Latest Articles

Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).
Bioinformatics
Neural Mechanism Subserving Memory of Chinese Abstract and Concrete Words
YAN Jing, LI Wenjuan, ZHANG Tingting, ZHANG Junjun, JIN Zhenlan, LI Ling
, Available online  , doi: 10.12178/1001-0548.2023103
Abstract:
The neural mechanism subserving the processing of abstract and concrete words is widely studied in cognitive neuroscience. The previous studies focused on the alphabetic languages, which found that the processing of abstract words relies more on verbal system and the processing of concrete words relies more on perceptual system. This study investigates the brain activity elicited by the abstract and concrete words in the process of the memory encoding with functional magnetic resonance imaging. The results are that the memory encoding of the two types of words activates temporal-occipital cortex and left inferior frontal gyrus. Besides, abstract words elicit default mode network additionally while concrete words elicit sensorimotor cortex additionally. The results indicate that the memory of the abstract and concrete words activates verbal and executive control systems, but they also rely on different systems for memory encoding.
Dual-Path Vision Transformer for Auxiliary Diagnosis of Acute Ischemic Stroke
ZHANG Taohong, GUO Xueqiang, ZHENG Han, LUO Jichang, WANG Tao, JIAO Liqun, TANG Anying
, Available online  , doi: 10.12178/1001-0548.2023081
Abstract:
Acute ischemic stroke is one of the fatal brain dysfunction diseases caused by the interruption of blood supply to the brain tissue. Digital Subtract Angiography (DSA) is the gold standard for diagnosing such cerebrovascular diseases. Based on the frontal and lateral DSA images of the patients, a dual-path image classification intelligent model, Dual-Path Vision Transformer (DPVF), is constructed in this paper to evaluate the treatment effectiveness of acute ischemic stroke in a graded manner. In order to improve the speed of auxiliary diagnosis, the model is constructed based on the lightweight design idea of EdgeViT. And in order to make the model have high accuracy, the spatial-channel self-attention module is proposed to promote the transformer model to capture more comprehensive feature information and improve the model representation. In addition, for the feature fusion of two branches of DPVF, a cross-attention module is constructed to cross-fuse the outputs of the two branches, which promotes the model to extract richer features and thus improves the model performance. The experimental results show that the accuracy of DPVF on the test set reaches 98.5%, which can effectively meet the practical requirements.
Application of Machine Learning to Parkinson’s Disease Diagnosis
LI Xi, JIANG Meng
, Available online  , doi: 10.12178/1001-0548.2023180
Abstract:
Machine learning is one of the research hotspots and focuses of medical artificial intelligence. For the early diagnosis of neurodegenerative Parkinson’s Disease (PD), the existing clinical rating scales have certain subjectivity and limitations. This paper reports the research progress of machine learning in the diagnosis of PD based on behavioral (speech, gait, and writing), electrophysiology (Electroencephalogram, EEG), radiomics (magnetic resonance imaging, single-photon emission tomography, and positive photon emission tomography), and genomics data. The report finds that the application of machine learning is more accurate than the traditional method in the diagnosis of PD, which provides reference for the research and application of artificial intelligence intelligent diagnosis in the future.
Electronic Science and Technology
A 16-Bit Low Power SAR ADC with 110 dB Spurious Free Dynamic Range
XING Xianglong, WANG Qian, KANG Cheng, PENG Jiangling, LI Qing, YU Jun
, Available online  , doi: 10.12178/1001-0548.2023272
Abstract:
A 16-bit 625 kS/s Successive Approximation Register Analog-to-Digital Converter (SAR ADC) is presented. An improved sampling and hold circuit is proposed to optimize sampling linearity and noise performance. Segmented Capacitor Digital-to-Analog Converter (CDAC) is designed and hybrid capacitor switching method is adopted to reduce layout area and switching energy. Dither injection technique is used to improve ADC’s linearity. Two-stage integrating preamplifier is adopted to reduce comparator’s noise. Output offset storage and optimized circuit design techniques reduce comparator’s offset and noise induced by offset calibration. Comparator speed is also improved by circuit design. The prototype is fabricated using CMOS 0.18 μm process and occupies an active area of 1.15 mm2. With 1 kHz sinusoid input, the measured differential input peak-to-peak amplitude is 8.8 V. Signal to Noise and Distortion (SINAD) and Spurious Free Dynamic Range (SFDR) are 85.9 dB and 110 dB respectively. Differential Nonlinearity (DNL) and Integral Nonlinearity (INL) are −0.27/+0.32 LSB and −0.58/+0.53 LSB respectively with a power consumption of 4.31 mW.
A Self-Formed Silicon Surface Microstructure: Surface Characterization and Potential Applications
LI Jiacheng, LIU Shuang, WU Shenglan, LIU Yong, ZHONG Zhiyong
, Available online  , doi: 10.12178/1001-0548.2022206
Abstract:
The morphology of hydrogenated amorphous silicon (a-Si: H) films grown by plasma enhanced chemical vapor deposition (PECVD) is studied. Nonuniformly distributed bubble defects are formed during the process of a-Si: H films deposited on crystalline silicon substrate. The SiNx/a-Si alternative layers are deposited on the as-prepared a-Si: H film, a perfect dome-shaped multi-shell microstructure was formed at the site of the blister defect, and no distinct structural collapse was observed. Three unique characters of the self-formed dome-shape microstructure are concluded, and the potential applications of the proposed self-formed dome-shape microstructure in nanophotonics and MEMS are also pointed out.
Effect of Low-Voltage Si MOSFETs on Short-Circuit Characteristics of SiC/Si Cascode Devices
ZHOU Yuming, CHU Jinkun, ZHOU Jiahui
, Available online  , doi: 10.12178/1001-0548.2023050
Abstract:
SiC/Si cascode device, formed by low-voltage Silicon Metal-Oxide-Semiconductor Field-Effect Transistor (Si MOSFET) and Silicon Carbon Junction Field-Effect Transistor (SiC JFET), has several advantages such as low driving-voltage of Si MOSFET, high blocking-voltage, and low loss of SiC JFET. In this paper, the effect of low-voltage Si MOSFET on the short-circuit performance of SiC/Si cascode device has been investigated with experiment and numerical simulation. The results give that during the short circuit, the highest temperature of SiC JFET in the cascode case is lower than that of single SiC JFET case, so the short-circuit failure duration of SiC/Si cascode device is longer than that of single SiC JFET. Moreover, with the increasing in the rated voltage of Si MOSFET, the short-circuit failure duration for SiC/Si cascode device also increases.
A Review of the Applications and Development Trends of Electromagnetic Spectrum Space
XIE Chunmao, ZHANG Chuan, HUANG Ming, LIU Hongjun, LI En, YANG Shiwen
, Available online  , doi: 10.12178/1001-0548.2023059
Abstract:
In recent years, the electromagnetic spectrum space has gradually developed into a competitive field in the game of big powers in the world, which is also one of the national strategic scarce resources. In civil electromagnetic spectrum, along with the spectrum congestion and fast growing of application services, the applications of broadband spectrum monitoring, spectrum security, big data of spectrum and intelligence have been facing great challenges. Competition games of military electromagnetic spectrum has become even more intense, and electromagnetic spectrum perception, monitoring and impact effect evaluation are facing problems such as limited sensing approaches and insufficient capabilities in spectrum management, control and collaborative applications. Firstly, the recent status, challenges, and development trends in abroad of typical electromagnetic spectrum space applications are reviewed. Then, the domestic studies on overall design, planning management, typical engineering practices and key techniques for electromagnetic spectrum applications are analyzed. Finally, some development suggestions on domestic electromagnetic spectrum space applications are summarized and presented.
Quantum Information
Probabilistic Teleportation of Arbitrary Three-Particle GHZ and W State
WANG Haozhen, TAN Xiaoqing, XU Qingshan, BAO Daipengwei
, Available online  , doi: 10.12178/1001-0548.2022356
Abstract:
A new quantum channel composed of two multi-particle partially entangled states for quantum teleportation (QT) is constructed. Two explicit controlled probabilistic teleportation tripartite schemes are proposed for arbitrary three-particle partially entangled GHZ state and W state. The arbitrary GHZ state and W state can be teleported with a certain probability to the receiver who introduces an auxiliary particle and performs the corresponding unitary operation, with the help of the measurement of Bell-state and single-particle from the sender and the controller. The overall success probabilities of two QT schemes are both \begin{document}$8d^2n^2$\end{document} (\begin{document}$d$\end{document} and \begin{document}$n$\end{document} are the coefficients of the quantum channel).
Research on Mathematical Model of Quantum Fuzzy Information Management
ZHANG Shibin, HUANG Chenyi, LI Xiaoyu, ZHENG Fangcong, LI Chuang, LIU Zhaolin, YANG Yongxi
, Available online  , doi: 10.12178/1001-0548.2022355
Abstract:
In order to efficiently deal with the complexity and uncertainty of big data, this paper integrates “uncertainty problem + intuitionistic fuzzy set theory + quantum computing”, to build a quantum fuzzy information management mathematical model based on intuitionistic fuzzy set theory. To verify the feasibility, rationality and validity of this model, a simulation experiment of quantum fuzzy neural network based on parameterized quantum circuit is designed under uncertainty environment. The experimental results show that the quantum fuzzy neural network based on this model can more objectively, accurately and comprehensively reflect the knowledge information contained in each object in the uncertainty problem, and improve the accuracy of the algorithm processing big data.
Complexity Sciences
Analysis of Time-Delay SEQS Model Based on Multi-Layer Network with Active Nodes
CAO Lingling, YANG Hongchun, GAO Yachun, ZHANG Hu, YANG Chun, FU Chuanji
, Available online  , doi: 10.12178/1001-0548.2023062
Abstract:
The study of disease transmission is of great significance to make more accurate prediction of the epidemic dynamics. Most of the existing disease transmission models are based on single-layer networks and do not consider the coupling effect between multi-layer networks. In this paper, a Susceptible-Exposed-Quarantined-Susceptible (SEQS) model with time delay under the action of multi-layer networks with active nodes is proposed. A time-delayed SEQS model is used to simulate a disease that has an incubation period as well as a recovery period, and can be reinfected after recovery. The static network in the multi-layer network represents the social network of different cities, and the active nodes represent the floating population between cities. Because of the existence of active nodes, the multiple layer network is time-varying, and a detection mechanism is introduced to detect the adjacent nodes within the detection radius with the isolated node as the center. Research show that the final evolution state of the propagation curve of the model has three modes: steady state, periodic oscillation, and non-periodic oscillation, also show that determining the accurate detection range can effectively control the large-scale spread of the disease, when the rate of disease transmission can be effectively reduced.
Analysis of Gender Differences in Market Influence of Chinese Film Actors and Actresses
ZHAO Shiyue, ZHOU Tao, HAN Xiaopu, ZHOU Yinzuo
, Available online  , doi: 10.12178/1001-0548.2023053
Abstract:
This paper builds a database consisting of more than 10000 actors and actresses and more than 3000 movies from 2000 to 2020 in China, mainly collected from Douban, supplemented by Cateye, IMBD and Baidu. This paper focuses on gender differences in actors’ market influence. The market influence of a film is measured by it’s box office, and the contribution of an actor/actress to the box office is defined as the inversely proportional to his/her ranking in the cast list of the film engaged. Our research shows that the performances of actors are significantly better than actresses in terms of market influence, career length, and subject diversity. Moreover, the ratio of average box office of actresses to that of actors fluctuates around 0.6 each year after 2015, which is lower than the ratio of female to male salaries in the whole industry in China, showing more significant gender inequality. Further mediating effect analysis and moderating effect analysis show that the difference of career length and subject diversity are the main reasons for gender difference in market influence, and actors achieve significantly higher rewards from market influence than those of actresses given the same extent in expanding their career length and subject diversity.
Study on the Influence of Control Strategy on the First Arrival Time of Epidemics
KANG Ze, WANG Jianbo, YANG Cheng, XU Xiaoke, DU Zhanwei
, Available online  , doi: 10.12178/1001-0548.2022419
Abstract:
Aiming at the problem of network structure influence on the control strategy on delaying the spatial spread of epidemic, the dual-population structure, hub structure, one-dimensional linear structure, and path superposition structure existing in the composite population network are studied, and the effects of patient isolation strategy and air traffic control strategy on delaying the first time of epidemic are analyzed, and the simulation verification is carried out through the American Airlines network. The results show that in the early stage of the outbreak, the control effect of timely patient isolation strategy is better than that of air traffic control strategy. The length of the epidemiological transmission structure is an important factor affecting the time of first arrival; The path overlay structure hardly affects the control effect of air traffic control strategy, but to a large extent, it inhibits the control effect of patient isolation strategy.
Computer Engineering and Applications
3D Face Recognition Based on Key Feature Enhancement Mechanism
WANG Qi, QIAN Weizhong, LEI Hang, WANG Xupeng
, Available online  , doi: 10.12178/1001-0548.2023012
Abstract:
3D face recognition is an important part of the field of computer vision. Pointnet relies on deep learning to solve the disorder of point clouds and realize the global feature extraction. However, due to the lack of detailed texture of point clouds, it is difficult to realize face recognition in complex situations only by global features. In deal with the above problems, a local feature descriptor is proposed to describe the local spatial geometric features of the point clouds, and a key feature enhancement mechanism is introduced to enhance the key features of the face through the probability distribution, which can reduce the interference of unnecessary features and effectively improve the accuracy of the model. Experiments were carried out on public data sets CASIA-3D, Lock3DFace and Bosphorus. The results show that our method can deal well with the change of expression, partial occlusion and interference of head pose, especially in weak light conditions, compared with RP-Net, the accuracy is improved by 1.1 percent, and the method also has good real-time performance.
Extractive Document Summarization Model Based on Heterogeneous Graph and Keywords
ZHU Qilin, WANG Yu, XU Jian
, Available online  , doi: 10.12178/1001-0548.2023019
Abstract:
Extractive document summarization uses certain strategies to select some sentences from lengthy texts to form a summary, whose key is to use as much semantic and structural information of the text as possible. In order to better mine such information and then use it to guide the summarization, an extractive document summarization model based on heterogeneous graph and keywords (HGKSum) is proposed, which models the text as a heterogeneous graph composed of sentence nodes and word nodes. The model uses the graph attention networks to learn the features of the nodes in the graph. The multi-task learning is applied to the model, which considers the keywords extraction task as an auxiliary task of the document summarization task. The candidate summary which derived from the prediction of the neural networks in the model is often highly redundant, so the model refines it to create the final summary of low redundancy. The comparative experiment on the document summarization benchmark shows that the proposed model outperforms the baselines. Besides, ablation studies also demonstrate the necessity of introducing heterogeneous nodes and keywords.
Research on Pedestrian Re-Identification Data Augmentation Method Based on Multi-Factor Guidance
LIU Zhigang, ZHANG Guohui, GAO Yue, LIU Miaomiao
, Available online  , doi: 10.12178/1001-0548.2023056
Abstract:
To solve the difficulty in obtaining annotated pedestrian images in the field of pedestrian re-identification research, a novel data augmentation method guided by multi-factor is proposed in this paper. Firstly, a local multi-scale guidance mechanism is designed in the generator network. It can suppress the local artifacts in generated images through feature fusion. Secondly, a long-distance correlation guidance mechanism is proposed to improve the overall visual quality of the generated pedestrian image by guiding the long-distance dependence of the generated image with external attention. Lastly, an adversarial discrimination network is designed and embed into original generative adversarial networks. The three network stability architecture model increases the stability of generative adversarial network training. The experiment are validated on the VIPeR, Market-1501 and DukeMTMC-reID benchmark datasets. The results demonstrate our method outperforms the state-of-the-art with the mAP and rank-1 scores, especially in small-scale datasets.
Nonlinear System Prediction Method of Physical Neural Networks Based on Frequency Domain Control Constraints
QIAN Kui, SONG Aiguo, TIAN Lei
, Available online  , doi: 10.12178/1001-0548.2023036
Abstract:
To address the problems of high computational cost and boundary condition limitations associated with the existing physical information neural network using numerical simulation to approximate the physical control equations, a nonlinear system prediction method of physical neural networks based on frequency domain control constraints is proposed. Firstly, a nonlinear prediction network model with alternating updates of temporal features is constructed, followed by a physical control equation constraint based on the Fourier spectrum method (FSM) in the frequency domain, and then the spatio-temporal data are trained without labels under the coupling of the network model and the frequency domain control constraint to complete the system evolution learning. The experimental results show that the proposed method can achieve unlabeled nonlinear complex modeling under physical rule constraints, and has faster learning speed and prediction accuracy compared with the mainstream Physics Informed Neural Network (PINN) model and its variants. In the case of t≤0.25 s and t≤0.5 s short-time prediction, the Mean Square Error (MSE) of the system is reduced by 86% and 95% compared with that of the mainstream baseline model in the same period of time after 20 times of pre-training, and the MSE of the system can be reduced by 80% in the case of t≤2 s long-time prediction after sufficient training.
Information and Communication Engineering
High-Speed Signal Transmission Synchronization Method Between FPGAs in Digital Oscilloscopes
GAO Yuan, ZHAO Yu, WANG Houjun, YE Peng
, Available online  , doi: 10.12178/1001-0548.2023320
Abstract:
The data acquisition system is the core component of Digital Storage Oscilloscopes (DSO). With the gradual increase in the bandwidth and sampling rate of oscilloscopes, the single-chip Analog to Digital Converter (ADC) + Field Programmable Gate Array (FPGA) architecture is difficult to meet ultra-high-speed and multi-channel application scenarios. Therefore, data acquisition systems in high-end oscilloscopes generally adopt a 'Master-Slave' FPGA control architecture. Under this architecture, the synchronous transmission of signals between multiple FPGAs is an important prerequisite for achieving synchronization and accurate acquisition of the acquisition system. This paper proposes a method for high-speed signal synchronous transmission between FPGAs to solve the problem of signal synchronization transmission between multiple FPGA boards. With help of FPGA's IODELAY unit, the most stable synchronization transmission interval is found through test data training, and synchronization transmission between multiple FPGAs is realized. Experiments on a domestic digital oscilloscope shows that this method can effectively achieve synchronous transmission of high-speed signals between FPGAs.
Pipeline Design of LDPC Decoder Based on Frame-Interleaving
HAN Guojun, YANG Weize, YE Zhenliang, ZHAI Xiongfei, SHI Zhiping
, Available online  , doi: 10.12178/1001-0548.2023023
Abstract:
The decoder of low density parity check code (LDPC) generally adopts an iterative algorithm based on node confidence update, which can be implemented in parallel and has very high throughput. In this paper, we propose a frame-interleaving decoding structure with high hardware utilization efficiency (HUE) features and develop a dynamic planning method for node reordering within layers, which can solve the memory access conflict problems. Compared with the existing structures, the proposed structure shows more efficiency with respect to hardware utilization.
Design of Dual-Function Radar Communication System Based on Delay Breakpoint Mapping FMCW
XU Rui, WEN Ruiming, HUANG Zihan, LI Gang, WEN Guangjun
, Available online  , doi: 10.12178/1001-0548.2023171
Abstract:
Dual-Functional Radar-Communication (DFRC) system based on Frequency Modulated Continuous Wave (FMCW) has the characteristics of high time-frequency utilization, strong anti-Doppler performance and low range sidelobe. The existing FMCW-DFRC waveform designs generate dual-function deterioration and increase the link complexity of communication receivers. A DFRC system based on Delayed Breakpoint Mapping (DBM)-FMCW is designed to modulate data by mapping the location of the delayed breakpoint in the chirp period and the phase between the divided clusters. After carrier mixing and undersampling, dechirping, chip alignment and data demodulation are realized in the digital domain. In the radar processing link, a Breakpoint Area Deletion and Splicing (BADS) scheme is designed to make the radar performance of DFRC consistent with that of unmodulated FMCW. The simulation results show that, compared with the uncompensated scheme, the BADS scheme can reduce the sidelobe amplitude of the image by about 37 dB and is not affected by the modulation data. Compared with the communication performance of existing DFRC schemes based on chirped waveform, DBM-FMCW reduces the symbol error rate.
Blind Recognition for Composite Modulation Signal Based on Frequency-Domain Data Compressed Sensing
HE Ling, YANG Pengfei, YAN Xiao, ZHONG Xunuo, BAI Taili
, Available online  , doi: 10.12178/1001-0548.2023096
Abstract:
Modern TT&C (Tracking, Telemetry and Command) system mostly adopts the composite modulation in a form of “pulse coding/multi-subcarrier internal modulation/external modulation”. This complicated scheme brings great challenges to signal accurate recognition in the absence of prior information and low signal-to-noise ratio (SNR) scenario. The existing composite modulation blind recognition methods based on feature extraction and pattern recognition are sensitive to signal features and sample size, and the whole process becomes even more cumbersome in the case of multiple subcarriers. In this paper, based on the unified carrier system composite modulated signal modeling, a new idea of blind recognition is proposed to train and classify the compressed composite modulated signal frequency domain data by using the inverse residual packet convolutional structure of lightweight neural network. By means of experiment platform construction and Python code designing, the proposed method verification for 10 composite modulated signals in condition of various SNRs is implemented. The results show that the recognition accuracy of the proposed method can reach 94.5% (SNR=0 dB) and 100% (SNR=5 dB) respectively; moreover, the sample size required for equal recognition accuracy is less than the existing statistical features and decision tree-based methods, and both the performance and amount of neural networks parameters used for classification are better than those of the benchmark network.
Rating-Trustworthy Recommendation Model Based on Generative Adversarial Networks
WANG Yong, WANG Songli, Deng Jiangzhou
, Available online  , doi: 10.12178/1001-0548.2023116
Abstract:
Existing deep learning-based recommendation models have mainly focused on improving the accuracy of recommendation systems. However, beyond recommendation accuracy, the reliability of the model's recommendations is also of great concern. Therefore, a rating-trustworthy recommendation model based on generative adversarial networks (GANs) is proposed to evaluate the effectiveness of prediction results and achieve a balance between recommendation accuracy and reliability. This model solely employs explicit user rating information to gauge the credibility of predicted ratings and screens out highly credible predicted ratings based on a predefined reliability threshold, thus ensuring the trustworthiness of recommended items. Furthermore, to enhance the prediction performance of the model and ensure fairness in training, a positive sample padding strategy is designed to mitigate the data imbalance problem in the rating reliability matrix. Experimental results on three real datasets show that the proposed model outperforms selected comparison methods in both Recall and NDCG metrics, effectively improving the performance of recommendation systems.
Entity-Relationship Joint Extraction Model Infused with Reinforcement Learning
ZHAI Sheping, LI Hang, KANG Xinnian, YANG Rui
, Available online  , doi: 10.12178/1001-0548.2023107
Abstract:
Existing joint extraction tasks of entities and relationships introduce distant supervision strategies to automatically generate large-scale training data, leading to severe problems of noisy data during data processing. To address the issue of noisy data, this paper proposes an entity relation joint extraction model with reinforcement learning integration. The model consists of two components: reinforcement learning and joint extraction model. The joint extraction model is composed of a graph convolutional network and a multi-head self-attention mechanism. Firstly, reinforcement learning is utilized to eliminate noisy sentences from the original dataset, and the denoised high-quality sentences are input into the joint extraction model. Secondly, the joint extraction model is employed to predict and extract entities and relationships from the input sentences, and provide feedback rewards to the reinforcement learning component to guide it in selecting high-quality sentences. Finally, the reinforcement learning and joint extraction models are jointly trained and iteratively optimized. The experiments demonstrating that the proposed model can effectively address the issue of data noise and outperform baseline methods in entity relationship extraction.
Characterization of Electrical Properties of Flexible Transistors Based on van der Walls Heterostructure under Dynamic Strain Modulation
CHEN Jianglong, WANG Zenghui
, Available online  , doi: 10.12178/1001-0548.2023104
Abstract:
Conventional electronic devices are mainly composed of inorganic semiconductor materials such as silicon-based and rigid polymer insulating substrate materials, which are the basis of existing electronic technology. Although the traditional electronic technology has been developed, but due to the limitations of the material, resulting in the field of flexible electronics, its application still exists in a certain limit. Two-dimensional materials have high crystallinity, near-perfect lattice structure, atomic-level thickness and high mechanical tensile strength, and exhibit excellent charge-transfer properties, making them promising for applications in the field of flexible electronics. In this paper, we combine the excellent mechanical properties of graphene, molybdenum disulfide and hexagonal boron nitride to prepare molybdenum disulfide-based field-effect transistors by mechanical exfoliation method with dry transfer. We test the flexible electrical properties of the devices using a flexible test platform built in-house. The test results show that the designed two-dimensional heterojunction transistor has only a small change in electrical properties under static test conditions; however, under dynamic test conditions, due to the too small van der Waals force between layers, the sliding or displacement between layers makes the electrical properties of the device change significantly.
Regional Difference and Temporal-Spatial Pattern of Chinese Preschool Education Development
Xie Mei, Zhou Tao
, Available online  , doi: 10.12178/1001-0548.2022226
Abstract:
Preschool education is an essential people’s livelihood field related to the development of children, family happiness, and the future of the country, which has significance and value in promoting the fairness of social resources and welfare and maintaining the stability of social order. In order to guarantee the equality and accessibility of preschool education, the government has committed to making attributions to narrow both regional and urban-rural preschool education gap for a long time. Based on related data from the China Education Statistics Yearbook and China Population & Employment Statistics Yearbook, we analyze the evolution trend of preschool education at nation level, at region level, and at province level, respectively. The results show that the gross enrollment rate for three-year preschool education has dramatically enhanced in the past two decades, especially in the central and west regions. The distribution disparity of the gross enrollment rate has decreased, which is mainly attributable to intra-regional differences. During 2000-2015, the gross enrollment rate had a significantly positive spatial aggregation. During 2016-2019, the spatial aggregation pattern disappeared over time. The findings reveal that the level of preschool education has made remarkable progress in quantity and balanced development. In the end, this paper provides some suggestions for the balanced development of preschool education.
Research on the legitimacy and practice path of public data paid service
ZHONG Shuli, HAN Shijiao, ZHANG Yaoyao, ZHAO Na, CHEN Yi, ZHANG Yanling, GU Qin, ZHANG Qianming, ZHOU Tao
, Available online  , doi: 10.12178/1001-0548.2022314
Abstract:
Public data paid service is the path to the valued and factored public data. However, there is still a lack of sufficient research and feasible solutions on the legitimacy, effective path and risk prevention of public data paid service. This paper analyzes the concept of public data, analyzes the scope of public data paid service, and from the social economic value, data ownership and the rationality and necessity of charging three aspects, demonstrate the legitimacy of public data paid service. This paper deeply analyzes the effective path to develop public data paid service and clearly proposes the strategies and solutions that should be adopted in the four key links of authorization and operation mode, service delivery mode, pricing strategy and benefit compensation mechanism. At the end of the paper, it also discusses the possible risks and preventive measures in the public data paid service, and summarizes and refines the six specific suggestions. This paper can be an important reference for promoting the paid service of public data continuously and healthily.