Authors: Yao Wenkai , Xing Liwen
Published: August 2020

As a high-precision and widely used navigation method, satellite navigation can provide users with all-weather and all-time services. In recent years, with the improvement of satellite positioning accuracy requirements, using conventional Kalman filter algorithm to solve the problem can not meet the requirements of high accuracy and low cost of satellite navigation. To solve these problems, this paper proposes the RBF Kalman fusion filtering algorithm. The original data without processing is compared with the results of traditional model and RBF Kalman fusion filtering model through the simulation experiment.The results show that the algorithm can better improve the positioning accuracy.
Research on Container Big Data Application
Authors: Xu Shibo , Wang Jing , Li Jichun , Cao Wensheng
Published: August 2020

The container transport is the most important equipment of global supply chain, which connects economy of most countries in the world. A huge amount of data will be generated during container transportation. In this paper, the current transportation risks and problems in container transportation are presented in detail, and the construction plan of the container big data platform is proposed, including how to obtain data, what types of data are obtained and how the big data platform optimizes the existing transportation safety and efficiency, and finally specific recommendations are put forward to better promote the construction of container big data platform.
Collaborative Filtering Recommendation Algorithm Based on User Characteristics and User Interests
Authors: Li Shangsong , Li Xuesong
Published: August 2020

Among many e-commerce platforms, collaborative filtering recommendation algorithm is currently the most widely used recommendation technology. In order to alleviate the deficiencies of the traditional user-based collaborative filtering algorithm in cold start and recommendation accuracy, this paper proposes a collaborative filtering recommendation algorithm based on user characteristics and user interests. The similarity of the algorithm in this paper is composed of user score similarity, user attribute feature similarity and user interest similarity, in which user registration information is used to extract attribute features to calculate user feature similarity; use the number of user evaluations of project attributes to measure users’ interest in different project attributes, use the similarity calculation formula to calculate the interest similarity value between users. The user attribute feature similarity and user interest similarity are combined with the user rating similarity to obtain the final similarity for recommendation. Finally, a simulation experiment is performed on the MovieLens movie data set. Through the experimental results, it can be seen that the improved collaborative filtering algorithm based on user characteristics and user interests not only solves the cold start problem, but also improves the recommendation accuracy.
Research on Spatial Pattern of Cultural and Creative Industries in Shanghai Based on POI Data
Authors: Zhao Qian , Hu Ziyi , Wu Tianyan
Published: August 2020

The combination of big data and cultural and creative industry provides reference for the smart layout of urban planning. Based on nuclear density estimation, location entropy analysis and Ripley’s K function analysis, this paper uses ArcGIS10.2 to study the spatial pattern of cultural and creative industries in Shanghai based on the point of interest (POI) data of various cultural and creative industries in Shanghai. The results show that the spatial distribution of Shanghai’s cultural and creative industries is characterized by agglomeration, presenting a “multi-core” spatial structure, and the industry space of various cultural and creative industries is distributed as “center - periphery”, showing different distribution patterns. The results also show that the multi-scale spatial clustering of all kinds of cultural and creative industries shows different manifestations. They all converge on a larger spatial scale and have a larger range of spatial choices.Then, there are obvious regional differences in the professional functions of various cultural and creative industries. The mature blocks show multi-functional elements and multi-advantageous industries are featured by agglomeration. The functions of the developing blocks are relatively single.
Personal Data Protection and Anonymization in the Process of Data Commodity Trading
Authors: Su Jing , Gai Jiale , Si Yaqing , Zheng Xinyu , Li Guangkai , Liu Zhixue
Published: August 2020

Big data brings tremendous commercial value, but at the same time, it also leads to personal information leakage and other problem. This paper analyses the laws and regulations of the personal information and anonymization at home and abroad, and discusses the legal identification criterion of personal information, personal data and data anonymization systematically. And combining with the practice, this paper provides general methods for anonymizing data with personal privacy when they are used or traded by companies.
Study on Taxi Driver Decision-Making of Beijing Capital International Airport Based on M / M / S model
Authors: Xu Baiyan , Liu Jiawei , Chen Long , JiabinDeng
Published: August 2020

Based on the ideas of queuing theory and data fitting, this paper uses Dev-cpp software to write C ++ programs and establishes a driver decision model. Firstly, the selection scheme of taxi drivers for Beijing Capital International Airport is given, and the rationality of the model and its dependence on related factors are analyzed using Dev-cpp software programming. Secondly, through the M / M / S model in the queuing theory model, it is verified that the “boarding point” arrangement scheme with the highest total boarding efficiency can be achieved under the premise of ensuring safety.
Identifying the Dimensions of Data Science as a Space
Authors: Huang Ruxin , Cai Xinlin , Liu Yuhang
Published: August 2020

An increasing number of data with great potential value are produced continuously, which exceeds the capacity of the existing computing system and promotes the generation of new basic theories about big data and its processing. Such problems as the relationship between big data and small data and that between big data and the existing scientific system need to be accurately solved by the academic circle through the establishment of data science. However, data science is still in its infancy and the basic dimensions of data science need to be identified. In this paper, five categories demanding for establishment of data science are summarized. A new way to understand big data that gives the most important speculation and research object of data science from a global perspective is developed.
DNS based Measurement and Analysis of IPv6 Domain Name Support
Authors: Li Feng , Cao Huaping , Ma Weijun , Hu Chao , Wang Zhanfeng , Lu Xiyu
Published: August 2020

At present, the IPv4 addresses has been fully allocated, and the worldwide deployment of IPv6 addresses is accelerating due to its advantages. In order to investigate current development of IPv6, the IPv6 addresses of domain names are firstly resolved by DNS servers, and the obtained IPv6 addresses data, mainly including the geographic location statistics of IPv6, the proportion of southeast coastal cities, the comprehensive statistics of top-level domain names, etc., can be used to analyze the domain name supported by IPv6. The analysis of the measurement results has great guiding significance for further understanding the future development and application of IPv6.
Application status and optimization prospect of China’s electricity prosperity index
Authors: Wang Xiang , Guo Lijie , Zhang Lili , Wu Shanshan
Published: August 2020

Electric power data can more realistically and objectively reflect the development trend of the national economy. The application of the boom index based on electric power data is an effective way for macroeconomic monitoring and early warning. This paper combs the application of the power prosperity index of the power industry, especially power grid companies, and finds that There are problems such as insufficient application of power data, traditional synthesis methods, irregular data processing, unscientific selection of benchmark indicators, and incomplete evaluation of index validity. Finally, the paper proposes the future direction of the research and application of power boom index optimization.
Bank Queuing Optimization Based on Markov Process
Authors: He Yuqi , Li Haoxuan
Published: August 2020

Queuing optimization is a troubling problem in the field of operations research and it is difficult to solve completely due to the complexity and uncertainty of the queuing environment. Among them, bank queuing optimization is the most widespread and representative in the queuing theory. This article focuses on using customer arrival and service of cashiers to establish birth-death process and its transfer rate matrix. By introducing service intensity and customer satisfaction index, it is concluded that the optimal number of open cashiers in different time period can be calculated and predicted.
Research on Travelers’ Travel Behaviour in Urban Central Business Districts Based on Nested Logit Model
Authors: Ying Dan , Guo Zhida , Ye Tianrui
Published: August 2020

From the perspective of individual travel, this paper studies the behavior of travel mode selection of travelers in downtown business districts. In this regard, this paper uses survey data on Dalian residents’ travel to summarize the travel characteristics of travelers and to explore the key factors that influence travelers’ choice of travel mode. Using all samples and considering that travelers with different travel purposes may have differences in the choice of travel modes, the travel behavior of travelers in the business center is modeled. Finally, the Nested Logit(NL)model is used to predict the traffic mode split rate of travelers in the central business district. And the variation trend of traffic mode split rate is predicted when the important influencing factor (car ownership) in the model changes. The results show that the sharing rate of cars increases significantly, while that of other modes of transportation decreases, and the fastest decline is in the order of bus and walking. This indicates that car ownership has a certain influence on the sharing rate of buses and cars. Therefore, reducing car ownership can, to a certain extent, discourage the use of cars and increase the proportion of travelers who choose public transportation.
Analysis of CO Concentration Monitoring Data in Apron Environment
Authors: Xu Enhua , Xu Yan
Published: August 2020

The paper analysed CO concentration monitoring data in apron environment to explore the influence factors, and proposed the additive model for CO considering the influence of internal and external factors. Linear interpolation filling the missing values could effectively solve the problem of data missing and improved the effect of the addictive model of ARIMA and multivariate linear regression. The addictive calibration model by ARIMA and Multiple regressions for CO was reconstructed based on linear interpolation filling. The error analysis showed that the accuracy of CO was improved. The prediction effect was also improved by considering the interaction effect.
Deep Learning Semantic Statistical Features’ Labeling Of SAR Image Based On K-MEANS Clustering And Wavelet Transform
Authors: Peng Jinxi , Su Yuanqi , Xue Xiaorong , Song Donghong , Xue Xiaoyong , Wu Aihua
Published: August 2020

[Purpose] Because Synthetic Aperture Radar (SAR) image contains much speckle, at present, it’s hard to obtain good results for SAR image with the traditional image classification methods. However, SAR image has rich texture information which facilitates SAR image segmentation. There is a Deep Learning method of K-means data clustering that is used to calculate the optimal k value to avoid more-segmentation or less-segmentation, the advantages of fuzzy theory and neural network are used to improve the classification accuracy of image processing.[Methods] According to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis, an efficient image segmentation method based on Deep Learning Semantic analysis and wavelet transform is proposed to achieve precision of classification; Firstly, the texture features of SAR image are extracted by Deep Learning Semantic clustering; Secondly, the SAR image are characterized according to SAR semantics. Based on one of the Deep Learning Semantic k-means algorithms, the best k-value iterations of SSE(Sum of the Squared Errors)and SC(Silhouette Coefficient) are performed, and the suitable value k is selected; Finally, by Deep Learning segmentation based on the texture feature of the SAR image and the filtered gray component vector, and the SAR image is classified to facilitate the change detection of images.[Results] The experimental results show that a good k value detection is performed on the CPU/GPU platform. The SAR images is compared with two different results between the wavelet filtering and Deep Learning semantic method images which are single or multiple standard classified through detection-change SAR is better than the Empirical Approach, then the labels are classified for the accuracy and computational efficiency that are calculated. The classification results were accurately ameliorated. [Conclusions] The similarity calculation of k-value cluster is an important precondition, so it’s necessary to select the optimal k-value suitable for the segmentation condition. The method achieves are taken an adequate SAR image analysis of effect and improvement for performance by semantic classification of k-means clustering.
A novel method for global minimization combined filled function method and dimensionality reduction technique
Authors: Wang Weixiang , Shang Youlin , Sun Guanglei
Published: August 2020

This paper introduces a new filled function method based on the dimensionality reduction technique for the global minimization problem. By utilizing the so called α - dense curves, we first transform the original function of n-variables into a single variable function, and then minimize the transformed function with the filled function method. The constructed filled function contains just one parameter which can be adjusted readily during the iterative process. The theoretical properties of the filled function are discussed, and the filled function algorithm is given. At last, a few numerical experiments are included.
Research on Sales Forecast Based on Prophet-SARIMA Combination Model
Authors: Zhao Jiantao , Zhang Chunwei
Published: August 2020

The stable development of e-commerce enterprises cannot be separated from scientific replenishment planning, and forecasting the sales volume of products helps e-commerce to make a reasonable replenishment plan, so as to speed up cash flow and reduce inventory pressure. Based on the in-depth study of Prophet model and seasonal differential autoregressive moving average model, as well as the analysis of daily sales volume of an e-commerce product from January 2015 to March 2018, a Prophet-SARIMA combination model for sales volume prediction is proposed. The principle of the combination model is to use prophet and SARIMA model to forecast the sales data respectively, and then weighted combination to get the final forecast results. Analyze and compare the prediction effect of single model and combination model. The first mock exam shows that the Prophet-SARIMA combination model has higher accuracy and stability in sales volume forecasting than single model. The sales volume forecast results of Prophet SARIMA combination model can provide an important basis for the e-commerce to supplement orders scientifically.
Survey and Statistical Analysis of Hangzhou Citizens’ Awareness of Harmful Blue Light and Consciousness of Protection Against Blue Light
Authors: Shen Qiuxin , Xu Liting , Jiang Yiyan , Zheng Ran , Zhang Yiping
Published: August 2020

The blue light hazards of electronics display screen have been concerned by people, and the corresponding protection of the blue light hazards has become the need of consumers. We took the citizens of Hangzhou as the sample survey object and we got the cognition degree of the citizens of Hangzhou to the harmful blue light and blue light-proof mobile phone membrane with the methods of the questionnaire survey and SPSS data analysis. The results showed that most people were not aware of the dangers of blue light, and many people had more or less certain eye problems. At the same time, the use of blue light-proof mobile phone membrane was not popular. The public still needs to increase their awareness of the dangers of blue light as well as the awareness of blue light prevention.
Research on Comprehensive Development of Shandong Province Based on Bayesian Quantile Regression
Authors: Guo Mengdi , Zhao Xiaoshan , Ge Yang
Published: August 2020

Based on the theme of coordinated development in Shandong Province, the article proposes coordinated indicators based on the three aspects of economy, society and ecology, and uses the principal component analysis method and entropy method to integrate the three aspects of economy, society and ecology in Shandong The indicators were selected. Then the Bayesian quantile model was used to construct an evaluation model and the coordinated development of Shandong Province was evaluated. It shows that Shandong Province needs to vigorously develop ecology and vigorously develop indicators that affect Shandong Province’s great changes if it wants to rapidly increase its comprehensive strength.
The Construction of College Students’ Habit Guidance System based on Action Control Theory
Authors: Li Guangyu
Published: August 2020

In modern society with rich information, the behavior of college students born after the year 2000 shows new characteristics. In the face of temptations, as well as the significant differences in learning methods between universities and high schools, freshmen are prone to be confused and shaken in their self-control, which brings pressure of academic difficulties. In order to help freshmen quickly adapt to the university learning methods and develop good learning habits, based on the theory of behavior control, this paper constructs the habit guidance system of freshmen from three aspects: Students’ network behavior, work and rest habits, and reading interest. We achieved satisfactory results through two years’ practice in Beihang University.
Improving Abstractive Summarization via Dilated Convolution
Authors: Jin Dawei , Kang Ruizhi , Zhang Hongjun , Hao Wening , Chen Gang
Published: August 2020

In this paper, a sequence-to-sequence based hybrid neural network model is proposed for abstractive summarization. Our method utilizes Bi-directional Long Short-Term Memory (Bi-LSTM) and multi-level dilated convolutions (MDC) to capture the global semantic information and semantic-unit level information, respectively. In decoding phrase, our model generates words according to summary relevant information captured by attention mechanism. Experiment shows that this proposed model outperforms several strong baselines on both of Gigawords corpus and DUC-2004 task.