• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model

    2023-12-12 15:50:16HaoMengXinguoYuBinHeLitianHuangLiangXueandZongyouQiu
    Computers Materials&Continua 2023年10期

    Hao Meng,Xinguo Yu,Bin He,Litian Huang,Liang Xue and Zongyou Qiu

    Faculty of Artificial Intelligence in Education,Central China Normal University,Wuhan,430079,China

    ABSTRACT Solving arithmetic word problems that entail deep implicit relations is still a challenging problem.However,significant progress has been made in solving Arithmetic Word Problems(AWP)over the past six decades.This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations (DIR-AWP),such as entailing commonsense or subject-domain knowledge involved in the problem-solving process.This paper proposes to take three steps to solve DIR-AWPs,in which the first three steps are used to conduct the qualia inference process.The first step uses the prepared set of qualia-quantity models to identify qualia scenes from the explicit relations extracted by the Syntax-Semantic(S2)method from the given problem.The second step adds missing entities and deep implicit relations in order using the identified qualia scenes and the qualia-quantity models,respectively.The third step distills the relations for solving the given problem by pruning the spare branches of the qualia dependency graph of all the acquired relations.The research contributes to the field by presenting a comprehensive approach combining explicit and implicit knowledge to enhance reasoning abilities.The experimental results on Math23K demonstrate hat the proposed algorithm is superior to the baseline algorithms in solving AWPs requiring deep implicit relations.

    KEYWORDS Arithmetic word problem;implicit quantity relations;qualia syntax-semantic model

    1 Introduction

    Solving arithmetic word problems entailing deep implicit relations is a critical branch problem of solving arithmetic word problems.Arithmetic word problems entailing deep implicit relations are the AWPs that can be solved only after adding the deep implicit relations.Two example types of DIRAWPs are the AWPs of entailing commonsense or specific domain knowledge.“Chicken and rabbit in the same cage”is an example of entailing commonsense.People use this problem as a touchstone to judge whether the solver is powerful.In other words,people think that DIR-AWPs are the most difficult AWPs.However,there are only so many satisfactory algorithms for solving DIR-AWPs.The paper has studied this problem.The reason is that the type of problem represents the highest degree of difficulty in solving AWPs.This paper proposes discovering deep implicit relations by qualia inference to solve DIR-AWPs,such as entailing commonsense or subject-domain knowledge.

    In response to this issue,deep implicit knowledge has been proposed to tackle the challenge of multi-steps of implicit relations reasoning during the problem-solving of DIR-AWPs.The syntaxsemantic relation graph(S2RG)is a middle-state that facilitates the qualia structure from the S2model.The S2RG enhances the knowledge representation and reasoning capability of solving DIR-AWPs in real-world scenarios.This paper proposes the three steps used to conduct the inference process.

    Yu et al.proposed a Vectorized Syntactic-Semantic(V-S2)method[1,2]for solving word problems.This method encapsulates mathematical knowledge into S2models and leverages a neural network miner to discover implicit quantity relations.The authors extend this paper by proposing a novel approach called the Qualia Syntax-Semantic Model (QS2M).The QS2M method leverages qualiabased relation inference to discover deep implicit relations.Compared to traditional similarity matching and pattern recognition-based inference approaches,the graph-based inference method QS2M provides a more logically controllable and understandable solution to solving word problems.This modification offers a more sophisticated approach to discovering implicit relations in word problems.This understanding is achieved through QS2M methods,which extract relations from external qualiabased datasets and uncover relations from an expanded understanding of the problem.The proposed algorithm highlights the AWP text,scenario understanding and inference of deep implicit knowledge.

    This paper uses a tutorial based on an algorithm approach that leverages the generalized problemsolving principle.They contend that learners can more effectively learn by focusing on relational operations instead of concentrating only on the system of equations.This approach divides the task of obtaining a system of equations into two simpler sub-tasks:identifying relations and their transformation into equations.The proposed algorithm,known as the “relation-centric solving algorithm”,addresses the growing demand for advanced intelligent tutoring systems[3].The contributions of this paper can be summarized as follows:

    1.The QS2M has been proposed for solving DIR-AWP characterized by complex problem scenarios.The QS2M approach utilizes graph-based inference,which provides a logically controlled and coherent framework compared to traditional methods.

    2.Implicit knowledge addition by the QS2M model represents the relationships between mathematical entities and their attributes.The qualia role patterns in different problem scenarios are to extract the DIR-AWP quantity relations from fully connected S2RG.

    2 Related Work

    The development of methods for acquiring quantity relations from problem texts has involved using manually crafted rules[4,5]or templates in their early stages[6].Rule-based[7]systems rely on predefined rules,such as predicate logic,for unambiguous deductions.Alternatively,semantic parsingbased methods [8] utilized the semantic structure of problems to retrieve historical knowledge more efficiently.However,this approach came at the expense of ambiguity and inference interpretation[9,10].Yan et al.[11]proposed a seq2seq model that translated problem sentences into expressions.

    Furthermore,Liang et al.[12,13]designed the teacher module to associate the encoding to match the correct solution and analogical pairs in a latent space.Above all,Yu et al.[1] proposed a stateaction paradigm that utilized knowledge expressions and action transformations.Advanced methods can be categorized into knowledge-addition and state action-based methods that adopt a relationcentric approach based on this paradigm.

    2.1 The Knowledge-Addition Method Solving DIR-AWP

    The main objective of the research is to employ S2RG to bridge the gap of implicit knowledge for DIR-AWP.The paper reviewed related knowledge acquisition and reasoning work to achieve this goal.Walter et al.[14]proposed a two-frame framework for solving AWPs,utilizing knowledge and solution frames to store problem-understanding outcomes.In Natural Language Processing(NLP),knowledge-addition methods have shown the potential to enhance problem comprehension through the utilization of explicit expert knowledge.Several recent studies [15–17] have focused on using additional information to aid in understanding problems.For instance,Graph2Tree [18]was introduced to capture relationships and order information among quantities.In the field of observation,there is a particular emphasis on improving the expression reasoning process [19,20].Researchers have proposed various methods,such as Goal-Driven Tree-Structured (GTS) Neural Model [21],which utilizes a goal-driven decomposition mechanism to reason an expression tree.Shen et al.[19] also created an ensemble of multiple encoders and decoders,combining semantic understanding and reasoning strengths.The deep learning framework approach to reasoning implicit relations is based on the semantic hint of the shallow implicit knowledge,which directly adding shallow implicit mathematical relationships cannot represent the content of the DIR-AWPs.

    Overall,by building on these related studies,the research aims to apply S2RG to enhance the acquisition of implicit knowledge for solving DIR-AWP.A knowledge-addition solver is an automated system that utilizes a knowledge base to represent subject-domain knowledge.It surpasses the performance of traditional problem-solving methods by leveraging its superior computing capacity.This approach to problem-solving is characterized by its reliance on subject-specific knowledge and its ability to generate innovative solutions.

    2.2 The State-Action Framework Reasoning DIR-AWP

    This study aims to develop a syntax-semantic relation graph-based approach to enhance the efficiency of quantity relation extraction for resolving DIR-AWPs.The quantity relations and solution goals in such problems are founded on ontology,and hence,recognizing and extracting ontology relations can pave the way for generating the quantity relations.Therefore,the associations between words in the text can be leveraged to obtain ontology relations.Prior research proposes the concept of qualia role[22],a set of relations referred to as qualia that can signify the meaning of a word based on the concept of the words.Furthermore,a set of semantic roles called qualia structure,including formal,constitutive,agentive,and telic roles,is proposed to represent the meaning of nominal and implicit information described in[23].

    Knowledge graph completion [24,25] is a relevant task in S2RG due to the explicit knowledge graph formed.This task involves learning unknown edges in the knowledge graph using existing edges.Various approaches have been proposed,such as Bordes et al.[26] interpreting knowledge semantics through translation operations and Pei [27] capturing structure information and longrange dependencies through a geometric perspective.Other types of knowledge,including background knowledge[28],logical knowledge[29],and implicit knowledge in pre-trained language models,have also been investigated.Special forms of knowledge,such as logic rules[30]and mathematical properties[31],have also been studied in various research works.

    The study differs from previous knowledge acquisition research in that it utilizes implicit mathematical knowledge through its reasoning approach S2RG,a general framework based on a stateaction paradigm,and a relation-centric approach.Moreover,the paper introduces a QS2M within S2RG,improving solution accuracy and reasoning interpretability.The work contributes to the field by presenting a comprehensive approach that combines explicit and implicit knowledge learning to enhance reasoning abilities.

    3 The Qualia DIR-AWPs Solver

    3.1 Overview

    This section details the proposed qualia-based DIR-AWPs solver to discover implicit quantity relations for solving AWPs with complex problem scenarios.The proposed QS2M framework represents two main steps in Fig.1.First,solving AWPs is based on relations from the S2model,and the entity relation representation S2RG is an intermediate state for solving the problem in Fig.2.Second,based on the S2model,the implicit relations are extracted from expanded S2RG of implicit knowledge.

    Figure 1:The framework for solving DIR-AWPs using the proposed QS2M method

    Figure 2:The example of solving DIR-AWPs by using the proposed QS2M method

    Compared to traditional knowledge models,the advantage of QS2M is the cross-scenario multistep inference for discovering implicit knowledge entities N and quantity relations R.A given arithmetic word problemPcould be translated into a triple ofthat contains a set of knowledge entities N=Ne∪Niand quantity expressions R=Re∪Rias well as the solution goal g to be solved,where Neand Reare knowledge entities and quantity expressions that are directly stated in P.Niand Riare implicit knowledge entities and quantity expressions indicated by implicit knowledge of DIR-AWPs.

    Definition 1 (Knowledge entity):A knowledge entity ea={e1,e2,...,ei} mentioned is a word in solving DIR-AWPs,e.g.,“speed” and “uniform linear motion” are often used to explain relevant knowledge points in knowledge scenarios.For these terms with a clear knowledge orientation,emis a knowledge attribute,where m is the number of knowledge attribute words.These knowledgeattributing words are selected from many teaching resources,including textbooks and test questions.

    Definition 2(Syntax-semantic relation graph):Each DIR-AWP is constructed as a syntax-semantic relation graph,denoted as S2RG=.which captures the relations between the DIRAWP knowledge words and their neighbors to highlight the knowledge point.As shown in Fig.2.The knowledge entity is directly connected to nodes,and neighboring entities are connected to their corresponding knowledge entities.The qualia relations of knowledge entities also form a scenarioaware knowledge representation.

    Definition 3(Implicit knowledge space):The implicit knowledge space S is based on the knowledge point to enrich the connotation and extension of knowledge points.Its knowledge entity combinations in different knowledge scenarios are S={i1,i2,...,ik},and k is the number of knowledge points.In the hidden knowledge space,the closer the knowledge is to each other,the more similar the knowledge features are.

    Algorithm 1:Qualia-based Solver for Solving DIR-AWPs

    3.2 Qualia Syntax-Semantic Model

    The solvable state of DIR-AWPs requires constructing a connected S2RG of relations,which provides a comprehensive understanding of the process involving using the S2RG to represent and reason knowledge in knowledge entities.On the S2RG,quantity relations are represented by a set of connected attribute nodes belonging to one or more entity nodes,which can be viewed as sub-graphs by applying a graph traversal.A quantity relation mining algorithm translates such sub-graphs into quantity relations.

    The quantity relations indicated by the relations among knowledge entities and their attributes can be modeled as a set of knowledge models named the QS2M.The QS2M is structured as a quintuple,

    where:Neis the explicit knowledge entity stated in the AWPs to be calculated in the solving process.

    Niis the knowledge entity to link AWP entities in the solving process.

    Qris the semantic pattern AWPs that constructs the qualia relation to link Niscenario entities.

    Qpis the syntax-semantic structure pattern for converting math relations from the S2RG.

    Riis the quantity relations associated with the qualia role pair.

    The QS2M linked the AWP explicit relation and the implicit relation.The set of QS2M,Mi=(Ne,Ni,Qr,Qp,Ri),i=1,2,...,m denotes the pool of qualia-based knowledge models.

    As a syntax relationship between entities,the qualia structure can be incorporated into the existing S2model to construct S2RG.The knowledge description ability of S2RG lies in the concept network centered on nouns as entities.The QS2M allows the model to perform multi-step reasoning.The quantity expressions indicated by knowledge entities and attributes can be modelled as a set of S2RG.Inspired by the qualia structure system [19],the knowledge base uses the QES2to represent the structure of AWP,and entities form its object eo,entity attribution ea,and values.The entity is independent and used to distinguish different knowledge entities,and attribution is attached to the entity and used to present numeric values.A hierarchical structure of Entity-Attribution-Value can represent the quantity relations.Object entities and their attributions form the basic form for representing quantitative relations.Identifying an application’s object entity is the key to extracting quantitative relations and understanding and solving problems.

    The AWPs scenario has three main categories of factual facts: reflexive fact,connective fact,and con-vergence fact.The expressions are presented as facts that facts could further translate into mathematical operations to calculate the final answers.The knowledge entity relation can be described as a qualia structure denoted as Rc.Each element rcis,where esrcand edstare two knowledge entities,Qrdenotes the semantic role of edstassociated with esrc,Qpdenotes the syntaxsemantic pattern related to Qr.The six kinds of qualia roles Qrfor solving AWPs:Formal role(FOR),Constitutive role(CON),Unit role(UNI),Material role(MAT),Telic role(TEL),Evaluation role(EVA),Handle role(HAN),Action role(ACT)andOrientation role(ORI).

    As a result,quantity facts in AWPs could be divided into the following three categories accordingly:

    Reflexive Fact:reflexive fact presents the expressions amount different attributes eaof a knowledge entity object eo.The relation between the target entity and its quantity,length,weight,speed,the relation between the speed-time distance of the target entity,etc.,which associates with the qualia roles of FOR and UNI.

    Connective Fact:connective fact presents the expressions amount different entity objects eoi,e.g.,comparative relations:“there are five more apples than pears”,multiplicative/proportional relations:“the number of pears is twice the number of peaches”.Which associates with qualia roles of EVA,MAT,and ORI.

    Convergence Fact:convergence fact describes the convergence relation between an object entity and two or more object entities,e.g.,summation relation:“38 trees were planted in Year 3 and 22 trees were planted in Year 4.How many trees were planted in both years?”.which usually associates with qualia roles of CON,TEL,ACT,HAN,MAT,and ORI.

    Based on the aforementioned definition,reflexive facts can be represented as constitutive roles linking a mathematical entity with its associated attributes.For instance,the constitutive role C(rabbit,leg)establishes a connection between the knowledge entity“rabbit”and its attribute“l(fā)egs”.Similarly,the constitutive role C (circle,area,radius) links the attributes “area” and “radius” of a “circle”mathematical entity.Unlike reflexive facts,connective facts are context-dependent and may take on various forms,such as“is-a”relationships between knowledge entities,“used-for”relations,“createdby”relations,and so forth.For instance,the source formula“rabbit.Legs=4”can be deduced from the constitutive role C(rabbit,leg),and the formula“area=PI ?radius ?radius”can be derived from the C(circle,area,radius).

    This study uses the Language Technology Platform(LTP)[32]natural language processing tool for word segmentation and part-of-speech tagging of word problems.For a DIR-AWP text P entered in natural language,a lexical tagging(POS) algorithm uses transformers’tokenizer to separate the AWP into lexical subdivisions of the text andtheir lexical roles.In Algorithm 2,the explicit entities are extracted from the S2model,and then the entities are constructed as S2RG through the entity dependency relation and S2relation.

    Algorithm 2:Qualia syntax-semantic model for S2RG construction

    3.3 Implicit Knowledge Addition by Qualia Syntax-Semantic Model

    Implicit relation Rirecovery refers to an entity eocorresponding to attribute eain qualia disciplines.However,its value does not explicit in the problem text;the paper defines this knowledge as implicit knowledge fact.AWPs implicit knowledge contains two types of implicit relation sources: missing entity and subject-domain relations.The generated prompt questions Qrare embedded in the S2RG and as clues to traverse the nodes in the S2RG.Implicit quantity relation mining is designed to discover implicit quantity relations from the updated S2RG.

    The S2RG nodes as an indicator to match the pattern of POS:entities Eeand attributes Aefrom the expressions Re,and solution goalsg.The S2RG generated math relations from explicit entities connect the solution goal g,inference domain knowledge Rd in the knowledge model.The purpose of the S2RG generation algorithm is to implement the addition and inference of implicit expressions.On the S2RG,the entities and attribute values are represented as nodes and relations.In the qualia role description system,entity relations are modelled by the qualia roles of entities.Specifically,the qualia roles of entities are determined by the syntactic structures,and the paper uses six qualia roles[19]to describe the knowledge entity relations in Chinese AWPs.

    The reasoning of the solution chains is achieved from the discrete S2RG to be holistic knowledge as a fully connected S2RG.By modeling the entity relations of the DIR-AWP,the object roles between entities are obtained and added to R.R holds the entity qualia roles obtained after modeling for the input DIR-AWP.The related information in R must be completed by classifying the AWP scenario and obtaining the entity relation combination of the current AWP scenario in Table 1.

    Table 1:The six DIR-AWP examples of the implicit knowledge and their corresponding QS2M models

    3.3.1 Commonsense Foreground Knowledge:S2RG Node Generation

    The S2RG of DIR-AWPs is the solving process state of reasoning missing nodes.Based on the S2RG scenario feature,the model defines the input position sequence nodes Ne.The knowledge model combined the manual prompt pattern for inquiring about implicit AWP knowledge entity candidates for AWPs from the pre-train language model.

    After setting up the template,the explicit entities Nefollow prompt Qrto complement a node Ni,the attributes are ea,and the links Qr.The model needs to fill the candidate entities into the structure of the incomplete triple u=:the label words match the Chinese pre-trained language model L=chinese-roberta-wwm-ext for MLM(Masked Language Model)to get the Chinese grammatical words[MASK].Ranking the candidate entities according to scores.

    Where Nirepresents the output of the prompts f(Ne):

    The Neis the indicator to match pattern Qrto traverse the nodes in the S2RG.The implicit nodes Niand domain Rdthrough Qr.The prompt pattern Ni=fQr(Ne)defines the input position and explicit nodes Ne.

    3.3.2 Subject-Domain Background Knowledge:S2RG Implicit Relation Generation

    Subject-domain relations exist in the DIR-AWP scenario-solving process,represented as S2RG complements the DIR-AWP’s problem-solution chain.The construction of entities by obtaining the qualia roles of entities based on a syntactic format and then describing the entity relations through the qualia roles.

    Compared with the S2model defined in Yu et al.[1],this definition extends the QS2M method that provides a mechanism for acquiring the knowledge items from function problem text.The paper manually designs 150 logical cues for arithmetic reasoning based on the problem context of the topic by splitting the solution expression into multiple sub-expressions based on different topic contexts and giving logical explanations described in natural language based on each sub-operational unit according to the pattern of thought chain reasoning.which covered the five contextual categories summarised in Table 1,including 20 for the plane problem;32 for the task problem;38 for price problems;36 for task problems;24 for MovePath problems.

    Solving DIR-AWPs involves combining explicit and implicit knowledge into a fully connected graph S2RG,which involves searching for a chain of nodes that connect the known information to the solution goalg.The S2RG is enhanced using a structural qualia syntax-semantic pattern,transforming it into a relation-centric representation.This pattern includes various elements such as lexical markers,keywords,dependency relations,and sequential relations within the sentence to construct a semantic scenario.Overall,the approach provides a more comprehensive and rigorous framework for problemsolving in Algorithm 3.

    Algorithm 3:Implicit knowledge acquisition and transformation into quantity relations

    Algorithm 3(continued)

    4 Experiment

    The paper presents the“scenario category”for the DIR-AWPs and constructs a comprehensive entity relation graph S2RG.Specifically,the study investigates five scenario categories of primary school DIR-AWPs and provides a detailed account of each category’s entity and qualia role combinations.These findings shed light on the underlying structures of the DIR-AWP types and offer insights into how to model entity relations effectively.This section presents the empirical findings compared to the Math23K,a publicly available dataset.

    4.1 Dataset

    In this study,the paper employed the Math23K[11]dataset,which is widely used to evaluate math problem solvers,and contains both story and non-story problems(e.g.,equations,formulas,numbers).The approach,QS2M,was explicitly applied to story problems.Previous researches by Mayer [33],Cheng et al.[34],Hong et al.[35],and He et al.[36] have shown that AWPs can be classified into various scenarios based on their storylines,which impact the problem-solving process and the quantity relations involved.To assess the effectiveness of the algorithm on different problem categories,the paper classified 6030 problems from Math23K into five distinct groups to create a new dataset for evaluation.The goal was to evaluate the performance of QS2M across these different categories.The newly created dataset comprises only one-quarter of the original Math23K dataset and comprises five DIR-AWPs types.The dataset includes 6030 problems,sufficient to demonstrate the universality of AWP solvers,as it represents typical cases encountered in AWPs.Table 2 provides detailed information regarding the new dataset and is available for download.

    Table 2:The distribution of Math23K over five types of problems

    Baselines.Three methods compared the model as below:

    ?S2 model[2]: a theoretical framework has been developed for addressing arithmetic word problems that involve explicit statements and require the use of a set of S2models.This framework offers a systematic approach to solving such problems by incorporating various linguistic and mathematical concepts to represent and manipulate the problem’s elements effectively.

    ? GTS[21]: a math word problem solver,structured as a goal-oriented tree,is utilized for the purpose of producing solution expressions.

    ? Graph2Tree[18]: a deep learning architecture that integrates the strengths of graph-based encoders and tree-based decoders to generate expressive solutions.This model achieves enhanced performance in generating solution expressions by leveraging the inherent structural properties of both graph and tree representations.

    ?QS2M:the model proposed in this paper.

    4.2 Performance on Quantity Relation Extraction

    The evaluation of quantity relation extraction is not commonly performed by all neural solvers,and the lack of large-scale ground truth for quantity relation evaluation posed a significant challenge.To assess the performance of quantity relation extraction,accuracy (Acc),recall (R),and F1-score metrics were compared to the V-S2model.The results of the test are summarized in Table 3 and show that the proposed QS2M model significantly outperforms the V–S2model by 7.8% in overall accuracy.Specifically,the QS2M model outperforms neural models by 11.8%and 11.1%on the Plane and MovePath problems,respectively.

    Table 3:The extraction result(%)of math relations compared with the V-S2 method

    The authors of [37] employed a methodology where they encoded the problem statement and the output of Algorithm 2 using the AWP solvers,such as GTS and Graph2Tree.This approach was designed to evaluate problem-solving accuracy,and the results were reported in Table 4.According to the results presented in Table 4,it can be observed that the incorporation of the QS2M tasks has led to a significant improvement in the average accuracy of both the Graph2Tree and GTS models.

    Table 4:The accuracy result(%)of problem-solving

    Specifically,the injection of the extracted quantity expressions has resulted in an average accuracy of 82.0%and 78.5%for the Graph2Tree and GTS models,respectively.These findings suggest that the proposed approach is highly effective in solving AWPs that require more implicit relations.

    5 Conclusions and Future Work

    This paper acknowledges the notable advancements achieved in solving AWPs.However,it also recognizes the absence of an effective method for uncovering deep implicit relations for addressing DIR-AWPs,including those related to common sense or subject-specific knowledge.The present paper suggests utilizing the three-step qualia-quantity approach for discovering deep implicit relations.In the initial stage,the S2method extracts all the explicit relations and identifies scenarios using a preexisting set of qualia-quantity models.Subsequently,the missing entities are incorporated under the identified scenarios,and qualia-quantity models are employed to establish deep implicit relations.Finally,an S2RG is proposed to represent all the obtained relations,which is then condensed by pruning superfluous branches to solve the given problem.The answers are obtained by solving the distilled relations.

    The study proposed a novel method called QS2M that represents quantity expressions linked with uncorrelated entities to address the lack of hidden relations in complex scenarios.In future endeavors,the paper aims to enhance this solver for a broader range of issues and construct a more comprehensive knowledge repository,based on qualia role,to construct problem solvers.Furthermore,the paper has plans to design an intelligent tutoring system and to explore more efficient educational strategies utilizing the system to guide and teach students.

    Acknowledgement:I would like to express my deep gratitude to Professor Xinguo Yu,my research supervisors,for his patient guidance,enthusiastic encouragement and useful critiques of this research work.I would also like to thank Dr.Bin He,for her advice and assistance in keeping my progress on schedule.My grateful thanks are also extended to Mr.Litian Huang for his help in doing the meteorological data analysis,to Mr.Liang Xue,who helped me calculate the wind pressure coefficient and to Mr.Zongyou Qiu for their support in the site measurement.I would also like to extend my thanks to the technicians of the laboratory of National Engineering Research Center for E-Learning(NERCEL)for their help in offering me the resources in running the program.Finally,I wish to thank my parents for their support and encouragement throughout my study.

    Funding Statement:The National Natural Science Foundation of China(No.61977029)supported the work.This work was supported partly by Nurturing Program for Doctoral Dissertations at Central China Normal University(No.2022YBZZ028).

    Author Contributions:H.Meng: review and editing,writing original draft,funding acquisition;X.Yu:conceptualization,methodology,funding acquisition;B.He:investigation,project administration,supervision;L.Huang:software,validation,visualization;L.Xue,Z.Qiu:data collection.

    Availability of Data and Materials:Data will be made available on request.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    乱系列少妇在线播放| 国产在线免费精品| 日本与韩国留学比较| 国产真实伦视频高清在线观看| 午夜久久久在线观看| av国产久精品久网站免费入址| 另类精品久久| 亚洲精华国产精华液的使用体验| freevideosex欧美| 美女xxoo啪啪120秒动态图| 国产精品嫩草影院av在线观看| 高清在线视频一区二区三区| h日本视频在线播放| 精华霜和精华液先用哪个| 国产视频首页在线观看| 国产片特级美女逼逼视频| 乱人伦中国视频| 久久精品国产亚洲av涩爱| 国产女主播在线喷水免费视频网站| 日日撸夜夜添| 国语对白做爰xxxⅹ性视频网站| 狠狠精品人妻久久久久久综合| 人妻一区二区av| 久久热精品热| 91久久精品国产一区二区三区| 国产亚洲精品久久久com| 99久久人妻综合| 精品少妇黑人巨大在线播放| 国产精品无大码| 国产男女内射视频| 丰满迷人的少妇在线观看| 亚洲,一卡二卡三卡| 一级二级三级毛片免费看| 色视频在线一区二区三区| 亚洲色图综合在线观看| 卡戴珊不雅视频在线播放| 亚洲欧美清纯卡通| 国产美女午夜福利| 极品教师在线视频| 丁香六月天网| 蜜桃在线观看..| 国产一区二区三区综合在线观看 | 国精品久久久久久国模美| 一本久久精品| 少妇熟女欧美另类| 国产成人一区二区在线| 亚洲真实伦在线观看| 丰满乱子伦码专区| 另类精品久久| 国产在线视频一区二区| 国产一区二区在线观看av| 高清不卡的av网站| 欧美 日韩 精品 国产| 国产精品99久久久久久久久| 哪个播放器可以免费观看大片| 噜噜噜噜噜久久久久久91| 欧美xxxx性猛交bbbb| 国产欧美日韩精品一区二区| 蜜臀久久99精品久久宅男| 久久久久人妻精品一区果冻| 亚洲精品456在线播放app| 麻豆成人av视频| 只有这里有精品99| 免费观看性生交大片5| 在线观看www视频免费| 尾随美女入室| 国内揄拍国产精品人妻在线| 亚洲va在线va天堂va国产| tube8黄色片| 国产有黄有色有爽视频| 亚洲内射少妇av| 美女cb高潮喷水在线观看| tube8黄色片| 亚洲av日韩在线播放| 国产亚洲91精品色在线| 国产美女午夜福利| 成年人免费黄色播放视频 | 国产精品99久久99久久久不卡 | 婷婷色麻豆天堂久久| 伦理电影免费视频| 欧美 日韩 精品 国产| 精品酒店卫生间| 视频区图区小说| 91精品伊人久久大香线蕉| 又爽又黄a免费视频| 免费av不卡在线播放| 精品一区二区免费观看| 欧美精品亚洲一区二区| 亚洲久久久国产精品| 久久精品国产自在天天线| 久久韩国三级中文字幕| 久久久久久久亚洲中文字幕| freevideosex欧美| 在线观看免费高清a一片| 亚洲美女搞黄在线观看| 午夜精品国产一区二区电影| 国产白丝娇喘喷水9色精品| 亚洲国产av新网站| 在线 av 中文字幕| 色5月婷婷丁香| 亚洲精品日韩av片在线观看| 亚洲综合精品二区| 蜜桃在线观看..| 少妇人妻一区二区三区视频| 日韩免费高清中文字幕av| 久久国产精品男人的天堂亚洲 | 日本免费在线观看一区| 成人特级av手机在线观看| 人妻系列 视频| 欧美+日韩+精品| 免费观看a级毛片全部| 日日爽夜夜爽网站| 一级爰片在线观看| 久久99精品国语久久久| 久久6这里有精品| 熟妇人妻不卡中文字幕| 三级国产精品欧美在线观看| 欧美性感艳星| 91成人精品电影| 永久网站在线| 狂野欧美激情性xxxx在线观看| 青春草国产在线视频| 欧美激情国产日韩精品一区| 各种免费的搞黄视频| 亚洲国产精品成人久久小说| 伦精品一区二区三区| 国产熟女午夜一区二区三区 | 老熟女久久久| 日本黄大片高清| 丰满乱子伦码专区| 亚洲av在线观看美女高潮| 男女免费视频国产| 视频中文字幕在线观看| 国产日韩欧美亚洲二区| 日韩av在线免费看完整版不卡| 国产日韩一区二区三区精品不卡 | 亚洲av成人精品一区久久| 日韩欧美 国产精品| 伦理电影免费视频| 老熟女久久久| 亚洲精品久久久久久婷婷小说| 丰满迷人的少妇在线观看| 午夜精品国产一区二区电影| 成年人免费黄色播放视频 | 色婷婷久久久亚洲欧美| 亚洲国产毛片av蜜桃av| 亚洲欧美一区二区三区黑人 | 亚洲av二区三区四区| 黑人高潮一二区| 99热6这里只有精品| 久久精品国产亚洲av涩爱| 久久久久久伊人网av| 国产淫片久久久久久久久| 免费看不卡的av| 3wmmmm亚洲av在线观看| 免费观看性生交大片5| 51国产日韩欧美| 丰满饥渴人妻一区二区三| 久久精品国产亚洲av天美| 高清视频免费观看一区二区| 久久99一区二区三区| av免费在线看不卡| 亚洲色图综合在线观看| 美女视频免费永久观看网站| 国产伦理片在线播放av一区| 日日啪夜夜爽| 熟妇人妻不卡中文字幕| 三级国产精品欧美在线观看| 国产免费视频播放在线视频| 99久久精品一区二区三区| 久久99蜜桃精品久久| 午夜福利视频精品| 成人午夜精彩视频在线观看| 日日撸夜夜添| 91精品国产九色| 久久久久久久久久人人人人人人| 一级片'在线观看视频| 麻豆精品久久久久久蜜桃| 伦理电影大哥的女人| 国产男人的电影天堂91| 中文字幕制服av| 国产有黄有色有爽视频| 91在线精品国自产拍蜜月| 国产色爽女视频免费观看| av在线观看视频网站免费| videossex国产| 精品久久久噜噜| 亚洲av.av天堂| 91精品伊人久久大香线蕉| 一个人免费看片子| 黄片无遮挡物在线观看| 亚洲欧美中文字幕日韩二区| 黄色一级大片看看| 日本猛色少妇xxxxx猛交久久| 另类精品久久| 在线看a的网站| 久久久久久久精品精品| 久久精品熟女亚洲av麻豆精品| 午夜av观看不卡| 春色校园在线视频观看| 女人久久www免费人成看片| 国产男人的电影天堂91| 熟妇人妻不卡中文字幕| 亚洲精品,欧美精品| 性色avwww在线观看| 91精品一卡2卡3卡4卡| 搡女人真爽免费视频火全软件| 熟女人妻精品中文字幕| 国产熟女欧美一区二区| 啦啦啦中文免费视频观看日本| 啦啦啦中文免费视频观看日本| 午夜影院在线不卡| 又大又黄又爽视频免费| 青春草国产在线视频| 99视频精品全部免费 在线| 成人漫画全彩无遮挡| 亚洲精品一区蜜桃| 一区二区三区精品91| videossex国产| 国产精品不卡视频一区二区| 五月玫瑰六月丁香| 老熟女久久久| 美女主播在线视频| 秋霞在线观看毛片| 亚洲国产精品一区二区三区在线| 国产综合精华液| 国产色爽女视频免费观看| 如日韩欧美国产精品一区二区三区 | 亚洲欧美精品专区久久| 曰老女人黄片| 99视频精品全部免费 在线| 9色porny在线观看| a级一级毛片免费在线观看| 久久久久精品久久久久真实原创| 久久精品国产亚洲网站| 欧美精品人与动牲交sv欧美| 中文字幕精品免费在线观看视频 | 免费看不卡的av| 国产精品秋霞免费鲁丝片| a 毛片基地| 亚洲经典国产精华液单| 亚洲色图综合在线观看| 中国美白少妇内射xxxbb| 国产精品国产三级国产av玫瑰| 三级国产精品片| 夜夜看夜夜爽夜夜摸| 国产精品女同一区二区软件| 国产av一区二区精品久久| 亚洲怡红院男人天堂| 99热这里只有是精品在线观看| 一级毛片黄色毛片免费观看视频| 啦啦啦视频在线资源免费观看| 亚洲丝袜综合中文字幕| 最近最新中文字幕免费大全7| 欧美3d第一页| 亚洲一区二区三区欧美精品| 五月天丁香电影| 国产在线免费精品| 久久久久久久精品精品| 亚洲无线观看免费| √禁漫天堂资源中文www| 99久久人妻综合| 亚洲美女黄色视频免费看| av卡一久久| 久久精品国产a三级三级三级| 亚洲av中文av极速乱| 久久国产亚洲av麻豆专区| 自线自在国产av| 日韩av不卡免费在线播放| 午夜老司机福利剧场| 精品人妻熟女毛片av久久网站| 国产亚洲欧美精品永久| 亚洲国产毛片av蜜桃av| 亚洲av.av天堂| 18禁动态无遮挡网站| 最新的欧美精品一区二区| 欧美 亚洲 国产 日韩一| 久久鲁丝午夜福利片| 国产亚洲最大av| 亚洲国产毛片av蜜桃av| 十分钟在线观看高清视频www | 亚洲av中文av极速乱| 国产精品久久久久成人av| 日韩,欧美,国产一区二区三区| 黑人高潮一二区| 国产成人aa在线观看| 欧美+日韩+精品| 亚洲av国产av综合av卡| 精品久久久久久久久av| 人妻系列 视频| 女性生殖器流出的白浆| 丰满人妻一区二区三区视频av| 精品一区二区三区视频在线| 欧美高清成人免费视频www| 黄片无遮挡物在线观看| 一区二区三区免费毛片| 亚洲av成人精品一区久久| 亚洲精品亚洲一区二区| 天堂8中文在线网| 黑人巨大精品欧美一区二区蜜桃 | 亚洲人成网站在线播| 高清午夜精品一区二区三区| 午夜久久久在线观看| 香蕉精品网在线| 欧美区成人在线视频| 一区二区三区四区激情视频| 精品少妇黑人巨大在线播放| 日韩成人av中文字幕在线观看| 在线观看一区二区三区激情| 中国三级夫妇交换| 新久久久久国产一级毛片| 免费播放大片免费观看视频在线观看| 国产91av在线免费观看| 日韩伦理黄色片| 免费看日本二区| 免费人妻精品一区二区三区视频| 少妇 在线观看| 91久久精品电影网| 成年人午夜在线观看视频| 欧美日韩综合久久久久久| 国产av国产精品国产| 高清视频免费观看一区二区| 视频中文字幕在线观看| 国产成人freesex在线| 亚洲情色 制服丝袜| 亚洲人成网站在线观看播放| 一边亲一边摸免费视频| 一级毛片aaaaaa免费看小| 大码成人一级视频| tube8黄色片| av免费在线看不卡| 一个人免费看片子| 在线观看三级黄色| 中文在线观看免费www的网站| 草草在线视频免费看| 国产精品久久久久久久久免| 少妇被粗大猛烈的视频| 国产精品伦人一区二区| 看非洲黑人一级黄片| 人妻系列 视频| 亚洲一区二区三区欧美精品| 国产精品国产三级国产专区5o| 午夜日本视频在线| 麻豆乱淫一区二区| 只有这里有精品99| 国产精品伦人一区二区| 日韩伦理黄色片| 亚洲av不卡在线观看| 国产精品无大码| 久久精品久久久久久久性| av在线观看视频网站免费| 99热全是精品| 国产精品无大码| 免费看av在线观看网站| 少妇被粗大猛烈的视频| av线在线观看网站| 亚洲伊人久久精品综合| 又黄又爽又刺激的免费视频.| 99热网站在线观看| 国产成人精品一,二区| 国产欧美另类精品又又久久亚洲欧美| 男女无遮挡免费网站观看| 国产亚洲91精品色在线| 国产在线男女| 18禁在线播放成人免费| 亚洲国产日韩一区二区| 亚洲,一卡二卡三卡| 九草在线视频观看| 王馨瑶露胸无遮挡在线观看| 日韩熟女老妇一区二区性免费视频| 中文字幕av电影在线播放| 国产在线视频一区二区| 热re99久久国产66热| 久久女婷五月综合色啪小说| 亚洲欧美清纯卡通| 永久免费av网站大全| 久久久久网色| 在线观看av片永久免费下载| 久久精品国产亚洲网站| 亚洲精品日韩av片在线观看| 亚洲不卡免费看| 视频区图区小说| 五月天丁香电影| 亚洲欧美一区二区三区黑人 | 国产精品成人在线| 亚洲国产成人一精品久久久| 少妇人妻 视频| 欧美成人午夜免费资源| 色婷婷av一区二区三区视频| 欧美一级a爱片免费观看看| 亚洲自偷自拍三级| 精品视频人人做人人爽| 亚洲精品国产成人久久av| a级片在线免费高清观看视频| 久久久久人妻精品一区果冻| 国产一区二区三区综合在线观看 | 国产精品女同一区二区软件| 国产成人精品福利久久| 王馨瑶露胸无遮挡在线观看| 国产免费福利视频在线观看| 97精品久久久久久久久久精品| 日韩av不卡免费在线播放| 99热全是精品| 最黄视频免费看| 99热这里只有精品一区| 一本—道久久a久久精品蜜桃钙片| 又黄又爽又刺激的免费视频.| 国产成人精品婷婷| 国产精品不卡视频一区二区| 一区二区三区四区激情视频| 99视频精品全部免费 在线| 国内精品宾馆在线| 久久热精品热| 成人二区视频| 国产一区二区在线观看av| 久久久国产欧美日韩av| 久久久国产一区二区| 少妇被粗大的猛进出69影院 | 日本午夜av视频| 在线亚洲精品国产二区图片欧美 | 婷婷色综合www| 成人毛片60女人毛片免费| 最近的中文字幕免费完整| 自拍偷自拍亚洲精品老妇| 亚洲av综合色区一区| 久久精品久久久久久噜噜老黄| 亚洲国产精品国产精品| 中文精品一卡2卡3卡4更新| 日韩精品免费视频一区二区三区 | 91aial.com中文字幕在线观看| 国产精品久久久久久精品古装| 国产欧美日韩综合在线一区二区 | 欧美bdsm另类| 丁香六月天网| 黑人猛操日本美女一级片| 国产毛片在线视频| 在线观看免费高清a一片| 高清午夜精品一区二区三区| 狂野欧美激情性bbbbbb| 69精品国产乱码久久久| 97在线人人人人妻| 国产国拍精品亚洲av在线观看| 久久毛片免费看一区二区三区| 日韩成人av中文字幕在线观看| 国产黄片美女视频| 色婷婷久久久亚洲欧美| 久久99精品国语久久久| 自线自在国产av| 国产一区亚洲一区在线观看| 80岁老熟妇乱子伦牲交| 亚洲情色 制服丝袜| 女人久久www免费人成看片| 日韩免费高清中文字幕av| 伊人亚洲综合成人网| 热re99久久精品国产66热6| av福利片在线| 一个人免费看片子| 成人影院久久| www.av在线官网国产| 亚洲婷婷狠狠爱综合网| av网站免费在线观看视频| 丰满饥渴人妻一区二区三| 国内揄拍国产精品人妻在线| 亚洲国产精品一区二区三区在线| 久久久精品94久久精品| 国产成人精品一,二区| 少妇人妻 视频| 欧美 日韩 精品 国产| 久久99蜜桃精品久久| 一级黄片播放器| 国产免费福利视频在线观看| 人妻人人澡人人爽人人| 欧美最新免费一区二区三区| 久久韩国三级中文字幕| 黄色配什么色好看| 最近中文字幕高清免费大全6| 麻豆成人av视频| 中国国产av一级| 国产av一区二区精品久久| 色哟哟·www| h视频一区二区三区| 国产国拍精品亚洲av在线观看| 国产精品麻豆人妻色哟哟久久| 色吧在线观看| 精品亚洲成a人片在线观看| 极品人妻少妇av视频| 久久精品国产鲁丝片午夜精品| 久久久国产一区二区| 亚洲,欧美,日韩| 一级毛片我不卡| 久久久亚洲精品成人影院| 国产精品久久久久久精品古装| 七月丁香在线播放| 在线播放无遮挡| 美女内射精品一级片tv| 久久久亚洲精品成人影院| 纯流量卡能插随身wifi吗| 在线观看三级黄色| 99国产精品免费福利视频| 日韩免费高清中文字幕av| freevideosex欧美| 十八禁高潮呻吟视频 | 亚洲在久久综合| 日韩免费高清中文字幕av| 2022亚洲国产成人精品| 高清在线视频一区二区三区| 伦理电影免费视频| av天堂中文字幕网| 一级黄片播放器| 亚洲国产av新网站| 久久毛片免费看一区二区三区| 国产成人免费无遮挡视频| 制服丝袜香蕉在线| 嘟嘟电影网在线观看| 色吧在线观看| 亚洲性久久影院| 亚洲综合精品二区| 街头女战士在线观看网站| 亚洲精品一区蜜桃| 搡老乐熟女国产| 国产男女超爽视频在线观看| 秋霞在线观看毛片| 99热网站在线观看| 尾随美女入室| 国产亚洲精品久久久com| 街头女战士在线观看网站| 在线观看人妻少妇| 综合色丁香网| 夜夜看夜夜爽夜夜摸| 在线观看www视频免费| 18禁裸乳无遮挡动漫免费视频| 午夜福利,免费看| 美女cb高潮喷水在线观看| 亚洲欧美精品专区久久| 在线观看一区二区三区激情| 亚洲精品久久午夜乱码| 国产免费福利视频在线观看| 如何舔出高潮| 午夜久久久在线观看| 看十八女毛片水多多多| 亚洲欧美日韩另类电影网站| 日本wwww免费看| 久热这里只有精品99| 日日爽夜夜爽网站| 天天躁夜夜躁狠狠久久av| 久久久久久久国产电影| 国产精品三级大全| .国产精品久久| 国产日韩欧美视频二区| 十分钟在线观看高清视频www | √禁漫天堂资源中文www| 久久久久久久久久久免费av| 在线免费观看不下载黄p国产| 日本vs欧美在线观看视频 | 亚洲高清免费不卡视频| 汤姆久久久久久久影院中文字幕| 日本91视频免费播放| 观看美女的网站| 成年美女黄网站色视频大全免费 | www.av在线官网国产| 黄色视频在线播放观看不卡| 少妇人妻 视频| 蜜桃在线观看..| 春色校园在线视频观看| 哪个播放器可以免费观看大片| 美女中出高潮动态图| 91久久精品国产一区二区成人| 一级,二级,三级黄色视频| av在线app专区| 免费大片18禁| 亚洲精品aⅴ在线观看| 高清毛片免费看| 国产精品一区二区在线不卡| 免费观看av网站的网址| 美女视频免费永久观看网站| 色哟哟·www| 色网站视频免费| 超碰97精品在线观看| 国产亚洲欧美精品永久| 国产精品一区二区在线观看99| 日韩一区二区三区影片| 日韩三级伦理在线观看| 大香蕉久久网| 久久精品国产亚洲网站| 精品午夜福利在线看| 性高湖久久久久久久久免费观看| 韩国av在线不卡| 精品午夜福利在线看| 国产精品.久久久| 十分钟在线观看高清视频www | 欧美另类一区| 全区人妻精品视频| 久久久久久久大尺度免费视频| 欧美少妇被猛烈插入视频| 高清午夜精品一区二区三区| 国产精品蜜桃在线观看| √禁漫天堂资源中文www| 人体艺术视频欧美日本| 香蕉精品网在线| a级毛片免费高清观看在线播放| 国产精品久久久久久久电影| 中文字幕亚洲精品专区| 黑人猛操日本美女一级片| 一级毛片黄色毛片免费观看视频| 亚洲av不卡在线观看| 亚洲欧洲精品一区二区精品久久久 | 日韩,欧美,国产一区二区三区| 欧美精品亚洲一区二区| 亚洲一级一片aⅴ在线观看| 岛国毛片在线播放| 国产乱来视频区| 少妇的逼好多水| 熟女电影av网| 亚洲人成网站在线播| 久久人人爽av亚洲精品天堂|