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Table of Content

    20 January 2022, Volume 58 Issue 1
    Construction and Inference Technique of Large-Scale Chinese Concreteness Lexicon
    XIE Zhipeng, BI Ran
    2022, 58(1):  1-6.  DOI: 10.13209/j.0479-8023.2021.100
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    To solve the resource-lack problem of Chinese word concreteness, this paper designs and implements an automatic method to construct Chinese concreteness lexicon. By making full use of the existing resource of English word concreteness, it builds up a large-scale Chinese concreteness lexicon based on pretrained word embeddings and an MLP concreteness regression model. In addition, it proposes the concreteness inference tasks on the word level and on the sentence level, and manually constructs the corresponding datasets for evaluation the performance of the Chinese concreteness lexicon on these tasks. Experimental results show that the constructed concreteness lexicon can perform the two inference tasks effectively.
    Knowledge Bases Completion Based on Multi-hop Paths
    WANG Yinmiao, HAN Zhimin
    2022, 58(1):  7-12.  DOI: 10.13209/j.0479-8023.2021.103
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    In order to complement knowledge bases (KBs), the authors propose a new path-based reasoning method, which uses the attention mechanism to combine entities and their types to represent the entities in the path and use the attention mechanism to summarize the absolute value of the difference between the relationship vector predicted by each path and the representation vector of the given relationship to calculate the confidence of the model. The results of experiment on benchmark data sets WN18RR and FB15k-237 show that the proposed model has better performance than the existing path-based relational reasoning methods.
    Research on Dialogue Entity Relation Extraction with Enhancing Character Information
    XU Yang, JIANG Yuru, ZHANG Yuyao, HE Weikai
    2022, 58(1):  13-20.  DOI: 10.13209/j.0479-8023.2021.107
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    A multi-party dialogue relationship extraction model that integrates character reference information is proposed based on a previous model of graph attention network. Specifically, this article adds character nodes in graph, connects them with the word nodes referred by the corresponding character and uses graph attention networks for encoding. The F1 score based on DialogRE Dataset improved by 2.9% on the valid set and 4.6% on the test set compared with the baseline model.
    An Emotion-Cause Pair Extraction Model Based on Multichannel Compact Bilinear Pooling
    HUANG Jin, XU Shi, CAI Ercong, WU Zhijie, GUO Meimei, ZHU Jia
    2022, 58(1):  21-28.  DOI: 10.13209/j.0479-8023.2021.108
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    The authors propose a model based on multichannel compact bilinear pooling to rank pair candidates in a document. The proposed model firstly extracts the emotion-specific and cause-specific representation containing position information via graph attention network, then further learns the local relation representation between emotion clause and cause clause through the local relation-aware module. Finally, these representations are fused via multichannel compact bilinear pooling to learn the emotion-cause pairs representation for effective ranking. Experimental results show that the proposed approach achieves the best performance among all compared approaches on the task.
    Neural Machine Translation Based on XLM-R Cross-lingual Pre-training Language Model
    WANG Qian, LI Maoxi, WU Shuixiu, WANG Mingwen
    2022, 58(1):  29-36.  DOI: 10.13209/j.0479-8023.2021.109
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    The authors explore the application of XLM-R cross-lingual pre-training language model into the source language, into the target language and into both of them to improve the quality of machine translation, and propose three neural network models, which integrate pre-trained XLM-R multilingual word representation into the Transformer encoder, into the Transformer decoder and into both of them respectively. The experimental results on WMT English-German, IWSLT English-Portuguese and English-Vietnamese machine translation benchmarks show that integrating XLM-R model into Transformer encoder can effectively encode the source sentences and improve the system performance for resource-rich translation task. For resource-poor translation task, integrating XLM-R model can not only encode the source sentences well, but also supplement the source language knowledge and target language knowledge at the same time, thus improve the translation quality.
    Drug-Target Interactions Prediction Based on Meta-path of Heterogeneous Information Network
    LIAO Yiming, OUYANG Chunping, LIU Yongbin, HU Fuyu
    2022, 58(1):  37-44.  DOI: 10.13209/j.0479-8023.2021.105
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    The paper proposes a graph neural network model based on meta-path to predict drug target interactions (GMDTI). Firstly, based on drugs, targets, diseases and side effects in eight datasets, and the eight different types of action relationships between them, the authors construct a drug-target heterogeneous information network (HIN). Then, two different meta-paths are defined to capture the different sub-topology information of HIN and the latent semantic information between different nodes. Especially, the graph neural network method is applied to represent the node by aggregating the information of the first-order neighbor nodes and the nodes of the meta-path. Finally, DTIs prediction is completed effectively by end-to-end learning method. This method takes the first-order topology and the semantic information of meta-path of the drug-target HIN into account, which is helpful to learn more potential drug target relationships. The experiment results show that the proposed method achieves 98.6% in AUC and 94.5% in AUPR, which are higher than all baseline models. At the same time, GMDTI has better robustness than all baseline models by sparsity experiments of datas and reduction experiments of noise.
    Multi-modality Paraphrase Generation Model Integrating Image Information
    MA Chao, WAN Zhang, ZHANG Yujie, XU Jin’an, CHEN Yufeng
    2022, 58(1):  45-53.  DOI: 10.13209/j.0479-8023.2021.110
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    In multi-modality scenarios such as commodity descriptions and news comments, existing paraphrase generation models can not utilize information from image and therefore result in the loss of semantics in the generated paraphrases. In order to solve this problem, this paper first propose the Multi-modality Paraphrase Generation (MPG) model to integrate image information for paraphrase generation. In MPG, in order to integrate the image information corresponding to the original sentence, the authors first construct an abstract scene graph and transform the image features into node features of the scene graph. Furthermore, the constructed scene graph was utilized to generate paraphrase, by using the relational graph convolutional neural network for encoder and graph-based attention mechanism for decoder. In the evaluation stage, a sentence pair similarity calculation method was proposed to select sentence pairs describing same objects from the MSCOCO data set, and then evaluation experiments were conducted. Experimental results show that the proposed MPG model achieve better semantic fidelity, which indicates that the integration of image information is effective in improving the quality of the paraphrase generation in multi-modality scenarios.
    Data Augmentation Method for Question Answering
    DING Jiajie, XIAO Kang, YE Heng, ZHOU Xiabing, ZHANG Min
    2022, 58(1):  54-60.  DOI: 10.13209/j.0479-8023.2021.112
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    Aiming at the problem that the current data augmentation method for automatic question answering requires a large amount of external data, a new method oriented to the defects of the question answering model is proposed. Firstly, the question answering (QA) model, question generating (QG) model and question answering matching (QAMatch) model are trained on the training set. Secondly, all the answers predicted by the QA model on the training set are obtained and the wrong ones are selected. Then, the QG model is used to generate corresponding questions for these answers. Finally, the question-answer pairs are filtered by the QAMatch model and the high-quality data are retained as the final augmented data. This method does not require additional data and domain knowledge, and can construct specific data for QA model, improving the performance with less training cost. Experimental results show that the proposed data augmentation method is effective for R-NET, Bert-Base and Luke. Compared with other methods, the QA model achieves better performance improvement with less data scale.
    Incorporating Clause Alignment Knowledge into Chinese-English Neural Machine Translation
    MIAO Guoyi, LIU Mingtong, CHEN Yufeng, XU Jin’an, ZHANG Yujie, FENG Wenhe
    2022, 58(1):  61-68.  DOI: 10.13209/j.0479-8023.2021.111
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    Currently, neural machine translation (NMT) is insufficient in capturing the semantic and structural relationships between clauses in complex sentences, which often results in poor discourse coherence of long and complex sentence translation. To address this problem, the paper proposes a Chinese-English NMT approach by integrating the clause alignment knowledge into NMT. Firstly, a labeling scheme combining manual and automatic annotation is introduced to annotate a large-scale clause aligned Chinese-English parallel corpus that provides rich clause-level Chinese-English bilingual alignment knowledge for model training. Then, a NMT model is designed based on clause alignment learning for enhancing the ability of the model to learn the semantic structure relationships between clauses within complex sentences. Experimental results on WMT17, WMT18 and WMT19 Chinese-English translation tasks demonstrate that proposed method can significantly improve the NMT performance. Evaluation and analysis show that proposed method can effectively improve the discourse coherence of complex sentence in Chinese-English machine translation.
    A Context-Fusion Method for Entity Extraction Based on Residual Gated Convolution Neural Network
    SU Fenglong, SUN Chengzhe, JING Ning
    2022, 58(1):  69-76.  DOI: 10.13209/j.0479-8023.2021.102
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    Due to the convolutional receptive field size, the current word has a limited relevance to the context. It brings about a problem, that is, the semantics of the entity words in the whole sentence is under-considered. The Residual Gated Convolution Neural Network (RGCNN) uses dilated convolution and residual gated linear units to simultaneously consider the associations between words from different dimensions, which adjusts the amount of information flowing to the next layer of neurons. And then by this way the vanishing gradient can be alleviated in cross-layer propagation. At the same time, RGCNN combines the attention mechanism to calculate the semantics between words in the last layer. The results on datasets show that RGCNN has a competitive advantage in speed and accuracy, which reflects the superiority and robustness of the algorithm.
    A Category Hybrid Embedding Based Approach for Power Text Hierarchical Classification
    CHEN Xiaona, GAO Pengfei, LIANG Yue, MA Yinglong
    2022, 58(1):  77-82.  DOI: 10.13209/j.0479-8023.2021.104
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    Aiming at the problem that the current power text classification methods ignore the latent semantic association between category labels and therefore lead to low classification performance, a hierarchical multi-label power text classification method is proposed. Firstly, a power multi-label text dataset is built using automatic information extraction based on power unstructured texts, and the hierarchical structural relationships between categories are constructed by leveraging relevant domain knowledge. Secondly, a text classification method HONLSTM-BERT is proposed based on hybrid embeddings of category structure and label semantics for hierarchically classifying power texts in a top-down manner. At last, experiments were made in comparison with some popular text classification methods, and the experimental results show that proposed HONLSTM-BERT method achieves superior classification accuracy, and can efficiently improve the performance of automatic text classification.
    Multi-task Semantic Matching with Self-supervised Learning
    CHEN Yuan, QIU Xinying
    2022, 58(1):  83-90.  DOI: 10.13209/j.0479-8023.2021.101
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    In semantic matching, the interaction information between pairs of texts is critical in predicting a matching score for the pairs. This paper proposes a multi-task learning framework with self-supervised learning for deep learning semantic matching problem. Specifically, a self-supervised model is designed for the paired sentences to regenerate each other with sequence-to-sequence generation method. Then a multi-task learning framework integrates the representation from the self-supervised generation with that of the deep matching model to predict the similarity score of the texts. Experimentations with 9 deep matching models prove that the proposed framework can improve the performances of the traditional deep matching models.
    An Automatic Classification Model of Tibetan La Case Example Sentences with Fusion Dual-channel Syllable Features
    BAN Mabao, CAI Rangjia, ZHANG Rui, SE Chajia, ZHUO Mazhaxi
    2022, 58(1):  91-98.  DOI: 10.13209/j.0479-8023.2021.106
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    Based on the importance of automatic classification of Tibetan La case example sentences in Tibetan natural language processing, according to the usage and adding rules of Tibetan La case, this paper classifies Tibetan La case example sentences and defines the classification concept, and proposes an automatic classification model of Tibetan La case example sentences with fusion dual-channel syllable features. The proposed model first uses word2vec and Glove to construct a dual-channel Tibetan syllable embedding, and combines the dual-channel syllable features in each convolution respectively to enrich the expression of input features and improve the spatial representation ability of the convolutional layer. Then in each convolution, the Bi-LSTM combined with the hierarchical attention mechanism is used to learn the timing features, and the multi-channel features are spliced to improve the learning ability of the context timing features. Finally, the automatic classification of Tibetan La case example sentences is realized through the full link layer and the Softmax layer. Experiments show that proposed model has an accuracy of 90.26% in the classification of Tibetan La case example sentences on the test set.
    Consistency Check for Chinese Word Segmentation via Contextual Similarity
    LIU Wei, HUANG Kaiyu, YU Hao, HUANG Degen
    2022, 58(1):  99-105.  DOI: 10.13209/j.0479-8023.2021.099
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    The authors propose a method of consistency check for Chinese word segmentation based on contextual similarity. First, the classification constraints based on word formation, part of speech and dependency syntax are designed by using the features of morphology and syntax. Then, the semantic information of the context in which the inconsistent strings are located is encoded by using pretrained word embeddings, and the inconsistent strings are classified by semantic similarity between contexts. Experimental results show that proposed method can effectively improve the accuracy of consistency check for Chinese word segmentation. Further, three mainstream Chinese word segmentation models are used to re-implement in the revised Chinese word segmentation corpus. The result shows that proposed method can effectively improve the quality of Chinese word segmentation corpus, and the F1 scores of three Chinese word segmentation models are improved by 1.18%, 1.25% and 1.04% respectively.
    A study on the Construction of Chinese Near-Synonyms Knowledge Base
    LI Juan
    2022, 58(1):  106-112.  DOI: 10.13209/j.0479-8023.2021.098
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    Near-synonyms discrimination dictionaries often overlook the nuances of near-synonyms, and lack of detailed description of the knowledge of syntactic distribution, semantic characteristics, combination restrictions and usage templates of near-synonyms. The author constructs a knowledge base of near-synonyms based on corpus, which takes collocation as the carrier and the relationship between words as the means to provide rich usage and context knowledge for near-synonyms discrimination. The proposed knowledge base contains usage information: collocation, collocational relationship, collocational frequency, distributional information, semantic features, example sentences, etc. The knowledge of syntax, semantics, and usage of near-synonyms are presented in a visual way, so it is more intuitive and more friendly to Chinese second language learners.
    Research on Key Techniques of Smoothing Transition of LOD Texture Blending and Antialiasing Based on Shader
    JIANG Zhan, LI Mei, SUN Zhenming, MAO Shanjun
    2022, 58(1):  113-122.  DOI: 10.13209/j.0479-8023.2021.051
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    Aiming at the problem of LOD (level of detail) popping and aliasing when the LOD Level is switched, this paper proposes a shader-based smooth transition algorithm for LOD texture blending and antialiasing. According to the distance between the 3D model and the viewpoint, the opaque mask algorithm based on Alpha test and weighted adjacent frames antialiasing algorithm are used to generate transition materials between LODs to achieve smooth transition for LOD switching. It can not only improve the texture quality, but also ensure the fluency and authenticity of loading 3D scene. The experimental results show that, compared with the smoothing method of Unreal Engine 4, the average GPU time of the proposed algorithm is reduced by more than 8%, and the frame rate is increased by more than 8%. Compared with the existing texture smooth transition methods, this algorithm can optimize the GPU rendering performance, stabilize and improve the frame rate and maintain good visual effect, effectively solve the popping problem when the LOD Level is switched.
    Spatial Pattern and Evolution Characteristics of the Production-Living-Ecological Space in the Mountainous Area of Northern Hebei Province: A Case Study of Zhangjiakou City
    JI Zhengxin, XU Yueqing, HUANG An, LU Longhui, DUAN Yaming
    2022, 58(1):  123-134.  DOI: 10.13209/j.0479-8023.2021.121
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    Taking Zhangjiakou City, the central city of mountainous area of northern Hebei Province, as a study area, the spatial pattern and evolution characteristics were analyzed by using land transfer matrix, land use dynamic degree and landscape index, from the aspects of quantitative structure, spatial layout, vertical gradient and landscape spatial pattern. The results were as follows. 1) There was great regional diversity in the evolution characteristics of the “production-living-ecological” (PLE) space of Zhangjiakou City. The ecological space was mainly distributed in Yanshan mountain and Taihang mountain in the east of the study area, and production-living space and production-ecological space were concentrically distributed in Yanghe river valley basin, Sanggan river basin and Huliu river basin, potential space was scattered around the central and western production-living space. 2) In the past 25 years, the PLE space of Zhangjiakou City had different degrees of mutual transformation, while the mutual transformation of ecological space and production-ecological constituted the main type of evolution of the territory space pattern. Compared with 1990–2000, the PLE space changed more frequently from 2000 to 2015. 3) The types of territorial space were more diverse in middle and lower mountain and gentle slope zones from the vertical spectrum. In the past 25 years, ecological space expanded to high-altitude and high-slope zones, while production-ecological space transferred to low-altitude and low-slope zones. 4) The landscape pattern of the PLE space in Zhangjiakou City tended to be fragmented and complicated. 5) The natural geographical environment was the natural basis and spatial carrier of the spatial distribution of the PLE. The aggravation of industrialization and urbanization, social and economic development and policy factors were the reasons for the spatial pattern changes of the PLE space in Zhangjiakou City.
    A Meta-Analysis of the Overall Accuracy of Extent and Species of the Coastal Mangroves
    SHEN Xiaoxue, ZHANG Zhi, ZHAI Chaoyang, LI Ruili
    2022, 58(1):  135-146.  DOI: 10.13209/j.0479-8023.2021.096
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    A meta-analysis of the research on the extent and species identification of mangroves has been conducted using remote sensing since 2000. The study clarified the overall accuracy status of mangrove extent and species identification, as well as the influence of remote sensing data source, classification algorithm, feature type and species number on the overall accuracy. The results showed that the overall accuracy range of mangrove extent identification was 55.7% – 99.7% and about 66% of the researches were based on Landsat series satellite data, and had the highest overall accuracy (75% – 99.7%). Optical remote sensing and radar data fusion could effectively improve the overall accuracy of extent identification (>90%). The simpler the type of ground features (≤3 types) or the more complex (≥6 types), the overall accuracy of extent identification is higher and more stable. The overall accuracy of mangrove species identification ranged from 64% to 98.6%; the closer the spatial resolution is to the size of the plant canopy, the higher the overall accuracy of species identification. Among the high spatial resolution remote sensing data sources, the overall accuracy of the species identification of data sources with shortwave infrared bands was higher than that of non-shortwave infrared bands. Multi-source remote sensing data fusion and plants themselves feature information that helps to improve the overall accuracy of category identification. Support vector machines (SVM), maximum likelihood classification (MLC), and random forest (RF) algorithms in supervised machine learning algorithms were the most widely used and had better overall accuracy. As the number of species increases, the overall accuracy of species identification varies with remote sensing data sources and classification algorithms. In summary, the identification accuracy of mangrove extent and species may be improved to a certain extent, and remote sensing data sources, classification algorithms, feature types, and the number of species will all affect the identification accuracy.
    Experimental Study of the Influence of Water Temperature on Pan Evaporation
    GAO Huihui, CHEN Zhi, SHI Zhe, YAN Chunhua, WANG Bei, ZOU Zhendong, QIU Guoyu
    2022, 58(1):  147-156.  DOI: 10.13209/j.0479-8023.2021.093
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    Based on a pair of black and white standard A-type evaporation pans set under the same meteorological conditions, the dynamic characteristics of meteorological elements, water temperature and evaporation of the two pans were observed. The influence of water temperature on the evaporation of the evaporation pan was explored based on the existing 6 evaporation models. The results showed that 1) the designed observation method can be used to study the effect of water temperature on the pan evaporation. The water temperature and evaporation rate between the two pans were significantly different. During the 50-day observation period, the average water temperature difference between the two pans was 0.4°C, and the average daily evaporation difference was 1.1 mm/d. 2) 1°C increase in water temperature difference between black and white pans will produce an evaporation difference of 0.808 mm/d under the same conditions of solar radiation and other meteorological elements. 3) Under the condition of rising water temperature, the estimated value of the classical evaporation model without considering the water temperature would be smaller than the actual observed value, and the error of the estimation will also increase correspondingly.
    Ecological Sensitivity Assessment and Spatial Pattern Analysis of Land Resources in China
    WEI Chanjuan, MENG Jijun
    2022, 58(1):  157-168.  DOI: 10.13209/j.0479-8023.2021.091
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    Based on the basic database of resources and environment, an index system was constructed to evaluate the ecological sensitivity of land resources in China. We revealed its spatial distribution characteristics among different land use types and different agricultural areas. The main conclusions are as follows. 1) There are significant spatial differences in ecological sensitivity of land resources in China. Highly ecological sensitive areas are concentrated in four regions: the northern arid/semi-arid deserts with land desertification sensitivity, Loess Plateau with soil-erosion sensitivity, the southern hills with soil-erosion sensitivity, and the karst region in the southwest which is both sensitive to rocky desertification and soil erosion. 2) The ecological sensitivity of cultivated land, forests and grassland is significantly different. The sensitivity of cultivated land is lower than the others. Dry land in the north is more sensitive than paddy field in the south; forests are more sensitive to soil erosion and rocky desertification, mainly in the Greater Khingan Range-Changbai Mountains; grassland is highly sensitive to land desertification, mainly refers to the low-cover grassland in eastern Inner Mongolia. 3) The ecological sensitivity of land resources in the nine agricultural areas is generally high in the north and low in the south. According to the characteristics of land ecological sensitivity in different agricultural areas, various land use measures and ecological protection strategies should be implemented.
    Diatom Community Structure and Water Quality Evaluation in the Maqu-Linhe Section of the Yellow River
    ZHAO Mengyao, LIANG Enhang, CHEN Ying, HE Yifan, WANG Jiawen
    2022, 58(1):  169-176.  DOI: 10.13209/j.0479-8023.2021.124
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    To further investigate the water quality in the Maqu-Linhe section of the Yellow River, which is an important water source, diatoms were collected from ten sites in spring and autumn of 2015. Based on the morphological identification method, 24 species belonging to 2 classes, 9 orders, 12 families, and 13 genera were characterized. The cell density and biomass of diatom were lower in spring than those in autumn, which was further proved by NMDS and ANOSIM analysis. Water quality assessment based on indicator diatoms revealed that Nitzschia palea indicating polysaprobic zone was detected in some sites such as Linhe, Qingtongxia, Lanzhou, and Maqu, which was consistent with the assessment from the Shannon diversity index (H). Water quality was characterized as slight-moderate pollution with low diatom diversity in the Maqu-Linhe section of the Yellow River. The water quality of this river section in spring (H=1.91±0.67) was slightly better than that in autumn (H=1.58±0.49), which might be related to the higher DNR in spring.
    Split Fluidized Bed Catalytic Ozone-Flocculation Process for Advanced Treatment of Biochemical Tail Water from Coking Wastewater
    CHU Yongbao, CHEN Delin, LIU Sheng, XU Yi, ZHAO Huazhang
    2022, 58(1):  177-185.  DOI: 10.13209/j.0479-8023.2021.095
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    The catalytic ozonation-flocculation combined process was applied for the treatment of the biochemical tail water of coking wastewater. The optimal treatment performance was investigated and the characteristic and degradation process of dissolved organic pollutants in wastewater was studied. A self-designed split type fluidized bed catalytic ozonation reactor was adopted for the experiment. The results showed that under the optimal reaction conditions (30% catalyst dosage ratio, 3 L/min ozone flow rate, and 700 mg/L flocculant dosage), the COD removal rate of coking wastewater biochemical tail water was 83.7%, and the TOC removal rate was 72.3%. Through the analysis of ultraviolet-visible spectrum and three-dimensional fluorescence spectrum, the smell aromatic compounds, humic acid, soluble microbial metabolites, and fulvic acid material, generally presented in coking wastewater biochemical tail water, was partially degraded during the stage of catalytic ozonation, which was eventually removed in the flocculation stage. The intermediate was also degraded in the flocculation stage.
    Research on the Fast-Response Air Pressure Sensor and Spectral Characteristics of the Pressure Fluctuations in the Turbulent Atmosphere
    WEI Zhuorui, ZHANG Hongsheng, LI Qianhui, REN Yan, KANG Ling, WANG Pengfei, LIU Haibo
    2022, 58(1):  186-194.  DOI: 10.13209/j.0479-8023.2021.122
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    Based on the observational data of a self-developed fast-response air pressure sensor at the Atmospheric Boundary Layer and Atmospheric Environment Comprehensive Experimental Station in the Horqin area, Inner Mongolia in the summer of 2019, the characteristic parameters of the pressure fluctuations were calculated, and the spectral characteristics of the pressure fluctuations and the characteristics of the pressure standard deviation were studied. The results show that the self-developed fast-response air pressure sensor can reflect the rapid fluctuations of pressure, and the frequency response is close to 1 Hz. The variance spectra of the pressure fluctuations satisfy the n-2 scaling law in the frequency range from 0.0006 to 0.5 Hz, and the peak frequency is lower than that of the wind speed and temperature. The normalized variance spectra of pressure fluctuations under different atmospheric stabilities merge into a single line in the high-frequency range and distribute around the stability parameter in the low-frequency range. The contribution of pressure fluctuations to turbulent energy is mainly at larger scales, while that of the wind speed and temperature is mainly at smaller scales. The standard deviation and fluctuation intensity of the pressure have obvious diurnal variation characteristics, which is strong during the daytime and weak during the nighttime.