bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electrochemical studies of ceramic-like MAO-coatings on the E110 alloy
Authors: Sivtsova G V , Savushkina S V , Ashmarin A A , Gerasimov M V , Suminov I V , Vinogradov A V 
Published: June 2021

Abstract
Ceramic-like coatings with the thickness up to 35 μm were formed on E110 zirconium alloy by microarc oxidation in electrolyte with the addition of yttrium oxide powder with average particle size 0.28 μm. The electrochemical behavior in 0.2M LiOH, structure, and phase composition of the obtained coatings have been studied. Polarization study in 0.2MLiOH at room temperature showed the anode currents on the coating up to 4 times less than on the uncoated alloy. Increasing the electrolyte temperature to 100 °C leads to growth in the anode currents on the coated samples, but current density values still correspond to the passive state.
Development of TiO2- and MWCNT based photocatalysts with Au and Cu clusters by electrophoretic deposition
Authors: Sorokina L I , Lebedev E A , Dubkov S V , Savitskiy A I , Maniecki T , Kedziora A , Gromov D G 
Published: June 2021

Abstract
This paper presents the features of electrophoretic deposition of TiO2, TiO2-$$$MWCNT composites. The influence of various dispersant additives in the suspension on the electrophoretic mobility of TiO2 particles and the morphology of the formed layer has been studied. The effect of modification by Au and Cu clusters on the photocatalytic activity was investigated.
Composition of glaze layer of the 18th century tiles
Authors: Vakhrushev V O , Wainstein D L , Kovalev A I , Konovalov E P , Mukhsinova A D , Shlykova T V 
Published: June 2021

Abstract
There were studied glaze layers of fragments of painted tiles estimated by art historians basing on external signs as fabricated in the Netherlands in the 18th century. The paper presents results of glaze surface composition measurements by X-ray fluorescence (XRF) and X-ray photoelectron spectroscopy (XPS), time-of-flight mass-spectroscopy (TOF-$$$SIMS) methods in comparison with literature data. Information on composition of glaze layers including pigments will be used obtained in this research will be used in further conservation efforts.
Mutual mass transfer between the target and the substrate in the process of ion sputtering. Flat and cylindrical target
Authors: Volpian O D , Dronsky R V , Alybin K V , Kuzmichev A I , Melnichenko M S 
Published: June 2021

Abstract
The ion sputtering process for the deposition of coatings from alternating layers of light and heavy metals are considered, taking into account the mutual mass transfer from the target to the substrate and from the substrate to the target as the result of resputtering the coating matter by reflected fast neutral gas atoms (former primary bombarding ions, backscattered from the target towards the substrate). The resputtering effect is more important in the case of deposition of a heavy metal of Ta type on layer from a light matter of Si type since it forms the intermediate layer of mixing composition “Ta + Si” between layers of pure metals.
Numerical model of the structural element complex geometry with a coating
Authors: Yakupov S N , Kiyamov H G , Yakupov N M 
Published: June 2021

Abstract
A model of the composition of complex geometry based on three-dimensional elements with a cubic approximation of the desired variables for the structural element and two-dimensional elements with a cubic approximation of the desired variables for the coating is described. An example of calculating the stress-strain state of a plate with thin steel coatings is considered.
Application of Data Mining Based on Computer Algorithm in Personalized Recommendation Service of University Smart Library
Authors: Jing Zhang 
Published: June 2021

Abstract
Smart library is an important channel to improve the reading ability of college students. It is an important means to cultivate students’ reading interest and cultivate the reading style of the whole people. The wisdom library focuses on wisdom, including service intelligence, management intelligence, and facility intelligence. In the process of promoting smart libraries, big data computing is at an important position, and computer algorithms are the core technology support. Therefore, it is of great significance to combine computer algorithm data mining with computer algorithms to analyze the application of data mining in the personalized recommendation service of university libraries. This paper analyzes the application of computer algorithm and big data mining technology in the personalized recommendation service of smart library, analyzes readers’ reading preferences and behavior habits, and proposes three measures for reference.
Research on the impact of R&D investment on the performance of equipment manufacturing enterprises
Authors: Zhao Xiangtong , Yu Zipeng 
Published: June 2021

Abstract
R&Dinvestment is a key factor for enterprises to enhance core competitiveness and achieve sustainable development, and also a key factor to improve the performance of equipment manufacturing enterprises. Taking 292 listed companies in equipment manufacturing industry as the research object, this paper explores the impact of R&D investment intensity and R&D personnel ratio on enterprise performance by analyzing the impact of R&D investment intensity and R&D personnel ratio on enterprise performance. The R&D input intensity of Listed Companies in China’s equipment manufacturing industry is at a medium level, and their competitiveness is not strong. There is a significant negative correlation between the current R&D input intensity and the current performance of the enterprise. There is a significant positive correlation between the current R&D input intensity and the current business performance of the enterprise. However, there is no significant relationship between the current R&D personnel ratio and the current performance of the enterprise.
Analysis of the Smart Library Construction in Colleges Based Big Data and Artificial Intelligence
Authors: Chen Zesheng , Zhou Min , Feng Lichun 
Published: June 2021

Abstract
With the rapid development of artificial intelligence in recent years, colleges and universities committed to information construction are seeking to transform from digital to intelligent. Among them, the smart library is an important part of university information construction. However, set up smart libraries face such problems as low rate of library entry and poor sense of experience. This paper analyses the status and difficulties of research and practices in smart libraries by crawler technology and questionnaire, and puts forward a smart library information construction scheme. The results show setting up a smart campus is in line with the new trend of education.
(λ, μ) Hesitant fuzzy subalgebras of Boolean algebras
Authors: Man Jiang 
Published: June 2021

Abstract
In this paper, the concept of (λ, μ)-hesitant fuzzy subalgebras is introduced in Boolean algebra. Some properties of (λ, μ)-hesitant fuzzy subalgebras are discussed. Finally, we proved that the intersection and direct product of two (λ, μ)-hesitant fuzzy subalgebras are also (λ, μ)-hesitant fuzzy subalgebras in Boolean algebra.
Evolving Data Monitoring Algorithm based on Data Stream Clustering
Authors: Yang Chunhua , Wang Cong , Hu Xiao , You Niankang , Yang Xuguang 
Published: June 2021

Abstract
Clustering is one of the most important methods in the data stream mining. The nature of evolving data streams puts forward the following requirements for clustering: discovery of clusters with arbitrary shape, determination of the optimal number of clusters, anomaly detection and processing the influence of time. In the communication process, timely detection of anomalies is very important for the coordination of various devices. In this paper, we propose an adaptive density-based approach for clustering in evolving data streams(SagsDStream), which automatically updates parameters in subsequent stages based on the offline clustering result, so that the parameters can adapt to the change of data stream. In the online mode, the judging rules are saved in Improved Time Cluster Feature (ITCF). In the offline model, we use the density of the data distribution and the cluster distance to set the parameters of clustering algorithm, which overcome the sensitivity of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) threshold setting. Experimental results show that SagsDStream has better processing efficiency and clustering effect when dealing with data streams.
Air Quality Index Monitoring Based on Profile-MEWMA Control Chart
Authors: Liu Yingjie , Zi Xuemin 
Published: June 2021

Abstract
At present, due to the rapid development of domestic economy, the environmental pollution problems are becoming increasingly serious, especially the pollution problems such as PM2.5 and PM10 caused by industrial emissions and waste gas pollution are most urgent. The general linear profile model and multivariate exponential weighted moving average control chart (MEWMA) is designed to perform monitoring and evaluating the air quality index (AQI) in Tianjin, so as to provide some statistical information for the relevant departments. Six main pollutants SO2,NO2,PM10,PM2.5,O3,CO in Tianjin were collected which taken as factors affecting AQI under consider establish a general linear profile model where AQI was taken as the response variable. The coefficients of the model are monitoring by an MEWMA control chart.
MLU-Net: Efficient Segmentation for Retinal Layers In Optical Coherence Tomography Images
Authors: Xu Xiangcong , Wang Xuehua , Han Dingan , Guo Xuedong , Lin Jingyi , Xiong Ke , Zheng Yixu , Zeng Yaguang 
Published: June 2021

Abstract
The automatic segmentation of retinal images obtained by optical coherence tomography is increasingly important for ophthalmologists to diagnose and monitor many kinds of ophthalmic diseases. U-Net is the most widely used deep learning network in retinal segmentation, but the limited number of data-flow paths made it hard to capture complex features. We proposed here an optimized Mobile Ladder U-Net (MLU-Net), which consists of a Ladder Connection for increasing the network’s data-flow paths and a depthwise separable convolution for reducing the model’s parameters. Experiments on 100 B-scans from 10 human eyes demonstrated that the 9 retinal layer boundaries can be segmented accurately with the MLU-Net. In addition, compared with the original U-Net and LadderNet, the segmentation result of our method is closest to the expert label.
Enhancing the Transferability of Adversarial Attacks with Input Transformation
Authors: Yang Bo , Zhang Hengwei , Xu Kaiyong 
Published: June 2021

Abstract
Deep neural networks (DNNs) are challenged by their vulnerability to adversarial examples, which are crafted by adding small, human-imperceptible perturbations to the original images, but make the model output inaccurate predictions. Adversarial attacks can thus be an important tool to evaluate and select robust models before they are deployed. However, under the challenging black-box setting, most of existing adversarial attacks can only fool a model with a low success rate. Based on image augmentation methods, we found that random transformation of image size can eliminate overfitting in the generation of adversarial examples and improve their transferability. To this end, we propose an adversarial example generation method based on this phenomenon, which can be integrated with Fast Gradient Sign Method (FGSM)-related methods to build a stronger gradient-based attack and generate adversarial examples with better transferability. Extensive experiments on the ImageNet dataset demonstrate that our methods attack both normally trained models and adversarially trained models with higher attack success rates than existing baseline attacks. We hope that our proposed attack method can serve as a benchmark for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods.
The Representation of Large-Scale Graph Based on Semi-Supercised Learning
Authors: Tian-qing Yang , Jing-zong Yang , Hui Wang 
Published: June 2021

Abstract
With the development of artificial intelligence, the technology of graph representation has been widely used in graph structure data mining. The traditional graph representation mostly uses adjacency matrix. The representation of adjacency matrix is faced with two major challenges. One reason is extremely inefficient in the storage and computing of large graphs with the massive growth of data; the other is that the characterized data is difficult to retain the original structural information of graphs. Aiming at the above two points, an efficient graph representation method is proposed in this paper. The first-order similarity and second-order similarity of the graph are used as constraints to train the original graph structure data, and the nodes in the graph are mapped to a low-dimensional space, which greatly reduces the cost of storage space and computing while retaining the graph structure information. Experimental results show that the proposed method is significantly better than the Deepwalk and spectral clustering methods in different proportion of label data. Meanwhile, the proposed method can be applied to the semi-supervised learning task with a small number of labels, which provides convenience for the subsequent data analysis.
The Impact of Assets Accelerated Depreciation on Enterprise Investment Based on Statistical Analysis
Authors: Wang Chaofan 
Published: June 2021

Abstract
With the continuous upgrade of consumption structure, the supply and demand contradictions continue to deepen, and the Ministry of Finance issued by the Ministry of Finance in October 2014 stipulates that the six major industries can adopt accelerated depreciation of newly purchased fixed assets. The purpose of item initiative is to further promote the reform of the supply side. Based on the newly purchased fixed assets in 2010-2017, this paper discovered the research of difference-in-difference model analysis by using statistical methods. The policy significantly reduced the tax burden of the pilot industry, significantly improved the fixed asset investment in the pilot industry, further Promote the structural reform of the supply side of the company, and promoting the company’s improvement efficiency. This paper helps to deepen the exploration of the impact of relevant depreciation policies, which provides a reference for China’s better supply side structural reform, and promoting the development of enterprises, which is helpful to the development of deep application research of mathematical economics
The Theoretical Breakthrough of Self-Supervised Learning : Variational Autoencoders and Its Application In Big Data Analysis
Authors: Ji Yanqiu , Lu Zhiyi 
Published: June 2021

Abstract
In the study of big data statistics, a large number of unknown latent variables bring great difficulties to modeling. Variational autoencoders(VAE) can overcome the shortcomings of traditional variational methods such as low efficiency and poor generality, and provide an efficient and extensible framework for variational posteriori inference and approximate maximum likelihood learning based on gradient. On the basis of reviewing the development history of variational autoencoders and taking the deep latent variable model(DLVM) as an example, this paper introduces the basic principle of variational autoencoders and analyzes its application under the background of big data. The problems in theory and application of variational autoencoders are presented, as well as the topics to be further studied. The combination of variational autoencoders and other statistical modeling methods may become a new idea for big data research.
An ensemble learning method for variable selection and its application on Railroad Fatal Accidents.
Authors: Chen Jing , Yan Yan , Chen Xi 
Published: June 2021

AbstractScalars based on linear and generalized linear models are commonly used in fatality and disease predictions. Currently, standard approaches for variable selection in linear and generalized linear models are not explored well with high dimensional data. This manuscript proposed a new variable selection method for logistics models with inspiration from heterogeneous ensemble methods. The goal of this study is to extend classical variables selection methods such as stepwise, lasso, or RF in the case of high dimensional accident data with missing values. Real world accidents-collated data sets are collected from the official website of US Railroad Administration and US Police Department from 2000 to 2015. Preprocessing includes log transformation and z score transformation. Simulated sampling was applied due to the imbalanced outcome. The base variables was selected from the Empirical Model (EM) [1], Lasso model[2], Xgboost[3] and the traditional stepwise backward. An intermediate selection was proposed by ensemble training of base variables. Final models built by variables selected improved Area Under the Curve (AUC) from 0.79 to 0.84 and from 0.79 to 0.84 in test data compared to arbitrary APS model. Comparison with the existing model shows that the logistics model based on selected variables has high accuracy.
Study of cell clustering optimization algorithm for load balancing in 5G scenario
Authors: Wang Yuhan 
Published: June 2021

Abstract
In the context of 5G networks at this stage, massive data transmission and highly dense deployment of base stations make severe interference and huge load volumes an inevitable new challenge. The traditional interference coordinated cell-clustering scheme only considers the inter-micro base station path loss without jointly considering load balancing, an important factor affecting system performance. This paper first introduces the existing cell-clustering schemes, and then proposes a cell-clustering optimization algorithm that combines path loss and load balancing on the basis of 5G cellular network architecture, and then simulate the optimization algorithm and compare the results with the traditional schemes to verify its ability to balance the load, increase the amount of transceiver data, and optimize the resource allocation and performance effects.
Link Prediction Algorithm Based on Node Structure Similarity Measured by Relative Entropy
Authors: Yuyu Meng , Jing Guo 
Published: June 2021

Abstract
To solve the problem that the link prediction method based on local information ignores the influence of neighbor structure information on the similarity measurement of nodes, a link prediction method based on relative entropy and local structure of nodes is proposed. Firstly, the second-order local network is introduced to describe the local structure of nodes; then, the structural similarity between nodes is described by redefining the relative entropy; finally, the structural similarity of nodes is measured based on relative entropy, and the structural similarity index of the node structure is proposed considering the structure information of the neighbor. Experimental results on 7 real network data sets show that the proposed method can achieve better results and can be applied to networks with a small average aggregation coefficient compared with other similarity indexes based on local and global information, and also have better performance on large-scale networks.
Research on equipment health diagnosis system of railway passenger station based on cloud-edge collaboration
Authors: He Xiaoling , Xu Chunjie , Chen Ruifeng , Wang Jing , Li Jun 
Published: June 2021

Abstract
In view of the current operation and maintenance mode for railway station equipment, the method of traditional manual and post-diagnosis for the equipment faults is still used, which cannot meet the reliability requirements of equipment operation and maintenance management. A health diagnosis system based on cloud-edge collaboration for railway station equipment is proposed. Firstly, it collects online operation information of multi-source perception equipment, which realized through edge intelligent gateway by componentized micro- service and Docker technology. Moreover, it realizes data storage and publishing, edge detection, monitoring online and early warning through the intellectual server models. Secondly, a life prediction model is established based on HSMM to calculate the health status and service life of the equipment. Finally, the cloud-edge collaboration model is designed based on KubeEdge to realize the cloud-edge communication and collaborative service of resources. It effectively solves the problems of remote condition monitoring, health diagnosis and predictive maintenance for railway station equipment, and it provides technical support for the safe operation of railway station.
Attack path prediction based on Bayesian game model
Authors: Sun Pengyu , Zhang Hengwei , Li Chenwei 
Published: June 2021

Abstract
The current network risk assessment model often ignores the impact of attack cost and intrusion intention on network security. In order to better solve the problem of information security defense strategy selection and accurately assess the target network risk, this paper proposes an attack path prediction method based on game model.The atomic attack probability is calculated by vulnerability value, attack cost and attack benefit. The static risk assessment model is established combined with Bayesian belief network quantitative attack graph. And the dynamic update model of intrusion intention is used to realize the effective prediction of attack action under rational assumption, which provides the basis for dynamic defense measures of attack surface. The experimental results verify the feasibility and effectiveness of the model and method.
Research on the spatio-temporal characteristics of hornets’ nests based on K-means clustering
Authors: Mijia Li , Lei Ma , Zhejun Cai 
Published: June 2021

Abstract
Asia giant hornet is a predator of European honeybees, invading and destroying their nests. Nowadays, the State of Washington is suffering the attack of this hornet. Due to the potential severe impact on local honeybee populations, it will probably cause a great deal of anxiety.
The most informative factors we consider are Detection Date,latitude and longitude. We propose a “time-space-state model” to evaluate the information evaluated by these two indicators. The time-space-state model can be divided into two kinds: the general model of time discretization and space continuum; The general model of space discretization and time semi-continuity. In the previous model, we mainly use K-means algorithm to determine the nest position in discrete time. In the latter model, we use the improved K-means algorithm to obtain the distribution model of hornets with seasonal variation.
Through research, we have discovered the temporal and spatial laws of hornets’ nests, which are of great significance for maintaining the stability of agricultural production.