bluuebunny/crossref_metadata_2025 huggingface dataset:
Editorial
Authors: Baerwald Christoph
Published: October 2020

Unverändert ist der Schmerz das häufigste Symptom, was einen
Patienten zum Arzt führt. Gerade bei rheumatischen Erkrankungen ist der
Schmerz das führende Symptom und wenn wir epidemiologische Daten unter
anderem auch aus der Kerndokumentation sehen, dann ist der Schmerz auch im Verlauf
der Erkrankungen ein immenses Problem für viele Patienten. Das jetzt
vorliegende Heft hat sich des Themas „Schmerz“ angenommen und es
werden verschiedene Facetten beleuchtet.
In situ x-ray and electron scattering studies of oxide molecular beam epitaxial growth
Authors: Yan Xi , Wrobel Friederike , Li Yan , Zhou Hua , Wang Huan-hua , Bhattacharya Anand , Sun Jirong , Hong Hawoong , Fong Dillon D.
Published: October 2020

We perform in situ synchrotron x-ray ray diffraction (SXRD)/reflection high energy electron diffraction (RHEED) studies on the growth of complex oxide thin films by molecular beam epitaxy. The unique deposition chamber, located at the Advanced Photon Source, allows the preparation of complex oxide samples with monolayer precision and facilitates the formation of direct correlations between in situ x-ray studies and the more prevalent RHEED investigations. Importantly, because SXRD and RHEED probe different atomic-scale processes during thin film synthesis, their concomitant use enables the extraction of details concerning growth behavior that one cannot determine from either probe alone. We describe the results of such in situ studies on the epitaxial growth of perovskite LaNiO3 on (La0.18Sr0.82)(Al0.59Ta0.41)O3 (001). We find that during the earliest stages of growth, the RHEED and x-ray signals do not agree with each other, demonstrating that while regular RHEED oscillations may imply high quality growth, the film–substrate interface can undergo significant changes during deposition due to the occurrence of interdiffusion at the growth temperature.
Mechanical and acoustic properties of a hybrid organic–inorganic perovskite, TMCM-CdCl3, with large piezoelectricity
Authors: Guo Tian-Meng , Gao Fei-Fei , Li Zhi-Gang , Liu Yiming , Yu Mei-Hui , Li Wei
Published: October 2020

Piezoelectric hybrid organic–inorganic perovskites have attracted significant attention due to their outstanding piezoelectricity comparable to perovskite oxides. However, their elastic properties, which determine the piezoelectric performance, have not yet been explored. Here, the elastic properties of a perovskite ferroelectric TMCM-CdCl3 (TMCM = trimethylchloromethyl ammonium) with a large piezoelectric effect are comprehensively investigated. High-pressure synchrotron powder x-ray diffraction experiments reveal that it exhibits a low bulk modulus of 14.58 GPa, which is an order of magnitude lower than those of perovskite oxides. In addition, Young’s moduli and shear moduli were calculated using density functional theory, and their values are significantly smaller than perovskite piezoceramics. Furthermore, the acoustic properties of TMCM-CdCl3 were calculated. Its longitudinal wave velocity is 3.86 km s−1–4.94 km s−1, and the corresponding specific acoustic impedance is 8.28 MRayl–10.21 MRayl. These maximum values are, respectively, 31.5%, 74.4%, and 8.4%, 73.1% lower than those from BaTiO3 and PbTiO3. Our findings disclose the soft nature and low acoustic impedance of hybrid perovskite piezoelectrics, which are critical to their applications in energy conversion devices.
Overlapping subjunctive forms in Gallo- and Ibero-Romance verb paradigms
Authors: Esher Louise
Published: October 2020

In several varieties of Catalan, Valencian and Occitan, inflectional exponents originating in the imperfect
subjunctive (reflex of Latin pluperfect subjunctive) are analogically extended into the first and second person plural present
subjunctive forms, resulting in syncretism between present and imperfect subjunctive forms for the relevant persons. The scope and
directionality of such extensions are remarkably consistent, and are indicative of a change driven by the structure of the
inflectional paradigm in the relevant varieties. A significant consequence of this development, which fits into a general Romance
tendency for analogical remodelling of first and second person plural forms, is alignment between previously overlapping
distributions of stem allomorphs and stress placement, and thus greater predictability of inflected forms.
Plasma augmented structural and electrical properties of half doped neodymium strontium manganites
Authors: Chettri Pronita , Sarma Arun , Okram G S , Rao Ashok , Deka Utpal
Published: October 2020

The reduction in resistivity and modification of the thermoelectric power of perovskite oxides is in continuous demand because of its large scale industrial applications. Currently, electron or ion beam irradiations are the most preferred methods employed to improve its properties. The quantum of improvement achieved with these methods is appreciable, however, it involves intriguing technicalities in the production of the beams. Plasma is a natural medium with the presence of both electrons and ions that can be easily produced using the glow discharge technique. In this work, the effect of oxygen plasma exposure on the structural properties, electrical resistivity, and thermopower of Nd0.5Sr0.5MnO3 manganites is investigated. Powdered Nd0.5Sr0.5MnO3 manganites samples are prepared using the solid-state reaction method. It is found that plasma exposure has changed the lattice parameters thereby varying the cell volume, which are not normally seen in the case of electron beam exposure. Further, the electrical resistivity is observed to decrease significantly after plasma exposure that changed the transport properties. It is also observed that plasma exposure decreases the absolute value of thermopower at low temperatures but increases it at high temperatures.
Optimal parameter estimation of open quantum systems*
Authors: Ji Yinghua , Ke Qiang , Hu Juju
Published: October 2020

In quantum information technologies, quantum weak measurement is beneficial for protecting coherence of systems. In order to further improve the protection effect of quantum weak measurement on coherence, we propose an optimization scheme of quantum Fisher information (QFI) protection in an open quantum system by combing no-knowledge quantum feedback control with quantum weak measurement. On the basis of solving the dynamic equations of a stochastic two-level quantum system under feedback control, we compare the effects of different feedback Hamiltonians on QFI and find that via no-knowledge quantum feedback, the observation operator σx
(or σx
and σz
) can protect QFI for a long time. Namely, no-knowledge quantum feedback can improve the estimation precision of feedback coefficient as well as that of detection coefficient.
An electromagnetic view of relay time in propagation of neural signals*
Authors: Xu Jing-Jing , Xu San-Jin , Wang Fan , Xu Sheng-Yong
Published: October 2020

We review the experimental and computational data about the propagation of neural signals in myelinated axons in mice, cats, rabbits, and frogs published in the past five decades. In contrast to the natural assumption that neural signals occur one by one in time and in space, we figure out that neural signals are highly overlapped in time between neighboring nodes. This phenomenon was occasionally illustrated in some early reports, but seemed to have been overlooked for some time. The shift in time between two successive neural signals from neighboring nodes, defined as relay time τ, was calculated to be only 16.3 μs–87.0 μs, i.e., 0.8 %–4.4 % of the average duration of an action potential peak (roughly 2 ms). We present a clearer picture of the exact physical process about how the information transmits along a myelinated axon, rather than a whole action potential peak, what is transmitted is only a rising electric field caused by transmembrane ion flows. Here in the paper, τ represents the waiting time until the neighboring node senses an attenuated electric field reaching the threshold to trigger the open state. The mechanisms addressed in this work have the potential to be universal, and may hold clues to revealing the exact triggering processes of voltage-gated ion channels and various brain functions.
Evaporation of nanoscale water on solid surfaces*
Authors: Wan Rongzheng , Fang Haiping
Published: October 2020

The evaporation of water is essential in the macroscopic world. Recent researches show that, on solid surfaces, the evaporation of nanoscale water is quite different from that on bulk water surfaces. In this review, we show the theoretical progress in the study of nanoscale water evaporation on various solid surfaces: the evaporation rate of nanoscale water does not show a monotonic decrease when the solid surface changes from hydrophobic to hydrophilic; the evaporation of nanoscale water on hydrophobic–hydrophilic patterned surfaces is unexpectedly faster than that on uniform surface; the evaporation of nanoscale water on patterned graphene oxide is faster than that on homogeneous one; how temperature affects the evaporation of nanoscale water on solid surface; how ions affect the evaporation of nanoscale water on graphene oxide.
Machine learning identification of impurities in the STM images*
Authors: Wang Ce , Li Haiwei , Hao Zhenqi , Li Xintong , Zou Changwei , Cai Peng , Wang Yayu , You Yi-Zhuang , Zhai Hui
Published: October 2020

We train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope (STM) measurements. The neural network is first trained with a large number of simulated data and then the trained neural network is applied to identify a set of experimental images taken at different voltages. We use the convolutional neural network to extract features from the images and also implement the attention mechanism to capture the correlations between images taken at different voltages. We note that the simulated data can capture the universal Friedel oscillation but cannot properly describe the non-universal physics short-range physics nearby an impurity, as well as noises in the experimental data. And we emphasize that the key of this approach is to properly deal with these differences between simulated data and experimental data. Here we show that even by including uncorrelated white noises in the simulated data, the performance of the neural network on experimental data can be significantly improved. To prevent the neural network from learning unphysical short-range physics, we also develop another method to evaluate the confidence of the neural network prediction on experimental data and to add this confidence measure into the loss function. We show that adding such an extra loss function can also improve the performance on experimental data. Our research can inspire future similar applications of machine learning on experimental data analysis.
Electron dynamics of active mode-locking terahertz quantum cascade laser*
Authors: Hou Qiushi , Wang Chang , Cao Juncheng
Published: October 2020

The pulse generation from active mode-locking terahertz quantum cascade laser is studied by Maxwell–Bloch equations. It is shown that longer dephasing time will lead to multiple pulses generation from the laser. The dependence of output field on modulation length and radio-frequency parameters is obtained. In order to achieve short pulse generation, the DC bias should close to threshold value and modulation length should be shorter than 0.256 mm. The output pulse is unstable and the envelope shows many oscillations in the presence of spatial hole burning, resulting destabilization of mode-locking.
Chaotic dynamics of complex trajectory and its quantum signature*
Authors: Zhao Wen-Lei , Gong Pengkai , Wang Jiaozi , Wang Qian
Published: October 2020

We investigate both the quantum and classical dynamics of a non-Hermitian system via a kicked rotor model with

P
T

symmetry. For the quantum dynamics, both the mean momentum and mean square of momentum exhibit the staircase growth with time when the system parameter is in the neighborhood of the

P
T

symmetry breaking point. If the system parameter is much larger than the

P
T

symmetry breaking point, the accelerator mode results in the directed spreading of the wavepackets as well as the ballistic diffusion in momentum space. For the classical dynamics, the non-Hermitian kicking potential leads to the exponentially-fast increase of classical complex trajectories. As a consequence, the imaginary part of the trajectories exponentially diffuses with time, while the real part exhibits the normal diffusion. Our analytical prediction of the exponential diffusion of imaginary momentum and its breakdown time is in good agreement with numerical results. The quantum signature of the chaotic diffusion of the complex trajectories is reflected by the dynamics of the out-of-time-order correlators (OTOC). In the semiclassical regime, the rate of the exponential increase of the OTOC is equal to that of the exponential diffusion of the complex trajectories.
Modeling, simulations, and optimizations of gallium oxide on gallium–nitride Schottky barrier diodes
Authors: Fang Tao , Li Ling-Qi , Xia Guang-Rui , Yu Hong-Yu
Published: October 2020

With technology computer-aided design (TCAD) simulation software, we design a new structure of gallium oxide on gallium–nitride Schottky barrier diode (SBD). The parameters of gallium oxide are defined as new material parameters in the material library, and the SBD turn-on and breakdown behavior are simulated. The simulation results reveal that this new structure has a larger turn-on current than Ga2O3 SBD and a larger breakdown voltage than GaN SBD. Also, to solve the lattice mismatch problem in the real epitaxy, we add a ZnO layer as a transition layer. The simulations show that the device still has good properties after adding this layer.
A local refinement purely meshless scheme for time fractional nonlinear Schrödinger equation in irregular geometry region*
Authors: Jiang Tao , Jiang Rong-Rong , Huang Jin-Jing , Ding Jiu , Ren Jin-Lian
Published: October 2020

A local refinement hybrid scheme (LRCSPH-FDM) is proposed to solve the two-dimensional (2D) time fractional nonlinear Schrödinger equation (TF-NLSE) in regularly or irregularly shaped domains, and extends the scheme to predict the quantum mechanical properties governed by the time fractional Gross–Pitaevskii equation (TF-GPE) with the rotating Bose–Einstein condensate. It is the first application of the purely meshless method to the TF-NLSE to the author’s knowledge. The proposed LRCSPH-FDM (which is based on a local refinement corrected SPH method combined with FDM) is derived by using the finite difference scheme (FDM) to discretize the Caputo TF term, followed by using a corrected smoothed particle hydrodynamics (CSPH) scheme continuously without using the kernel derivative to approximate the spatial derivatives. Meanwhile, the local refinement technique is adopted to reduce the numerical error. In numerical simulations, the complex irregular geometry is considered to show the flexibility of the purely meshless particle method and its advantages over the grid-based method. The numerical convergence rate and merits of the proposed LRCSPH-FDM are illustrated by solving several 1D/2D (where 1D stands for one-dimensional) analytical TF-NLSEs in a rectangular region (with regular or irregular particle distribution) or in a region with irregular geometry. The proposed method is then used to predict the complex nonlinear dynamic characters of 2D TF-NLSE/TF-GPE in a complex irregular domain, and the results from the posed method are compared with those from the FDM. All the numerical results show that the present method has a good accuracy and flexible application capacity for the TF-NLSE/GPE in regions of a complex shape.
Machine learning in materials design: Algorithm and application*
Authors: Song Zhilong , Chen Xiwen , Meng Fanbin , Cheng Guanjian , Wang Chen , Sun Zhongti , Yin Wan-Jian
Published: October 2020

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning (ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm: (i) data acquisition → (ii) feature engineering → (iii) algorithm → (iv) ML model → (v) model evaluation → (vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’: (i) structure and composition → (ii) property → (iii) synthesis → (iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.
Bulk viscosity for interacting strange quark matter and r-mode instability windows for strange stars *
Authors: Xu Jian-Feng , Kang Dong-Biao , Peng Guang-Xiong , Xia Cheng-Jun
Published: October 2020

We investigate the bulk viscosity of strange quark matter in the framework of the equivparticle model, where analytical formulae are obtained for certain temperature ranges, which can be readily applied to those with various quark mass scalings. In the case of adopting a quark mass scaling with both linear confinement and perturbative interactions, the obtained bulk viscosity increases by

orders of magnitude compared with those in bag model scenarios. Such an enhancement is mainly due to the large quark equivalent masses adopted in the equivparticle model, which are essentially attributed to the strong interquark interactions and are related to the dynamical chiral symmetry breaking. Due to the high bulk viscosity, the predicted damping time of oscillations for a canonical 1.4

strange star is less than one millisecond, which is shorter than previous findings. Consequently, the obtained

-mode instability window for the canonical strange stars well accommodates the observational frequencies and temperatures for pulsars in low-mass X-ray binaries (LMXBs).
A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG
Authors: Bahador Nooshin , Erikson Kristo , Laurila Jouko , Koskenkari Juha , Ala-Kokko Tero , Kortelainen Jukka
Published: October 2020

Objective. When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance. Approach. A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact. Main results. The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset. Significance. This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.
Research on the Visual Interaction Design of Tourism Destination Brands Based on Regional Features
Authors: Zhang Yanhua , Wang Xiaoyan
Published: October 2020

In the process of rapid development of my country’s tourism industry, there are serious problems in the homogeneity of tourist destinations and tourism projects, which will affect the brand building effect of tourist destinations to a certain extent. Through the analysis of the design theory and design model of tourism destination image in the current development process of tourism industry, we can find that regional brand is the main core in the design process of tourism destination. Visual design plays an important role in shaping the brand image of tourist destinations. Therefore, we need to pay attention to the effective connection between the image design of regional tourism destinations, and explore the methods and approaches of image design for regional tourism destinations. Only in this way can the brand design level of the tourist destination be improved.
Application and Research of Computer VR Technology in Digital Media System Design
Authors: Yu Sihui , Chen Fuzhong , Guo Tianyi , Meng Hui , Chen Xiaoman , Li Zhengpeng
Published: October 2020

This paper first analyzes the definition of computer VR technology and the transmission of information data, and then expounds how to use VR technology to establish a complete digital media design system; Finally, deeply analyzes the application of VR technology in the design of digital media system, including the detailed discussion and planning of user interaction, information dissemination mode and information reception mode. According to the current situation of digital media development in China, the computer VR technology is introduced to improve the operation process of digital media, so that the digital media design system can be improved.
RETRACTED: Innovation and Practice of College English Teaching Model Based on Computer Network
Authors: Zhao Qian , Li Chunyan
Published: October 2020

This article analyzes the current status of the application of the college English teaching model, including students’ low interest in learning, strong unity of teaching model, and a large proportion of theoretical courses. The author combines the application characteristics and application value of the computer network, through studying how to do a good job of innovation in the way of thinking, innovative supervision methods and teaching methods, make full use of the advantages of the network, establish a teaching resource database, and improve the security of data information. The purpose of this article is to give full play to the advantages of computer network applications and to improve the applicability and effectiveness of the college English teaching model.
The Application of Art Design Thinking in Visual Works from the Perspective of Digital Media
Authors: Wang Xiaoyan , Zhang Yanhua
Published: October 2020

In the process of rapid development of information technology, the development speed of the information age is also relatively fast. In the information age, digital media has influenced modern design art to a certain extent. In the process of continuous development of media technology, visual works have developed and progressed significantly in terms of space. Because art has a crucial position in the design process of modern works. Therefore, we are supposed to study and analyze the application of art design thinking in visual works in the field of digital media, and explore the positive role of art in the design process of works. It is proposed that the application of artistic thinking in the design of works has a positive significance in enhancing the application effect of artistic design thinking.
The Application of Cloud Computing in the Practice Teaching of Business English Major in Higher Vocational Colleges
Authors: Mei Zhao
Published: October 2020

With the rapid development of information technology, cloud computing is also gradually applied to business English teaching practice in higher vocational education. Cloud computing can build a broad training platform for students, extend students’ English knowledge, and also meet the practical training needs of business English students, greatly improving the efficiency of business English teaching in vocational colleges. The effective connection between cloud computing and business English practice teaching meets the requirements of quality education, so higher vocational colleges should pay attention to it. This article analyzes the overview of cloud computing, explores the current status of business English teaching, and proposes strategies for cloud computing applications.
Research on career success of computer information technology research and development (R&D) personnel
Authors: Li Xuemei , Chen Yiwen , Zhang Yuting
Published: October 2020

To explore the influence mechanism of psychological capital on career success, a questionnaire survey was conducted among 288 research and development (R&D) personnel of computer information technology in China. The results showed that psychological capital, career commitment, person-organization fit, total score and each dimension of career success were significantly positively correlated with each other; psychological capital significantly positively predicts objective and subjective career success, and the predictive effect of both can be realized through the mediating role of career commitment; in the second half of the mediated path between psychological capital and subjective career success, career commitment is regulated by person-organization fit. Compared with individuals with a low level of person-organization fit, individuals with a high level of person-organization fit have a stronger mediating effect on the relationship between psychological capital and subjective career success.
Lightweight Crowd Counting Network based on Depthwise Separable Convolution
Authors: Yao Dengguo , Xu Yuanping , Zhang Chaolong , Xu Zhijie , Huang Jian , Guo Benjun
Published: October 2020

Crowd counting on the image is a challenging problem. Many neural network-based methods usually use two-branch and multi-branch networks to extract high-level features of different scales or densities, and then merge these features by a fusion operation. Although these methods can reduce the error of crowd counting, it makes the amount of parameters is enormous, so that the efficiency of training and optimization of the model is low, and the calculation resource consumption is high. To this end, a residual network based on depthwise separable convolution is proposed for image crowd counting. The network can not only reduce the amount of calculation through depthwise separable convolution, but also deepen the network depth through the residual structure to extract more effective high-level features. The experiment proves that, compared with the start-of-the-art methods, the method in this paper dramatically reduces the parameter amount to 1.91 Million when the accuracy is comparable.
Video fire recognition based on multi-channel convolutional neural network
Authors: Zhong Chen , Shao Yu , Ding Hongjun , Wang Ke
Published: October 2020

A video fire detection method based on multi-channel convolutional neural network(CNN) is presented in this paper. With improved OTSU and connectivity analysis, an adaptive threshold segmentation for flame image is implemented. According to the flame combustion characteristics, the feature of flame area is extracted using parameters such as colour, roundness and area change. Using TensorFlow platform, the CNN and the video database is formed. In order to improve the ability of dynamic fire detection, the method of combining random gradient descent and momentum correction is used to train CNN. Experimental results show that the accuracy of CNN fire detection method is 86%, compared with 68.2% of the traditional k-nearest algorithm (KNN).
Design and Implementation of a Web Crawler System based on an Adaptive Page-Rank algorithm
Authors: Zhang Xin , Cheng Zhi , Zhang Chen
Published: October 2020

Web crawlers have the ability to automatically extract web page information, but there exists the issue that some pages reuse keywords to improve their search rankings. Therefore, we propose an adaptive Page-rank algorithm to build a crawler system to resolve the issue mentioned above. Specifically, we generate a relationship matrix based on the crawled web page access relationships, and then an probability matrix based on the number of web pages is generated iteratively, and finally the web pages crawled are displayed in descending order of calculated weights. Besides, we propose to control the iterative process in Page-rank with the coherence of anchor texts. The system uses Python language to realize the functions of web crawling. Experimental results demonstrate that this system has a high speed in data collection. Comparing with Hints and classical Page-rank crawler systems, The results show that the proposed method outperforms in precision and recall.
Design of a Web-based Personalized E-learning Platform
Authors: Xue Chengfeng , Zeng Xiting
Published: October 2020

It is important to carry out the distant learning especially in the COVID-19 period when most of schools have to close down. Therefore, a Web-based personalized e-learning platform is presented to satisfy the requests of the middle school student. Firstly, the features of the learner’s needs for e-learning are analysed as the base of the platform. Secondly a novel architecture is proposed that describes the components necessary for the distribution of courses and knowledge in Internet. Thirdly the design of the platform is enabled by the real time collaboration system, multimedia transmission and knowledge repository to help the middle school student study in their own natural ways such as gaming and blogging with friends from interests. The experiment results show that the designed e-learning platform can improve the performance of network throughput and the learner’s satisfactory level.
The feature selection based on AndroidManifest.xml
Authors: Guan Jun , Mao Baolei , Jiang Xu
Published: October 2020

Nowadays, Android malicious applications are rampant, and information security is facing serious challenges. To prevent being decompiled, Android malicious applications often use software packers to protect themselves. After decompilation, the only available file is the AndroidManifest.xml file. Therefore, when detecting shell applications, the researchers can only rely on the features provided by the AndroidManifest.xml file, including permissions and intent filters. How to select effective features from the AndroidManifest.xml file is the key to detect Android malicious applications. This paper proposes the feature selection method for Android malware detection based on AndroidManifest.xml.
Research on related properties of two types of self-similar networks
Authors: Zhong Wang , Mafuxiang
Published: October 2020

Self-similarity is the whole and the part of a complex system, that is, the similarity of structure or property between the part and that part. Studying self-similar networks is helpful for us to better understand the complex networks in the real world. Because the fractal networks have self-similarity, a type of modified Koch network and a type of Austria network were firstly described by this paper, and the exact expressions of degree distribution and aggregation coefficient of the two types of networks were secondly derived, finally, the relationship between the Randic indicator of the two types of network and other invariants is studied.
Recognition of Common Insect in Field Based on Deep Learning
Authors: Cao Xu , Wei Ziyi , Gao Yinjie , Huo Yingqiu
Published: October 2020

Traditional insect taxonomy methods have high technical requirements, and the ability of artificial identification of insects is insufficient. In order to solve the problem, this paper proposes a method of common insects recognition in field based on transfer learning. A total of 9 kinds of insects, such as mythimna separata, rice borer, rice plant hopper, mole cricket, mantis, locust, grass fly, ladybug, and ditch beetle are collected for classification and identification, those include the main insect pests and some natural enemies of the main food crops in the field, such as wheat, rice, corn, etc. Then we use the digital image processing technology and the confrontation generation network to expend the insect dataset, and build a model based on transfer learning to transfer the knowledge learned by VGG16, VGG19, InceptionV3, and InceptionV4 on the ImageNet dataset to the insect classification and recognition. Experimental results show that the transfer learning training model has better classification performance and higher convergence speed, and data expansion can help extend sample and avoid overfitting. The highest recognition accuracy is up to 97.39% among models, which adopt the VGG19 convolutional neural network to pretrain the model for transfer learning. This method has the high recognition accuracy, less time consumption, simple and convenient, robustness in particular for the translation and rotation, which provides a reference for the identification and classification method of field insects.
Research on Construction and Application of Power Distribution Internet of Things
Authors: Shao Siyang , Ma Xiang , Yuan Wei
Published: October 2020

As an important part of power grid aggregation and distribution, the distribution network is becoming more and more important and intelligent with the development of the Internet of Things technology. As a high-priority system for power supply services, the distribution network faces customers directly, so it also faces greater challenges. On this basis, based on the traditional distribution network, this paper studies and comparatively analyzes the advantages of the new distribution Internet of Things model, proposes a four-tier architecture system for the construction of distribution Internet of Things. Furthermore, the application and development process of the Internet of Things technology in the distribution network is explained from the three key technologies of comprehensive perception technology, diverse communications, and edge computing. Finally, from the perspective of operation and maintenance management and fault diagnosis and early warning, the application prospects of the Internet of things for power distribution are discussed.
Analysis on the Mechanism of Superconducting Quantum Computer
Authors: Wang Yuhui
Published: October 2020

Nowadays the quantum computer has been drawing public attention, and how to realize a quantum computer is one of the difficult problems. However, superconducting circuit is a good system for solving the physics of quantum computers, as it has a solid development history and a perfect fit with quantum computers. In this paper, the development history of the superconducting quantum computer will be reviewed. In addition, the Josephson junction is the basis of superconducting quantum computation, based on which as well as in combination with DiVincenzo’s five requirements, superconducting quantum computers have a great potential. For the main part, the advantages and disadvantages of superconducting a quantum computer are analyzed. Some suggestions are also given and the future of superconducting computer is predicted. In conclusion, the superconducting system is found to be a suitable system for quantum computers.
1553B self-check abnormal analysis of a certain computer
Authors: Wang Yongchao
Published: October 2020

During the factory test of a certain computer, the 1553B self-test is abnormal. Through the analysis, it is found that the RAMLB signal pin constraint error in the FPGA firmware caused the failure. In the case of a certain difference in the characteristics of the chip, the 1553B extended SRAM storage function is unstable, and the existing drivers and applications use the extended storage area, which eventually leads to unstable communication. This problem was solved by modifying the 1553B driver. The research in this paper enriches the research of computer 1553B communication and provides theoretical support and basis for subsequent development, design, and solving practical problems.
Design of Financial Early Warning System Based on Elman_Adaboost Algorithm
Authors: Wu Guoqiang , Li Qingping
Published: October 2020

This article analyzes the relevance of the Elman_Adaboost algorithm and combines the design points of the financial early warning system. This article includes data integration platform design, operating system platform design, data warehouse platform design, system integration platform design, system logic structure design, system software function design, financial warning level division, financial risk warning situation division, etc. The author of this paper continuously improves the financial information system, the financial risk responsibility system, etc. The purpose of this article is to improve the practicality of the financial early warning system and lay the foundation for the stable development of the enterprise economy.
Algorithms on Graph Coloring Problem
Authors: Yang Mingtao
Published: October 2020

Graph coloring problem is one of the most famous NP-complete problem and it is an important problem in the study of set theory and graph theory. Since it was raised, lots of people has tried many methods to solve the problem or to optimize the solution to this problem. In this article, the author will introduce the concept of the graph coloring problem and two solutions of the problem. One is based on genetic algorithm and the other is based on greedy algorithm. Each solution has its own traits. Through the analysis, this paper aims to reduce the complicity of the algorithms for making them useful in various applications.
Content analysis and topic correlation of financial news related to big data concept
Authors: Teng Yuanyang , Wu Xiaobo , Li Yicun
Published: October 2020

This paper combines the traditional content analysis method with the emerging natural language processing algorithm, LDA, and takes the financial news related to big data as the research object of content analysis, so as to extract the topics and vocabulary concerned by different categories of financial news related to big data, and judge the correlation of different categories of financial news on topics.
Virtual simulation of communication between KUKA robot and PLC
Authors: Shen Bin , Yang Zhihang , Wang Yanwu
Published: October 2020

Virtual simulation can effectively provide the training environment for the robot. It can solve many problems in robot practice, such as complex equipment, high price and so on. Virtual simulation and communication of KUKA robot can increase more possibilities. Based on the study of its trajectory planning and kinematics, this paper further increases the research content of virtual policy. This paper takes uniy3d virtual reality engine as the development platform, and studies the virtual motion simulation and control of industrial robot from the perspective of interaction between virtual robot and real robot,
Research on algorithm and implementation of Fourier transform in the case of N < M
Authors: Gao Yang , Zhu Jia Li , Li Yun Fei , Liu Jian
Published: October 2020

In this paper, Fourier transform (FT) is studied under a special situation in which the transform lengthNis less than the number of input dataM. First, the corresponding Fourier transform formula is deduced, and then an implementation example is given as well as performance simulation and implementation analysis. The results show that the mixed radix FT method has less resource consumption and less processing latency compared with fast Fourier transform (FFT) and discrete Fourier transform (DFT) method, respectively. The proposedN<MFT method increases the flexibility of traditional Fourier transform algorithm.
Hamiltonian claw-free graphs involving induced cycles
Authors: Yin Jun , Xiong Liming
Published: October 2020

The hamiltonian problem is an important topic in structural graph theory, which is closely related to Four Color Problem. Hence lots of graph scholars are dedicated to this topic. There are many authors working for finding some sufficient conditions for hamiltonian property of graphs. Let G be a claw-free graph with n vertices and δ(G)≥3. In this paper, we show that if G has an induced cycle of length more than (4n – 2δ(G)–4)(δ(G)+2)−1, then G is hamiltonian. The result is best possible if δG
is 3 or 4.
Optimization of the Recovery Time of Pyramid Code in Distributed Storage System
Authors: Geng Wei , Zhang Hang , Liu Longxiang , He Rui , Tang Dan
Published: October 2020

In large scale distributed storage systems, erasure code is a basic technology that provides high reliability at low cost. Compared with traditional redundancy technology, erasure coding technology has low redundancy and high flexibility. Therefore, it is a good choice for distributed systems to construct Pyramid codes that are flexible and suitable for a variety of application scenarios, but its disadvantage is that the data recovery time is still long. To address the above problems, in this paper, we propose an Active Fault-Tolerant Pyramid (AFTP) based code, which dynamically adjusts the length of the group in the Pyramid code and the original data block correlation with redundant blocks by using the hard disk fault prediction model based on the decision tree to reduce the length of the group of data blocks in a potentially faulty hard disk, which can be used for multiple hard disk failures. All read and recovery operations are performed within the group, reducing recovery time without adding additional storage overhead. To verify the validity of the AFTP code, we conduct intensive experiments on the distributed storage system based on Ceph. The results show that, compared with Basic-Pyramid (BP), the recovery time of AFTP code is reduced by 8%-64%, and compared with the commonly used classic block codes, the recovery time of AFTP code is reduced by 11%-52%.
Group strategy of dissertation defense based on greedy retrospective hybrid algorithm
Authors: Su Pei , Luo Bing Xue , Deng Fang Yi , Xia Ai Xue , Guo Yan
Published: October 2020

Thesis defense is an important link to examine the quality of graduate thesis. In order to ensure the fairness of evaluation, the examiners and students are subject to many restrictions when the thesis defense is grouped. Meanwhile, with the popularization of higher education and the increasing number of graduates, grouping becomes very complicated. This paper designs a paper defense grouping strategy based on greedy backtracking hybrid algorithm. In this paper, the best solution of the group of judges and the group of students is found by using the idea of greed, and then the solution space tree is established. Then combined with the idea of backtracking algorithm, the search was carried out from the root node, and the schemes that did not meet the constraints were eliminated until all the results that met the requirements were found. In this way, a grouping strategy can meet the constraints of the arrangement of thesis defense in universities and improve the efficiency and fairness. Finally, the correctness of the algorithm strategy is verified by experiments.
Nuclide Spectrum Peak Searching Algorithm Based on Multiple Morphological Structuring Elements
Authors: Huang Yao , Liu Mingzhe , Liu Xianghe , Luo Rui
Published: October 2020

Considering the characteristics of the traditional peak searching methods of nuclide spectrum and morphological transformation in the image processing, a nuclide spectrum peak searching method based on multiple morphological structuring elements is proposed. Four flat structural elements at an angle of 0, 45, 90, and 135 to the horizontal line are utilized, then the morphological transformation of the nuclide spectrum is performed, and the details of the different directions of the nuclide spectrum curve are better controlled. The algorithm proposed in this paper improves the ability of the morphological peak searching and the peak information is preserved well, meanwhile, the method has a better peak recognition effect.
Exploration and Practice of online teaching mode of operating system course
Authors: Zhang Wenhua
Published: October 2020

In the spring of 2020, the Ministry of Education proposed to suspend classes but keep learning and carry out online teaching. In this paper, after analyzing the advantages and disadvantages of online teaching, combined with the ongoing operation system, some ideas of operating system online teaching reform are put forward from three aspects of leading to learn, live to learn in class and after class superintendent. Course evaluation and network teaching platform evaluation pay attention to the usual inspection and evaluation of learning process, which makes the evaluation more reasonable and fair.
A Facial Expression Recongnition Method Based on Dlib, RI-LBP and ResNet
Authors: Yang Jianxing , Adu Jianhua , Chen Hongguang , Zhang Jie , Tang Jianfeng
Published: October 2020

A method of facial expression recognition using a composite feature is proposed. The method combines the expanded Dlib facial feature detector, the rotation-invariant local binary pattern (RI-LBP) and the 50-layer ResNet neural network model (ResNet_50). First, the expanded Dlib was used to locate 83 feature points on the face, obtainting the Dlib feature after preprocessing and dimentionality reduction (PCA). Then, the rotation-invariant LBP feature was extracted from 8 important regions after tilt correction. Furthermore, a 50-layer ResNet neural network was used to extract the low level features from the images. Finally, the three features were combined and extreme learning machine (ELM) was used to classify the composite facial features. The experimental results on Jaffe and CK+ datasets showed that the proposed method performs better compared with other methods.
Corporate Performance, Agency Costs and Non-financial Information Disclosure
Authors: Wu Jing , Yuan Fangying
Published: October 2020

Taking Shanghai and Shenzhen A-share listed companies as samples, this paper studies the relationship between corporate performance, agency cost and non-financial information disclosure. It is found that non-financial information disclosure is positively correlated with corporate performance and negatively correlated with agency cost. Further analysis shows that the higher the level of non-financial information disclosure, the stronger the ability to control risks. Based on the stakeholder - agency theory, this paper studies the non-financial information disclosure of listed companies in China in reducing the agency cost among stakeholders. The conclusion shows that the managers should promote good non-financial information disclosure, which can not only improve the performance of the company, but also reduce the risk and agency cost.