Top Read Articles

Published in last 1 year |

In last 2 years |

In last 3 years |

All

Please wait a minute...


For Selected: Download Citations EndNote Reference Manager ProCite BibTeX RefWorks Toggle Thumbnails

Select

Research progress in Chinese named entity recognition in the financial field Qiurong XU, Peng ZHU, Yifeng LUO, Qiwen DONG Journal of East China Normal University(Natural Science)    2021, 2021 (5): 1-13.   DOI: 10.3969/j.issn.1000-5641.2021.05.001 Abstract （352）   HTML （598）    PDF （821KB）（206）       Knowledge map    As one of the basic components of natural language processing, named entity recognition (NER) has been an active area of research both domestically in China and abroad. With the rapid development of financial applications, Chinese NER has improved over time and been applied successfully throughout the financial industry. This paper provides a summary of the current state of research and future development trends for Chinese NER methods in the financial field. Firstly, the paper introduces concepts related to NER and the characteristics of Chinese NER in the financial field. Then, based on the development process, the paper provides an overview of detailed characteristics and typical models for dictionary and rule-based methods, statistical machine learning-based methods, and deep learning-based methods. Next, the paper summarizes public data collection tools, evaluation methods, and applications of Chinese NER in the financial industry. Finally, the paper explores current challenges and future development trends. Table and Figures | Reference | Related Articles | Metrics

Select

Survey of early time series classification methods Mengchen YANG, Xudong CHEN, Peng CAI, Lyu NI Journal of East China Normal University(Natural Science)    2021, 2021 (5): 115-133.   DOI: 10.3969/j.issn.1000-5641.2021.05.011 Abstract （336）   HTML （374）    PDF （1503KB）（193）       Knowledge map    With the increasing popularity of sensors, time-series data have attracted significant attention. Early time series classification (ETSC) aims to classify time-series data with the highest level of accuracy and smallest possible size. ETSC, in particular, plays a critical role in fintech. First, this paper summarizes the common classifiers for time-series data and reviews the current research progress on minimum prediction length-based, shapelet-based, and model-based ETSC frameworks. There are pivotal technologies, advantages, and disadvantages of the representative ETSC methods in separate frameworks. Next, we review public time-series datasets in fintech and commonly used performance evaluation criteria. Lastly, we explore future research directions pertinent to ETSC. Table and Figures | Reference | Related Articles | Metrics

Select

A fuzzer for query processing functionality of OLAP databases Zhaokun XIANG, Ting CHEN, Qian SU, Rong ZHANG Journal of East China Normal University(Natural Science)    2021, 2021 (5): 74-83.   DOI: 10.3969/j.issn.1000-5641.2021.05.007 Abstract （233）   HTML （37）    PDF （831KB）（113）       Knowledge map    Query processing, including optimization and execution, is one of the most critical functionalities of modern relational database management systems (DBMS). The complexity of query processing functionalities, however, leads to high testing costs. It hinders rapid iterations during the development process and can lead to severe errors when deployed in production environments. In this paper, we propose a tool to better serve the testing and evaluation of DBMS query processing functionalities; the tool uses a fuzzing approach to generate random data that is highly associated with primary keys and generates valid complex analytical queries. The tool constructs constrained optimization problems to efficiently compute the exact cardinalities of operators in queries and furnish the results. We launched small-scale testing of our method on different versions of TiDB and demonstrated that the tool can effectively detect bugs in different versions of TiDB. Table and Figures | Reference | Related Articles | Metrics

Select

YOLO-S: A new lightweight helmet wearing detection model Hongcheng ZHAO, Xiuxia TIAN, Zesen YANG, Wanrong BAI Journal of East China Normal University(Natural Science)    2021, 2021 (5): 134-145.   DOI: 10.3969/j.issn.1000-5641.2021.05.012 Abstract （215）   HTML （33）    PDF （1182KB）（196）       Knowledge map    Traditional worker helmet wearing detection models commonly used at construction sites suffer from long processing times and high hardware requirements; the limited number of available training data sets for complex and changing environments, moreover, contributes to poor model robustness. In this paper, we propose a lightweight helmet wearing detection model—named YOLO-S—to address these challenges. First, for the case of unbalanced data set categories, a hybrid scene data augmentation method is used to balance the categories and improve the robustness of the model for complex construction environments; the original YOLOv5s backbone network is changed to MobileNetV2, which reduces the network computational complexity. Second, the model is compressed, and a scaling factor is introduced in the BN layer for sparse training. The importance of each channel is judged, redundant channels are pruned, and the volume of model inference calculations is further reduced; these changes help increase the overall model detection speed. Finally, YOLO-S is achieved by fine-tuning the auxiliary model for knowledge distillation. The experimental results show that the recall rate of YOLO-S is increased by 1.9% compared with YOLOv5s, the mAP of YOLO-S is increased by 1.4% compared with YOLOv5s, the model parameter is compressed to 1/3 of YOLOv5s, the model volume is compressed to 1/4 of YOLOv5s, FLOPs are compressed to 1/3 of YOLOv5s, the reasoning speed is faster than other models, and the portability is higher. Table and Figures | Reference | Related Articles | Metrics

Select

Erasure code partition storage based on the CITA blockchain Furong YIN, Chengyu ZHU, Bin ZHAO, Zhao ZHANG Journal of East China Normal University(Natural Science)    2021, 2021 (5): 48-59.   DOI: 10.3969/j.issn.1000-5641.2021.05.005 Abstract （200）   HTML （44）    PDF （1572KB）（180）       Knowledge map    Blockchain system adopts full replication data storage mechanism, which retains a complete copy of the whole block chain for each node. The scalability of the system is poor. Due to the existence of Byzantine nodes in the blockchain system, the shard scheme used in the traditional distributed system cannot be directly applied in the blockchain system. In this paper, the storage consumption of each block is reduced from O(n) to O(1) by combining erasure code and Byzantine fault-tolerant algorithm, and the scalability of the system is enhanced. This paper proposes a method to partition block data, which can reduce the storage redundancy and affect the query efficiency less. A coding block storage method without network communication is proposed to reduce the system storage and communication overhead. In addition, a dynamic recoding method for entry and exit of blockchain nodes is proposed, which not only ensures the reliability of the system, but also reduces the system recoding overhead. Finally, the system is implemented on the open source blockchain system CITA, and through sufficient experiments, it is proved that the system has improved scalability, availability and storage efficiency. Table and Figures | Reference | Related Articles | Metrics

Select

Electricity theft detection based on t-LeNet and time series classification Xiaoqin MA, Xiaohui XUE, Hongjiao LUO, Tongyu LIU, Peisen YUAN Journal of East China Normal University(Natural Science)    2021, 2021 (5): 104-114.   DOI: 10.3969/j.issn.1000-5641.2021.05.010 Abstract （163）   HTML （23）    PDF （855KB）（69）       Knowledge map    Electricity theft results in significant losses in both electric energy and economic benefits for electric power enterprises. This paper proposes a method to detect electricity theft based on t-LeNet and time series classification. First, a user’s power consumption time series data is obtained, and down-sampling is used to generate a training set. A t-LeNet neural network can then be used to train and predict classification results for determining whether the user exhibits behavior reflective of electricity theft. Lastly, real user power consumption data from the state grid can be used to conduct experiments. The results show that compared with the time series classification method based on Time-CNN (Time Convolutional Neural Network) and MLP (Muti-Layer Perception), the proposed method offers improvements in the comprehensive evaluation index, accuracy rate, and recall rate index. Hence, the proposed method can successfully detect electricity theft. Table and Figures | Reference | Related Articles | Metrics

Select

Research on the integration and application of industrial point source emission permit management technology in Taihu Basin Xiaochun GUO, Zhenyang HAN, Shaoyong LU, Binghui ZHENG, Zebin TIAN Journal of East China Normal University(Natural Science)    2021, 2021 (4): 39-45.   DOI: 10.3969/j.issn.1000-5641.2021.04.005 Abstract （162）   HTML （38）    PDF （607KB）（11）       Knowledge map    In this paper, we provide an overview of the development of emission permit systems domestically and globally, and analyze the problems and technology requirements for an emission permit management system at the initial stage of the National Major Science and Technology Program for Water Pollution Control and Treatment (referred to hereinafter as the “Water Program”) in the Taihu Basin. Based on a summary of technical achievements from the 11th and 12th Five-Year Plans for the Taihu Basin Water Program, a comprehensive set of industrial point source emission permit management technology methods was developed for unit division, control unit pollution load verification, control unit water environmental capacity calculation, assessment of water pollution control and management for key industries, allocation of emission permits, and dynamic monitoring. Furthermore, the effects of implementing a complete set of technologies in Taihu Lake Basin were explored and will serve as a reference for the implementation of a pollution permit management system. Table and Figures | Reference | Related Articles | Metrics

Select

Response of soil greenhouse gas emissions to temperature and moisture across different land-use types Wenxiu SANG, Hualei YANG, Jianwu TANG Journal of East China Normal University(Natural Science)    2021, 2021 (4): 109-120.   DOI: 10.3969/j.issn.1000-5641.2021.04.013 Abstract （161）   HTML （45）    PDF （1000KB）（55）       Knowledge map    In this paper, soil samples were collected from the red soil region of southern China (namely, the Sunjiaba small watershed in Yingtan, Jiangxi) across four different land-use types. Laboratory incubation experiments were subsequently carried out from June 2019 to October 2019. We used a closed chamber to measure soil greenhouse gases (CO2, CH4, N2O) simultaneously with the help of an advanced greenhouse gas analyzer (Picarro-G2508). The aim was to explore the response of soil greenhouse gas emissions across different land-use types to changes in temperature and soil moisture levels under the premise of global climate change. The results showed that the global warming potential (GWP) of the four land-use types increases with paddy, orangery, forest, and upland, respectively. This suggests that greenhouse gas emissions from paddy soils have the greatest relative impact on global warming. In a temperature-controlled experiment, soil CO2 emissions were shown to have a significant positive correlation with soil temperature. The Q10 values of soil respiration coefficients for the four land-use types were: 2.61 (forest), 2.51 (upland), 3.12 (orangery), and 3.17 (paddy). Thus, paddy soil respiration has the highest temperature sensitivity, indicating that paddy soil has a higher CO2 emission potential. Correlations were not significant between CH4 and N2O emissions to soil temperature. In the moisture-controlled experiment, the results indicated that soil CO2 emissions increased at the beginning and then decreased with increasing soil moisture, with the maximum emission rate at 20% GWC (gravity water content). CH4 emissions from paddy soils increased with soil moisture (R2 = 0.8875); CH4 fluxes from the other three land-use types, however, were not significantly related to soil moisture. The soil N2O emissions increased at the beginning and then decreased across the soil moisture range measured; all land-use types had the highest N2O fluxes at 25% GWC. Table and Figures | Reference | Related Articles | Metrics

Select

The impacts of climate and land use changes on water yield in the Beisan River Basin Wenjing LI, Sheng WANG, Qing LI, Taoli WU, Xinyue ZHAO Journal of East China Normal University(Natural Science)    2021, 2021 (4): 99-108.   DOI: 10.3969/j.issn.1000-5641.2021.04.012 Abstract （159）   HTML （44）    PDF （1255KB）（62）       Knowledge map    The Beisan River Basin is an important water source for the Jing-Jin-Ji region. It is important to analyze the temporal and spatial changes in basin water yield and the corresponding driving factors to maintain the security and stability of the ecosystem. Based on meteorology, land use, and soil data, the water production module of the InVEST model was used to analyze the temporal and spatial change characteristics of water yield in the Beisan River Basin from 2000 to 2017. The contribution of climate and land use change to the change in water yield was explored through scenario simulation. The results showed that from 2000 to 2017, the average annual water yield of the Beisan River Basin was 17.8 × 108 m3; the annual change showed an increasing trend at a rate of 1.03 × 108 m3/a. The spatial distribution pattern of water yield was high in the south and low in the north. The average depth of water production in the south and north was 70.85 mm and 8.83 mm, respectively. The high value area of water yield was transferred from the southeast Juhe River and Huanxiang River Basin to the southwest Wenhe River and Yongdingbei River Basin. The water supply per unit area, ranked from high to low, across different land use types showed the following order: construction land > cultivated land > water area > unused land > forest land > grassland. From 2000 to 2015, the water yield of cultivated land was the highest, accounting for 51.3% of the total water yield of the basin, while that of construction land increased the most, reaching 144.3%. Scenario simulation results showed that climate and land use change contributed 70.7% and 29.3%, respectively, to the water yield increase, and the surge in precipitation played a leading role. Table and Figures | Reference | Related Articles | Metrics

Select

Blockchain-oriented data management middleware Sijia DENG, Xing TONG, Haibo TANG, Zhao ZHANG, Cheqing JIN Journal of East China Normal University(Natural Science)    2021, 2021 (5): 60-73.   DOI: 10.3969/j.issn.1000-5641.2021.05.006 Abstract （157）   HTML （25）    PDF （1224KB）（75）       Knowledge map    As a decentralized distributed ledger, blockchain technology is widely used to share data between untrusted parties. Compared with traditional databases that have been refined over many years, blockchains cannot support rich queries, are limited to single query interfaces, and suffer from slow response. Simple organizational structures and discrete storage limits are the main barriers that limit the expression of transaction data. In order to make up for the shortcomings of existing blockchain technology and achieve efficient application development, users can build abstract models, encapsulate easy-to-use interfaces, and improve the efficiency of queries. We also propose a general data management middleware for blockchain, which has the following characteristics: ① Support for custom construction of data models and the flexibility to add new abstractions to transaction data; ② Provide multiple data access interfaces to support rich queries and use optimization methods such as synchronous caching mechanisms to improve query efficiency; ③ Design advance hash calculation and asynchronous batch processing strategies to optimize transaction latency and throughput. We integrated the proposed data management middleware with the open source blockchain CITA and verified its ease of use and efficiency through experiments. Table and Figures | Reference | Related Articles | Metrics

Select

Data augmentation technology for named entity recognition Xiaoqin MA, Xiaohe GUO, Yufeng XUE, Lin YANG, Yuanzhe CHEN Journal of East China Normal University(Natural Science)    2021, 2021 (5): 14-23.   DOI: 10.3969/j.issn.1000-5641.2021.05.002 Abstract （156）   HTML （395）    PDF （689KB）（128）       Knowledge map    A named entity recognition task is as a task that involves extracting instances of a named entity from continuous natural language text. Named entity recognition plays an important role in information extraction and is closely related to other information extraction tasks. In recent years, deep learning methods have been widely used in named entity recognition tasks; the methods, in fact, have achieved a good performance level. The most common named entity recognition models use sequence tagging, which relies on the availability of a high quality annotation corpus. However, the annotation cost of sequence data is high; this leads to the use of small training sets and, in turn, seriously limits the final performance of named entity recognition models. To enlarge the size of training sets for named entity recognition without increasing the associated labor cost, this paper proposes a data augmentation method for named entity recognition based on EDA, distant supervision, and bootstrap. Using experiments on the FIND-2019 dataset, this paper illustrates that the proposed data augmentation techniques and combinations thereof can significantly improve the overall performance of named entity recognition models. Table and Figures | Reference | Related Articles | Metrics

Select

Research on multi-objective cargo allocation based on an improved genetic algorithm Ping YU, Huiqi HU, Weining QIAN Journal of East China Normal University(Natural Science)    2021, 2021 (5): 185-198.   DOI: 10.3969/j.issn.1000-5641.2021.05.016 Abstract （156）   HTML （29）    PDF （1063KB）（76）       Knowledge map    In this paper, we propose a mathematical model to solve the multi-objective cargo allocation problem with greater stability and efficiency; the model for cargo allocation maximizes the total cargo weight, minimizes the total number of trips, minimizes the number of cargo loading and unloading points, and offers fast convergence based on the elitism genetic algorithm (FEGA). First, a hierarchical structure with the Pareto dominance relation and an elitism retention strategy were added on the basis of the genetic algorithm. This helped to improve the population diversity while accelerating the local search ability of the algorithm. Then, the random structure of the initial population was modified, and a double population strategy was designed. An adaptive operation was subsequently added to sequentially improve the global search ability of the algorithm and accelerate the convergence speed of the population. Based on the new algorithm, real cargo data were used to demonstrate the feasibility and optimization potential of the new method. The results show that compared with the traditional genetic algorithm, the proposed algorithm has a better optimization effect in solving the cargo allocation process with strong constraints and a large search space; the search performance and convergence, moreover, are also improved. Table and Figures | Reference | Related Articles | Metrics

Select

Joint extraction of entities and relations for domain knowledge graph Rui FU, Jianyu LI, Jiahui WANG, Kun YUE, Kuang HU Journal of East China Normal University(Natural Science)    2021, 2021 (5): 24-36.   DOI: 10.3969/j.issn.1000-5641.2021.05.003 Abstract （156）   HTML （31）    PDF （842KB）（194）       Knowledge map    Extraction of entities and relationships from text data is used to construct and update domain knowledge graphs. In this paper, we propose a method to jointly extract entities and relations by incorporating the concept of active learning; the proposed method addresses problems related to the overlap of vertical domain data and the lack of labeled samples in financial technology domain text data using the traditional approach. First, we select informative samples incrementally as training data sets. Next, we transform the exercise of joint extraction of entities and relations into a sequence labeling problem by labelling the main entities. Finally, we fulfill the joint extraction using the improved BERT-BiGRU-CRF model for construction of a knowledge graph, and thus facilitate financial analysis, investment, and transaction operations based on domain knowledge, thereby reducing investment risks. Experimental results with finance text data shows the effectiveness of our proposed method and verifies that the method can be successfully used to construct financial knowledge graphs. Table and Figures | Reference | Related Articles | Metrics

Select

Optimization of LSM-tree storage systems based on non-volatile memory Yang YU, Huiqi HU, Xuan ZHOU Journal of East China Normal University(Natural Science)    2021, 2021 (5): 37-47.   DOI: 10.3969/j.issn.1000-5641.2021.05.004 Abstract （156）   HTML （35）    PDF （1368KB）（242）       Knowledge map    With the advent of the big data era, the financial industry has been generating increasing volumn of data, exerting pressure on database systems. LevelDB is a key-value database, developed by Google, based on the LSM-tree architecture. It offers fast writing and a small footprint, and is widely used in the financial industry. In this paper, we propose a design method for the L0layer, based on non-volatile memory and machine learning, with the aim of addressing the shortcomings of the LSM-tree architecture, including write pause, write amplification, and unfriendly reading. The proposed solution can slow down or even solve the aforementioned problems; the experimental results demonstrate that the design can achieve better read and write performance. Table and Figures | Reference | Related Articles | Metrics

Select

Characteristics of dissolved organic matter and its effects on denitrification in urban river sediments Rui WENG, Zheng WEI, Yanmei YANG, Jing HAN, Yan HE, Minsheng HUANG Journal of East China Normal University(Natural Science)    2021, 2021 (4): 46-54.   DOI: 10.3969/j.issn.1000-5641.2021.04.006 Abstract （151）   HTML （26）    PDF （1032KB）（15）       Knowledge map    Understanding the impact of dissolved organic matter (DOM) on the denitrification process is critical to addressing the challenges associated with nitrogen removal in urban river treatment. In this paper, we show that DOM in urban rivers are mainly comprised of small-molecule fulvic acids. The humic acid content and aromaticity of the DOM, moreover, were found to be low. Compared with the control case, DOM can promote the denitrification process; specifically, the removal efficiency of TN and NO3–-N in the DOM-added group increased by 7.24% ± 0.36% and 23.52% ± 1.17%, respectively. DOM with an acetate group had an even better effect on the removal of TN and NO3–-N, reaching 74.48% ± 1.29% and 98.62% ± 0.07%, respectively. Microbiological analysis showed that the DOM-added group can significantly increase the diversity and richness of the bacteria community compared with the control case. However, the relative abundance of the heterotrophic denitrifiers Pseudomonas and Brevundimonas as well as the nirK-type denitrifier Paracoccus in the DOM-added group was less than that of the DOM with an acetate group. Additionally, a relatively high concentration of NH4+-N (> 3.7 mg/L) was observed in the DOM-added group. The addition of DOM can significantly increase the relative abundance of Anaeromyxobacter related to dissimilatory nitrate reduction to ammonium (DNRA) functional genes. It is speculated that DOM promotes the denitrification process and induces the DNRA process simultaneously. Table and Figures | Reference | Related Articles | Metrics

Select

Comprehensive evaluation of engineering applications for multi-pond constructed wetlands in Erhai Lake Basin Dan LI, Binghui ZHENG, Zhaosheng CHU, Xing WANG, Minsheng HUANG Journal of East China Normal University(Natural Science)    2021, 2021 (4): 8-16.   DOI: 10.3969/j.issn.1000-5641.2021.04.002 Abstract （145）   HTML （474）    PDF （1433KB）（41）       Knowledge map    In this study, the rank evaluation method was used to comprehensively assess engineering applications for integrated multi-pond constructed wetlands (MPCWs) using a multi-dimensional evaluation system. We used pollutant purification performance, sewage storage capacity, vegetation ecological restoration, and economic investment as indicators for the evaluation. The results showed that the application of large-scale integrated MPCWs for controlling non-point source pollution was helpful for intercepting pollutants. Accumulated and purified reclaimed water was available for nearby rural agricultural water use. The implementation of MPCWs can result in water savings, pollution reduction, water resource allocation, and sewage reuse. The inclusion of vegetation within MPCWs was beneficial for ecological vegetation restoration and sewage purification. Given the economic investment requirement for MPCWs and the high potential security risks of deep-water MPCWs, we proposed application suggestions for different groups of MPCWs based on functional requirements. Shallow free water surface flow constructed wetlands could be used in populous areas with small volumes of highly polluted water, and eco-floating treatment wetlands could be used in sparsely populated areas with large volumes of highly polluted water. The scientific application of different groups of MPCWs also requires consideration of other factors, such as local special land resource endowments, pollution source structures, and the allocation of rural agricultural water resources. Table and Figures | Reference | Related Articles | Metrics

Select

Immobilization and efficacy of an aerobic denitrifier Chao YIN, Ying LI, Tingyue ZHANG, Jiamin LIU, Tida CHEN, Dan CUI, Minsheng HUANG Journal of East China Normal University(Natural Science)    2021, 2021 (4): 1-7.   DOI: 10.3969/j.issn.1000-5641.2021.04.001 Abstract （144）   HTML （501）    PDF （769KB）（48）       Knowledge map    To improve the environmental tolerance and nitrogen removal efficiency of an aerobic denitrifier, polyvinyl alcohol (PVA), sodium alginate (SA), and rice hull powder were used as immobilized carriers for an aerobic denitrifier and the performance was subsequently evaluated. The results showed that the optimal ratio of immobilized particles was a mixture of 12% PVA, 8% sodium alginate (SA), 0.5 g rice hull powder, and 10 mL bacterial solution. The immobilized particles had strong stability and mass transfer capability; the removal efficiency of TN was 89.35% ~ 90.12% over 48h. The immobilized particles had good tolerance to pH and rotating speed. When the pH was 11, the removal efficiency of TN was 90%. The removal efficiency of TN and NH4+-N was the highest (91.29% and 93.30%, respectively) when the speed was 120 r/min. The immobilized particles were not resistant to low temperatures (10℃ and 15℃), and the TN removal efficiency was only about 20% at 10℃. The TN removal efficiency, however, achieved 90.59% at 30℃. Table and Figures | Reference | Related Articles | Metrics

Select

Simulation analysis for remote sensing inversion of ocean wavelength and water depth by the Complex Morlet Wavelet method Shanling CHENG, Shouxian ZHU, Gui ZHANG, Wenjing ZHANG Journal of East China Normal University(Natural Science)    2021, 2021 (4): 134-144.   DOI: 10.3969/j.issn.1000-5641.2021.04.015 Abstract （141）   HTML （22）    PDF （3298KB）（16）       Knowledge map    Using the wave-shaped features of remote sensing images, the wavelength of ocean waves can be determined based on the wavelet method. Shallow water depths can then be estimated from the wavelength because the wavelength becomes shorter as the water depth decreases. In this paper, remote sensing data were replaced by ideal elevation data, and numerical simulation data were used to study the performance of the Complex Morlet Wavelet method in estimating wavelength and water depth. In particular, the effects of data resolution and sub-image size on water depth estimation were explored. The results from the ideal elevation data shows that: when the wavelength has no spatial change and the size of the sub-image is greater than the wavelength, the data resolution has no substantial effect on the wavelength estimation if there are more than nine evenly distributed data grids in one image. This phenomenon can be explained by the wavelength-energy spectrum. When the wavelength changes spatially, accurate estimation of the wavelength requires that the sub-image size is larger than twice the wavelength and there are four data grids in one wavelength. The estimation of wavelength by numerical simulated data requires a similar size for sub-images and the data number. The error of water depth estimation increases slightly if the sub-image size is too large, and also increases slightly as the resolution of the data decreases. Table and Figures | Reference | Related Articles | Metrics

Select

The Chinese experience at the International Mathematical Olympiad Bin XIONG, Peijie JIANG Journal of East China Normal University(Natural Science)    2021, 2021 (6): 1-14.   DOI: 10.3969/j.issn.1000-5641.2021.06.001 Abstract （141）   HTML （690）    PDF （1371KB）（170）       Knowledge map    The International Mathematical Olympiad (IMO) is one of the most important and influential global youth intellectual competitions. However, there is little research on how to effectively organize the competition at the national level to help cultivate talent in mathematics, science, and technology. The Mathematical Olympiad originated from a competition to solve mathematical problems. Many outstanding mathematicians and scientists have been prior winners of the IMO and have reaped benefits subsequently to some extent. The Mathematical Olympiad helps to select and train gifted students in mathematics. China’s outstanding historical achievements in the IMO have attracted the attention of the world. Many of China’s students, who exhibited exceptional performance at the IMO, later became outstanding mathematicians, scientists, and technologists. These achievements need to be publicized, and the Chinese experience at the Mathematical Olympiad needs to be summarized and promoted. This article summarizes the history of the IMO and reviews the practices of the IMO in China based on the literature. China uses a number of strategies to ensure outstanding results in the IMO, including: the selection of contestants from existing domestic programs (National High School Mathematics Joint Competition, Chinese Mathematical Olympiad, and National Training Team); a multi-level educational system based on school training; and the accumulation and publication of relevant learning materials. The outbreak of the novel coronavirus has affected the normal proceedings of the IMO, but China has implemented effective countermeasures. There are still some misunderstandings about the Mathematical Olympiad in China. By introducing prior contestants, who have participated in the IMO and made outstanding contributions, China can help the public better appreciate the Mathematical Olympiad. At the same time, the Chinese experience at the IMO is an important reference for other countries in organizing competition training and selecting and nurturing gifted students in mathematics. Table and Figures | Reference | Related Articles | Metrics

Select

Query optimization technology based on an LSM-tree Jiabo SUN, Peng CAI Journal of East China Normal University(Natural Science)    2021, 2021 (5): 94-103.   DOI: 10.3969/j.issn.1000-5641.2021.05.009 Abstract （140）   HTML （27）    PDF （746KB）（117）       Knowledge map    Given challenges with poor query performance for databases using LSM-trees, the present research explores the use of index and cache technologies to improve the query performance of LSM-trees. First, the paper introduces the basic structure of an LSM-tree and analyzes the factors that affect query performance. Second, we analyze current query optimization technologies for LSM-trees, including index optimization technology and cache optimization technology. Third, we analyze how index and cache, in particular, can improve the query performance of databases using LSM-trees and summarize existing research in this area. Finally, we present possible avenues for further research. Table and Figures | Reference | Related Articles | Metrics