Ruya Tian
email: tianruya@126.com

Lei Wu

Xuefu Yijun Liu
email: cyijunliu@casipm.ac.cn

Xuefu Zhang
email: dzhangxuefu@caas.cn

Meta-Synthetic Methodology: A New Way to Study Agricultural Rumor Intervention

Keywords: meta-synthetic methodology, online public opinion system, agricultural rumor intervention, intervention mechanism

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

With the popularity of the web, BBS and Twitter on Internet have increasingly become the most important channel for people to publish, exchange and access to information. It is now an important platform for Internet users to leave comments on emergencies, to express attitudes and to vent their emotions. Furthermore, it even becomes an important force in promoting the development of the incidents.

Monitoring and management of agricultural rumors are increasingly becoming an important aspect of the national interests and a matter of national security, which make the emergency management of the online rumors become a hot spot [START_REF] Liu | Formation and application of public opinion based on supernetwork analysis[END_REF][START_REF] Li | Dynamical model of public opinion and its application based on supernetwork[END_REF][START_REF] Tian | Intervention of public opinion and its application based on supernetwork analysis[END_REF][START_REF] Ma | Recognition of online opinion leaders based on supernetwork analysis[END_REF]. At present, researches on online public opinion intervention in China focused more on the qualitative ways, which takes case analysis as the main method. While scientific, systematic, and quantitative research is rare [START_REF] Tian | Advances in intervention of online public opinion[END_REF].

System is the general form of the existence of things. One of the founders of general systems theory, Bertalanffy, defined the system as "A synthesis of all related factors", which is an organic integrity with a specific function combined of several interactional and interdependent components [START_REF] Bertalanffy | General system theory: Foundations, development, applications[END_REF]. General systems theory requires people to see things from a global standpoint, to comprehensively analyze the relationships between the elements in the system, elements and their system, system and its environment, this system and other systems. By doing so, internal relations and laws of a system can be grasped, as well as the way to control and transform it effectively. Meta-synthetic methodology proposed by the Chinese famous scholar Xue-Sen Qian, illustrates a comprehensive approach of qualitative and quantitative methods [START_REF] Qian | A new discipline of science: the study of open complex giant system and its methodology[END_REF]. It is a creative sublimation to systems theory, providing a new way of thinking and methods to solve the problem of open complex giant system [START_REF] Gu | Practice on Wuli-Shili-Renli system approach[END_REF].

This paper investigated the intervention mechanism of online public opinion from a meta-synthetic methodological point of view. Online public opinion can be seen as a system. It has general constituent elements. Each element in the system is on a particular position with specific functions. Links and constraints between elements of each other constitute an indivisible whole. On the basis of elaborating its composition, structure, and function qualitatively, this paper quantified the intervention mechanism for online public opinion to provide a new research perspective for online public opinion intervention.

The rest of this paper is organized as follows. Section 2 describes online public opinion system, including its composition, structure, and function. In Section 3, we built an Opinion SuperNetwork according to the system characteristics to explore the online public opinion, and then discussed the intervention mechanism based on this supernetwork. In Section 4, we applied the methods described in Section 3 to a certain food safety event, and the results are presented and compared. Section 5 concludes, and suggests possible future research approaches.

Online Public Opinion System

Online public opinion is a system of particular composition, structure, and function.

Intervention and guide for online public opinion can be highly effective or even gets twice the result with half the effort by fully considering the inherent properties and operation mechanism of the online public opinion system. Online public opinion system is the sum of beliefs, attitudes, opinions and emotions, etc. It is public opinion subject published on the Internet about certain social events under certain psychologies at a certain time and space. It can impact on the development of the events.

Online public opinion system consists of opinion subjects, opinion objects, and system dynamics, as is shown in Fig. 1. Public opinion system exists in a particular system environment, and is divided from its environment by the system boundary.

There are certain inputs and outputs between the public opinion system and its environment. Opinion subjects of public opinion system includes the opinion agents discussing the events and the parties of the events. For instance, Netizens are opinion subjects in public opinion system. The opinion object is the sum of beliefs, attitudes, opinions and emotions, etc., which are published by public opinion subjects on the Internet, such as netizens' posts. System dynamic is the psychology of public opinion subjects, which is the internal driving force for subjects to express different objects.

For example, hatred answers for the deep psychological reason of certain netizens who publish rumors.

Fig. 1. Online public opinion system and its intervention mechanism

Public opinion system environment is the event evolvement, which is the input of the system, and also the external driving force of the development of the system.

System boundary is the certain space and time, as well as specific event. Effect of opinion system on the event is the system output. There are two fundamentally different types of system output. One is the direct result caused by the system. The other type is the conclusion with more universal significances. As in "Guo Mei-mei rumor event", the direct result of the online public opinion system is Guo Mei-mei clarified that there is no relationship between her and the Chinese Red Cross, and Red Cross disclosed its money flows. While universal conclusions are we need to reflect on the values of the post-90s generation, to explore methods resolving the government confidence crisis, and to ponder the role of the media.

Relation mode between various elements of the opinion system is the system's structure. First, opinion subjects interact one another. For instance, opinion of the opinion leaders influences the others' opinion [START_REF] Katz | Personal Influence, The part played by people in the flow of mass communications[END_REF][START_REF] Tian | Leader-Guiding model of online opinion supernetwork[END_REF]. Second, there are temporal and spatial dependencies between opinion objects. Some posts published earlier on time result in preconception impacts on later posts. And information nonidentity of different forums caused the isolation between the opinion objects in space. Thirdly, there is a relationship of action and reaction between opinion subject and object.

Subjects express different viewpoints under different system dynamics, and different viewpoints in turn affect the psychology of the subjects. Last but not least, system dynamic is the connector, mediator, and promoter associating system subjects and objects. It is the internal driving force of system development and evolvement.

Interactions between this subject and that subject, this object and that object, as well as between subject and object are driven by the system dynamic.

Online public opinion impacts on its environment, i.e. the evolvement of incidents. This is the output of the public opinion system. It can reflect the function of the system. Discussions in online public opinion system have positive effects on the development of the event. It is a collection and a reflection of public opinion. Online public opinion system is a promoter for government policy to be transparent and open.

But the negative impacts of the online public opinion should not be overlooked.

First, the online public opinion can sometimes become an amplifier of false information and rumors, leading to misinformation and opinion anomie. Internet is a place mixed of good and evil. People with different ages, professions, and interests gathered in this discourse space. Anyone can feel free to post or retweet the information of social events after he or she registered an ID as a forum user or had a personal twitter, which opens the door for false information and rumors.

Second, the online public opinion can be the fuse of public discontent sometimes, with negligible impact on social stability. In the online public discourse space, multi-dimensional interacts among Internet users include not only text, but also an emotional interaction. It is noteworthy that, discontent of netizens often start from criticizing a social event, and be further aggravated with the criticism going deeper. It sometimes bring bad impacts on social stability.

Third, the online public opinion can sometimes become a splitter digesting social cohesion, which leads to social crisis of confidence. With the increase of online rumor, social confidence is facing a deeper crisis. In recent years, the social crisis of confidence problems caused by the spread of online rumor is worth more increasingly of concern.

Intervention Mechanism of Online Public Opinion System

Qualitative descriptions for components, structure, and function of the online public opinion system provide a theoretical basis for quantitative calculation of system mechanism. Online public opinion intervention can be implemented based on the characteristics of the system. Regulating the composition and structure of the system changes function of the system. This will make public opinion interacting with the event positively and achieve the goal of rumor intervention.

Online Public Opinion System Modeling

A supernetwork is defined as a network that exists above and beyond existing networks [START_REF] Nagurney | On the relationship between supply chain and transportation network equilibria: a supernetwork equivalence with computations[END_REF]. In recent years, it is applied in many aspects such as knowledge networks [START_REF] Xi | Knowledge supernetwork model and its application in organizational knowledge systems[END_REF], logistics (supply chain) [START_REF] Estrada | Subgraph centrality and clustering in complex hyper-networks[END_REF], traffic [START_REF] Wakolbinger | Dynamic supernetworks for the integration of social networks and supply chains with electronic commerce: modeling and analysis of buyer-seller relationships with computations[END_REF], ecology [START_REF] Nagurney | Management of knowledge intensive systems as supernetworks: modeling, analysis, computations, and applications[END_REF], and so on.

According to the compositional and structural characteristics of online public opinion system, a supernetwork model including Social Subnetwork, Environment Subnetwork, Psychological Subnetwork, and Viewpoint Subnetwork can be created (Fig. 2). The model in this study can not only depict the relationships between the elements in the public opinion formation process, but better reveal the dynamics and evolution mechanisms of the system [START_REF] Tian | Isolation, insertion, and reconstruction: three strategies to intervene in rumor spread based on supernetwork model[END_REF][START_REF] Tian | SSIC Model: a multi-layer model for intervention of online rumors spreading[END_REF].

Fig. 2. Online public opinion system modeling

Social Subnetwork

Take the nodes of the network for the agents issued public opinion, and the network edges for the reply relationships between the agents. By doing so, we form the "Social Subnetwork of Opinion SuperNetwork ( A G )".Opinion agents who issued positive viewpoints are positive agents in the public opinion system. The one who issued negative viewpoints are negative agents.

Environment Subnetwork

Take the nodes of the network for the environments under which public opinion issued, and the network edges for relationships between issued time of pieces of information. By doing so, we form the "Environment Subnetwork of Opinion SuperNetwork ( E G )". A negative environment is the one with rumor spreading, while positive environment refutes rumor spreading.

Psychological Subnetwork

Take the nodes of the network for agents' psychology who issued public opinion, and the network edges for their transformation relationships. By doing so, we create the "Psychological Subnetwork of Opinion SuperNetwork ( P G )". The agents with positive psychology are reluctant to believe the rumors, and the ones with negative psychology are inclined to believe rumors.

Viewpoint Subnetwork

Take the nodes of the network for viewpoint keywords issued by public opinion agents, and network edges for various keywords' affiliation relationships. By doing so, we create the "Viewpoint Subnetwork of Opinion SuperNetwork ( K G )". The viewpoint with rumor spreading is negative, while the one with rumor clarifying is positive.

Opinion SuperNetwork

Take opinion agents, opinion environment, agent's psychology and viewpoints as nodes, the relationships in and between the Subnetworks for superedges ( SE ) to create "Opinion SuperNetwork (OSN)".

 

, , , ,

A E P K a e p k OSN G G G G SE     (1)
This model can be used to describe the overall features and the general rules of online public opinion system. Based on this model, we explore the quantitative intervention mechanisms, including how to make the best timing to intervene, select feasible intervention mode, as well as master the appropriate intervention intensity (Fig. 1).

Intervention Timing

Currently, the intervention of online agricultural rumor is implemented after the event had become opinion hotspot. Government departments are involved in the interference with netizens' urgent requirement, missed the opportunity to change the negative momentum of the public opinion. This often caused netizens' emotion siltation and conditions that public opinion is detrimental to the development of the event. Due to time rush, a lot of intervention agents often have too little expertise to manage the crisis. Researchers have proved that it is better to manage public opinion crisis with responding to the crisis within "golden four hours" [START_REF] Liu | Study on the guidance of online public opinion for innovation social management[END_REF].

It is an ideal choice to implement intervention according to the characteristics of the online public opinion system, before system output, i.e. before the system caused impact or significant influence to the event. So quantitative prediction of system output is particularly important. Superlink (i.e. superedges) prediction referred to the prediction of existent yet unknown superlinks and future superlinks in a supernetwork structure [START_REF] Liu | Superedge prediction[END_REF]. Superlink prediction between opinion agents can be conducted by method shown in Fig.

3 [START_REF] Liu | Superedge prediction: what opinions will be mined based on an opinion supernetwork model?[END_REF].

Fig. 3. Flow diagram of superlink prediction

Through superlink prediction, we can know superedges that may appear in the next moment in opinion supernetwork. The predicted superedges contain opinion subjects, information environment, opinion psychology, and opinion viewpoints. This portrays how the subjects issued their point of view under an external driven force of environment and an internal driven force of psychology. And these viewpoints will in turn affect the development of events, becoming an output of the system. So we can predict the intervention timing of the system in accordance with opinion superlink prediction results. Suppose a supernetwork model of a public opinion system is as shown in Fig. 4. In the figure, red hollow graphics are those with negative attribute.

Green solid graphics are those with positive attribute. The red dashed line represents a negative predicted superedge, and the green double-dashed line represents a positive predicted superedge. Intervention for the system is required when the predicted superedges in negative is more than positive ones.

Fig. 4. Diagram of intervention timing

Intervention Mode

Experience has shown that when serious emergencies occur, the mainstream media should proactive and timely report the truth of emergencies objectively, with the support and guidance of the government and the propaganda department. This may inhibit the spread of rumors, and maintain social stability. Speaking of the mainstream media in the first time will resolve irrational pressure of public opinion. In the Internet era, it is fast and convenient for people accessing to information through the Internet, SMS and other channels. When a serious emergency occurs, the message will spread like wildfire, and it will have a strong psychological impact on the public in the short term. Mainstream media should occupy the initiative of the incident reporting.

Intervention of online public opinion system can be implemented by changing system input. Input of the system is the opinion environment, that is, different development stages of events. Thus changing opinion environment, i.e. publishing the latest information of the event, can promote the event to positive direction. This will achieve the intervention purpose of online public opinion system. After the release of the new positive information, the psychology of the opinion subjects will change under the impacts of the external driving force of opinion environment. Opinion subjects will update their opinion objects under the internal driving force of opinion psychology, namely the views expressed by opinion objects further varies with changes in their psychology. In Fig. 5, the bigger solid green triangle represents the newly added opinion environment. The green double-dashed line represents intervention of public opinion environment on the various elements of the system. It should be emphasized that environmental impact will be different between the opinion subjects in the center position of reply relationship in the system (core agent) and the ones in the edge position (leaf agent). So how to control the intervention intensity to get best effect under particular cost becomes the key point of the rumor intervention study.

Fig. 5. Diagram of intervention mode

Intervention Intensity

Psychological state of public opinion subject is the dynamic of system evolution.

Each subject will produce specific psychology in a particular environment, and published their viewpoints on social events under this psychological impact. Dynamic of system evolution is adjusted after subject's psychology changes, i.e. condition basis of viewpoints expressing is changed. Therefore, for public opinion system, intervention intensity must be strong enough to change the subject's psychology.

After the release of positive information to the public opinion system for intervention, the overall evolution rules of the model are: changes of the environment cause the changes of the subject's psychology; changes of the subject's psychology cause the changes of subject's attribute; changes of subject's attribute affect the attribute of newly published viewpoint. Specific changes of each Subnetwork in the model are as follows:

Evolution of System Environment

The new environment of agent i A is E . Its property depends on environment information E and the neighbors of agent i A (i.e. other agents have replying

relationships with i A ), namely, 2 Num j E E Num        (2)
Num is the neighbors' number of agent i A . 1

E    , it is the property of a new environmental information. j A are neighbors of agent i A , j  are their points of view. If a viewpoint is a positive one, 1 j    ; otherwise 1 j    . 1, 2, , j Num  L
, and ji  .

Evolution of System Dynamics

Opinion subject's psychology changes when the environment If the subject is exposed to a changed environment, we may calculate the new environment of the agent in according to formula [START_REF] Li | Dynamical model of public opinion and its application based on supernetwork[END_REF]. And then we calculate the psychology transition probability under the new positive environment. The probability that the positive psychology of an agent's transform to a negative one is

        EC E C E C e P ee         (3)
And the probability that the negative psychology of an agent's transform to a positive one is

        EC E C E C e P ee        (4)
C reflects the concern degree on an event by the public.

Evolution of Opinion Subjects

The opinion subject property is judged according to the attribute of its psychology in accordance with the following rules: a. ∃ subject psychology is negative, the subject is negative.

b. ∀ subject psychology is positive, the subject is positive.

Evolution of Opinion Objects

Objects, i.e. viewpoints of the subject changes when the subject's property changed. Strategy 1. The released positive information affects all subjects at the same time.

Step 1: Initialize the property values of all nodes and the superedges of the Opinion SuperNetwork.

Step 2: Calculate the positive agents' proportion. When the ratio is <50%, continue to next step, otherwise end.

Step 3: Simulate the release of a positive information. Add a positive environment node to simulate intervention implement.

Step 4: Choose a negative agent node randomly to update the node's property and other nodes' in the opinion supernetworks according to the evolution rules described in 3.4.

Step 5: Return to Step 2 and cycle.

Strategy 2. The released positive information affects core subjects at the first time.

In Step 4, choose core subjects of negative property first. Strategy 3. The released positive information affects leaf subjects at the first time.

In Step 4, choose leaf subjects of negative property first.

Result Analysis

Implement all the above three intervention strategies. Conduct 1000 simulation experiments independently under each of the strategies, and take the average value of the 1000 simulation experiments as the result (Fig. 6).

As can be seen from Fig. 6, after the release of positive information, the proportion

Conclusions

Meta-synthetic methodology provides a new perspective to study online agricultural rumor intervention. Online public opinion is seen as an organic integrity with specific functions composed of several elements of interaction and interdependence. This helps us to make a comprehensive analysis of the relationships among elements, elements and the system, as well as the system and its environment. Thus we can grasp its internal relationship and law, and achieve the purpose of controlling and transforming the system effectively.

Our investigation made two important contributions to the investigation of online agricultural rumor intervention. First, based on the meta-synthetic methodology, intervention mechanism of online public opinion system was investigated under a way of "from qualitative to quantitative". We described the general characteristics of online public opinion system qualitatively, including its composition, structure, and function. Then we built a supernetwork model which includes Social Subnetwork, Environment Subnetwork, Psychological Subnetwork and Viewpoint Subnetwork, to quantitatively research public opinion intervention mechanism to get the general intervention methods of public opinion system.

Second, this paper contributes to the research of online agricultural rumor intervention mechanism. We used superlink prediction to provide a theoretical basis for predicting the intervention timing. The release of new environmental information to impact the system input is used as the specific intervention mode. Changing the system dynamic by intervening in the psychological condition of the opinion agents was taken as the measure of intervention intensity. And the simulation results show that build a supernetwork model based on meta-synthetic methodology to implement rumor intervention can achieve desired effects. The analysis of the intervention strategies in this paper provides the government a comprehensive measure standard of online agricultural rumor events, which allows them to get a much deeper understanding to the rumor spreading influence elements, and make more strategic decisions intervening in rumor spreading.

Our study provides a useful consultation to agricultural rumor intervention theoretical studies from a meta-synthetic methodology perspective. And in future studies, more work may be done to improve our research. This article only analyzed the system changes caused by the environmental Subnetwork. The next step, we will continue to investigate the system changes caused by the other three Subnetworks, and also explore the corresponding intervention strategies.

  a. If the subject property changes from a negative one to a positive one, the negative standpoints of the subject change to the majority of the positive standpoints of the neighbors'. b. If the subject property changes from a positive one to a negative one, the positive standpoints of the subject change to the majority of the negative standpoints of the neighbors'. used in this study to seek the variation of the proportion of positive agents over time under the release of positive information. We picked an agricultural rumor event as a study case. The model of the case contains four Subnetworks, including 108 nodes of agents in relationships of nodes in and between different Subnetworks form the superedges of the Opinion SuperNetwork. This simulation experiment aims at analyzing how the proportion of positive agents change with time under the opinion intervention implement, and exploring the efficiency of rumor intervention based on the meta-synthetic methodology. Here we proposed three different intervention strategies, and compared the efficiency of various strategies. The experiment procedure is as follows:

  of positive public opinion gradually increased under all the three intervention strategies. It indicates that establishing supernetwork model based on the meta-synthetic methodology to conduct rumor interventions can achieve the desired outcomes. (a) Strategy 1. Both core and leaf agents at the same time (b) Strategy 2. Core agents first affecting core public opinion agents first. While in practice the letter takes a much higher cost. So in the real agricultural rumor intervention, take the strategy of affecting all public opinion agents at the same time can achieve a higher effect under a lower cost.

Fig. 7 .

 7 Fig. 7. Effect Comparison of Different Intervention Strategies Simulation results show that analyzing rumor intervention mechanism from the perspective of meta-synthetic methodology to develop intervention strategies can not only quantify intervention strategies, but also draw influence mechanisms and laws of intervention strategies by changes and interlocking mechanisms of each element in the online public opinion system. This will help to provide a reliable basis for the development of more sophisticated and effective intervention strategies.

Acknowledgements

Funds for this research was provided by the National Social Science Foundation of China (15CTQ030), the National Natural Science Foundation of China (91324009), the National Sci-Tech Pillar Program during the 12th Five-year Plan Period (2011BAH10B06), and the Science and Technology Innovation Projects of the Chinese Academy of Agricultural Sciences (CAAS).

In addition, efficiency of the three different intervention strategies is in comparison (Fig. 7). In the beginning, the strategy that positive information first affects the of leaf public opinion agents has the highest efficiency, followed by the strategy that both kinds of opinion agents being affected at the same time. The strategy that information impacts core public opinion agents first is the least efficient. That may because the leaf agent kind is superior in numbers to core ones. Soon, the advantage of affecting core agents first shows up. By the 15th time step, effect of affecting core agents first surpasses the other two strategies. After the system is steady, strategy of affecting core agents first has a dominant effect, followed by the strategy that both kinds of opinion agents being affected at the same time.

It is commonly known that publishing positive information, and making it affect the core public opinion agents first is the best strategy for rumor intervention. This intervention mechanism research based on a meta-synthetic methodology approach has provided a reliable quantitative basis for evidence. However, it can be seen from the efficiency comparison of the three kinds of strategies that the efficiency of affecting all the public opinion agents together is only slightly inferior to the one of


Yingyi Chen

Jiani Xue

Huihui Yu

Jing Xu

Zhumi Zhen

Xingyue Tu

Zhijie Ma

Yun Zhao

Yanzhong Liu

Liu Yanzhong

Quick and Automatic Generation Method for the Evaluation Report of the Small Hydropower Substitute Fuel Project

Keywords: small hydropower substitute fuel (SHSF), multi-indicator evaluation report template, quick and automatic generation method

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The small hydropower substitute fuel (SHSF) project is a public welfare project that consolidate the achievements of returning farming land to forestry, and solve the problem of fuel for rural life. It gives prominence to the ecology benefit and was named as "projects lightening the hope of mountain area "by local people. From 2009 to 2015, and there are 1022 small hydropower for fuel ecological protection projects in construction in fragile areas. Different from conventional construction of small hydropower energy issues, the small hydropower replacing fuel small hydropower project is to use firewood instead of rural life, so as to achieve the purpose of protecting the forest [START_REF] Huizhou | Relevant Issues at Project Planning of Using Small hydropower Replacing Woody Fuel for Ecological Protection[END_REF][START_REF] Tingrong | Estimation of the rural electric power consumption in the ecological protection areas with implementing the project of small hydropower for fuel [J][END_REF] . At present, the experiment unit work of the small hydropower replacing firewood project is just start, with China's great efforts to developing the small hydropower replacing firewood projects, a large number of project data in urgent need of regulation and evaluation. As an important part of project evaluation and management, efficient and rapid method to generate standardized project evaluation reports is critical.

In order to facilitate the management of the documents generated for further processing and reuse, Word is used as the report output of the application tools can further extend the application's functionality, improve utilization and shareability of data [START_REF]Using Delphi Creates the Word Report and the Dynamical Structured Form[END_REF][START_REF] Weixiang | A Research on and Realization of Output of Structured Documents[END_REF][START_REF] Hui | The Design and Realize of Physical Report Generation System[END_REF] . In general, report document formats are relatively fixed [START_REF] Fen | A Platform for Automatically Producing Word Document Based on Multiple techniques[END_REF][START_REF] Baoyu | Using Delphi Creates the Word Report and the Dynamical Structured Form[END_REF] , so in the most of these reports generated methods, report template is often designed well in advance and the label, or specific keywords are insert into the specific location where need to be filled in by the program. And then the reports are generated by replacing the label, or specific keyword in the build process. The benefit of this approach that the report is product by the program document style is to be designed outside has the advantage of reducing the amount of code, improved implementation efficiency [START_REF] Yurong | Word Document Automatically Generated Technology Based on VC[END_REF] .

Facing great development need of small hydropower for fuel ecological protection projects, drawing on the domestic and international water energy management experience as well as the sustainable development of small hydropower management structure, this paper presents a multiindicator comprehensive evaluation module based on the study of eco-benefit evaluation methods and overall framework of the management and evaluation system of the small hydropower project, it produces evaluation report by using default evaluation report template, which includes environmental impacts on climate change, conversion of cropland to forest and natural forest protection project, greenhouse gas emission and the role of social and poverty alleviation. In order to achieve automated building of the comprehensive evaluation report of the small hydropower project, we construct a quick and automatic generation method for the evaluation report based on C#.Net development environment. It produces evaluation report by reading, traversing and replacing the corresponding position data placeholder of customized multi-indicator evaluation report template .The quick and automatic generation method for the evaluation report of the small hydropower project can provide technical support for benefit analysis of ecological environment.

Materials and Methods

The structure of Evaluation report generation module

The development objective of the comprehensive evaluation module based on Microsoft Visual Studio integrated development environment and .Net Framework 4.5 platform is to generate Evaluation report quickly and automatically according to the user requirement. The structure of Evaluation report generation module is shown in Figure 1.

The evaluation module design includes three parts: document template customization part, database and report generation part: (1) Document template customization: Document template customization is a tool that runs on a client for the administrator to use for making report document template according to their requirement. Document templates, including fixed and replaceable parts. The replaceable parts is specified using the designated (For example, label in word). ( 2

Encapsulating data processing method and establishing label

Word report including data recording method and data processing method of data query. ( 1) the main methods of data entry manual entry of text information. ( 2) data querying method based on userselected criteria from the database to retrieve the corresponding data, and graphically displayed. Dataprocessing methods, including methods and method body, method header indicates the method name, method calls the conditions and method return value type, including method return value type into text strings, images, and tables, one for each input class methods and query class method. Features in each data processing method using the C# language adds a description only shows that it features, the format is a text string [START_REF] Troelsen | Pro C# 2010 and the[END_REF] , and finally through the compiler to encapsulate the data processing method for the dynamic-link library (DLL) files. Technical data processing methods using reflection file description of the analytic methods, sorting packages for labels, and configured service interfaces using Web Service technology release. Labels include tags categories, tags and the corresponding data source [10] .

2.3Report template customization

Report template customization is a tool that runs on a client for the administrator to use for making report document template according to their requirement. Report templates, including fixed and replaceable parts, which is specified using the designated (For example, label in word), are stored in database.

Evaluating indicator of the small hydropower replacing firewood project

Report templates makes up of multiple evaluation, which is environmental impacts on climate change, conversion of cropland to forest and natural forest protection project, greenhouse gas emission and the role of social and poverty alleviation.

(1) Environmental impacts on climate change indicators include eco-environment, energy saving and emission reduction, new dam construction technology and capacity to respond to climate change.

(2) Conversion of cropland to forest and natural forest protection project indicator includes the absorption of water conservation, soil conservation, nutrients accumulation, the atmosphere and environment purification, biological diversity protection.

(3) Greenhouse gas emission indicator includes greenhouse gas emissions of the small hydropower replacing firewood station and greenhouse gas emissions of returning farmland to forest.

(4) The role of social and poverty alleviation indicator includes per capita income, Engel's coefficient, increase in employment and social support degree.

Customization of the multi-indicator comprehensive evaluation report template

After establishing the corresponding position label is accomplished, the report template can be customize. The steps of creating a custom template as follows: (1) creates a new, blank Word document, in accordance with the changes to the normal document methodology and demand, add reports to be automatically generated by content in the document including fixed static text, header, footer, header, separator(such as symbols) and dynamic text. Among them, the placeholder for dynamic text with the corresponding logo. If the report needs to insert a table, where the paragraph you want to set the Insert table, and then inserts a page break in the next paragraph, make your spreadsheet into a region. If multiple sets of graphs and charts is needed, the appropriate place to insert a text box in Word document template for each size format, such as a text box, and then inserts a placeholder inside a text box. In order to distinguish data placeholders from normal text, you can set a special style for it. (2) Save the word document that contains placeholders for data as a report template.

Figure 2 The Partial of the evaluation report template

Conversation of dynamic web pages

Using report templates to create a report in Word format directly, does not have a function that interacts with a database query based on a condition [11], you also need to convert a Word report templates in the form of dynamic Web pages, follow these steps: (1) The way to traverse the bookmark lookup data placeholders in a template and read the corresponding value, and data processing method of descriptive information. (2) Through reflection technology, according to the description corresponds to the one by one the relationship between information and data processing methods to find data processing methods.(3) the ASPX data display controls is determined depend on the value type of data processing method Reflected .(4) The Word report templates are transformed into Html forms.( 5) Using regular expressions to remove comments and style information in the Html file, and add the Aspx pages such as Js and css information. ( 6) Through the template engine, using Aspx data components (such as TextBox, PictureBox and GridView) replacing data placeholders in your Html to generate dynamic pages.

Evaluation report generation

The variety of information in static and dynamic data in the database tables is the input section of the evaluation report, and the evaluation report of the small hydropower replacing fuel project as the output. The generation process of the small hydropower replacing firewood evaluation report is as follows. User finished editing the evaluation report on client under through the data operation of query and inserts by visiting a web page dynamically controls. The Placeholder data and data processing method is one to one in database. The server calls data processing method corresponding to the data placeholders of multi-indicator evaluation report template customized for reading data, and binding Data display components in Dynamic pages. Page structure is similar to final report. In page, queries and modifications operations repeatedly are allowed, and temporarily saved is supported. Then, the contents of all controls are serialized in backed server, and stored to the database.

When the user selects the generating reports Operation, backed server traverse the each data placeholder of multi-indicator evaluation report template customized. At the same time, the corresponding position data placeholder of multi-indicator evaluation report template customized is replaced by the content of the corresponding control that pass through the page. All data placeholders in the template are replaced, and product the word document .Then convert WORD to PDF, and save it as the final evaluation report.

Create a Word document

The original document is Exist?

Read the evaluation report template

The build process cancelled?

Stop the build process and all word process Based on the characteristics of the management and evaluation system of the small hydropower project, in this paper, a quick and automatic generation method based on Microsoft Visual Studio integrated development environment and C#.Net Framework 4.5 platform is proposed .The application results show that this report generation method based on C#.Net language is efficient and rapid, and meets the requirements of the management and evaluation system of the small hydropower replacing firewood project successfully, and can improve the availability and shareability of the material of the management and evaluation system.

Figure 1

 1 Figure 1 The structure of Evaluation report generation module

  Figure 3 WO control flow diagram

Acknowledgements

We acknowledge the financial assistance provided by the Key Program for International S&T Cooperation Projects of China (2012DFA60830).


Min Sun

Daoliang Li

Aquatic animal disease diagnosis system based on Android

Keywords: Android, Aquatic animals, Disease diagnosis

In recent years, as long as the rapid development of aquaculture, the occurrence of aquatic animal diseases increase year by year, which restrict the sustained, steady, and healthy development of aquaculture. So the rapid diagnosis of aquatic animal disease is particularly important for their prevention and control. In the present work, an Android-based aquatic animal disease diagnostic system has been developed in order to provide more convenient and effective aquatic animal disease diagnostic services to ordinary farmers. The designed system includes the part of the man-machine interface (user interface, expert interface), inference engine, the case base, knowledge base and so on. Interactive interface is a visual display interface. Inference engine was written in the Java programming language. Case base and knowledge base were created using SQLite. The results show that ordinary farmers can easily use this system to realize the convenient, fast, and accurate access to the findings of disease diagnosis and prevention measures as well as aquaculture expert can add, delete, modify, view the case base and knowledge base at any time.

Introduction

Fishing industry has been rapid development since the 1990s in China, and has Fishing is the competitive industries of agriculture economic development, fisheries and aquaculture in the world compared with other edible animal breeding, world aquaculture industry has developed very quickly [1].However, with the rapid development of aquaculture, aquatic animal disease has become increasingly serious. It is the major factor constraints the sustained and healthy development of aquaculture industry. During April to October in 2004,126 kinds of disease of 74 kinds of aquaculture species are monitored by 30 provinces, autonomous regions and municipalities of China. The results show that from April to October 2004, the direct economic losses due to diseases reached 15.144 billion yuan. 4.56 billion yuan more than 10.584 billion yuan of 2003 [2]. So, how can we make the production of aquaculture on the way to the healthy development has become an issue for aquatic animal experts. The research and promotion of diagnostic methods and prevention of diseases of aquatic animals has become urgent priority of aquaculture development.

In order to reduce the losses caused by diseases in aquaculture, research and develop various aquatic animal disease diagnosis expert system to assist aquaculture farmers has become the main topic among various research institutions. Currently because the aquatic animal disease experts is lack, the personnel of aquaculture are mainly farmers, and the distribution of farms tend to be more dispersed, the technical level of the breeding staff is not high. Thus in the process of diagnosis of aquatic animal disease, losses take places often due to the lack of experts or the experts can not get the scene as soon as possible. In addition, because the disease can not be accurately diagnosed, science prescription and grasp dose, farmers often abused medicines, antibiotics, it also had a significant impact on ecological environment and food safety and the healthy and sustainable development of aquaculture is restricted [3][4][5][6][7]. In order to reduce the economic losses, resolve the conflicts between frequent aquatic animal diseases and lack of experts, many research institutions developed a variety of expert systems for aquatic animal disease diagnosis by using some advanced technology products and information technology. We can use these expert systems to mimic the effect of working off-site, by this way the problem of lacking experts can be resolved.

At present, all kinds of aquatic animal disease diagnostic systems research and development of the country is mainly aimed at the personal computer, Hand personal computer and personal digital assistant applications terminal [8][9][START_REF] Lei | Windows Mobile technology applications in agriculture[END_REF][START_REF] Ouyang | The design and implement of PDA-Oriented expert system in agriculture[END_REF][START_REF] Wei | A knowledge representation and inference strategy for a development platform of agricultural intelligence system[END_REF][START_REF] Shi | Application and prospects of agriculture expert system[END_REF].But for the actual results now, on the one hand, in terms of ordinary farmers, PC machine, PDA, and HPC applications such as intelligent terminal system applications in agriculture, is not only expensive, single function, but also a strong dependence on the network environment, resulting in very limited in its application crowd. On the other hand, the current agricultural intelligent system development platform almost based on Windows operating systems [START_REF] Li | Research on the internet-based agricultural expert system[END_REF], it leading to poor results in terms of free sex, open source, and human-computer interaction. Therefore, it is important to seek one simpler interface and more friendly agricultural intelligent system application terminal and build an open source and free development environment for the popularization of aquatic animal disease prevention and control technology to farmers.

In recent years, with the development of the global smart phone, Google released Android operating system and promoting China's 3G network coverage project, China's 3G smart phone has been rapid development [START_REF] Li | Research on automation testing on windows mobile-based device[END_REF]. Especially after thousands of smart phones launched within Android3G, Android smart phones in China's mobile phone market share has been increasing [START_REF] Guan | Research on rural mobile information service in 3G era[END_REF][START_REF] Sharon | Operating systems for mobile computing[END_REF][START_REF] Chen | Android-driven smart phone shipments grew[END_REF]. Android 3G intelligent mobile phone is a set of call, Internet, multimedia and other functions in one of the intelligent terminal, its price is low, and can avoid the "two into". In addition, the Android operating system is a free and open source code operating system based on Linux. It is not only able to provide flexible independent design space for software designers, but also supports voice, touch screen and a series of new interactive technology.

In this paper, an aquatic animal disease diagnosis system based on Android is developed.

Design of aquatic animal disease diagnosis system based on Android

Structural design of aquatic animal disease diagnosis system based on Android

The design of the structure of aquatic animal disease diagnosis system based on Android is composed of four modules, human-machine interface, inference engine, case base and knowledge base. Its structure is as shown in Fig. 1. The man-machine interface includes a user interface and expert interface. The user interface is a visual display interface used by ordinary farmers. Aquatic animal disease expert can use the expert interface to add, delete, modify or view the case database and knowledge base. The inference engine is programmed by JAVA programming language. Case base and knowledge base are developed by SQLite3 and independent of application development.

Human-machine interface

Build the application development environment

Android development platform not only supports Windows, Linux also supports Mac OS systems. In this paper, the development environment is built on the Linux operating system and constructed by "JDK (Java development kit) + Eclipse + Android SDK (Software development kit) + ADT (Android development tools)".

Build the application development environment

SQLite is a compliant relational database management system that embedded lightweight and comply with the ACID. It takes resources are very low, open source and supports most of the SQL statement. This paper uses SQLite database to store large amounts of data, and to achieve the use of data, updating, maintenance and other operations. And install SQLite3 as "aquatic animal disease diagnosis system based on Android" database development platform on Linux operating systems.

Design of aquatic animal disease diagnosis system based on Android

Database design

The system includes three main types of data tables, tables of various types of aquatic animal disease knowledge, case table or case weight table. Table 1

System inference engine Design and Analysis

This system establish a aquatic animal disease diagnostic reasoning model by using case-based diagnostic reasoning combined with expert symptom scoring method. In order to achieve complementary advantages of the two diagnostic methods, improve the accuracy of diagnosis of aquatic animal diseases. Diagnostic reasoning as follows:

Step 1: Use case diagnosis and numerical diagnosis to do disease diagnosis respectively based on the symptoms information that farmer input, then compare the two results obtained by the two diagnostic methods.

Step 2: If the two diagnostic results are different, the system will show the symptoms information of both diseases and ask farmer input symptoms information again, then diagnosis again.

Step 3: If the two diagnostic results are same, then judge whether the value of Similarity that obtained by case diagnosis is between diagnostic threshold(0.65) and Very similar threshold(0.95), when the condition is satisfied then multiplexed on this case and display diagnostic results.

Step 4: If there is no enough conditions then ask the user whether they are satisfied with the result or not, if they satisfied with it then show the results of the diagnosis. When they are not satisfied with it then back to step 1.

Reasoning process flow chart shown in Fig. Because in the implementation process of inference engine need to involve the processing of the database, so SQLiteDatabase categories should be imported, and then create or open the database by the static method of SQLiteDatabase categories.

System migration and updates

Under the development environment of aquatic animal disease diagnostic system based on android, perform "Run As Android Application" can generate a installed package (ie APK file) of system, then upload it to the server. Users can download the system to the Android smart phone by 3G wireless network, or download it to the Android smart phone SD by PC, after this the migration of the system is finished.

The system updates is divided into two parts, expert and client. Experts can add, delete, modify or view the case base and knowledge base timely by the end of expert. Then upload the new database to the server when the condition of network is smooth. Farmers can get the information of system update by the client, they can choose whether to update the system or not.

In this paper, LinearLayout, RelativeLayout classes and AbsoluteLayout class provided by Android SDK are used to design the human-computer interaction of the system. We also use a variety of controls, such as Button, TextView, EditText, etc. to complete the interactive interface design.

After the system is installed on the user's or experts' smart phone, users can enter the login interface and choose breeding objects, then start the operation of disease diagnosis(show as Fig. 3, Fig. 4 and Fig. 5). Experts enter the login interface and choose the operation such as modify the rules, delete rules(show as Fig. 6).

Conclusions

This paper chooses the Android operating system as a development platform and develops an aquatic animal disease diagnosis system based on Android. Allow users to get rid of the constraints and inconvenience caused by computer and network. This system provides a more convenient and effective aquatic animal disease diagnostic services for farmers.

Farmers can use the system put forward in this paper to get the result of disease diagnosis and prevention measures. Experts can operate the case base and knowledge base timely, they also can upload the new data to server when the network is smooth, so that the farmers can get the latest data.

Fig. 1 .

 1 Fig. 1. Aquatic Animal Disease Diagnostic System structure

Fig. 2 .

 2 Fig. 2. Aquatic Animal Disease Diagnostic Flowchart

Fig. 3 .

 3 Fig. 3. Breeding user selects diagnostic object interface

Fig. 4 .Fig. 5 .Fig. 6 .

 456 Fig. 4. Disease diagnosis process interface

Table 1 .

 1 to Table 4 are the associated data table design. An aquatic animal disease cases table

	Primary key	Field Name	Field Type	Length	Empty	Description
	/Foreign key
	PK	ID	Int	5	N	Case
						Number
		type	Int	5	N	Case Type
		Dis_name	varchar	60	N	Names	of
						diseases
		Dis_type	varchar	60	N	Type	of
						disease
		Sym_set	varchar	300		Symptoms
						collection

Table 2 .

 2 Weight information table

	Primary key	Field Name	Field Type	Length	Empty	Description
	/Foreign key
	PK	Id	Int	5	N	weight
						number
		type	Int	5	N	Case Type
		Weight_set	varchar	1000	N	sets	of
						weight

Table 3 .

 3 An aquatic animal disease information table

	Primary key	Field Name	Field Type	Length	Empty	Description
	/Foreign key
	PK	ID	Int	5	N	Disease
						Number
		Dis_name	varchar	60	N	Names	of

Table 4 .

 4 Symptom Chart

	Primary key	Field Name	Field Type	Length	Empty	Description
	/Foreign key
	PK	ID	Int	5	N	Symptoms
						Number
		Sym_name	varchar	60	N	Symptoms
						Name
		Sym_type	varchar	60		Symptoms
						Type

Acknowledgment

This work was supported by the Key Program for International S&T Cooperation Projects of China (2013DFA11320).


Wenyu Zhang

Weixin Zhang

Daokuo Ge
email: cgedakuo@163.com

Hongxin Cao
email: dcaohongxin@hotmail.com

Yan Liu

Kunya Fu

Chunhuan Feng

Weitao Chen

Chuwei Song

Biomass-Based Leaf Curvilinear Modelfor Rapeseed (Brassica napus L.)

Keywords: rapeseed(Brassica napus L.), biomass, leaf curve, functional-structural plant models (FSPMs)

Leaf is one of the most important photosynthetic organs of rapeseed (Brassica napus L.). To quantifyrelationships between the leaf curve and the corresponding leaf biomassfor rapeseed on main stem, this paper presents a biomass-based leaf curvilinear model for rapeseed. Various model variables, includingleaf length, bowstring length, tangential angle,and bowstring angle, were parameterized based on data derived from the field experiments withvarieties, fertilizer, andtransplanting densities during 2011 to 2012, and 2012 to 2013growingseasons. And then weanalysed the biological significance of curvilinear equation for straight leaves,constructed the straight leaf probabilistic modelon main stem,quantified the relationship between leaf curvature and the corresponding leaf biomass, and constructed the leaf curvilinear model based on the assumption and verification of the curvilinear equation form for curvingleaf. The probability of straight leaf can be quantified with piecewise function according tothe different trend in the normalized leaf ranks ((0, 0.4],and (0.4, 1]). The leaf curvature decreased with the increasing of leaf biomass, and can be described with reciprocal function. The curve of straight leaf and the curving leafcan besimulated by linear equation and thequadratic function, respectively. Our models were validated withthe independent dataset from the field experiment, and the results indicatedthat the model could effectively predict thestraight leaf probability and leaf curvature, which would be useful for linking the rapeseed growth model with the rapeseed morphological model, and set the stagefor the development of functional-structural rapeseed models.

Introduction

Rapeseed is the world's important oil crops [START_REF] Hongxin | Researches of optimum shoot and ramification number dynamic models for rapeseed (Brassica napus L.)[C][END_REF] with harvest area of 25.3 to 30.9 million ha and total yield of 46.5~72.5 million tons during 2004~2013 [2]. At the same time, it is the main oilseed crop in China [START_REF] Hongxin | Researches of optimum leaf area index dynamicmodels for rape(Brassica napusL[END_REF], whose harvest area is about 5.6~7.5 million ha, and the total yieldis about 10.6~14.4 million tons [2] in general. Also, it is one of the main raw material of biodiesel [START_REF] Hama | Enzymatic biodiesel production: An overview of potential feedstocks and process development[END_REF]. Therefore, it isvery important for ensure food and ecological securitythatpromotthe development of rapeseed production.

Light distribution characteristics in crop canopiesdirectly affectthe light energy utilization efficiency for photosynthesis, dry matter accumulation, and yield formation.Allmost all the growth models predicted the crop canopy light distribution through the Beer's law [START_REF] Kropff | ORYZA1: An ecophysiological model for irrigated rice production[END_REF][START_REF] Bootekj | Modeling photosynthesis of row crop canopies[END_REF][START_REF] Liangzhi | Wheatcultivational simulation-optimization-decision marking system (WCSODS) [J][END_REF][START_REF] Liang | RiceGrow: A rice growth and productivity model[END_REF], in that the two key factors for light distribution simulation process, the extinction coefficient and the layered leaf area index, are closely related with the leaf curving characteristics [START_REF] Wang | Comparison of leaf angle distribution functions: Effects on extinction coefficient and fraction of sunlit foliage[END_REF]. Therefore, quantitatively modeling of the leaf curve could provide a mechanistic way for precisely simulating the crop canopy structure, light distribution, and photosynthesis, and lay a foundation for the predicting of light energy utilization efficiency and yield formation.

At present, there aremany studies on leaf curve modeling. In the study of mathematical characterization of maize canopies [START_REF] Stewart | Mathematical characterization of maize canopies[END_REF], leaf curve was described asa general quadratic equation expressed by the initial leaf angle, the coordinates of the leaf tipand theleaf'smaximum height. The general quadratic equation was also used to simluate the leaf curve of maize [START_REF] Guoyan | Mathematical description and three-dimensional reconstruction of maize canopy[END_REF][START_REF] Zhao Chunjiang | The computer simulation of maize leaf [J][END_REF][START_REF] Wengang | Geometry modeling of maize leaf canopy[END_REF], rice [START_REF] Hongwei | Research on rice leaf ceometric model and its visualization[END_REF], and other crops by many researchers.

Leaf curve was also fitted into a quadratic function for rice [START_REF] Hongyun | 3D Morphology modeling and computer simulation of rice main stem[END_REF][START_REF] Luowei | A study based on multi-segment & proportion geometrical model of rice leaf and 3D morphology modeling[END_REF] and winter wheat [START_REF] Chenguoqing | Studies on simulation and visualization of morphogenesis in wheat[END_REF], or a Gaussian function for spring barley [START_REF] Dornbuschtino | Description and visualization of graminaceous plants with an organ-based 3D architectural model, exemplified for spring barley (Hordeumvulgare L.)[END_REF] and rice [START_REF] Yonghui | Dynamic simulation of leaf curve in rice based on Gaussian function[END_REF]. Furthermore, Espana et al. [START_REF] Españamarı Á Luisa | Modeling maize canopy 3D architecture: Application to reflectance simulation[END_REF] decomposed leaf curvature into two parts, the ascending part was described as a parabolic curve, and the descending part, when existing, was characterized by a portion of an ellipse, and then applied the leaf curvature model to maize canopy 3D architecture and reflectance simulation. Watanabe et al. [START_REF] Tomonari | Rice morphogenesis and plant architecture: measurement, specification and the reconstruction of structural development by 3D architectural modelling[END_REF]found that leaf curves could be fitted using Hermite functions though analyzing three angles related to the basal, mid, and tip of leaf. Shi et al. [START_REF] Shi Chunlin | A quantitative analysis on leaf curvature characteristics in rice[END_REF] characterized the rice leaf curve by a second order differential equation,including the synthetic effect of leaf blade length, width,specific leaf weight,initial leaf angle, and the deformation coefficient on leaf space shape, using force analyzing on rice leaf. Zheng et al. [START_REF] Zhengbangyou | Three-dimensional digitization in situ of rice canopies and virtual stratified-clipping method[END_REF] obtain leaf midrib coordinate points by cubic spatial B-spline interpolation, and characterized the leaf curves as the connecting line of these points.

It is difficult to measure leaf curve because of the intricate leaf shapefor rapeseed.Therefore, the objectives of this researchwere to develop straight leaf probabilistic model, straight leaf curvilinear model, and biomass-based leaf curvilinear model by linking leaf morphological parameters with the corresponding leaf biomass, to validate the hypothesis that the curvilinear function for curving leaves could be fitted as quadratic function, and to provide a reference for linking morphological parameters with corresponding organ biomass, and for the establishment of the FSPMs.

Materials and Metheods

Materials

We used 2 rapeseedcultivars, they are:"Ningyou 18" (V1, conventional),and "Ningza 19" (V2, hybrid), breed by Institute of Economic Crops Research, Jiangsu Academy of Agricultural Sciences.

Methods

Experimental Conditions and Design

In order to determine the parameters andverify the models, three experimentswere conducted involving different varieties, transplanting densities, and fertilizer during the 2011-2012,and2012-2013 growing seasons at the experimental farm of our Academy (32.03° N, 118.87° E). The soil type is a hydragric anthrosol (organic carbon, 31.4 g kg -1 ; total nitrogen, 2.03g kg -1 ; available phosphorus, 20.3 mg kg -1 ; available potassium, 139.0 mg kg -1 ; and pH 7.31).

Exp. 1, variety and the fertilizer experiment (2011-2012):The Experiment was deployed insplit block designwiththree replications.Two fertilizerlevels (N0 = no fertilizer; N2 = 180kgha -1 ) were the whole-plottreatments while two cultivars (V1 and V2) constituted the sub-plots. The plots arranged random with 0.4 m row spacing, 0.17-0.20 m plant spacing in 7.0×5.7 m area.Fertilizer contained 12kgP 2 O 5 ha -1 ,18kgK 2 Oha -1 , and15 kgboron ha -1 .

Exp. 2, varietyexperiment (2012-2013):The experiment was deployed in randomized completeblockdesign with 2 varieties (V1 and V2) and 3replications. Nitrogen fertilizer included 90kg N ha -1 (N1), and the transplanting density was 1.2×10 5 plantha -1 (D2).

Exp. 3, variety, fertilizer and transplanting density experiment (2012-2013): The Experiment was deployed insplit block designwiththree replications.Three fertilizerlevels (N0 = no fertilizer; N1 = 90kgha -1 ; N2 = 180kgha -1 ) were the whole-plot treatments while variety (V1) and three transplanting densities (D1= 6×10 4 plantha -1 ;D2 = 1.2×10 5 plantha -1 ; D3= 1.8×10 5 plantha -1 ) constituted the sub-plots.

The plots of Exp. 2 and Exp. 3 arranged random with 0.42 m row spacing in 3.99 by 3.5 m area, and the plant spacing was calculated by row spacing and transplanting density. Fertilizer contained 90kgP 2 O 5 ha -1 and90kgK 2 Oha -1 for N1 plots, and180kgP 2 O 5 ha -1 and 180 kgK 2 Oha -1 for N2 plots, and 15 kgboron ha -1 was used as foliage spray for both N1 and N2 plots after bolting.

Measurements

The leafrank on the main stem of 50 randomly selected seedlings for each plot were marked using a red number stampbefore transplanting. Leaf morphological parameters including leaf length (the distance between leaf basal and leaf tip in the straight state, including the leaf blade and petiole, if it exists. LL,forshort), leaf tangentialangle (the angle between the tangential direction of leaf basalandthe main stem. TA, for short), leaf bowstring angle (the angle between the straight line from leaf basal to leaf tip in natural stateand the main stem. BA, for short), and leaf bowstring length (the distance between leaf basal and leaf tip in natural state. LBL, for short) were measured usingstraightedge andprotractordirectly (Fig. 1).

Fig. 1.Diagram of TA, BA and LBL

Data Analysis

We writed a C# Program to solve the approximate solution of leaf curve equation parameters through a step of 10 -5 cm. Leaf rank data were normalized to (0, 1] interval, in order to eliminate the apparent differences between treatments and replications. The data from Exp. 1 was used for model development, data from Exp. 2, and Exp. 3 were used for validation.

Model Validation

We validated the models developed in this paper by calculating the correlation (r), the root mean square error (RMSE), the average absolute difference (d a ), and the ratio of d a to the average observation (d ap ) [START_REF] Hongxin | Comparison of Crop Model Validation Methods [J[END_REF], and 1:1 line of simulated and observed properties. Some statistical indiceswere defined as follows:

𝑅𝑀𝑆𝐸 = √ ∑ (𝑂 𝑖 -𝑆 𝑖 ) 2 𝑛 𝑖=1 𝑛 𝑑 𝑎 = 1 𝑛 ∑ (𝑂 𝑖 -𝑆 𝑖 ) 𝑛 𝑖=1 𝑑 𝑎𝑝 (%) = |𝑑 𝑎 |/𝑂 ̅ × 100
whereiis sample number, n is total number of measurements,n-1=n when n≥30, S i is simulated value, and O i is observedvalue.

Results

Model Description

Probability and CurvilinearModel for Straight Leaves

In order to represent the extension state of leafbetter, we set the leaf in the Cartesian coordinate system with the leaf basal point as the origin and growth direction of main stem as y-axis, regardless of leaf distorting.According to observations in the Exp. 1, some rapeseed leaves could be considered as straight leaves with small difference between leaf length and leaf bowstring length, as well as leaf tangentialangle and leaf bowstring angle. Therefore, we treated the leaves asstraight leaves if difference between the leaf length and the leaf bowstring length was less than 1 cm or difference between the leaf tangentialangle and the leaf bowstring angle is less than 10°. The curvilinearequation (f(x))of straight leaves could be expressed asa linear functionwhich passes through the originand with the cotangent value of the leaf tangentialangle as the slope, and the function could be described byEq. [START_REF] Hongxin | Researches of optimum shoot and ramification number dynamic models for rapeseed (Brassica napus L.)[C][END_REF].

f(x) = cot(TA)•x (1)
where TA can be simulatedby our previous model [START_REF] Zhangweixin | Morphological Structure Model of Leaf Space Based on Biomass at Pre-Overwintering Stage in Rapeseed (Brassica napus L.) Plant [J][END_REF].

The data in the Exp. 1 showed thatchanges inthe probabilityof differenttreatments for straight leavesby the normalized leaf rankswas close to quadratic curve with significantr (r=0.725, P<0.01, n=16, r (14, 0.01) =0.623, Table 1) in the interval (0, 0.4] ( Fig. 2a), and logarithmic curvewith significantr (r=0.925, P<0.001, n=33, r (31, 0.001) =0.547, Table 1)in the interval (0.4, 1] ( Fig. 2b). So that the probabilityof straight leavesby the normalized leaf ranks (P LS ) could be expressed as a piecewise function as Eq. 2.

𝑃 𝐿𝑆 = { 𝐴 1 • 𝑁𝐿𝑅𝑠 2 + 𝐵 1 • 𝑁𝐿𝑅𝑠 + 𝐶 1 𝑁𝐿𝑅𝑠 ∈ (0, 0.4] 𝐴 2 • ln(𝑁𝐿𝑅𝑠) + 𝐵 2 𝑁𝐿𝑅𝑠 ∈ (0.4, 1] (2)
where NLRs is normalized leaf rank; A 1 ,A 2 ,B 1 ,B 2 , and C 1 are model parameters whose values and testing datashown in Table 1. 2.

b-ac b-ac=cot(TA)

From Table 2, wesawthat only quadratic function, sine function, and Hoerl-like function had specific biological significance for all the parameters: for quadratic functionax 2 +bx, -aexpresses the leaf curvature, and bexpresses the cotangentvalue of leaf tangentialangle; for sine functionasin(bx), aexpresses the ordinate value of leaf curve peak point, π/2b abscissa value of expresses theleaf curve peak point, and abexpresses the cotangent value of leaf tangentialangle; for Hoerl-like functionaxb x , aexpresses the cotangent value of leaf tangentialangle, and -bexpresses the leaf curvature. So that we addressedthem and validated the assumptions.

3.1.2.2Solution of the Leaf Curvilinear Equation

L t is leaf tip, x t is the abscissa of L t , y t is the ordinate of L t .

Fig. 3.The geometrical properties of leaf curve

As shown in Fig. 3, the leaf tip could be expressed as(sin (BA)•LBL, cos(BA)•LBL) by solving△OL t y t . We substitutedcoordinatesof origin and L t into curvilinear function for curving leaves to get equation set as Eq. 3.

{ 𝑓(sin(𝐵𝐴) • 𝐿𝐵𝐿) = cos(𝐵𝐴) • 𝐿𝐵𝐿 𝑓

′ (0) = cot(𝑇𝐴) (3)
The equation sets and their solutions corresponding to the three curvilinear functions for curving leaves we supposed above were shown in Table 3.

Table 3Three curvilinear functionsfor curving leaves and the corresponding equation setsand their solutions f(x)

Equation set Solution

ax 2 +bx { 𝑓(sin(𝐵𝐴) • 𝐿𝐵𝐿) = 𝑎 • sin(𝐵𝐴) 2 • 𝐿𝐵𝐿 2 + 𝑏 • 𝐿𝐵𝐿 • sin(𝐵𝐴) = cos(𝐵𝐴) • 𝐿𝐵𝐿 𝑓 ′ (0) = 𝑏 = cot(𝑇𝐴) 𝑎 = - sin(𝐵𝐴) • cot(𝑇𝐴) -cos (𝐵𝐴) 𝐿𝐵𝐿 • 𝑠𝑖𝑛(𝐵𝐴) 2 𝑏 = cot (𝑇𝐴) asin(bx) { 𝑓(sin(𝐵𝐴) • 𝐿𝐵𝐿) = 𝑎 • sin(𝑏 • 𝐿𝐵𝐿 • sin(𝐵𝐴)) = cos(𝐵𝐴) • 𝐿𝐵𝐿 𝑓 ′ (0) = 𝑎 • 𝑏 = 𝑐𝑜𝑡(𝑇𝐴) 𝑎 = 𝐿𝐵𝐿 • cos(𝐵𝐴) sin (𝑏 • 𝐿𝐵𝐿 • sin (𝐵𝐴)) 𝑎 • 𝑏 = cot (𝑇𝐴) axb x { 𝑓(sin(𝐵𝐴) • 𝐿𝐵𝐿) = 𝑎 • 𝐿𝐵𝐿 • sin(𝐵𝐴) • 𝑏 𝐿𝐵𝐿•sin(𝐵𝐴) = cos(𝐵𝐴) • 𝐿𝐵𝐿 𝑓 ′ (0) = 𝑎 = cot(𝑇𝐴) 𝑎 = cot (𝑇𝐴) 𝑏 = ( 𝑐𝑜𝑠(𝐵𝐴) sin (sin (𝐵𝐴) • sin (𝑇𝐴)) ) 1 sin (𝐵𝐴)•𝐿𝐵𝐿
As shown in Table . 3, quadratic function,and Hoerl-like function could be solved directly, but there was no analytical solution for sine function, and we writed a C# program to calculatethe approximate solutionwith a step of 10 -5 cm.

3.1.2.3Validation for Leaf Curvilinear Functions

We used the observed value of tangentialangle, bowstring angle, and bowstring length for solvingequation set, except leaf length. Meanwhile, the leaf length also could becalculated as the arc length between leaf basal and leaf tip by the formula of arc length. Therefore, we can validate theleaf curvilinear functionsthrough comparing theobserved leaf length with the calculatedarc length. The three leaf curvilinear functions were validated by 1:1 line of observed andcalculatedleaf length (Fig. 4), and the statistical parameterswere shownin Table 4. The results showed that the bestfittedone (with the highestr and lowestda,dap, andRMSE,Table 4) with specific biological significance (Table 2)for leaf curve was quadratic function, so that the curvilinear model for curving leaves could be described as aquadratic function asEq. [START_REF] Hama | Enzymatic biodiesel production: An overview of potential feedstocks and process development[END_REF].

f(x) = -Lc i •x 2 + cot(TA)•x (4)
where, Lc i is the leaf curvature on the ith day after emergence.

Biomass-Based Leaf CurvatureSimulation

The data in the experiments showed thatquadratic coefficientsof leaf curvilinear equationsincreased with the increase of corresponding leaf dry weightfrom leaf fully expandeduntil senescence. It means that leafcurvaturedecreased with thecorresponding leafbiomass (Fig. 5), and could by fitted by reciprocal function with significantr (V1: r=0.974, P<0.001, n=34, r (32, 0.001) =0.539; V2:r=0.637, P<0.001, n=51, r (49, 0.001) =0.447. Table 5).

𝐿𝑐 𝑖 =

-𝐿𝑐 𝑎 𝐷𝑊𝐿𝐵 𝑖 -𝐿𝑐 𝑏 [START_REF] Kropff | ORYZA1: An ecophysiological model for irrigated rice production[END_REF] whereDWLB i is the dry weighton the ith day after emergence; Lc a and Lc b are model parameters whose values and testing datashown in Table 5.

Validation

The models developed above were validatedwerevalidated with the independent datasets fromExp. 2 and Exp. 3, and the results showed that the correlation (r) of simulation and observationprobability for straight leavesand curvature for curving leavesall had significant level at P<0.001, and that the average absolute difference (d a ), the ratio of d a to the average observation (d ap ), and the root mean square error (RMSE) all weresmaller (Table 6). Fig. 6 and Fig. 7 indicated that the observed and simulated probability for straight leavesand curvature for curving leaves were all close to the 1:1 line. Functional-structural plant model with better mechanisticembodies the interaction between plant morphogenesis andcultivarsand environmental factors byintegrated the functionsof growth model with the structuresof morphological model [START_REF] Hongxin | Discussion on development of crop models[END_REF]. So far, there werelot of research on modeling the leaf curve [START_REF] Stewart | Mathematical characterization of maize canopies[END_REF][START_REF] Guoyan | Mathematical description and three-dimensional reconstruction of maize canopy[END_REF][START_REF] Zhao Chunjiang | The computer simulation of maize leaf [J][END_REF][START_REF] Wengang | Geometry modeling of maize leaf canopy[END_REF][START_REF] Hongwei | Research on rice leaf ceometric model and its visualization[END_REF][START_REF] Hongyun | 3D Morphology modeling and computer simulation of rice main stem[END_REF][START_REF] Luowei | A study based on multi-segment & proportion geometrical model of rice leaf and 3D morphology modeling[END_REF][START_REF] Chenguoqing | Studies on simulation and visualization of morphogenesis in wheat[END_REF][START_REF] Dornbuschtino | Description and visualization of graminaceous plants with an organ-based 3D architectural model, exemplified for spring barley (Hordeumvulgare L.)[END_REF][START_REF] Yonghui | Dynamic simulation of leaf curve in rice based on Gaussian function[END_REF][START_REF] Españamarı Á Luisa | Modeling maize canopy 3D architecture: Application to reflectance simulation[END_REF][START_REF] Tomonari | Rice morphogenesis and plant architecture: measurement, specification and the reconstruction of structural development by 3D architectural modelling[END_REF]whichaimed at 3D reconstruction of crop canopy with a high precision. Theywell explainedthe effects of structures on functions by combiningtechnical instrumentalities like geometrical ray trace [START_REF] Chellemichaë L | Taking into account the 3D canopy structure to study the physical environment of plants: the montecarlo solution[END_REF][START_REF] Yuntao | Efficient model for computing the distribution of direct solar radiation in maize canopy at organ level [J][END_REF], but could not change the structuresas a responseof the changed functions. Shi et al. [START_REF] Shi Chunlin | A quantitative analysis on leaf curvature characteristics in rice[END_REF]linked leaf curve with correspondingdry weightthroughforce analyzing on rice leaf, but the leaf curvilinear equation which waslimited by a form of second order differential equation in this research wasdifficult to use.

Groer et al. [START_REF] Groerchristian | A dynamic 3D model of rape (Brassica napus L.) computing yield components under variable nitrogen fertilization regimes[C]. 5th International Workshop on Functional-Structural Plant Models[END_REF]established a dynamic 3D model of rapeseed using the modellinglanguage XL [START_REF] Kniemeyer | Rule-based modelling with the XL/GroIMP software[END_REF]and made the morphological model response of different nitrogen levels using a sinks and sourcessystem like GREENLAB and theLEAFC3-N model [START_REF] Müller | LEAFC3-N: a nitrogen-sensitive extension of the CO2 and H2O gas exchange model LEAFC3 parameterised and tested for winter wheat (Triticumaestivum L.)[END_REF]for photosynthesis at different N-regimes. But the time from sowingto the rosette stage and the conditions except nitrogen was neglected in this model.Jullienet al. [START_REF] Alexandra | Characterization of the interactions between architecture and source-sink relationships in winter oilseed rape (Brassica napus) using the GreenLab model[END_REF] constructed a FSPM by characterizing the interactions between architecture and source-sinkrelationships in winter oilseed rapeusing the GREENLABmodel. However, it mainly consideredthe relationship betweenbiomass and leaf area [START_REF] Jullienalexandra | Variations in leaf mass per area according to N nutrition, plant age, and leaf position reflect ontogenetic plasticity in winter oilseed rape (Brassica napus L.)[END_REF],and the description of blade shape was relatively simple. Cao et al. [START_REF] Hongxin | Biomass-based rapeseed (Brassica napusL.) leaf geometric parameter model[C[END_REF] and Zhang et al. [START_REF] Zhangweixin | Morphological Structure Model of Leaf Space Based on Biomass at Pre-Overwintering Stage in Rapeseed (Brassica napus L.) Plant [J][END_REF]establishedmodels to meticulouspredict leaf morphological parameters like leaf length, width, and angles, apart from leaf curve. We developedbiomass-based leaf curvilinearmodel for rapeseed by linking the leaf curvature with the corresponding leaf biomass, which could realize combination of structures with functions,explainedeffects of environmental conditions on leaf morphogenesis, and set the stage for the development of functional-structural rapeseed models.

4.2The Research ProvidedAMechanistic and Universal Method for Leaf Curve Modeling

As to facilitate observation and simulation, we set the leaf intoa Cartesian coordinate system with the leaf basal point as the origin and growth direction of main stem as y-axis. To make the model more precisely, two cases of curving characteristic (straight or curving) were analyzed. In order to determiningthe form V2 of leaf curvilinear equation to be difficult to measure directly, we compared various functions based on their biological significance, and calculated and observed leaf length. For eliminating the apparent differences between treatments and replications, we normalized the leaf rank into the interval of (0, 1].

All these practices madethe model to havebetter mechanistic and universal.

4.3Models Developed In This PaperNeeds To Be Improved

The biomass-basedrapeseed leaf curvilinearmodel developed in this paper for the stage from leaf fully expandeduntil senescence, but the processesof leaf extension,senescence, and distorting is neglected.

Thus, it needs to be studied further. A similar method could also be applied in other crops likemaize andrice, and it could help to the regulation and selection for ideal plant typein the future.

Fig. 2 .

 2 Fig. 2. Changes in the probability of straight leaves with the normalized leaf ranks

  and There Biological Significance for Leaf Curve According to the leaves curving characteristicsobserved in experiments,and the research on curvilinear models for other crops, we supposedthat curvilinear function of rapeseedleaf (f(x)) could be fitted by functions such asquadratic function(ax 2 +bx),quartic function(ax 4 +bx 3 +cx 2 +dx), sine function(asin(bx)), Hoerl-like function(axb x ),(a+bx c )/(d+x c ),and (a+bx)/(1+cx+dx 2 ), whose diagram were similar to leaf curves in a interval. On the basis ofgeometric meaning of derivative, cot(TA) can be interpreted as f'(0) (value of leaf curvilinear equation'sderived function at origin of coordinates, thus, biological significance of leaf curvilinear equationparameters are showd in Table

Fig. 4 .

 4 Fig. 4. Comparision of the observed and the stimulated leaf length on main stem in 2011-2012

Fig. 5 .

 5 Fig. 5. Changes in the leaf curvature with the leaf dry weight in 2011-2012

Fig. 6 .Fig. 7 .

 67 Fig. 6. Comparision ofthe observed and the stimulatedprobability for straight leaves in 2012-2013

5Conclusions

  This paper presents a biomass-based leaf curvilinear model for rapeseed designed to explain effects of cultivars and environmental conditions on leaf curve. Various model variables, including leaf biomass, length, and angles were parameterized based on datasets derived from the experiments with rapeseed cv. Ningyou 18, and Ningza 19.With the help of our descriptive model, it will be easy tofulfill calculation of leaf curve via biomass, canopy structure via leaf curve,light distribution and the photosynthesis via canopy structure, and biomass via photosynthesis. It should be possible to connectionmorphological model with physiological model via biomass, and to development the FSRM.

Table 1Significance

 1Significance test of the straight leaf probabilistic model and its parameters

	Interval of
							Parameter	Unstandardize
	normalized	Function	n	r	Sig. for r	Sig. for F			t
							symbolic	d coefficients
	leaf ranks
	(0, 0.4]	𝐴 1 • 𝑁𝐿𝑅𝑠 2	16 0.725 **	r(14, 0.01)=0.623	0.008	A1	-10.519	-2.709 *
		+ 𝐵 1 • 𝑁𝐿𝑅𝑠					B1	1.572	3.199 **
		+ 𝐶 1					C1	0.139	-0.800
	(0.4, 1]	𝐴 2 • ln(𝑁𝐿𝑅𝑠)	33 0.925 **	r(31, 0.001)=0.547	0.000	A2	0.716	13.575 ***
		+ 𝐵 2		*			B2	0.965	36.223 ***

*** , ** , and * denote significance at P<0.001, P<0.01, and P<0.05, respectively. The same as below.

  Table 2The various functions of leaf curve, their derivative function and the derivative value at the origin, and the biological significance of the parameters

	f(x)	f'(x)					f'(0)	Biological	significance	of	the
								parameters
	ax 2 +bx	2ax+b					b	-a:curvature; b=cot(TA)
	ax 4 +bx 3 +cx 2 +dx	4ax 3 +3bx 2 +2cx+d	d	d=cot(TA)
	asin(bx)	abcos(bx)				ab	leaf curve peak: (π/2b,a); ab=cot(TA)
	axb x	ab x log(b)x+ab x		a	a= cot(TA); -b:curvature
	(a+bx c )/(d+x c )	𝑏𝑐𝑥 𝑐-1 𝑥 𝑐 + 𝑑	-	(𝑥 𝑐 + 𝑑) 2 𝑐𝑥 𝑐-1 (𝑏𝑥 𝑐 + 𝑎)	0	N/A
	(a+bx)/(1+cx+dx 2 )	𝑏 𝑑𝑥 2 + 𝑐𝑥 + 1	-	(𝑏𝑥 + 𝑎)(2𝑑𝑥 + 𝑐) (𝑑𝑥 2 + 𝑐𝑥 + 1) 2

Table 4Comparison

 4Comparison of statistical parameters of the observed and the stimulated leaf blade length on main stem in

				2011-2012
				Statistic parameters of simulation and observation
	f(x)
		n	da(cm)	dap(%)	RMSE(cm)	r	Sig.
	ax 2 +bx	509	2.521	10.426	3.974	0.956 ***	r(507,0.001)=0.145
	asin(bx)	509	3.730	15.427	6.083	0.904 ***	r(507,0.001)=0.145
	axb x	509	4.693	19.410	6.749	0.889 ***	r(507,0.001)=0.145

Table 5 .

 5 Significance test of the leaf curvature model and its parameter

	Cultivar	n	r	Sig. for r	Sig. for F	Parameter symbolic	Unstandardized coefficients	t
	V1	34 0.974 ***	r(32, 0.001)=0.539	0.000	Lca Lcb	-0.165 0.163	-24.127 *** 4.625 ***
	V2	51 0.637 ***	r(49, 0.001)=0.447	0.000	Lca	-.018	-5.789 ***
						Lcb	-0.015	-1.826

Table 6Comparison

 6Comparison

	of statistical parameters of simulation and observation in the probability for straight leaves
	and curvature for curving leaves on main stem in 2012-2013
					Statistic parameters of simulation and observation
	Models	Cultivar
				n	da	dap(%)	RMSE	r	Sig.
	Probability for straight leaves	V1,V2	52	0.001	0.245	0.191	0.762***	r(50,0.001)=0.443
			V1	93	0.007	-9.196	0.060	0.648***	r(91,0.001)=0.336
	Curvature for curving leaves
			V2	99	-0.005	-7.000	0.042	0.541***	r(97,0.001)=0.326
			1.2
	leaves
	straight		0.9
	probability for	simulated	0.6
	appearance		0.3
			0
			0	0.3	0.6	0.9	1.2
				appearance probability for straight leaves
					observed

Acknowledgments

This work is supported by the grants from the National Natural Science Foundation of China (31171455,31201127, and 31471415), the National High-Tech Research and Development Program of China (2013AA102305-1), the Jiangsu Agriculture Science and Technology Innovation Fund of China [CX(12)5060],andJiangsu Academy of Agricultural Sciences Basic Scientific Research Work Special Fund [ZX(15)2008,ZX(15)4001].